datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
suroRitch/PAW | ---
license: mit
---
|
chengli-thu/linghuchong | ---
license: cc-by-4.0
task_categories:
- text-generation
language:
- zh
size_categories:
- 1K<n<10K
---
支持ChatHaruhi2 的令狐冲数据，可以使用如下方式调用
```python
from chatharuhi import ChatHaruhi
chatbot = ChatHaruhi( role_from_hf = 'chengli-thu/linghuchong', \
llm = 'openai')
response = chatbot.chat(role='小师妹', text = '冲哥。')
print(response)
```
上传者: 李鲁鲁
更具体的信息，见 [ChatHaruhi](https://github.com/LC1332/Chat-Haruhi-Suzumiya)
欢迎加入我们的 [众筹角色创建项目](https://github.com/LC1332/Chat-Haruhi-Suzumiya/tree/main/characters/novel_collecting)
### Citation引用
Please cite the repo if you use the data or code in this repo.
```
@misc{li2023chatharuhi,
title={ChatHaruhi: Reviving Anime Character in Reality via Large Language Model},
author={Cheng Li and Ziang Leng and Chenxi Yan and Junyi Shen and Hao Wang and Weishi MI and Yaying Fei and Xiaoyang Feng and Song Yan and HaoSheng Wang and Linkang Zhan and Yaokai Jia and Pingyu Wu and Haozhen Sun},
year={2023},
eprint={2308.09597},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
lyimo/zindi_swahili | ---
license: mit
---
|
9rofe/med_reading_level | ---
license: agpl-3.0
task_categories:
- text-classification
- translation
language:
- en
size_categories:
- 1K<n<10K
--- |
vietgpt-archive/luatvietnam | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: url
dtype: string
- name: content
struct:
- name: text
dtype: string
- name: attribute_of_content
dtype: string
splits:
- name: train
num_bytes: 664999318
num_examples: 18330
download_size: 172216690
dataset_size: 664999318
---
# Dataset Card for "luatvietnam"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
aymanzeyada/test | ---
license: apache-2.0
---
|
skrishna/coin_flip_2 | ---
dataset_info:
features:
- name: targets
dtype: string
- name: targets_vec
sequence: int64
- name: inputs
dtype: string
splits:
- name: test
num_bytes: 280834
num_examples: 2000
- name: train
num_bytes: 279957
num_examples: 2000
download_size: 105065
dataset_size: 560791
---
# Dataset Card for "coin_flip_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Nexdata/Taiwanese_Mandarin_Speech_Data_by_Mobile_Phone_Guiding | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for Nexdata/Taiwanese_Mandarin_Speech_Data_by_Mobile_Phone_Guiding
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://www.nexdata.ai/datasets/64?source=Huggingface
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The data collected 203 Taiwan people, covering Taipei, Kaohsiung, Taichung, Tainan, etc. 137 females, 66 males. It is recorded in quiet indoor environment. It can be used in speech recognition, machine translation, voiceprint recognition model training and algorithm research.
For more details, please refer to the link: https://www.nexdata.ai/datasets/64?source=Huggingface
### Supported Tasks and Leaderboards
automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).
### Languages
Taiwanese Mandarin
## 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
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
### Citation Information
[More Information Needed]
### Contributions |
open-llm-leaderboard/details_SC44__Mistral-7B-private-oia | ---
pretty_name: Evaluation run of SC44/Mistral-7B-private-oia
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [SC44/Mistral-7B-private-oia](https://huggingface.co/SC44/Mistral-7B-private-oia)\
\ 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_SC44__Mistral-7B-private-oia\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-01-28T20:57:18.847869](https://huggingface.co/datasets/open-llm-leaderboard/details_SC44__Mistral-7B-private-oia/blob/main/results_2024-01-28T20-57-18.847869.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\": 0.6481514121645634,\n\
\ \"acc_stderr\": 0.03222955295349873,\n \"acc_norm\": 0.6482463305308075,\n\
\ \"acc_norm_stderr\": 0.032893811368600714,\n \"mc1\": 0.5789473684210527,\n\
\ \"mc1_stderr\": 0.017283936248136473,\n \"mc2\": 0.7314870038039903,\n\
\ \"mc2_stderr\": 0.014661019064531787\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.7013651877133106,\n \"acc_stderr\": 0.013374078615068747,\n\
\ \"acc_norm\": 0.7278156996587031,\n \"acc_norm_stderr\": 0.013006600406423702\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7261501692889862,\n\
\ \"acc_stderr\": 0.004450214826707175,\n \"acc_norm\": 0.892352121091416,\n\
\ \"acc_norm_stderr\": 0.0030930175559380035\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6148148148148148,\n\
\ \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n\
\ \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6973684210526315,\n \"acc_stderr\": 0.03738520676119669,\n\
\ \"acc_norm\": 0.6973684210526315,\n \"acc_norm_stderr\": 0.03738520676119669\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.62,\n\
\ \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.62,\n \
\ \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7094339622641509,\n \"acc_stderr\": 0.027943219989337124,\n\
\ \"acc_norm\": 0.7094339622641509,\n \"acc_norm_stderr\": 0.027943219989337124\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7638888888888888,\n\
\ \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.7638888888888888,\n\
\ \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \
\ \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.59,\n \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\"\
: 0.59,\n \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.653179190751445,\n\
\ \"acc_stderr\": 0.036291466701596636,\n \"acc_norm\": 0.653179190751445,\n\
\ \"acc_norm_stderr\": 0.036291466701596636\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.47058823529411764,\n \"acc_stderr\": 0.04966570903978529,\n\
\ \"acc_norm\": 0.47058823529411764,\n \"acc_norm_stderr\": 0.04966570903978529\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.73,\n \"acc_stderr\": 0.0446196043338474,\n \"acc_norm\": 0.73,\n\
\ \"acc_norm_stderr\": 0.0446196043338474\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5787234042553191,\n \"acc_stderr\": 0.03227834510146268,\n\
\ \"acc_norm\": 0.5787234042553191,\n \"acc_norm_stderr\": 0.03227834510146268\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5,\n\
\ \"acc_stderr\": 0.047036043419179864,\n \"acc_norm\": 0.5,\n \
\ \"acc_norm_stderr\": 0.047036043419179864\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5655172413793104,\n \"acc_stderr\": 0.04130740879555498,\n\
\ \"acc_norm\": 0.5655172413793104,\n \"acc_norm_stderr\": 0.04130740879555498\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4126984126984127,\n \"acc_stderr\": 0.025355741263055266,\n \"\
acc_norm\": 0.4126984126984127,\n \"acc_norm_stderr\": 0.025355741263055266\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.48412698412698413,\n\
\ \"acc_stderr\": 0.04469881854072606,\n \"acc_norm\": 0.48412698412698413,\n\
\ \"acc_norm_stderr\": 0.04469881854072606\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7806451612903226,\n\
\ \"acc_stderr\": 0.023540799358723302,\n \"acc_norm\": 0.7806451612903226,\n\
\ \"acc_norm_stderr\": 0.023540799358723302\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5024630541871922,\n \"acc_stderr\": 0.035179450386910616,\n\
\ \"acc_norm\": 0.5024630541871922,\n \"acc_norm_stderr\": 0.035179450386910616\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\
: 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7636363636363637,\n \"acc_stderr\": 0.03317505930009182,\n\
\ \"acc_norm\": 0.7636363636363637,\n \"acc_norm_stderr\": 0.03317505930009182\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\": 0.8963730569948186,\n \"acc_stderr\": 0.02199531196364424,\n\
\ \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.02199531196364424\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6615384615384615,\n \"acc_stderr\": 0.023991500500313036,\n\
\ \"acc_norm\": 0.6615384615384615,\n \"acc_norm_stderr\": 0.023991500500313036\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.32222222222222224,\n \"acc_stderr\": 0.028493465091028593,\n \
\ \"acc_norm\": 0.32222222222222224,\n \"acc_norm_stderr\": 0.028493465091028593\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6680672268907563,\n \"acc_stderr\": 0.030588697013783642,\n\
\ \"acc_norm\": 0.6680672268907563,\n \"acc_norm_stderr\": 0.030588697013783642\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3509933774834437,\n \"acc_stderr\": 0.03896981964257375,\n \"\
acc_norm\": 0.3509933774834437,\n \"acc_norm_stderr\": 0.03896981964257375\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8348623853211009,\n \"acc_stderr\": 0.01591955782997604,\n \"\
acc_norm\": 0.8348623853211009,\n \"acc_norm_stderr\": 0.01591955782997604\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5185185185185185,\n \"acc_stderr\": 0.03407632093854051,\n \"\
acc_norm\": 0.5185185185185185,\n \"acc_norm_stderr\": 0.03407632093854051\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8235294117647058,\n \"acc_stderr\": 0.02675640153807897,\n \"\
acc_norm\": 0.8235294117647058,\n \"acc_norm_stderr\": 0.02675640153807897\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8059071729957806,\n \"acc_stderr\": 0.025744902532290916,\n \
\ \"acc_norm\": 0.8059071729957806,\n \"acc_norm_stderr\": 0.025744902532290916\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.672645739910314,\n\
\ \"acc_stderr\": 0.03149384670994131,\n \"acc_norm\": 0.672645739910314,\n\
\ \"acc_norm_stderr\": 0.03149384670994131\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n\
\ \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228732,\n \"\
acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228732\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.75,\n\
\ \"acc_stderr\": 0.04186091791394607,\n \"acc_norm\": 0.75,\n \
\ \"acc_norm_stderr\": 0.04186091791394607\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n\
\ \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.44642857142857145,\n\
\ \"acc_stderr\": 0.04718471485219588,\n \"acc_norm\": 0.44642857142857145,\n\
\ \"acc_norm_stderr\": 0.04718471485219588\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7961165048543689,\n \"acc_stderr\": 0.039891398595317706,\n\
\ \"acc_norm\": 0.7961165048543689,\n \"acc_norm_stderr\": 0.039891398595317706\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8846153846153846,\n\
\ \"acc_stderr\": 0.020930193185179326,\n \"acc_norm\": 0.8846153846153846,\n\
\ \"acc_norm_stderr\": 0.020930193185179326\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621504,\n \
\ \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.04688261722621504\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8237547892720306,\n\
\ \"acc_stderr\": 0.013625556907993464,\n \"acc_norm\": 0.8237547892720306,\n\
\ \"acc_norm_stderr\": 0.013625556907993464\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7167630057803468,\n \"acc_stderr\": 0.02425790170532338,\n\
\ \"acc_norm\": 0.7167630057803468,\n \"acc_norm_stderr\": 0.02425790170532338\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.37318435754189944,\n\
\ \"acc_stderr\": 0.016175692013381957,\n \"acc_norm\": 0.37318435754189944,\n\
\ \"acc_norm_stderr\": 0.016175692013381957\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.6993464052287581,\n \"acc_stderr\": 0.026256053835718964,\n\
\ \"acc_norm\": 0.6993464052287581,\n \"acc_norm_stderr\": 0.026256053835718964\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7266881028938906,\n\
\ \"acc_stderr\": 0.025311765975426122,\n \"acc_norm\": 0.7266881028938906,\n\
\ \"acc_norm_stderr\": 0.025311765975426122\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7314814814814815,\n \"acc_stderr\": 0.024659685185967284,\n\
\ \"acc_norm\": 0.7314814814814815,\n \"acc_norm_stderr\": 0.024659685185967284\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.475177304964539,\n \"acc_stderr\": 0.029790719243829727,\n \
\ \"acc_norm\": 0.475177304964539,\n \"acc_norm_stderr\": 0.029790719243829727\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4726205997392438,\n\
\ \"acc_stderr\": 0.01275107578801506,\n \"acc_norm\": 0.4726205997392438,\n\
\ \"acc_norm_stderr\": 0.01275107578801506\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6654411764705882,\n \"acc_stderr\": 0.0286619962023353,\n\
\ \"acc_norm\": 0.6654411764705882,\n \"acc_norm_stderr\": 0.0286619962023353\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6813725490196079,\n \"acc_stderr\": 0.01885008469646872,\n \
\ \"acc_norm\": 0.6813725490196079,\n \"acc_norm_stderr\": 0.01885008469646872\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6909090909090909,\n\
\ \"acc_stderr\": 0.044262946482000985,\n \"acc_norm\": 0.6909090909090909,\n\
\ \"acc_norm_stderr\": 0.044262946482000985\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7306122448979592,\n \"acc_stderr\": 0.02840125202902294,\n\
\ \"acc_norm\": 0.7306122448979592,\n \"acc_norm_stderr\": 0.02840125202902294\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8308457711442786,\n\
\ \"acc_stderr\": 0.02650859065623327,\n \"acc_norm\": 0.8308457711442786,\n\
\ \"acc_norm_stderr\": 0.02650859065623327\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.87,\n \"acc_stderr\": 0.033799766898963086,\n \
\ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.033799766898963086\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5301204819277109,\n\
\ \"acc_stderr\": 0.03885425420866767,\n \"acc_norm\": 0.5301204819277109,\n\
\ \"acc_norm_stderr\": 0.03885425420866767\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n\
\ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5789473684210527,\n\
\ \"mc1_stderr\": 0.017283936248136473,\n \"mc2\": 0.7314870038039903,\n\
\ \"mc2_stderr\": 0.014661019064531787\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8374112075769534,\n \"acc_stderr\": 0.010370455551343343\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6459438968915845,\n \
\ \"acc_stderr\": 0.013172728385222574\n }\n}\n```"
repo_url: https://huggingface.co/SC44/Mistral-7B-private-oia
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|arc:challenge|25_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|gsm8k|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hellaswag|10_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-28T20-57-18.847869.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-28T20-57-18.847869.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- '**/details_harness|winogrande|5_2024-01-28T20-57-18.847869.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-01-28T20-57-18.847869.parquet'
- config_name: results
data_files:
- split: 2024_01_28T20_57_18.847869
path:
- results_2024-01-28T20-57-18.847869.parquet
- split: latest
path:
- results_2024-01-28T20-57-18.847869.parquet
---
# Dataset Card for Evaluation run of SC44/Mistral-7B-private-oia
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [SC44/Mistral-7B-private-oia](https://huggingface.co/SC44/Mistral-7B-private-oia) 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_SC44__Mistral-7B-private-oia",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-28T20:57:18.847869](https://huggingface.co/datasets/open-llm-leaderboard/details_SC44__Mistral-7B-private-oia/blob/main/results_2024-01-28T20-57-18.847869.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": 0.6481514121645634,
"acc_stderr": 0.03222955295349873,
"acc_norm": 0.6482463305308075,
"acc_norm_stderr": 0.032893811368600714,
"mc1": 0.5789473684210527,
"mc1_stderr": 0.017283936248136473,
"mc2": 0.7314870038039903,
"mc2_stderr": 0.014661019064531787
},
"harness|arc:challenge|25": {
"acc": 0.7013651877133106,
"acc_stderr": 0.013374078615068747,
"acc_norm": 0.7278156996587031,
"acc_norm_stderr": 0.013006600406423702
},
"harness|hellaswag|10": {
"acc": 0.7261501692889862,
"acc_stderr": 0.004450214826707175,
"acc_norm": 0.892352121091416,
"acc_norm_stderr": 0.0030930175559380035
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695235,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6148148148148148,
"acc_stderr": 0.04203921040156279,
"acc_norm": 0.6148148148148148,
"acc_norm_stderr": 0.04203921040156279
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6973684210526315,
"acc_stderr": 0.03738520676119669,
"acc_norm": 0.6973684210526315,
"acc_norm_stderr": 0.03738520676119669
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.62,
"acc_stderr": 0.048783173121456316,
"acc_norm": 0.62,
"acc_norm_stderr": 0.048783173121456316
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7094339622641509,
"acc_stderr": 0.027943219989337124,
"acc_norm": 0.7094339622641509,
"acc_norm_stderr": 0.027943219989337124
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7638888888888888,
"acc_stderr": 0.03551446610810826,
"acc_norm": 0.7638888888888888,
"acc_norm_stderr": 0.03551446610810826
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.42,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.42,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.59,
"acc_stderr": 0.04943110704237102,
"acc_norm": 0.59,
"acc_norm_stderr": 0.04943110704237102
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.32,
"acc_stderr": 0.04688261722621504,
"acc_norm": 0.32,
"acc_norm_stderr": 0.04688261722621504
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.653179190751445,
"acc_stderr": 0.036291466701596636,
"acc_norm": 0.653179190751445,
"acc_norm_stderr": 0.036291466701596636
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.47058823529411764,
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```
## Dataset Details
### Dataset Description
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### Source Data
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EgilKarlsen/BGL_RoBERTa_Finetuned | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
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dtype: float32
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dtype: float32
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dtype: float32
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dtype: float32
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dtype: float32
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dtype: float32
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dtype: float32
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dtype: float32
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dtype: float32
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dtype: float32
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dtype: float32
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dtype: float32
- name: '759'
dtype: float32
- name: '760'
dtype: float32
- name: '761'
dtype: float32
- name: '762'
dtype: float32
- name: '763'
dtype: float32
- name: '764'
dtype: float32
- name: '765'
dtype: float32
- name: '766'
dtype: float32
- name: '767'
dtype: float32
- name: label
dtype: string
splits:
- name: train
num_bytes: 115582709.0625
num_examples: 37500
- name: test
num_bytes: 38527570.0
num_examples: 12500
download_size: 211881880
dataset_size: 154110279.0625
---
# Dataset Card for "BGL_RoBERTa_Finetuned"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
joaodubeux/bloom-sandbox | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: input_ids
sequence: int32
splits:
- name: train
num_bytes: 409800.0
num_examples: 50
- name: test
num_bytes: 49176.0
num_examples: 6
download_size: 221705
dataset_size: 458976.0
---
# Dataset Card for "bloom-sandbox"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Justj493/test03_3 | ---
license: agpl-3.0
---
|
MaxReynolds/TestUpload3 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 1258070.0
num_examples: 10
download_size: 1259602
dataset_size: 1258070.0
---
# Dataset Card for "TestUpload3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
fuliucansheng/InstructionWild | ---
license: mit
---
|
dbdu/ShareGPT-74k-ko | ---
language:
- ko
pretty_name: ShareGPT-74k-ko
tags:
- conversation
- chatgpt
- gpt-3.5
license: cc-by-2.0
task_categories:
- text-generation
size_categories:
- 10K<n<100K
---
# ShareGPT-ko-74k
ShareGPT 90k의 cleaned 버전을 구글 번역기를 이용하여 번역하였습니다.\
원본 데이터셋은 [여기](https://github.com/lm-sys/FastChat/issues/90)에서 확인하실 수 있습니다.
Korean-translated version of ShareGPT-90k, translated by Google Translaton.\
You can check the original dataset [here](https://github.com/lm-sys/FastChat/issues/90).
## Dataset Description
json 파일의 구조는 원본 데이터셋과 동일합니다.\
`*_unclneaed.json`은 원본 데이터셋을 번역하고 따로 후처리하지 않은 데이터셋입니다. (총 74k)\
`*_cleaned.json`은 위의 데이터에서 코드가 포함된 데이터를 러프하게 제거한 데이터셋입니다. (총 55k)\
**주의**: 코드는 번역되었을 수 있으므로 cleaned를 쓰시는 걸 추천합니다.
The structure of the dataset is the same with the original dataset.\
`*_unclneaed.json` are Korean-translated data, without any post-processing. (total 74k dialogues)\
`*_clneaed.json` are post-processed version which dialogues containing code snippets are eliminated from. (total 55k dialogues)\
**WARNING**: Code snippets might have been translated into Korean. I recommend you use cleaned files.
## Licensing Information
GPT를 이용한 데이터셋이므로 OPENAI의 [약관](https://openai.com/policies/terms-of-use)을 따릅니다.\
그 외의 경우 [CC BY 2.0 KR](https://creativecommons.org/licenses/by/2.0/kr/)을 따릅니다.
The licensing status of the datasets follows [OPENAI Licence](https://openai.com/policies/terms-of-use) as it contains GPT-generated sentences.\
For all the other cases, the licensing status follows [CC BY 2.0 KR](https://creativecommons.org/licenses/by/2.0/kr/).
## Code
번역에 사용한 코드는 아래 리포지토리에서 확인 가능합니다. Check out the following repository to see the translation code used.\
https://github.com/dubuduru/ShareGPT-translation
You can use the repository to translate ShareGPT-like dataset into your preferred language. |
nelson2424/Grocery_chatbot_text_v1 | ---
dataset_info:
features:
- name: text
dtype: string
- name: category
dtype: string
- name: items
dtype: string
splits:
- name: train
num_bytes: 622317
num_examples: 2482
download_size: 204878
dataset_size: 622317
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "Grocery_chatbot_text_classification_v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
zxypro/storyteller-bot-intent-classification | ---
license: apache-2.0
---
# Storyteller intent classification dataset
Data to train a intent classification model for a typical story telling robot.
It has 5 labels, each with 150 sentences.
Labels:
- summarize
- took_action_and_continue
- other
- start_generating_stories
- exit |
tyzhu/squad_qa_wrong_rare_v5_full_recite_ans_sent_first_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: correct_id
dtype: string
- name: inputs
dtype: string
- name: targets
dtype: string
splits:
- name: train
num_bytes: 7960034.039930323
num_examples: 4778
- name: validation
num_bytes: 409972
num_examples: 300
download_size: 1615121
dataset_size: 8370006.039930323
---
# Dataset Card for "squad_qa_wrong_rare_v5_full_recite_ans_sent_first_permute_rerun"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
recogna-nlp/recognasumm | ---
license: mit
task_categories:
- summarization
language:
- pt
tags:
- pt
- pt-br
- summarization
- abstractive summarization
- news
pretty_name: RecognaSumm
size_categories:
- 100K<n<1M
---
# RecognaSumm Dataset
## Introduction
RecognaSumm is a novel and comprehensive database specifically designed for the task of automatic text summarization in Portuguese. RecognaSumm stands out due to its diverse origin, composed of news collected from a variety of information sources, including agencies and online news portals. The database was constructed using web scraping techniques and careful curation, re sulting in a rich and representative collection of documents covering various topics and journalis tic styles. The creation of RecognaSumm aims to fill a significant void in Portuguese language summarization research, providing a training and evaluation foundation that can be used for the development and enhancement of automated summarization models.
## News Categories
| Category | # of news|
| :-: | :-: |
|Brazil | 14,131 |
|Economy | 12,613 |
|Entertainment | 5,337|
|Health | 24,921|
|Policy | 29,909 |
|Science and Technology | 15,135 |
|Sports | 2,915 |
|Travel and Gastronomy | 2,893 |
| World | 27,418 |
| **Total** | **135,272** |
## PTT5-Summ Model
We also trained the [PTT5](https://github.com/unicamp-dl/PTT5) model on this dataset and made it available on HuggingFace. [Click here to access](https://huggingface.co/recogna-nlp/ptt5-base-summ).
# Citation
### RecognaSumm: A Novel Brazilian Summarization Dataset (PROPOR 2024)
Comming soon |
lamaeldo/ICEM | ---
dataset_info:
features:
- name: tokens
sequence: string
- name: ner_tags
sequence: string
splits:
- name: train
num_bytes: 9981
num_examples: 104
download_size: 4245
dataset_size: 9981
---
# Dataset Card for "ICEM"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/type64_girlsfrontline | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of type64/64式/64式 (Girls' Frontline)
This is the dataset of type64/64式/64式 (Girls' Frontline), containing 28 images and their tags.
The core tags of this character are `long_hair, brown_hair, bangs, breasts, red_eyes, hair_ornament, large_breasts, brown_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 28 | 31.78 MiB | [Download](https://huggingface.co/datasets/CyberHarem/type64_girlsfrontline/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 28 | 20.66 MiB | [Download](https://huggingface.co/datasets/CyberHarem/type64_girlsfrontline/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 54 | 35.20 MiB | [Download](https://huggingface.co/datasets/CyberHarem/type64_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 28 | 29.44 MiB | [Download](https://huggingface.co/datasets/CyberHarem/type64_girlsfrontline/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 54 | 47.42 MiB | [Download](https://huggingface.co/datasets/CyberHarem/type64_girlsfrontline/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/type64_girlsfrontline',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 10 |  |  |  |  |  | 1girl, solo, bare_shoulders, looking_at_viewer, smile, dress, chinese_clothes, holding, cleavage_cutout, full_body, hair_flower, medium_breasts |
| 1 | 5 |  |  |  |  |  | 1girl, closed_mouth, solo, black_gloves, black_hair, fingerless_gloves, green_shirt, headphones, simple_background, white_background, armband, collared_shirt, looking_at_viewer, military_uniform, short_sleeves, hair_between_eyes, holding_gun, pouch |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | bare_shoulders | looking_at_viewer | smile | dress | chinese_clothes | holding | cleavage_cutout | full_body | hair_flower | medium_breasts | closed_mouth | black_gloves | black_hair | fingerless_gloves | green_shirt | headphones | simple_background | white_background | armband | collared_shirt | military_uniform | short_sleeves | hair_between_eyes | holding_gun | pouch |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:-----------------|:--------------------|:--------|:--------|:------------------|:----------|:------------------|:------------|:--------------|:-----------------|:---------------|:---------------|:-------------|:--------------------|:--------------|:-------------|:--------------------|:-------------------|:----------|:-----------------|:-------------------|:----------------|:--------------------|:--------------|:--------|
| 0 | 10 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | X | | X | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
autoevaluate/autoeval-eval-aslg_pc12-default-041a04-95805146498 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- aslg_pc12
eval_info:
task: translation
model: HamdanXI/t5_small_aslg_pc12
metrics: ['rouge']
dataset_name: aslg_pc12
dataset_config: default
dataset_split: train
col_mapping:
source: gloss
target: text
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Translation
* Model: HamdanXI/t5_small_aslg_pc12
* Dataset: aslg_pc12
* Config: default
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@HamdanXI](https://huggingface.co/HamdanXI) for evaluating this model. |
piotr-rybak/legal-questions | ---
language:
- pl
library_name: transformers
---
This dataset contains questions and passages from Polish law.
The dataset was created by randomly searching for provisions and asking questions related to that provision, in the
style of SQuAD. As a result, the questions might be biassed towards the content of a specific provision.
The authors of this dataset are student from AGH University of Krakow, supervised by [Aleksander
Smywiński-Pohl](https://huggingface.co/apohllo), PhD.
If you use the dataset, please cite the following article:
```
@article{kobylinski2023poleval,
title={PolEval 2022/23 Challenge Tasks and Results},
author={Kobylinski, {\L}ukasz and Ogrodniczuk, Maciej and Rybak, Piotr and Przyby{\l}a, Piotr and Pezik, Piotr and Miko{\l}ajczyk, Agnieszka and Janowski, Wojciech and Marcinczuk, Micha{\l} and Smywinski-Pohl, Aleksander},
year={2023},
journal={Proceedings of the 18th Conference on Computer Science and Intelligence Systems},
pages={1243–1250}
}
```
|
otavinshow/minhavoz | ---
license: openrail
---
|
tiagoblima/du-qg-squadv1_pt | ---
dataset_info:
features:
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answer
dtype: string
- name: paragraph_id
dtype: string
splits:
- name: train
num_bytes: 73536399
num_examples: 75722
- name: validation
num_bytes: 10455240
num_examples: 10570
- name: test
num_bytes: 10735398
num_examples: 11877
download_size: 16965943
dataset_size: 94727037
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
AdapterOcean/med_alpaca_standardized_cluster_70_std | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
- name: cluster
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 13811447
num_examples: 24891
download_size: 6748784
dataset_size: 13811447
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "med_alpaca_standardized_cluster_70_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
liuyanchen1015/MULTI_VALUE_cola_analytic_whose_relativizer | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: dev
num_bytes: 406
num_examples: 4
- name: test
num_bytes: 557
num_examples: 6
- name: train
num_bytes: 937
num_examples: 11
download_size: 7392
dataset_size: 1900
---
# Dataset Card for "MULTI_VALUE_cola_analytic_whose_relativizer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
RikoteMaster/isear_for_llama2_v2 | ---
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: Text_processed
dtype: string
- name: Emotion
dtype: string
- name: Augmented
dtype: bool
- name: text
dtype: string
splits:
- name: train
num_bytes: 15503742
num_examples: 7499
- name: validation
num_bytes: 2734776
num_examples: 1324
- name: test
num_bytes: 3819549
num_examples: 1879
download_size: 3535009
dataset_size: 22058067
---
# Dataset Card for "isear_for_llama2_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Falah/angela_lora | ---
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 4453869.0
num_examples: 21
download_size: 3801094
dataset_size: 4453869.0
---
# Dataset Card for "angela_lora"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
nczarli/cherry_images_1 | ---
license: apache-2.0
---
|
CyberHarem/mudrock_arknights | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of mudrock/マドロック/泥岩 (Arknights)
This is the dataset of mudrock/マドロック/泥岩 (Arknights), containing 500 images and their tags.
The core tags of this character are `horns, long_hair, red_eyes, pointy_ears, breasts, white_hair, large_breasts, hair_ornament, grey_hair, very_long_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 1.08 GiB | [Download](https://huggingface.co/datasets/CyberHarem/mudrock_arknights/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 475.55 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mudrock_arknights/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1369 | 1.09 GiB | [Download](https://huggingface.co/datasets/CyberHarem/mudrock_arknights/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 895.00 MiB | [Download](https://huggingface.co/datasets/CyberHarem/mudrock_arknights/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1369 | 1.76 GiB | [Download](https://huggingface.co/datasets/CyberHarem/mudrock_arknights/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/mudrock_arknights',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 15 |  |  |  |  |  | 1girl, bare_shoulders, black_sports_bra, crop_top, midriff, solo, black_choker, infection_monitor_(arknights), navel, off_shoulder, oripathy_lesion_(arknights), stomach, looking_at_viewer, cleavage, collarbone, parted_lips, upper_body, black_gloves, long_sleeves, cowboy_shot, sarashi, standing, open_clothes, simple_background |
| 1 | 5 |  |  |  |  |  | 1girl, bare_shoulders, black_gloves, black_sports_bra, crop_top, holding_hammer, holding_weapon, infection_monitor_(arknights), looking_at_viewer, midriff, navel, off_shoulder, open_clothes, oripathy_lesion_(arknights), solo, choker, cleavage, long_sleeves, stomach, upper_body, collarbone, medium_breasts, open_mouth, piercing |
| 2 | 14 |  |  |  |  |  | 1girl, bare_shoulders, black_bikini, cleavage, hair_flower, infection_monitor_(arknights), looking_at_viewer, official_alternate_costume, oripathy_lesion_(arknights), solo, yellow_flower, necklace, navel, stomach, parted_lips, black_choker, cowboy_shot, collarbone, armlet, blush, sitting, standing, water |
| 3 | 6 |  |  |  |  |  | 1girl, apple, bare_shoulders, black_bikini, black_choker, hair_flower, holding_fruit, looking_at_viewer, navel, necklace, official_alternate_costume, oripathy_lesion_(arknights), solo, stomach, yellow_flower, cleavage, infection_monitor_(arknights), parted_lips, barefoot, medium_breasts, wariza |
| 4 | 5 |  |  |  |  |  | 1girl, apple, bare_shoulders, black_bikini, cleavage, cowboy_shot, hair_flower, holding_fruit, infection_monitor_(arknights), looking_at_viewer, navel, necklace, official_alternate_costume, oripathy_lesion_(arknights), solo, stomach, yellow_flower, parted_lips, standing, simple_background, white_background, black_choker, hand_up, medium_breasts, sarong |
| 5 | 52 |  |  |  |  |  | 1girl, bare_shoulders, black_dress, official_alternate_costume, solo, looking_at_viewer, necklace, cleavage, detached_sleeves, short_sleeves, black_gloves, black_choker, earrings, single_glove, drinking_glass, holding_cup, parted_lips, standing |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | black_sports_bra | crop_top | midriff | solo | black_choker | infection_monitor_(arknights) | navel | off_shoulder | oripathy_lesion_(arknights) | stomach | looking_at_viewer | cleavage | collarbone | parted_lips | upper_body | black_gloves | long_sleeves | cowboy_shot | sarashi | standing | open_clothes | simple_background | holding_hammer | holding_weapon | choker | medium_breasts | open_mouth | piercing | black_bikini | hair_flower | official_alternate_costume | yellow_flower | necklace | armlet | blush | sitting | water | apple | holding_fruit | barefoot | wariza | white_background | hand_up | sarong | black_dress | detached_sleeves | short_sleeves | earrings | single_glove | drinking_glass | holding_cup |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:-------------------|:-----------|:----------|:-------|:---------------|:--------------------------------|:--------|:---------------|:------------------------------|:----------|:--------------------|:-----------|:-------------|:--------------|:-------------|:---------------|:---------------|:--------------|:----------|:-----------|:---------------|:--------------------|:-----------------|:-----------------|:---------|:-----------------|:-------------|:-----------|:---------------|:--------------|:-----------------------------|:----------------|:-----------|:---------|:--------|:----------|:--------|:--------|:----------------|:-----------|:---------|:-------------------|:----------|:---------|:--------------|:-------------------|:----------------|:-----------|:---------------|:-----------------|:--------------|
| 0 | 15 |  |  |  |  |  | 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 | 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 | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 14 |  |  |  |  |  | X | X | | | | X | X | X | X | | X | X | X | X | X | X | | | | X | | X | | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | |
| 3 | 6 |  |  |  |  |  | X | X | | | | X | X | X | X | | X | X | X | X | | X | | | | | | | | | | | | X | | | X | X | X | X | X | | | | | X | X | X | X | | | | | | | | | | |
| 4 | 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 | | | | | | | |
| 5 | 52 |  |  |  |  |  | X | X | | | | X | X | | | | | | X | X | | X | | X | | | | X | | | | | | | | | | | X | | X | | | | | | | | | | | | X | X | X | X | X | X | X |
|
spr1916/building_type_classification_test | ---
dataset_info:
features:
- name: image
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 24630
num_examples: 312
download_size: 4454
dataset_size: 24630
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "building_type_classification_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
boopysaur/mistral-childrens-books | ---
language:
- en
dataset_info:
features:
- name: text
dtype: string
- name: title
dtype: string
splits:
- name: train
num_bytes: 342745
num_examples: 452
download_size: 203603
dataset_size: 342745
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
manu/project_gutenberg | ---
dataset_info:
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: de
num_bytes: 1070196924
num_examples: 3131
- name: en
num_bytes: 25616345280
num_examples: 61340
- name: es
num_bytes: 496728508
num_examples: 1202
- name: fr
num_bytes: 2338871137
num_examples: 5493
- name: it
num_bytes: 383733486
num_examples: 1008
- name: nl
num_bytes: 504939551
num_examples: 1420
- name: pl
num_bytes: 4864460
num_examples: 34
- name: pt
num_bytes: 204058452
num_examples: 1111
- name: ru
num_bytes: 943593
num_examples: 6
- name: sv
num_bytes: 116664385
num_examples: 388
- name: zh
num_bytes: 174238359
num_examples: 437
download_size: 14399256761
dataset_size: 30911584135
task_categories:
- text-generation
language:
- fr
- en
- zh
- pt
- pl
- nl
- ru
- sv
- it
- de
- es
pretty_name: Project Gutenberg
size_categories:
- 10K<n<100K
---
# Dataset Card for "Project Gutenberg"
Project Gutenberg is a library of over 70,000 free eBooks, hosted at https://www.gutenberg.org/.
All examples correspond to a single book, and contain a header and a footer of a few lines (delimited by a *** Start of *** and *** End of *** tags).
### Usage
```python
from datasets import load_dataset
ds = load_dataset("manu/project_gutenberg", split="fr", streaming=True)
print(next(iter(ds)))
```
### License
Full license is available here:
https://www.gutenberg.org/policy/license.html
#### Summary
For nearly all uses, in nearly all parts of the world, the opening words of all of our eBooks apply: This eBook is for the use of anyone anywhere in the United States and most other parts of the world at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at [www.gutenberg.org]. If you are not located in the United States, you’ll have to check the laws of the country where you are located before using this ebook.”
##### Using the Project Gutenberg Trademark
If you want to use the name Project Gutenberg anywhere in the ebooks you distribute or on the distribution medium or in advertising you have to obey these rules:
- you may only distribute verbatim copies of the ebooks. No changes are allowed to the ebook contents. (Though reformatting the ebook to a different file format is considered okay).
- If you charge money for the copies you distribute, you have to pay royalties to Project Gutenberg.
- You must refund your clients for defective copies or if they don’t agree with the Project Gutenberg license.
If you don’t agree with any of the above mentioned restrictions, you may not use the Project Gutenberg trademark. You may still distribute the ebooks if you strip the Project Gutenberg license and all references to Project Gutenberg. |
freshpearYoon/v3_train_free_7 | ---
dataset_info:
features:
- name: input_features
sequence:
sequence: float32
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 15366848936
num_examples: 10000
download_size: 2692781118
dataset_size: 15366848936
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
LukeGPT88/patient-doctor-text-classifier-eng-dataset | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 2167613
num_examples: 24746
- name: validation
num_bytes: 712512
num_examples: 8249
- name: test
num_bytes: 716933
num_examples: 8249
download_size: 2372348
dataset_size: 3597058
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
Label is used to give a context to the related text using the following map :
- 0 --> "PATIENT"
- 1 --> "DOCTOR"
- 2 --> "NEUTRAL" |
autoevaluate/autoeval-staging-eval-project-squad_v2-e06b4410-11855586 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: deepset/bert-medium-squad2-distilled
metrics: []
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: deepset/bert-medium-squad2-distilled
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@sjrlee](https://huggingface.co/sjrlee) for evaluating this model. |
hemachandher/new_datasetsingle | ---
dataset_info:
features:
- name: image
struct:
- name: bytes
dtype: binary
- name: path
dtype: 'null'
- name: text
dtype: string
splits:
- name: train
num_bytes: 943273
num_examples: 1
download_size: 945943
dataset_size: 943273
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
CyberHarem/ophelia_phamrsolone_fgo | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of Ophelia Phamrsolone (Fate/Grand Order)
This is the dataset of Ophelia Phamrsolone (Fate/Grand Order), containing 180 images and their tags.
The core tags of this character are `long_hair, brown_hair, eyepatch, blue_eyes, hair_over_one_eye, ribbon, black_ribbon, bangs, neck_ribbon`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 180 | 190.51 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ophelia_phamrsolone_fgo/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 180 | 120.42 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ophelia_phamrsolone_fgo/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 402 | 247.71 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ophelia_phamrsolone_fgo/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 180 | 176.32 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ophelia_phamrsolone_fgo/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 402 | 335.81 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ophelia_phamrsolone_fgo/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/ophelia_phamrsolone_fgo',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 22 |  |  |  |  |  | 1girl, solo, upper_body, long_sleeves, looking_at_viewer, collared_shirt, white_shirt, black_jacket, closed_mouth, simple_background, white_background |
| 1 | 6 |  |  |  |  |  | 1girl, black_pantyhose, closed_mouth, long_sleeves, looking_at_viewer, simple_background, solo, white_background, blue_skirt, collared_shirt, white_shirt, black_jacket, breasts, cowboy_shot |
| 2 | 10 |  |  |  |  |  | 1girl, looking_at_viewer, solo, blush, navel, collarbone, medium_breasts, cleavage, black_bikini, open_clothes, simple_background, jacket, long_sleeves, open_mouth |
| 3 | 5 |  |  |  |  |  | 1boy, 1girl, black_jacket, blush, hetero, long_sleeves, solo_focus, clothed_sex, condom_wrapper, looking_at_viewer, medium_breasts, open_mouth, pussy, thighs, vaginal, mosaic_censoring, open_jacket, open_shirt, pillow, white_shirt, cleavage, collared_shirt, condom_on_penis, missionary, navel, nipples, on_back, on_side, panties_aside, panty_pull, pantyhose_pull, pink_bra, sex_from_behind, used_condom |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | upper_body | long_sleeves | looking_at_viewer | collared_shirt | white_shirt | black_jacket | closed_mouth | simple_background | white_background | black_pantyhose | blue_skirt | breasts | cowboy_shot | blush | navel | collarbone | medium_breasts | cleavage | black_bikini | open_clothes | jacket | open_mouth | 1boy | hetero | solo_focus | clothed_sex | condom_wrapper | pussy | thighs | vaginal | mosaic_censoring | open_jacket | open_shirt | pillow | condom_on_penis | missionary | nipples | on_back | on_side | panties_aside | panty_pull | pantyhose_pull | pink_bra | sex_from_behind | used_condom |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:-------------|:---------------|:--------------------|:-----------------|:--------------|:---------------|:---------------|:--------------------|:-------------------|:------------------|:-------------|:----------|:--------------|:--------|:--------|:-------------|:-----------------|:-----------|:---------------|:---------------|:---------|:-------------|:-------|:---------|:-------------|:--------------|:-----------------|:--------|:---------|:----------|:-------------------|:--------------|:-------------|:---------|:------------------|:-------------|:----------|:----------|:----------|:----------------|:-------------|:-----------------|:-----------|:------------------|:--------------|
| 0 | 22 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 6 |  |  |  |  |  | X | X | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 10 |  |  |  |  |  | X | X | | X | X | | | | | X | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 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 |
|
thercyl/ROKU | ---
dataset_info:
features:
- name: 'Unnamed: 0'
dtype: float64
- name: Ticker
dtype: string
- name: Year
dtype: string
- name: Text
dtype: string
- name: Embedding
dtype: string
splits:
- name: train
num_bytes: 70396393
num_examples: 2010
download_size: 44893298
dataset_size: 70396393
---
# Dataset Card for "ROKU"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
bigbio/ncbi_disease |
---
language:
- en
bigbio_language:
- English
license: cc0-1.0
multilinguality: monolingual
bigbio_license_shortname: CC0_1p0
pretty_name: NCBI Disease
homepage: https://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
- NAMED_ENTITY_DISAMBIGUATION
---
# Dataset Card for NCBI Disease
## Dataset Description
- **Homepage:** https://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/
- **Pubmed:** True
- **Public:** True
- **Tasks:** NER,NED
The NCBI disease corpus is fully annotated at the mention and concept level to serve as a research
resource for the biomedical natural language processing community.
## Citation Information
```
@article{Dogan2014NCBIDC,
title = {NCBI disease corpus: A resource for disease name recognition and concept normalization},
author = {Rezarta Islamaj Dogan and Robert Leaman and Zhiyong Lu},
year = 2014,
journal = {Journal of biomedical informatics},
volume = 47,
pages = {1--10}
}
```
|
open-llm-leaderboard/details_garage-bAInd__Dolphin-Platypus2-70B | ---
pretty_name: Evaluation run of garage-bAInd/Dolphin-Platypus2-70B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [garage-bAInd/Dolphin-Platypus2-70B](https://huggingface.co/garage-bAInd/Dolphin-Platypus2-70B)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 61 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 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_garage-bAInd__Dolphin-Platypus2-70B\"\
,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\
\nThese are the [latest results from run 2023-08-10T02:32:56.587713](https://huggingface.co/datasets/open-llm-leaderboard/details_garage-bAInd__Dolphin-Platypus2-70B/blob/main/results_2023-08-10T02%3A32%3A56.587713.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\": 0.6895249670105975,\n\
\ \"acc_stderr\": 0.031417385723151066,\n \"acc_norm\": 0.6936032221534247,\n\
\ \"acc_norm_stderr\": 0.031387123187245417,\n \"mc1\": 0.397796817625459,\n\
\ \"mc1_stderr\": 0.017133934248559635,\n \"mc2\": 0.566489803511904,\n\
\ \"mc2_stderr\": 0.014977450728482283\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6629692832764505,\n \"acc_stderr\": 0.01381347665290228,\n\
\ \"acc_norm\": 0.7039249146757679,\n \"acc_norm_stderr\": 0.013340916085246261\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6672973511252739,\n\
\ \"acc_stderr\": 0.0047021810422158885,\n \"acc_norm\": 0.8669587731527584,\n\
\ \"acc_norm_stderr\": 0.0033892519914384936\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6444444444444445,\n\
\ \"acc_stderr\": 0.04135176749720385,\n \"acc_norm\": 0.6444444444444445,\n\
\ \"acc_norm_stderr\": 0.04135176749720385\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.7631578947368421,\n \"acc_stderr\": 0.03459777606810535,\n\
\ \"acc_norm\": 0.7631578947368421,\n \"acc_norm_stderr\": 0.03459777606810535\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.72,\n\
\ \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.72,\n \
\ \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.720754716981132,\n \"acc_stderr\": 0.027611163402399715,\n\
\ \"acc_norm\": 0.720754716981132,\n \"acc_norm_stderr\": 0.027611163402399715\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8263888888888888,\n\
\ \"acc_stderr\": 0.03167473383795718,\n \"acc_norm\": 0.8263888888888888,\n\
\ \"acc_norm_stderr\": 0.03167473383795718\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \
\ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.53,\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\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \
\ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6589595375722543,\n\
\ \"acc_stderr\": 0.03614665424180826,\n \"acc_norm\": 0.6589595375722543,\n\
\ \"acc_norm_stderr\": 0.03614665424180826\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.3235294117647059,\n \"acc_stderr\": 0.046550104113196177,\n\
\ \"acc_norm\": 0.3235294117647059,\n \"acc_norm_stderr\": 0.046550104113196177\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.76,\n \"acc_stderr\": 0.04292346959909281,\n \"acc_norm\": 0.76,\n\
\ \"acc_norm_stderr\": 0.04292346959909281\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.6382978723404256,\n \"acc_stderr\": 0.0314108219759624,\n\
\ \"acc_norm\": 0.6382978723404256,\n \"acc_norm_stderr\": 0.0314108219759624\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4473684210526316,\n\
\ \"acc_stderr\": 0.04677473004491199,\n \"acc_norm\": 0.4473684210526316,\n\
\ \"acc_norm_stderr\": 0.04677473004491199\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5586206896551724,\n \"acc_stderr\": 0.04137931034482757,\n\
\ \"acc_norm\": 0.5586206896551724,\n \"acc_norm_stderr\": 0.04137931034482757\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4576719576719577,\n \"acc_stderr\": 0.025658868862058325,\n \"\
acc_norm\": 0.4576719576719577,\n \"acc_norm_stderr\": 0.025658868862058325\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5158730158730159,\n\
\ \"acc_stderr\": 0.044698818540726076,\n \"acc_norm\": 0.5158730158730159,\n\
\ \"acc_norm_stderr\": 0.044698818540726076\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \
\ \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.049756985195624284\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.8,\n \"acc_stderr\": 0.02275520495954294,\n \"acc_norm\": 0.8,\n\
\ \"acc_norm_stderr\": 0.02275520495954294\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5320197044334976,\n \"acc_stderr\": 0.035107665979592154,\n\
\ \"acc_norm\": 0.5320197044334976,\n \"acc_norm_stderr\": 0.035107665979592154\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\"\
: 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.8545454545454545,\n \"acc_stderr\": 0.027530196355066573,\n\
\ \"acc_norm\": 0.8545454545454545,\n \"acc_norm_stderr\": 0.027530196355066573\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8535353535353535,\n \"acc_stderr\": 0.025190921114603918,\n \"\
acc_norm\": 0.8535353535353535,\n \"acc_norm_stderr\": 0.025190921114603918\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.917098445595855,\n \"acc_stderr\": 0.01989934131572178,\n\
\ \"acc_norm\": 0.917098445595855,\n \"acc_norm_stderr\": 0.01989934131572178\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.7051282051282052,\n \"acc_stderr\": 0.023119362758232294,\n\
\ \"acc_norm\": 0.7051282051282052,\n \"acc_norm_stderr\": 0.023119362758232294\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3037037037037037,\n \"acc_stderr\": 0.028037929969114986,\n \
\ \"acc_norm\": 0.3037037037037037,\n \"acc_norm_stderr\": 0.028037929969114986\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.7605042016806722,\n \"acc_stderr\": 0.027722065493361262,\n\
\ \"acc_norm\": 0.7605042016806722,\n \"acc_norm_stderr\": 0.027722065493361262\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.48344370860927155,\n \"acc_stderr\": 0.040802441856289715,\n \"\
acc_norm\": 0.48344370860927155,\n \"acc_norm_stderr\": 0.040802441856289715\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8844036697247707,\n \"acc_stderr\": 0.01370874953417264,\n \"\
acc_norm\": 0.8844036697247707,\n \"acc_norm_stderr\": 0.01370874953417264\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5879629629629629,\n \"acc_stderr\": 0.03356787758160831,\n \"\
acc_norm\": 0.5879629629629629,\n \"acc_norm_stderr\": 0.03356787758160831\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.9068627450980392,\n \"acc_stderr\": 0.020397853969427,\n \"acc_norm\"\
: 0.9068627450980392,\n \"acc_norm_stderr\": 0.020397853969427\n },\n\
\ \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\":\
\ 0.890295358649789,\n \"acc_stderr\": 0.020343400734868837,\n \"\
acc_norm\": 0.890295358649789,\n \"acc_norm_stderr\": 0.020343400734868837\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7802690582959642,\n\
\ \"acc_stderr\": 0.027790177064383595,\n \"acc_norm\": 0.7802690582959642,\n\
\ \"acc_norm_stderr\": 0.027790177064383595\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.816793893129771,\n \"acc_stderr\": 0.03392770926494733,\n\
\ \"acc_norm\": 0.816793893129771,\n \"acc_norm_stderr\": 0.03392770926494733\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.859504132231405,\n \"acc_stderr\": 0.03172233426002158,\n \"acc_norm\"\
: 0.859504132231405,\n \"acc_norm_stderr\": 0.03172233426002158\n },\n\
\ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8240740740740741,\n\
\ \"acc_stderr\": 0.036809181416738807,\n \"acc_norm\": 0.8240740740740741,\n\
\ \"acc_norm_stderr\": 0.036809181416738807\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7914110429447853,\n \"acc_stderr\": 0.031921934489347235,\n\
\ \"acc_norm\": 0.7914110429447853,\n \"acc_norm_stderr\": 0.031921934489347235\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5803571428571429,\n\
\ \"acc_stderr\": 0.04684099321077106,\n \"acc_norm\": 0.5803571428571429,\n\
\ \"acc_norm_stderr\": 0.04684099321077106\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8252427184466019,\n \"acc_stderr\": 0.037601780060266196,\n\
\ \"acc_norm\": 0.8252427184466019,\n \"acc_norm_stderr\": 0.037601780060266196\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9145299145299145,\n\
\ \"acc_stderr\": 0.01831589168562585,\n \"acc_norm\": 0.9145299145299145,\n\
\ \"acc_norm_stderr\": 0.01831589168562585\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \
\ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8633461047254151,\n\
\ \"acc_stderr\": 0.012282876868629234,\n \"acc_norm\": 0.8633461047254151,\n\
\ \"acc_norm_stderr\": 0.012282876868629234\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7427745664739884,\n \"acc_stderr\": 0.023532925431044287,\n\
\ \"acc_norm\": 0.7427745664739884,\n \"acc_norm_stderr\": 0.023532925431044287\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.6312849162011173,\n\
\ \"acc_stderr\": 0.01613575901503012,\n \"acc_norm\": 0.6312849162011173,\n\
\ \"acc_norm_stderr\": 0.01613575901503012\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7483660130718954,\n \"acc_stderr\": 0.024848018263875195,\n\
\ \"acc_norm\": 0.7483660130718954,\n \"acc_norm_stderr\": 0.024848018263875195\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7813504823151125,\n\
\ \"acc_stderr\": 0.023475581417861113,\n \"acc_norm\": 0.7813504823151125,\n\
\ \"acc_norm_stderr\": 0.023475581417861113\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.845679012345679,\n \"acc_stderr\": 0.020100830999850994,\n\
\ \"acc_norm\": 0.845679012345679,\n \"acc_norm_stderr\": 0.020100830999850994\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.5567375886524822,\n \"acc_stderr\": 0.029634838473766006,\n \
\ \"acc_norm\": 0.5567375886524822,\n \"acc_norm_stderr\": 0.029634838473766006\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5691003911342895,\n\
\ \"acc_stderr\": 0.012647695889547226,\n \"acc_norm\": 0.5691003911342895,\n\
\ \"acc_norm_stderr\": 0.012647695889547226\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.7169117647058824,\n \"acc_stderr\": 0.02736586113151381,\n\
\ \"acc_norm\": 0.7169117647058824,\n \"acc_norm_stderr\": 0.02736586113151381\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.7450980392156863,\n \"acc_stderr\": 0.01763082737514838,\n \
\ \"acc_norm\": 0.7450980392156863,\n \"acc_norm_stderr\": 0.01763082737514838\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7181818181818181,\n\
\ \"acc_stderr\": 0.04309118709946458,\n \"acc_norm\": 0.7181818181818181,\n\
\ \"acc_norm_stderr\": 0.04309118709946458\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7714285714285715,\n \"acc_stderr\": 0.02688214492230774,\n\
\ \"acc_norm\": 0.7714285714285715,\n \"acc_norm_stderr\": 0.02688214492230774\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8805970149253731,\n\
\ \"acc_stderr\": 0.02292879327721974,\n \"acc_norm\": 0.8805970149253731,\n\
\ \"acc_norm_stderr\": 0.02292879327721974\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.87,\n \"acc_stderr\": 0.033799766898963086,\n \
\ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.033799766898963086\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.536144578313253,\n\
\ \"acc_stderr\": 0.038823108508905954,\n \"acc_norm\": 0.536144578313253,\n\
\ \"acc_norm_stderr\": 0.038823108508905954\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.847953216374269,\n \"acc_stderr\": 0.02753912288906145,\n\
\ \"acc_norm\": 0.847953216374269,\n \"acc_norm_stderr\": 0.02753912288906145\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.397796817625459,\n\
\ \"mc1_stderr\": 0.017133934248559635,\n \"mc2\": 0.566489803511904,\n\
\ \"mc2_stderr\": 0.014977450728482283\n }\n}\n```"
repo_url: https://huggingface.co/garage-bAInd/Dolphin-Platypus2-70B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|arc:challenge|25_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hellaswag|10_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-10T02:32:56.587713.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-10T02:32:56.587713.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-10T02:32:56.587713.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-10T02:32:56.587713.parquet'
- config_name: results
data_files:
- split: 2023_08_10T02_32_56.587713
path:
- results_2023-08-10T02:32:56.587713.parquet
- split: latest
path:
- results_2023-08-10T02:32:56.587713.parquet
---
# Dataset Card for Evaluation run of garage-bAInd/Dolphin-Platypus2-70B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/garage-bAInd/Dolphin-Platypus2-70B
- **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 [garage-bAInd/Dolphin-Platypus2-70B](https://huggingface.co/garage-bAInd/Dolphin-Platypus2-70B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 61 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 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_garage-bAInd__Dolphin-Platypus2-70B",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-08-10T02:32:56.587713](https://huggingface.co/datasets/open-llm-leaderboard/details_garage-bAInd__Dolphin-Platypus2-70B/blob/main/results_2023-08-10T02%3A32%3A56.587713.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": 0.6895249670105975,
"acc_stderr": 0.031417385723151066,
"acc_norm": 0.6936032221534247,
"acc_norm_stderr": 0.031387123187245417,
"mc1": 0.397796817625459,
"mc1_stderr": 0.017133934248559635,
"mc2": 0.566489803511904,
"mc2_stderr": 0.014977450728482283
},
"harness|arc:challenge|25": {
"acc": 0.6629692832764505,
"acc_stderr": 0.01381347665290228,
"acc_norm": 0.7039249146757679,
"acc_norm_stderr": 0.013340916085246261
},
"harness|hellaswag|10": {
"acc": 0.6672973511252739,
"acc_stderr": 0.0047021810422158885,
"acc_norm": 0.8669587731527584,
"acc_norm_stderr": 0.0033892519914384936
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.32,
"acc_stderr": 0.04688261722621504,
"acc_norm": 0.32,
"acc_norm_stderr": 0.04688261722621504
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6444444444444445,
"acc_stderr": 0.04135176749720385,
"acc_norm": 0.6444444444444445,
"acc_norm_stderr": 0.04135176749720385
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7631578947368421,
"acc_stderr": 0.03459777606810535,
"acc_norm": 0.7631578947368421,
"acc_norm_stderr": 0.03459777606810535
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.72,
"acc_stderr": 0.04512608598542128,
"acc_norm": 0.72,
"acc_norm_stderr": 0.04512608598542128
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.720754716981132,
"acc_stderr": 0.027611163402399715,
"acc_norm": 0.720754716981132,
"acc_norm_stderr": 0.027611163402399715
},
"harness|hendrycksTest-college_biology|5": {
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"acc_stderr": 0.03167473383795718,
"acc_norm": 0.8263888888888888,
"acc_norm_stderr": 0.03167473383795718
},
"harness|hendrycksTest-college_chemistry|5": {
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"acc_norm": 0.48,
"acc_norm_stderr": 0.050211673156867795
},
"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": {
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"acc_norm": 0.4,
"acc_norm_stderr": 0.049236596391733084
},
"harness|hendrycksTest-college_medicine|5": {
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"acc_stderr": 0.03614665424180826,
"acc_norm": 0.6589595375722543,
"acc_norm_stderr": 0.03614665424180826
},
"harness|hendrycksTest-college_physics|5": {
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"acc_stderr": 0.046550104113196177,
"acc_norm": 0.3235294117647059,
"acc_norm_stderr": 0.046550104113196177
},
"harness|hendrycksTest-computer_security|5": {
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"acc_stderr": 0.04292346959909281,
"acc_norm": 0.76,
"acc_norm_stderr": 0.04292346959909281
},
"harness|hendrycksTest-conceptual_physics|5": {
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"acc_stderr": 0.0314108219759624,
"acc_norm": 0.6382978723404256,
"acc_norm_stderr": 0.0314108219759624
},
"harness|hendrycksTest-econometrics|5": {
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"acc_stderr": 0.04677473004491199,
"acc_norm": 0.4473684210526316,
"acc_norm_stderr": 0.04677473004491199
},
"harness|hendrycksTest-electrical_engineering|5": {
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"acc_stderr": 0.04137931034482757,
"acc_norm": 0.5586206896551724,
"acc_norm_stderr": 0.04137931034482757
},
"harness|hendrycksTest-elementary_mathematics|5": {
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"acc_norm": 0.4576719576719577,
"acc_norm_stderr": 0.025658868862058325
},
"harness|hendrycksTest-formal_logic|5": {
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"acc_norm": 0.5158730158730159,
"acc_norm_stderr": 0.044698818540726076
},
"harness|hendrycksTest-global_facts|5": {
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"acc_stderr": 0.049756985195624284,
"acc_norm": 0.43,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-high_school_biology|5": {
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"acc_norm": 0.8,
"acc_norm_stderr": 0.02275520495954294
},
"harness|hendrycksTest-high_school_chemistry|5": {
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"acc_stderr": 0.035107665979592154,
"acc_norm": 0.5320197044334976,
"acc_norm_stderr": 0.035107665979592154
},
"harness|hendrycksTest-high_school_computer_science|5": {
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"acc_stderr": 0.04351941398892446,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-high_school_european_history|5": {
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"acc_norm": 0.8545454545454545,
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},
"harness|hendrycksTest-high_school_geography|5": {
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},
"harness|hendrycksTest-high_school_government_and_politics|5": {
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"acc_norm_stderr": 0.01989934131572178
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"harness|hendrycksTest-high_school_macroeconomics|5": {
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"acc_norm": 0.7051282051282052,
"acc_norm_stderr": 0.023119362758232294
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"harness|hendrycksTest-high_school_mathematics|5": {
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"acc_norm": 0.3037037037037037,
"acc_norm_stderr": 0.028037929969114986
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.7605042016806722,
"acc_stderr": 0.027722065493361262,
"acc_norm": 0.7605042016806722,
"acc_norm_stderr": 0.027722065493361262
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.48344370860927155,
"acc_stderr": 0.040802441856289715,
"acc_norm": 0.48344370860927155,
"acc_norm_stderr": 0.040802441856289715
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8844036697247707,
"acc_stderr": 0.01370874953417264,
"acc_norm": 0.8844036697247707,
"acc_norm_stderr": 0.01370874953417264
},
"harness|hendrycksTest-high_school_statistics|5": {
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"acc_stderr": 0.03356787758160831,
"acc_norm": 0.5879629629629629,
"acc_norm_stderr": 0.03356787758160831
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.9068627450980392,
"acc_stderr": 0.020397853969427,
"acc_norm": 0.9068627450980392,
"acc_norm_stderr": 0.020397853969427
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.890295358649789,
"acc_stderr": 0.020343400734868837,
"acc_norm": 0.890295358649789,
"acc_norm_stderr": 0.020343400734868837
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.7802690582959642,
"acc_stderr": 0.027790177064383595,
"acc_norm": 0.7802690582959642,
"acc_norm_stderr": 0.027790177064383595
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.816793893129771,
"acc_stderr": 0.03392770926494733,
"acc_norm": 0.816793893129771,
"acc_norm_stderr": 0.03392770926494733
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.859504132231405,
"acc_stderr": 0.03172233426002158,
"acc_norm": 0.859504132231405,
"acc_norm_stderr": 0.03172233426002158
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.8240740740740741,
"acc_stderr": 0.036809181416738807,
"acc_norm": 0.8240740740740741,
"acc_norm_stderr": 0.036809181416738807
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7914110429447853,
"acc_stderr": 0.031921934489347235,
"acc_norm": 0.7914110429447853,
"acc_norm_stderr": 0.031921934489347235
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.5803571428571429,
"acc_stderr": 0.04684099321077106,
"acc_norm": 0.5803571428571429,
"acc_norm_stderr": 0.04684099321077106
},
"harness|hendrycksTest-management|5": {
"acc": 0.8252427184466019,
"acc_stderr": 0.037601780060266196,
"acc_norm": 0.8252427184466019,
"acc_norm_stderr": 0.037601780060266196
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.9145299145299145,
"acc_stderr": 0.01831589168562585,
"acc_norm": 0.9145299145299145,
"acc_norm_stderr": 0.01831589168562585
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.71,
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"acc_norm": 0.71,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-miscellaneous|5": {
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"acc_norm": 0.8633461047254151,
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},
"harness|hendrycksTest-moral_disputes|5": {
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},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.6312849162011173,
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"acc_norm": 0.6312849162011173,
"acc_norm_stderr": 0.01613575901503012
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7483660130718954,
"acc_stderr": 0.024848018263875195,
"acc_norm": 0.7483660130718954,
"acc_norm_stderr": 0.024848018263875195
},
"harness|hendrycksTest-philosophy|5": {
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"acc_norm": 0.7813504823151125,
"acc_norm_stderr": 0.023475581417861113
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"harness|hendrycksTest-prehistory|5": {
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"acc_norm": 0.845679012345679,
"acc_norm_stderr": 0.020100830999850994
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"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.5567375886524822,
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"acc_norm_stderr": 0.029634838473766006
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.5691003911342895,
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},
"harness|hendrycksTest-professional_medicine|5": {
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"acc_norm_stderr": 0.02736586113151381
},
"harness|hendrycksTest-professional_psychology|5": {
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},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.7181818181818181,
"acc_stderr": 0.04309118709946458,
"acc_norm": 0.7181818181818181,
"acc_norm_stderr": 0.04309118709946458
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7714285714285715,
"acc_stderr": 0.02688214492230774,
"acc_norm": 0.7714285714285715,
"acc_norm_stderr": 0.02688214492230774
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8805970149253731,
"acc_stderr": 0.02292879327721974,
"acc_norm": 0.8805970149253731,
"acc_norm_stderr": 0.02292879327721974
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.87,
"acc_stderr": 0.033799766898963086,
"acc_norm": 0.87,
"acc_norm_stderr": 0.033799766898963086
},
"harness|hendrycksTest-virology|5": {
"acc": 0.536144578313253,
"acc_stderr": 0.038823108508905954,
"acc_norm": 0.536144578313253,
"acc_norm_stderr": 0.038823108508905954
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.847953216374269,
"acc_stderr": 0.02753912288906145,
"acc_norm": 0.847953216374269,
"acc_norm_stderr": 0.02753912288906145
},
"harness|truthfulqa:mc|0": {
"mc1": 0.397796817625459,
"mc1_stderr": 0.017133934248559635,
"mc2": 0.566489803511904,
"mc2_stderr": 0.014977450728482283
}
}
```
### 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] |
mizinovmv/LLaVA-Instruct-150K-RU | ---
license: cc-by-4.0
task_categories:
- visual-question-answering
- question-answering
language:
- en
- ru
pretty_name: LLaVA Visual Instruct 150K Russian
size_categories:
- 100K<n<1M
---
# LLaVA Visual Instruct 150K Dataset Card
https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K
## Dataset details
**Dataset type:**
LLaVA Visual Instruct 150K is a set of GPT-generated multimodal instruction-following data.
It is constructed for visual instruction tuning and for building large multimodal towards GPT-4 vision/language capability.
**Dataset date:**
LLaVA Visual Instruct 150K was collected in April 2023, by prompting GPT-4-0314 API.
**Paper or resources for more information:**
https://llava-vl.github.io/
**License:**
Creative Commons Attribution 4.0 International; and it should abide by the policy of OpenAI: https://openai.com/policies/terms-of-use
## Intended use
**Primary intended uses:**
The primary use of LLaVA is research on large multimodal models and chatbots.
**Primary intended users:**
The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
|
explosion/ner-fashion-brands | ---
tags:
- prodigy
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: eval
path: data/eval-*
dataset_info:
features:
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dtype: string
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struct:
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num_bytes: 2222165
num_examples: 1235
- name: eval
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num_examples: 500
download_size: 839865
dataset_size: 3120984
---
# Ner Fashion Brands
This dataset originally appear as part of
[this tutorial](https://github.com/explosion/projects/tree/v3/tutorials/ner_fashion_brands). The goal
of the dataset is to detect fashion brands in Reddit Comments.
For more details, be sure to read [this blogpost](https://explosion.ai/blog/sense2vec-reloaded#annotation). |
Niche-Squad/COLO | ---
dataset_info:
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dtype: image
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- name: height
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features:
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dtype: image
- name: width
dtype: int64
- name: height
dtype: int64
- name: n_cows
dtype: int64
- name: annotations
sequence:
- name: id
dtype: int64
- name: image_id
dtype: int64
- name: category_id
dtype: int64
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dtype: int64
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dtype: float64
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sequence: float64
length: 4
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sequence:
sequence: int64
- name: image_id
dtype: int64
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dtype: string
splits:
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features:
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dtype: image
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dtype: int64
- name: height
dtype: int64
- name: n_cows
dtype: int64
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sequence:
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dtype: int64
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dtype: int64
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dtype: float64
- name: bbox
sequence: float64
length: 4
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sequence:
sequence: int64
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dtype: int64
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dtype: string
splits:
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num_examples: 1004
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num_bytes: 7430774.0
num_examples: 50
download_size: 151218220
dataset_size: 151534975.292
configs:
- config_name: 0_all
data_files:
- split: train
path: 0_all/train-*
- split: test
path: 0_all/test-*
- config_name: 1_top
data_files:
- split: daylight
path: 1_top/daylight-*
- split: indoorlight
path: 1_top/indoorlight-*
- split: infrared
path: 1_top/infrared-*
- split: train
path: 1_top/train-*
- split: test
path: 1_top/test-*
- config_name: 2_side
data_files:
- split: daylight
path: 2_side/daylight-*
- split: indoorlight
path: 2_side/indoorlight-*
- split: infrared
path: 2_side/infrared-*
- split: train
path: 2_side/train-*
- split: test
path: 2_side/test-*
- config_name: 3_external
data_files:
- split: train
path: 3_external/train-*
- split: test
path: 3_external/test-*
- config_name: a1_t2s
data_files:
- split: train
path: a1_t2s/train-*
- split: test
path: a1_t2s/test-*
- config_name: a2_s2t
data_files:
- split: train
path: a2_s2t/train-*
- split: test
path: a2_s2t/test-*
- config_name: b_light
data_files:
- split: train
path: b_light/train-*
- split: test
path: b_light/test-*
- config_name: c_external
data_files:
- split: train
path: c_external/train-*
- split: test
path: c_external/test-*
---
|
mariosasko/image_parquet_streaming_test | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': tench, Tinca tinca
'1': goldfish, Carassius auratus
'2': great white shark, white shark, man-eater, man-eating shark, Carcharodon
carcharias
'3': tiger shark, Galeocerdo cuvieri
'4': hammerhead, hammerhead shark
'5': electric ray, crampfish, numbfish, torpedo
'6': stingray
'7': cock
'8': hen
'9': ostrich, Struthio camelus
'10': brambling, Fringilla montifringilla
'11': goldfinch, Carduelis carduelis
'12': house finch, linnet, Carpodacus mexicanus
'13': junco, snowbird
'14': indigo bunting, indigo finch, indigo bird, Passerina cyanea
'15': robin, American robin, Turdus migratorius
'16': bulbul
'17': jay
'18': magpie
'19': chickadee
'20': water ouzel, dipper
'21': kite
'22': bald eagle, American eagle, Haliaeetus leucocephalus
'23': vulture
'24': great grey owl, great gray owl, Strix nebulosa
'25': European fire salamander, Salamandra salamandra
'26': common newt, Triturus vulgaris
'27': eft
'28': spotted salamander, Ambystoma maculatum
'29': axolotl, mud puppy, Ambystoma mexicanum
'30': bullfrog, Rana catesbeiana
'31': tree frog, tree-frog
'32': tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui
'33': loggerhead, loggerhead turtle, Caretta caretta
'34': leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea
'35': mud turtle
'36': terrapin
'37': box turtle, box tortoise
'38': banded gecko
'39': common iguana, iguana, Iguana iguana
'40': American chameleon, anole, Anolis carolinensis
'41': whiptail, whiptail lizard
'42': agama
'43': frilled lizard, Chlamydosaurus kingi
'44': alligator lizard
'45': Gila monster, Heloderma suspectum
'46': green lizard, Lacerta viridis
'47': African chameleon, Chamaeleo chamaeleon
'48': Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus
komodoensis
'49': African crocodile, Nile crocodile, Crocodylus niloticus
'50': American alligator, Alligator mississipiensis
'51': triceratops
'52': thunder snake, worm snake, Carphophis amoenus
'53': ringneck snake, ring-necked snake, ring snake
'54': hognose snake, puff adder, sand viper
'55': green snake, grass snake
'56': king snake, kingsnake
'57': garter snake, grass snake
'58': water snake
'59': vine snake
'60': night snake, Hypsiglena torquata
'61': boa constrictor, Constrictor constrictor
'62': rock python, rock snake, Python sebae
'63': Indian cobra, Naja naja
'64': green mamba
'65': sea snake
'66': horned viper, cerastes, sand viper, horned asp, Cerastes cornutus
'67': diamondback, diamondback rattlesnake, Crotalus adamanteus
'68': sidewinder, horned rattlesnake, Crotalus cerastes
'69': trilobite
'70': harvestman, daddy longlegs, Phalangium opilio
'71': scorpion
'72': black and gold garden spider, Argiope aurantia
'73': barn spider, Araneus cavaticus
'74': garden spider, Aranea diademata
'75': black widow, Latrodectus mactans
'76': tarantula
'77': wolf spider, hunting spider
'78': tick
'79': centipede
'80': black grouse
'81': ptarmigan
'82': ruffed grouse, partridge, Bonasa umbellus
'83': prairie chicken, prairie grouse, prairie fowl
'84': peacock
'85': quail
'86': partridge
'87': African grey, African gray, Psittacus erithacus
'88': macaw
'89': sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita
'90': lorikeet
'91': coucal
'92': bee eater
'93': hornbill
'94': hummingbird
'95': jacamar
'96': toucan
'97': drake
'98': red-breasted merganser, Mergus serrator
'99': goose
'100': black swan, Cygnus atratus
'101': tusker
'102': echidna, spiny anteater, anteater
'103': platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus
anatinus
'104': wallaby, brush kangaroo
'105': koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus
'106': wombat
'107': jellyfish
'108': sea anemone, anemone
'109': brain coral
'110': flatworm, platyhelminth
'111': nematode, nematode worm, roundworm
'112': conch
'113': snail
'114': slug
'115': sea slug, nudibranch
'116': chiton, coat-of-mail shell, sea cradle, polyplacophore
'117': chambered nautilus, pearly nautilus, nautilus
'118': Dungeness crab, Cancer magister
'119': rock crab, Cancer irroratus
'120': fiddler crab
'121': king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes
camtschatica
'122': American lobster, Northern lobster, Maine lobster, Homarus americanus
'123': spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish
'124': crayfish, crawfish, crawdad, crawdaddy
'125': hermit crab
'126': isopod
'127': white stork, Ciconia ciconia
'128': black stork, Ciconia nigra
'129': spoonbill
'130': flamingo
'131': little blue heron, Egretta caerulea
'132': American egret, great white heron, Egretta albus
'133': bittern
'134': crane
'135': limpkin, Aramus pictus
'136': European gallinule, Porphyrio porphyrio
'137': American coot, marsh hen, mud hen, water hen, Fulica americana
'138': bustard
'139': ruddy turnstone, Arenaria interpres
'140': red-backed sandpiper, dunlin, Erolia alpina
'141': redshank, Tringa totanus
'142': dowitcher
'143': oystercatcher, oyster catcher
'144': pelican
'145': king penguin, Aptenodytes patagonica
'146': albatross, mollymawk
'147': grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius
robustus
'148': killer whale, killer, orca, grampus, sea wolf, Orcinus orca
'149': dugong, Dugong dugon
'150': sea lion
'151': Chihuahua
'152': Japanese spaniel
'153': Maltese dog, Maltese terrier, Maltese
'154': Pekinese, Pekingese, Peke
'155': Shih-Tzu
'156': Blenheim spaniel
'157': papillon
'158': toy terrier
'159': Rhodesian ridgeback
'160': Afghan hound, Afghan
'161': basset, basset hound
'162': beagle
'163': bloodhound, sleuthhound
'164': bluetick
'165': black-and-tan coonhound
'166': Walker hound, Walker foxhound
'167': English foxhound
'168': redbone
'169': borzoi, Russian wolfhound
'170': Irish wolfhound
'171': Italian greyhound
'172': whippet
'173': Ibizan hound, Ibizan Podenco
'174': Norwegian elkhound, elkhound
'175': otterhound, otter hound
'176': Saluki, gazelle hound
'177': Scottish deerhound, deerhound
'178': Weimaraner
'179': Staffordshire bullterrier, Staffordshire bull terrier
'180': American Staffordshire terrier, Staffordshire terrier, American pit
bull terrier, pit bull terrier
'181': Bedlington terrier
'182': Border terrier
'183': Kerry blue terrier
'184': Irish terrier
'185': Norfolk terrier
'186': Norwich terrier
'187': Yorkshire terrier
'188': wire-haired fox terrier
'189': Lakeland terrier
'190': Sealyham terrier, Sealyham
'191': Airedale, Airedale terrier
'192': cairn, cairn terrier
'193': Australian terrier
'194': Dandie Dinmont, Dandie Dinmont terrier
'195': Boston bull, Boston terrier
'196': miniature schnauzer
'197': giant schnauzer
'198': standard schnauzer
'199': Scotch terrier, Scottish terrier, Scottie
'200': Tibetan terrier, chrysanthemum dog
'201': silky terrier, Sydney silky
'202': soft-coated wheaten terrier
'203': West Highland white terrier
'204': Lhasa, Lhasa apso
'205': flat-coated retriever
'206': curly-coated retriever
'207': golden retriever
'208': Labrador retriever
'209': Chesapeake Bay retriever
'210': German short-haired pointer
'211': vizsla, Hungarian pointer
'212': English setter
'213': Irish setter, red setter
'214': Gordon setter
'215': Brittany spaniel
'216': clumber, clumber spaniel
'217': English springer, English springer spaniel
'218': Welsh springer spaniel
'219': cocker spaniel, English cocker spaniel, cocker
'220': Sussex spaniel
'221': Irish water spaniel
'222': kuvasz
'223': schipperke
'224': groenendael
'225': malinois
'226': briard
'227': kelpie
'228': komondor
'229': Old English sheepdog, bobtail
'230': Shetland sheepdog, Shetland sheep dog, Shetland
'231': collie
'232': Border collie
'233': Bouvier des Flandres, Bouviers des Flandres
'234': Rottweiler
'235': German shepherd, German shepherd dog, German police dog, alsatian
'236': Doberman, Doberman pinscher
'237': miniature pinscher
'238': Greater Swiss Mountain dog
'239': Bernese mountain dog
'240': Appenzeller
'241': EntleBucher
'242': boxer
'243': bull mastiff
'244': Tibetan mastiff
'245': French bulldog
'246': Great Dane
'247': Saint Bernard, St Bernard
'248': Eskimo dog, husky
'249': malamute, malemute, Alaskan malamute
'250': Siberian husky
'251': dalmatian, coach dog, carriage dog
'252': affenpinscher, monkey pinscher, monkey dog
'253': basenji
'254': pug, pug-dog
'255': Leonberg
'256': Newfoundland, Newfoundland dog
'257': Great Pyrenees
'258': Samoyed, Samoyede
'259': Pomeranian
'260': chow, chow chow
'261': keeshond
'262': Brabancon griffon
'263': Pembroke, Pembroke Welsh corgi
'264': Cardigan, Cardigan Welsh corgi
'265': toy poodle
'266': miniature poodle
'267': standard poodle
'268': Mexican hairless
'269': timber wolf, grey wolf, gray wolf, Canis lupus
'270': white wolf, Arctic wolf, Canis lupus tundrarum
'271': red wolf, maned wolf, Canis rufus, Canis niger
'272': coyote, prairie wolf, brush wolf, Canis latrans
'273': dingo, warrigal, warragal, Canis dingo
'274': dhole, Cuon alpinus
'275': African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus
'276': hyena, hyaena
'277': red fox, Vulpes vulpes
'278': kit fox, Vulpes macrotis
'279': Arctic fox, white fox, Alopex lagopus
'280': grey fox, gray fox, Urocyon cinereoargenteus
'281': tabby, tabby cat
'282': tiger cat
'283': Persian cat
'284': Siamese cat, Siamese
'285': Egyptian cat
'286': cougar, puma, catamount, mountain lion, painter, panther, Felis concolor
'287': lynx, catamount
'288': leopard, Panthera pardus
'289': snow leopard, ounce, Panthera uncia
'290': jaguar, panther, Panthera onca, Felis onca
'291': lion, king of beasts, Panthera leo
'292': tiger, Panthera tigris
'293': cheetah, chetah, Acinonyx jubatus
'294': brown bear, bruin, Ursus arctos
'295': American black bear, black bear, Ursus americanus, Euarctos americanus
'296': ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus
'297': sloth bear, Melursus ursinus, Ursus ursinus
'298': mongoose
'299': meerkat, mierkat
'300': tiger beetle
'301': ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle
'302': ground beetle, carabid beetle
'303': long-horned beetle, longicorn, longicorn beetle
'304': leaf beetle, chrysomelid
'305': dung beetle
'306': rhinoceros beetle
'307': weevil
'308': fly
'309': bee
'310': ant, emmet, pismire
'311': grasshopper, hopper
'312': cricket
'313': walking stick, walkingstick, stick insect
'314': cockroach, roach
'315': mantis, mantid
'316': cicada, cicala
'317': leafhopper
'318': lacewing, lacewing fly
'319': dragonfly, darning needle, devil's darning needle, sewing needle,
snake feeder, snake doctor, mosquito hawk, skeeter hawk
'320': damselfly
'321': admiral
'322': ringlet, ringlet butterfly
'323': monarch, monarch butterfly, milkweed butterfly, Danaus plexippus
'324': cabbage butterfly
'325': sulphur butterfly, sulfur butterfly
'326': lycaenid, lycaenid butterfly
'327': starfish, sea star
'328': sea urchin
'329': sea cucumber, holothurian
'330': wood rabbit, cottontail, cottontail rabbit
'331': hare
'332': Angora, Angora rabbit
'333': hamster
'334': porcupine, hedgehog
'335': fox squirrel, eastern fox squirrel, Sciurus niger
'336': marmot
'337': beaver
'338': guinea pig, Cavia cobaya
'339': sorrel
'340': zebra
'341': hog, pig, grunter, squealer, Sus scrofa
'342': wild boar, boar, Sus scrofa
'343': warthog
'344': hippopotamus, hippo, river horse, Hippopotamus amphibius
'345': ox
'346': water buffalo, water ox, Asiatic buffalo, Bubalus bubalis
'347': bison
'348': ram, tup
'349': bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain
sheep, Ovis canadensis
'350': ibex, Capra ibex
'351': hartebeest
'352': impala, Aepyceros melampus
'353': gazelle
'354': Arabian camel, dromedary, Camelus dromedarius
'355': llama
'356': weasel
'357': mink
'358': polecat, fitch, foulmart, foumart, Mustela putorius
'359': black-footed ferret, ferret, Mustela nigripes
'360': otter
'361': skunk, polecat, wood pussy
'362': badger
'363': armadillo
'364': three-toed sloth, ai, Bradypus tridactylus
'365': orangutan, orang, orangutang, Pongo pygmaeus
'366': gorilla, Gorilla gorilla
'367': chimpanzee, chimp, Pan troglodytes
'368': gibbon, Hylobates lar
'369': siamang, Hylobates syndactylus, Symphalangus syndactylus
'370': guenon, guenon monkey
'371': patas, hussar monkey, Erythrocebus patas
'372': baboon
'373': macaque
'374': langur
'375': colobus, colobus monkey
'376': proboscis monkey, Nasalis larvatus
'377': marmoset
'378': capuchin, ringtail, Cebus capucinus
'379': howler monkey, howler
'380': titi, titi monkey
'381': spider monkey, Ateles geoffroyi
'382': squirrel monkey, Saimiri sciureus
'383': Madagascar cat, ring-tailed lemur, Lemur catta
'384': indri, indris, Indri indri, Indri brevicaudatus
'385': Indian elephant, Elephas maximus
'386': African elephant, Loxodonta africana
'387': lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens
'388': giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca
'389': barracouta, snoek
'390': eel
'391': coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus
kisutch
'392': rock beauty, Holocanthus tricolor
'393': anemone fish
'394': sturgeon
'395': gar, garfish, garpike, billfish, Lepisosteus osseus
'396': lionfish
'397': puffer, pufferfish, blowfish, globefish
'398': abacus
'399': abaya
'400': academic gown, academic robe, judge's robe
'401': accordion, piano accordion, squeeze box
'402': acoustic guitar
'403': aircraft carrier, carrier, flattop, attack aircraft carrier
'404': airliner
'405': airship, dirigible
'406': altar
'407': ambulance
'408': amphibian, amphibious vehicle
'409': analog clock
'410': apiary, bee house
'411': apron
'412': ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin,
dustbin, trash barrel, trash bin
'413': assault rifle, assault gun
'414': backpack, back pack, knapsack, packsack, rucksack, haversack
'415': bakery, bakeshop, bakehouse
'416': balance beam, beam
'417': balloon
'418': ballpoint, ballpoint pen, ballpen, Biro
'419': Band Aid
'420': banjo
'421': bannister, banister, balustrade, balusters, handrail
'422': barbell
'423': barber chair
'424': barbershop
'425': barn
'426': barometer
'427': barrel, cask
'428': barrow, garden cart, lawn cart, wheelbarrow
'429': baseball
'430': basketball
'431': bassinet
'432': bassoon
'433': bathing cap, swimming cap
'434': bath towel
'435': bathtub, bathing tub, bath, tub
'436': beach wagon, station wagon, wagon, estate car, beach waggon, station
waggon, waggon
'437': beacon, lighthouse, beacon light, pharos
'438': beaker
'439': bearskin, busby, shako
'440': beer bottle
'441': beer glass
'442': bell cote, bell cot
'443': bib
'444': bicycle-built-for-two, tandem bicycle, tandem
'445': bikini, two-piece
'446': binder, ring-binder
'447': binoculars, field glasses, opera glasses
'448': birdhouse
'449': boathouse
'450': bobsled, bobsleigh, bob
'451': bolo tie, bolo, bola tie, bola
'452': bonnet, poke bonnet
'453': bookcase
'454': bookshop, bookstore, bookstall
'455': bottlecap
'456': bow
'457': bow tie, bow-tie, bowtie
'458': brass, memorial tablet, plaque
'459': brassiere, bra, bandeau
'460': breakwater, groin, groyne, mole, bulwark, seawall, jetty
'461': breastplate, aegis, egis
'462': broom
'463': bucket, pail
'464': buckle
'465': bulletproof vest
'466': bullet train, bullet
'467': butcher shop, meat market
'468': cab, hack, taxi, taxicab
'469': caldron, cauldron
'470': candle, taper, wax light
'471': cannon
'472': canoe
'473': can opener, tin opener
'474': cardigan
'475': car mirror
'476': carousel, carrousel, merry-go-round, roundabout, whirligig
'477': carpenter's kit, tool kit
'478': carton
'479': car wheel
'480': cash machine, cash dispenser, automated teller machine, automatic
teller machine, automated teller, automatic teller, ATM
'481': cassette
'482': cassette player
'483': castle
'484': catamaran
'485': CD player
'486': cello, violoncello
'487': cellular telephone, cellular phone, cellphone, cell, mobile phone
'488': chain
'489': chainlink fence
'490': chain mail, ring mail, mail, chain armor, chain armour, ring armor,
ring armour
'491': chain saw, chainsaw
'492': chest
'493': chiffonier, commode
'494': chime, bell, gong
'495': china cabinet, china closet
'496': Christmas stocking
'497': church, church building
'498': cinema, movie theater, movie theatre, movie house, picture palace
'499': cleaver, meat cleaver, chopper
'500': cliff dwelling
'501': cloak
'502': clog, geta, patten, sabot
'503': cocktail shaker
'504': coffee mug
'505': coffeepot
'506': coil, spiral, volute, whorl, helix
'507': combination lock
'508': computer keyboard, keypad
'509': confectionery, confectionary, candy store
'510': container ship, containership, container vessel
'511': convertible
'512': corkscrew, bottle screw
'513': cornet, horn, trumpet, trump
'514': cowboy boot
'515': cowboy hat, ten-gallon hat
'516': cradle
'517': crane2
'518': crash helmet
'519': crate
'520': crib, cot
'521': Crock Pot
'522': croquet ball
'523': crutch
'524': cuirass
'525': dam, dike, dyke
'526': desk
'527': desktop computer
'528': dial telephone, dial phone
'529': diaper, nappy, napkin
'530': digital clock
'531': digital watch
'532': dining table, board
'533': dishrag, dishcloth
'534': dishwasher, dish washer, dishwashing machine
'535': disk brake, disc brake
'536': dock, dockage, docking facility
'537': dogsled, dog sled, dog sleigh
'538': dome
'539': doormat, welcome mat
'540': drilling platform, offshore rig
'541': drum, membranophone, tympan
'542': drumstick
'543': dumbbell
'544': Dutch oven
'545': electric fan, blower
'546': electric guitar
'547': electric locomotive
'548': entertainment center
'549': envelope
'550': espresso maker
'551': face powder
'552': feather boa, boa
'553': file, file cabinet, filing cabinet
'554': fireboat
'555': fire engine, fire truck
'556': fire screen, fireguard
'557': flagpole, flagstaff
'558': flute, transverse flute
'559': folding chair
'560': football helmet
'561': forklift
'562': fountain
'563': fountain pen
'564': four-poster
'565': freight car
'566': French horn, horn
'567': frying pan, frypan, skillet
'568': fur coat
'569': garbage truck, dustcart
'570': gasmask, respirator, gas helmet
'571': gas pump, gasoline pump, petrol pump, island dispenser
'572': goblet
'573': go-kart
'574': golf ball
'575': golfcart, golf cart
'576': gondola
'577': gong, tam-tam
'578': gown
'579': grand piano, grand
'580': greenhouse, nursery, glasshouse
'581': grille, radiator grille
'582': grocery store, grocery, food market, market
'583': guillotine
'584': hair slide
'585': hair spray
'586': half track
'587': hammer
'588': hamper
'589': hand blower, blow dryer, blow drier, hair dryer, hair drier
'590': hand-held computer, hand-held microcomputer
'591': handkerchief, hankie, hanky, hankey
'592': hard disc, hard disk, fixed disk
'593': harmonica, mouth organ, harp, mouth harp
'594': harp
'595': harvester, reaper
'596': hatchet
'597': holster
'598': home theater, home theatre
'599': honeycomb
'600': hook, claw
'601': hoopskirt, crinoline
'602': horizontal bar, high bar
'603': horse cart, horse-cart
'604': hourglass
'605': iPod
'606': iron, smoothing iron
'607': jack-o'-lantern
'608': jean, blue jean, denim
'609': jeep, landrover
'610': jersey, T-shirt, tee shirt
'611': jigsaw puzzle
'612': jinrikisha, ricksha, rickshaw
'613': joystick
'614': kimono
'615': knee pad
'616': knot
'617': lab coat, laboratory coat
'618': ladle
'619': lampshade, lamp shade
'620': laptop, laptop computer
'621': lawn mower, mower
'622': lens cap, lens cover
'623': letter opener, paper knife, paperknife
'624': library
'625': lifeboat
'626': lighter, light, igniter, ignitor
'627': limousine, limo
'628': liner, ocean liner
'629': lipstick, lip rouge
'630': Loafer
'631': lotion
'632': loudspeaker, speaker, speaker unit, loudspeaker system, speaker system
'633': loupe, jeweler's loupe
'634': lumbermill, sawmill
'635': magnetic compass
'636': mailbag, postbag
'637': mailbox, letter box
'638': maillot
'639': maillot, tank suit
'640': manhole cover
'641': maraca
'642': marimba, xylophone
'643': mask
'644': matchstick
'645': maypole
'646': maze, labyrinth
'647': measuring cup
'648': medicine chest, medicine cabinet
'649': megalith, megalithic structure
'650': microphone, mike
'651': microwave, microwave oven
'652': military uniform
'653': milk can
'654': minibus
'655': miniskirt, mini
'656': minivan
'657': missile
'658': mitten
'659': mixing bowl
'660': mobile home, manufactured home
'661': Model T
'662': modem
'663': monastery
'664': monitor
'665': moped
'666': mortar
'667': mortarboard
'668': mosque
'669': mosquito net
'670': motor scooter, scooter
'671': mountain bike, all-terrain bike, off-roader
'672': mountain tent
'673': mouse, computer mouse
'674': mousetrap
'675': moving van
'676': muzzle
'677': nail
'678': neck brace
'679': necklace
'680': nipple
'681': notebook, notebook computer
'682': obelisk
'683': oboe, hautboy, hautbois
'684': ocarina, sweet potato
'685': odometer, hodometer, mileometer, milometer
'686': oil filter
'687': organ, pipe organ
'688': oscilloscope, scope, cathode-ray oscilloscope, CRO
'689': overskirt
'690': oxcart
'691': oxygen mask
'692': packet
'693': paddle, boat paddle
'694': paddlewheel, paddle wheel
'695': padlock
'696': paintbrush
'697': pajama, pyjama, pj's, jammies
'698': palace
'699': panpipe, pandean pipe, syrinx
'700': paper towel
'701': parachute, chute
'702': parallel bars, bars
'703': park bench
'704': parking meter
'705': passenger car, coach, carriage
'706': patio, terrace
'707': pay-phone, pay-station
'708': pedestal, plinth, footstall
'709': pencil box, pencil case
'710': pencil sharpener
'711': perfume, essence
'712': Petri dish
'713': photocopier
'714': pick, plectrum, plectron
'715': pickelhaube
'716': picket fence, paling
'717': pickup, pickup truck
'718': pier
'719': piggy bank, penny bank
'720': pill bottle
'721': pillow
'722': ping-pong ball
'723': pinwheel
'724': pirate, pirate ship
'725': pitcher, ewer
'726': plane, carpenter's plane, woodworking plane
'727': planetarium
'728': plastic bag
'729': plate rack
'730': plow, plough
'731': plunger, plumber's helper
'732': Polaroid camera, Polaroid Land camera
'733': pole
'734': police van, police wagon, paddy wagon, patrol wagon, wagon, black
Maria
'735': poncho
'736': pool table, billiard table, snooker table
'737': pop bottle, soda bottle
'738': pot, flowerpot
'739': potter's wheel
'740': power drill
'741': prayer rug, prayer mat
'742': printer
'743': prison, prison house
'744': projectile, missile
'745': projector
'746': puck, hockey puck
'747': punching bag, punch bag, punching ball, punchball
'748': purse
'749': quill, quill pen
'750': quilt, comforter, comfort, puff
'751': racer, race car, racing car
'752': racket, racquet
'753': radiator
'754': radio, wireless
'755': radio telescope, radio reflector
'756': rain barrel
'757': recreational vehicle, RV, R.V.
'758': reel
'759': reflex camera
'760': refrigerator, icebox
'761': remote control, remote
'762': restaurant, eating house, eating place, eatery
'763': revolver, six-gun, six-shooter
'764': rifle
'765': rocking chair, rocker
'766': rotisserie
'767': rubber eraser, rubber, pencil eraser
'768': rugby ball
'769': rule, ruler
'770': running shoe
'771': safe
'772': safety pin
'773': saltshaker, salt shaker
'774': sandal
'775': sarong
'776': sax, saxophone
'777': scabbard
'778': scale, weighing machine
'779': school bus
'780': schooner
'781': scoreboard
'782': screen, CRT screen
'783': screw
'784': screwdriver
'785': seat belt, seatbelt
'786': sewing machine
'787': shield, buckler
'788': shoe shop, shoe-shop, shoe store
'789': shoji
'790': shopping basket
'791': shopping cart
'792': shovel
'793': shower cap
'794': shower curtain
'795': ski
'796': ski mask
'797': sleeping bag
'798': slide rule, slipstick
'799': sliding door
'800': slot, one-armed bandit
'801': snorkel
'802': snowmobile
'803': snowplow, snowplough
'804': soap dispenser
'805': soccer ball
'806': sock
'807': solar dish, solar collector, solar furnace
'808': sombrero
'809': soup bowl
'810': space bar
'811': space heater
'812': space shuttle
'813': spatula
'814': speedboat
'815': spider web, spider's web
'816': spindle
'817': sports car, sport car
'818': spotlight, spot
'819': stage
'820': steam locomotive
'821': steel arch bridge
'822': steel drum
'823': stethoscope
'824': stole
'825': stone wall
'826': stopwatch, stop watch
'827': stove
'828': strainer
'829': streetcar, tram, tramcar, trolley, trolley car
'830': stretcher
'831': studio couch, day bed
'832': stupa, tope
'833': submarine, pigboat, sub, U-boat
'834': suit, suit of clothes
'835': sundial
'836': sunglass
'837': sunglasses, dark glasses, shades
'838': sunscreen, sunblock, sun blocker
'839': suspension bridge
'840': swab, swob, mop
'841': sweatshirt
'842': swimming trunks, bathing trunks
'843': swing
'844': switch, electric switch, electrical switch
'845': syringe
'846': table lamp
'847': tank, army tank, armored combat vehicle, armoured combat vehicle
'848': tape player
'849': teapot
'850': teddy, teddy bear
'851': television, television system
'852': tennis ball
'853': thatch, thatched roof
'854': theater curtain, theatre curtain
'855': thimble
'856': thresher, thrasher, threshing machine
'857': throne
'858': tile roof
'859': toaster
'860': tobacco shop, tobacconist shop, tobacconist
'861': toilet seat
'862': torch
'863': totem pole
'864': tow truck, tow car, wrecker
'865': toyshop
'866': tractor
'867': trailer truck, tractor trailer, trucking rig, rig, articulated lorry,
semi
'868': tray
'869': trench coat
'870': tricycle, trike, velocipede
'871': trimaran
'872': tripod
'873': triumphal arch
'874': trolleybus, trolley coach, trackless trolley
'875': trombone
'876': tub, vat
'877': turnstile
'878': typewriter keyboard
'879': umbrella
'880': unicycle, monocycle
'881': upright, upright piano
'882': vacuum, vacuum cleaner
'883': vase
'884': vault
'885': velvet
'886': vending machine
'887': vestment
'888': viaduct
'889': violin, fiddle
'890': volleyball
'891': waffle iron
'892': wall clock
'893': wallet, billfold, notecase, pocketbook
'894': wardrobe, closet, press
'895': warplane, military plane
'896': washbasin, handbasin, washbowl, lavabo, wash-hand basin
'897': washer, automatic washer, washing machine
'898': water bottle
'899': water jug
'900': water tower
'901': whiskey jug
'902': whistle
'903': wig
'904': window screen
'905': window shade
'906': Windsor tie
'907': wine bottle
'908': wing
'909': wok
'910': wooden spoon
'911': wool, woolen, woollen
'912': worm fence, snake fence, snake-rail fence, Virginia fence
'913': wreck
'914': yawl
'915': yurt
'916': web site, website, internet site, site
'917': comic book
'918': crossword puzzle, crossword
'919': street sign
'920': traffic light, traffic signal, stoplight
'921': book jacket, dust cover, dust jacket, dust wrapper
'922': menu
'923': plate
'924': guacamole
'925': consomme
'926': hot pot, hotpot
'927': trifle
'928': ice cream, icecream
'929': ice lolly, lolly, lollipop, popsicle
'930': French loaf
'931': bagel, beigel
'932': pretzel
'933': cheeseburger
'934': hotdog, hot dog, red hot
'935': mashed potato
'936': head cabbage
'937': broccoli
'938': cauliflower
'939': zucchini, courgette
'940': spaghetti squash
'941': acorn squash
'942': butternut squash
'943': cucumber, cuke
'944': artichoke, globe artichoke
'945': bell pepper
'946': cardoon
'947': mushroom
'948': Granny Smith
'949': strawberry
'950': orange
'951': lemon
'952': fig
'953': pineapple, ananas
'954': banana
'955': jackfruit, jak, jack
'956': custard apple
'957': pomegranate
'958': hay
'959': carbonara
'960': chocolate sauce, chocolate syrup
'961': dough
'962': meat loaf, meatloaf
'963': pizza, pizza pie
'964': potpie
'965': burrito
'966': red wine
'967': espresso
'968': cup
'969': eggnog
'970': alp
'971': bubble
'972': cliff, drop, drop-off
'973': coral reef
'974': geyser
'975': lakeside, lakeshore
'976': promontory, headland, head, foreland
'977': sandbar, sand bar
'978': seashore, coast, seacoast, sea-coast
'979': valley, vale
'980': volcano
'981': ballplayer, baseball player
'982': groom, bridegroom
'983': scuba diver
'984': rapeseed
'985': daisy
'986': yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus,
Cypripedium parviflorum
'987': corn
'988': acorn
'989': hip, rose hip, rosehip
'990': buckeye, horse chestnut, conker
'991': coral fungus
'992': agaric
'993': gyromitra
'994': stinkhorn, carrion fungus
'995': earthstar
'996': hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola
frondosa
'997': bolete
'998': ear, spike, capitulum
'999': toilet tissue, toilet paper, bathroom tissue
splits:
- name: train
num_bytes: 7786747334.047
num_examples: 50889
download_size: 7777141900
dataset_size: 7786747334.047
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
open-llm-leaderboard/details_jefferylovely__ThetaMaven5 | ---
pretty_name: Evaluation run of jefferylovely/ThetaMaven5
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [jefferylovely/ThetaMaven5](https://huggingface.co/jefferylovely/ThetaMaven5)\
\ 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_jefferylovely__ThetaMaven5\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-01-26T21:02:42.271669](https://huggingface.co/datasets/open-llm-leaderboard/details_jefferylovely__ThetaMaven5/blob/main/results_2024-01-26T21-02-42.271669.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\": 0.6532234575515065,\n\
\ \"acc_stderr\": 0.032258628297869,\n \"acc_norm\": 0.6529181825680871,\n\
\ \"acc_norm_stderr\": 0.032928998699138026,\n \"mc1\": 0.5385556915544676,\n\
\ \"mc1_stderr\": 0.017451384104637452,\n \"mc2\": 0.6966525886236103,\n\
\ \"mc2_stderr\": 0.014890101664501334\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6936860068259386,\n \"acc_stderr\": 0.013470584417276514,\n\
\ \"acc_norm\": 0.7201365187713311,\n \"acc_norm_stderr\": 0.013119040897725922\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7093208524198367,\n\
\ \"acc_stderr\": 0.004531477407589652,\n \"acc_norm\": 0.883788090021908,\n\
\ \"acc_norm_stderr\": 0.0031982389518176203\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\
\ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6148148148148148,\n\
\ \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n\
\ \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6710526315789473,\n \"acc_stderr\": 0.03823428969926605,\n\
\ \"acc_norm\": 0.6710526315789473,\n \"acc_norm_stderr\": 0.03823428969926605\n\
\ },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\"\
: {\n \"acc\": 0.7018867924528301,\n \"acc_stderr\": 0.02815283794249387,\n\
\ \"acc_norm\": 0.7018867924528301,\n \"acc_norm_stderr\": 0.02815283794249387\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7708333333333334,\n\
\ \"acc_stderr\": 0.03514697467862388,\n \"acc_norm\": 0.7708333333333334,\n\
\ \"acc_norm_stderr\": 0.03514697467862388\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.46,\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\"\
: 0.57,\n \"acc_stderr\": 0.04975698519562428,\n \"acc_norm\": 0.57,\n\
\ \"acc_norm_stderr\": 0.04975698519562428\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.04793724854411019,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.04793724854411019\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6473988439306358,\n\
\ \"acc_stderr\": 0.03643037168958548,\n \"acc_norm\": 0.6473988439306358,\n\
\ \"acc_norm_stderr\": 0.03643037168958548\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.43137254901960786,\n \"acc_stderr\": 0.04928099597287534,\n\
\ \"acc_norm\": 0.43137254901960786,\n \"acc_norm_stderr\": 0.04928099597287534\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.8,\n \"acc_stderr\": 0.04020151261036845,\n \"acc_norm\": 0.8,\n\
\ \"acc_norm_stderr\": 0.04020151261036845\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5914893617021276,\n \"acc_stderr\": 0.032134180267015755,\n\
\ \"acc_norm\": 0.5914893617021276,\n \"acc_norm_stderr\": 0.032134180267015755\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5263157894736842,\n\
\ \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.5263157894736842,\n\
\ \"acc_norm_stderr\": 0.046970851366478626\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5517241379310345,\n \"acc_stderr\": 0.041443118108781526,\n\
\ \"acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.041443118108781526\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4126984126984127,\n \"acc_stderr\": 0.025355741263055263,\n \"\
acc_norm\": 0.4126984126984127,\n \"acc_norm_stderr\": 0.025355741263055263\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.47619047619047616,\n\
\ \"acc_stderr\": 0.04467062628403273,\n \"acc_norm\": 0.47619047619047616,\n\
\ \"acc_norm_stderr\": 0.04467062628403273\n },\n \"harness|hendrycksTest-global_facts|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_biology|5\": {\n \"acc\": 0.7741935483870968,\n\
\ \"acc_stderr\": 0.023785577884181012,\n \"acc_norm\": 0.7741935483870968,\n\
\ \"acc_norm_stderr\": 0.023785577884181012\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5073891625615764,\n \"acc_stderr\": 0.035176035403610105,\n\
\ \"acc_norm\": 0.5073891625615764,\n \"acc_norm_stderr\": 0.035176035403610105\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252607,\n \"acc_norm\"\
: 0.67,\n \"acc_norm_stderr\": 0.04725815626252607\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.0328766675860349,\n\
\ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.0328766675860349\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.803030303030303,\n \"acc_stderr\": 0.028335609732463362,\n \"\
acc_norm\": 0.803030303030303,\n \"acc_norm_stderr\": 0.028335609732463362\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8756476683937824,\n \"acc_stderr\": 0.023814477086593552,\n\
\ \"acc_norm\": 0.8756476683937824,\n \"acc_norm_stderr\": 0.023814477086593552\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.658974358974359,\n \"acc_stderr\": 0.02403548967633507,\n \
\ \"acc_norm\": 0.658974358974359,\n \"acc_norm_stderr\": 0.02403548967633507\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.337037037037037,\n \"acc_stderr\": 0.02882088466625326,\n \
\ \"acc_norm\": 0.337037037037037,\n \"acc_norm_stderr\": 0.02882088466625326\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6680672268907563,\n \"acc_stderr\": 0.03058869701378364,\n \
\ \"acc_norm\": 0.6680672268907563,\n \"acc_norm_stderr\": 0.03058869701378364\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.40397350993377484,\n \"acc_stderr\": 0.040064856853653415,\n \"\
acc_norm\": 0.40397350993377484,\n \"acc_norm_stderr\": 0.040064856853653415\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8477064220183487,\n \"acc_stderr\": 0.015405084393157074,\n \"\
acc_norm\": 0.8477064220183487,\n \"acc_norm_stderr\": 0.015405084393157074\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5277777777777778,\n \"acc_stderr\": 0.0340470532865388,\n \"acc_norm\"\
: 0.5277777777777778,\n \"acc_norm_stderr\": 0.0340470532865388\n },\n\
\ \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8137254901960784,\n\
\ \"acc_stderr\": 0.02732547096671632,\n \"acc_norm\": 0.8137254901960784,\n\
\ \"acc_norm_stderr\": 0.02732547096671632\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\
: {\n \"acc\": 0.8143459915611815,\n \"acc_stderr\": 0.025310495376944856,\n\
\ \"acc_norm\": 0.8143459915611815,\n \"acc_norm_stderr\": 0.025310495376944856\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6905829596412556,\n\
\ \"acc_stderr\": 0.031024411740572213,\n \"acc_norm\": 0.6905829596412556,\n\
\ \"acc_norm_stderr\": 0.031024411740572213\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7862595419847328,\n \"acc_stderr\": 0.0359546161177469,\n\
\ \"acc_norm\": 0.7862595419847328,\n \"acc_norm_stderr\": 0.0359546161177469\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7603305785123967,\n \"acc_stderr\": 0.03896878985070417,\n \"\
acc_norm\": 0.7603305785123967,\n \"acc_norm_stderr\": 0.03896878985070417\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7592592592592593,\n\
\ \"acc_stderr\": 0.04133119440243838,\n \"acc_norm\": 0.7592592592592593,\n\
\ \"acc_norm_stderr\": 0.04133119440243838\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.754601226993865,\n \"acc_stderr\": 0.03380939813943354,\n\
\ \"acc_norm\": 0.754601226993865,\n \"acc_norm_stderr\": 0.03380939813943354\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4732142857142857,\n\
\ \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.4732142857142857,\n\
\ \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.041858325989283136,\n\
\ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.041858325989283136\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8846153846153846,\n\
\ \"acc_stderr\": 0.020930193185179333,\n \"acc_norm\": 0.8846153846153846,\n\
\ \"acc_norm_stderr\": 0.020930193185179333\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \
\ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8352490421455939,\n\
\ \"acc_stderr\": 0.013265346261323792,\n \"acc_norm\": 0.8352490421455939,\n\
\ \"acc_norm_stderr\": 0.013265346261323792\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7456647398843931,\n \"acc_stderr\": 0.023445826276545546,\n\
\ \"acc_norm\": 0.7456647398843931,\n \"acc_norm_stderr\": 0.023445826276545546\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.47262569832402235,\n\
\ \"acc_stderr\": 0.016697420650642752,\n \"acc_norm\": 0.47262569832402235,\n\
\ \"acc_norm_stderr\": 0.016697420650642752\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7189542483660131,\n \"acc_stderr\": 0.025738854797818737,\n\
\ \"acc_norm\": 0.7189542483660131,\n \"acc_norm_stderr\": 0.025738854797818737\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7170418006430869,\n\
\ \"acc_stderr\": 0.025583062489984806,\n \"acc_norm\": 0.7170418006430869,\n\
\ \"acc_norm_stderr\": 0.025583062489984806\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7407407407407407,\n \"acc_stderr\": 0.024383665531035454,\n\
\ \"acc_norm\": 0.7407407407407407,\n \"acc_norm_stderr\": 0.024383665531035454\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.5,\n \"acc_stderr\": 0.029827499313594685,\n \"acc_norm\"\
: 0.5,\n \"acc_norm_stderr\": 0.029827499313594685\n },\n \"harness|hendrycksTest-professional_law|5\"\
: {\n \"acc\": 0.47196870925684486,\n \"acc_stderr\": 0.012750151802922435,\n\
\ \"acc_norm\": 0.47196870925684486,\n \"acc_norm_stderr\": 0.012750151802922435\n\
\ },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\"\
: 0.6801470588235294,\n \"acc_stderr\": 0.028332959514031208,\n \"\
acc_norm\": 0.6801470588235294,\n \"acc_norm_stderr\": 0.028332959514031208\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6928104575163399,\n \"acc_stderr\": 0.018663359671463667,\n \
\ \"acc_norm\": 0.6928104575163399,\n \"acc_norm_stderr\": 0.018663359671463667\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6454545454545455,\n\
\ \"acc_stderr\": 0.045820048415054174,\n \"acc_norm\": 0.6454545454545455,\n\
\ \"acc_norm_stderr\": 0.045820048415054174\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7061224489795919,\n \"acc_stderr\": 0.02916273841024977,\n\
\ \"acc_norm\": 0.7061224489795919,\n \"acc_norm_stderr\": 0.02916273841024977\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8258706467661692,\n\
\ \"acc_stderr\": 0.026814951200421603,\n \"acc_norm\": 0.8258706467661692,\n\
\ \"acc_norm_stderr\": 0.026814951200421603\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.85,\n \"acc_stderr\": 0.0358870281282637,\n \
\ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.0358870281282637\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.536144578313253,\n\
\ \"acc_stderr\": 0.038823108508905954,\n \"acc_norm\": 0.536144578313253,\n\
\ \"acc_norm_stderr\": 0.038823108508905954\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n\
\ \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5385556915544676,\n\
\ \"mc1_stderr\": 0.017451384104637452,\n \"mc2\": 0.6966525886236103,\n\
\ \"mc2_stderr\": 0.014890101664501334\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8263614838200474,\n \"acc_stderr\": 0.010646116480331\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6990144048521607,\n \
\ \"acc_stderr\": 0.012634504465211173\n }\n}\n```"
repo_url: https://huggingface.co/jefferylovely/ThetaMaven5
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|arc:challenge|25_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|gsm8k|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hellaswag|10_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-26T21-02-42.271669.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-26T21-02-42.271669.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- '**/details_harness|winogrande|5_2024-01-26T21-02-42.271669.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-01-26T21-02-42.271669.parquet'
- config_name: results
data_files:
- split: 2024_01_26T21_02_42.271669
path:
- results_2024-01-26T21-02-42.271669.parquet
- split: latest
path:
- results_2024-01-26T21-02-42.271669.parquet
---
# Dataset Card for Evaluation run of jefferylovely/ThetaMaven5
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [jefferylovely/ThetaMaven5](https://huggingface.co/jefferylovely/ThetaMaven5) 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_jefferylovely__ThetaMaven5",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-26T21:02:42.271669](https://huggingface.co/datasets/open-llm-leaderboard/details_jefferylovely__ThetaMaven5/blob/main/results_2024-01-26T21-02-42.271669.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": 0.6532234575515065,
"acc_stderr": 0.032258628297869,
"acc_norm": 0.6529181825680871,
"acc_norm_stderr": 0.032928998699138026,
"mc1": 0.5385556915544676,
"mc1_stderr": 0.017451384104637452,
"mc2": 0.6966525886236103,
"mc2_stderr": 0.014890101664501334
},
"harness|arc:challenge|25": {
"acc": 0.6936860068259386,
"acc_stderr": 0.013470584417276514,
"acc_norm": 0.7201365187713311,
"acc_norm_stderr": 0.013119040897725922
},
"harness|hellaswag|10": {
"acc": 0.7093208524198367,
"acc_stderr": 0.004531477407589652,
"acc_norm": 0.883788090021908,
"acc_norm_stderr": 0.0031982389518176203
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.35,
"acc_stderr": 0.0479372485441102,
"acc_norm": 0.35,
"acc_norm_stderr": 0.0479372485441102
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6148148148148148,
"acc_stderr": 0.04203921040156279,
"acc_norm": 0.6148148148148148,
"acc_norm_stderr": 0.04203921040156279
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6710526315789473,
"acc_stderr": 0.03823428969926605,
"acc_norm": 0.6710526315789473,
"acc_norm_stderr": 0.03823428969926605
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.61,
"acc_stderr": 0.04902071300001975,
"acc_norm": 0.61,
"acc_norm_stderr": 0.04902071300001975
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7018867924528301,
"acc_stderr": 0.02815283794249387,
"acc_norm": 0.7018867924528301,
"acc_norm_stderr": 0.02815283794249387
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7708333333333334,
"acc_stderr": 0.03514697467862388,
"acc_norm": 0.7708333333333334,
"acc_norm_stderr": 0.03514697467862388
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.46,
"acc_stderr": 0.05009082659620333,
"acc_norm": 0.46,
"acc_norm_stderr": 0.05009082659620333
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.57,
"acc_stderr": 0.04975698519562428,
"acc_norm": 0.57,
"acc_norm_stderr": 0.04975698519562428
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.35,
"acc_stderr": 0.04793724854411019,
"acc_norm": 0.35,
"acc_norm_stderr": 0.04793724854411019
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6473988439306358,
"acc_stderr": 0.03643037168958548,
"acc_norm": 0.6473988439306358,
"acc_norm_stderr": 0.03643037168958548
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.43137254901960786,
"acc_stderr": 0.04928099597287534,
"acc_norm": 0.43137254901960786,
"acc_norm_stderr": 0.04928099597287534
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.8,
"acc_stderr": 0.04020151261036845,
"acc_norm": 0.8,
"acc_norm_stderr": 0.04020151261036845
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5914893617021276,
"acc_stderr": 0.032134180267015755,
"acc_norm": 0.5914893617021276,
"acc_norm_stderr": 0.032134180267015755
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5263157894736842,
"acc_stderr": 0.046970851366478626,
"acc_norm": 0.5263157894736842,
"acc_norm_stderr": 0.046970851366478626
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5517241379310345,
"acc_stderr": 0.041443118108781526,
"acc_norm": 0.5517241379310345,
"acc_norm_stderr": 0.041443118108781526
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.4126984126984127,
"acc_stderr": 0.025355741263055263,
"acc_norm": 0.4126984126984127,
"acc_norm_stderr": 0.025355741263055263
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.47619047619047616,
"acc_stderr": 0.04467062628403273,
"acc_norm": 0.47619047619047616,
"acc_norm_stderr": 0.04467062628403273
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7741935483870968,
"acc_stderr": 0.023785577884181012,
"acc_norm": 0.7741935483870968,
"acc_norm_stderr": 0.023785577884181012
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5073891625615764,
"acc_stderr": 0.035176035403610105,
"acc_norm": 0.5073891625615764,
"acc_norm_stderr": 0.035176035403610105
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.67,
"acc_stderr": 0.04725815626252607,
"acc_norm": 0.67,
"acc_norm_stderr": 0.04725815626252607
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7696969696969697,
"acc_stderr": 0.0328766675860349,
"acc_norm": 0.7696969696969697,
"acc_norm_stderr": 0.0328766675860349
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.803030303030303,
"acc_stderr": 0.028335609732463362,
"acc_norm": 0.803030303030303,
"acc_norm_stderr": 0.028335609732463362
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8756476683937824,
"acc_stderr": 0.023814477086593552,
"acc_norm": 0.8756476683937824,
"acc_norm_stderr": 0.023814477086593552
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.658974358974359,
"acc_stderr": 0.02403548967633507,
"acc_norm": 0.658974358974359,
"acc_norm_stderr": 0.02403548967633507
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.337037037037037,
"acc_stderr": 0.02882088466625326,
"acc_norm": 0.337037037037037,
"acc_norm_stderr": 0.02882088466625326
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6680672268907563,
"acc_stderr": 0.03058869701378364,
"acc_norm": 0.6680672268907563,
"acc_norm_stderr": 0.03058869701378364
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.40397350993377484,
"acc_stderr": 0.040064856853653415,
"acc_norm": 0.40397350993377484,
"acc_norm_stderr": 0.040064856853653415
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8477064220183487,
"acc_stderr": 0.015405084393157074,
"acc_norm": 0.8477064220183487,
"acc_norm_stderr": 0.015405084393157074
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5277777777777778,
"acc_stderr": 0.0340470532865388,
"acc_norm": 0.5277777777777778,
"acc_norm_stderr": 0.0340470532865388
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8137254901960784,
"acc_stderr": 0.02732547096671632,
"acc_norm": 0.8137254901960784,
"acc_norm_stderr": 0.02732547096671632
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8143459915611815,
"acc_stderr": 0.025310495376944856,
"acc_norm": 0.8143459915611815,
"acc_norm_stderr": 0.025310495376944856
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6905829596412556,
"acc_stderr": 0.031024411740572213,
"acc_norm": 0.6905829596412556,
"acc_norm_stderr": 0.031024411740572213
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7862595419847328,
"acc_stderr": 0.0359546161177469,
"acc_norm": 0.7862595419847328,
"acc_norm_stderr": 0.0359546161177469
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7603305785123967,
"acc_stderr": 0.03896878985070417,
"acc_norm": 0.7603305785123967,
"acc_norm_stderr": 0.03896878985070417
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7592592592592593,
"acc_stderr": 0.04133119440243838,
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},
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},
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"harness|hendrycksTest-management|5": {
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"harness|hendrycksTest-marketing|5": {
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"harness|hendrycksTest-us_foreign_policy|5": {
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},
"harness|hendrycksTest-virology|5": {
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},
"harness|hendrycksTest-world_religions|5": {
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"acc_norm": 0.8362573099415205,
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},
"harness|truthfulqa:mc|0": {
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},
"harness|winogrande|5": {
"acc": 0.8263614838200474,
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},
"harness|gsm8k|5": {
"acc": 0.6990144048521607,
"acc_stderr": 0.012634504465211173
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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## More Information [optional]
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## Dataset Card Authors [optional]
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## Dataset Card Contact
[More Information Needed] |
harshithvh/alpaca_format2 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 582263
num_examples: 100
download_size: 169676
dataset_size: 582263
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "alpaca_format2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/type95_girlsfrontline | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of type95/95式/95式 (Girls' Frontline)
This is the dataset of type95/95式/95式 (Girls' Frontline), containing 486 images and their tags.
The core tags of this character are `long_hair, breasts, black_hair, large_breasts, bangs, yellow_eyes, hair_ornament, hairband, very_long_hair, hair_flower, white_hairband`, 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 | 486 | 676.96 MiB | [Download](https://huggingface.co/datasets/CyberHarem/type95_girlsfrontline/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 486 | 369.08 MiB | [Download](https://huggingface.co/datasets/CyberHarem/type95_girlsfrontline/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1115 | 739.83 MiB | [Download](https://huggingface.co/datasets/CyberHarem/type95_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 486 | 588.79 MiB | [Download](https://huggingface.co/datasets/CyberHarem/type95_girlsfrontline/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1115 | 1.03 GiB | [Download](https://huggingface.co/datasets/CyberHarem/type95_girlsfrontline/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/type95_girlsfrontline',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 9 |  |  |  |  |  | 1girl, black_pantyhose, bullpup, closed_mouth, holding_gun, looking_at_viewer, smile, solo, white_gloves, white_skirt, assault_rifle, between_breasts, blush, pleated_skirt, cleavage, fingerless_gloves, flower, white_shirt, cape, simple_background, white_background |
| 1 | 17 |  |  |  |  |  | 1girl, black_pantyhose, looking_at_viewer, solo, closed_mouth, smile, white_gloves, white_shirt, white_skirt, simple_background, between_breasts, blush, pleated_skirt, white_background, cleavage, cape, flower, blunt_bangs, cowboy_shot |
| 2 | 6 |  |  |  |  |  | 1girl, blush, looking_at_viewer, smile, solo, white_gloves, flower, simple_background, upper_body, between_breasts, closed_mouth, white_background |
| 3 | 47 |  |  |  |  |  | 1girl, solo, looking_at_viewer, official_alternate_costume, china_dress, white_dress, mole_under_eye, blush, black_thighhighs, blue_flower, pelvic_curtain, garter_straps, smile, thighs, white_background, closed_mouth, simple_background, holding_fan, fingerless_gloves, sitting, bridal_gauntlets, brown_thighhighs, brown_gloves, white_footwear, brown_eyes, elbow_gloves, panties, uchiwa, garter_belt |
| 4 | 9 |  |  |  |  |  | 1girl, cleavage, collarbone, flower, looking_at_viewer, official_alternate_costume, side-tie_bikini_bottom, simple_background, solo, white_bikini, white_thighhighs, cowboy_shot, navel, blush, white_background, closed_mouth, smile, front-tie_top, standing |
| 5 | 8 |  |  |  |  |  | 1girl, cleavage, closed_mouth, fingerless_gloves, looking_at_viewer, navel, official_alternate_costume, side-tie_bikini_bottom, smile, solo, white_bikini, white_gloves, white_thighhighs, flower, collarbone, front-tie_bikini_top, blush, cowboy_shot, full_body, standing |
| 6 | 12 |  |  |  |  |  | long_sleeves, looking_at_viewer, pleated_skirt, 1girl, animal_ear_fluff, solo, blush, miniskirt, school_uniform, closed_mouth, grey_scarf, smile, white_scarf, official_alternate_costume, blue_sweater, plaid_skirt, thigh_strap, bag, cat_ears, grey_skirt, hat, holding, kneehighs, simple_background, sitting, white_background, white_headwear, white_socks |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_pantyhose | bullpup | closed_mouth | holding_gun | looking_at_viewer | smile | solo | white_gloves | white_skirt | assault_rifle | between_breasts | blush | pleated_skirt | cleavage | fingerless_gloves | flower | white_shirt | cape | simple_background | white_background | blunt_bangs | cowboy_shot | upper_body | official_alternate_costume | china_dress | white_dress | mole_under_eye | black_thighhighs | blue_flower | pelvic_curtain | garter_straps | thighs | holding_fan | sitting | bridal_gauntlets | brown_thighhighs | brown_gloves | white_footwear | brown_eyes | elbow_gloves | panties | uchiwa | garter_belt | collarbone | side-tie_bikini_bottom | white_bikini | white_thighhighs | navel | front-tie_top | standing | front-tie_bikini_top | full_body | long_sleeves | animal_ear_fluff | miniskirt | school_uniform | grey_scarf | white_scarf | blue_sweater | plaid_skirt | thigh_strap | bag | cat_ears | grey_skirt | hat | holding | kneehighs | white_headwear | white_socks |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:------------------|:----------|:---------------|:--------------|:--------------------|:--------|:-------|:---------------|:--------------|:----------------|:------------------|:--------|:----------------|:-----------|:--------------------|:---------|:--------------|:-------|:--------------------|:-------------------|:--------------|:--------------|:-------------|:-----------------------------|:--------------|:--------------|:-----------------|:-------------------|:--------------|:-----------------|:----------------|:---------|:--------------|:----------|:-------------------|:-------------------|:---------------|:-----------------|:-------------|:---------------|:----------|:---------|:--------------|:-------------|:-------------------------|:---------------|:-------------------|:--------|:----------------|:-----------|:-----------------------|:------------|:---------------|:-------------------|:------------|:-----------------|:-------------|:--------------|:---------------|:--------------|:--------------|:------|:-----------|:-------------|:------|:----------|:------------|:-----------------|:--------------|
| 0 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 17 |  |  |  |  |  | X | X | | X | | X | X | X | X | X | | X | X | X | X | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 6 |  |  |  |  |  | X | | | X | | X | X | X | X | | | X | X | | | | X | | | X | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 47 |  |  |  |  |  | X | | | X | | X | X | X | | | | | X | | | X | | | | X | X | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 9 |  |  |  |  |  | X | | | X | | X | X | X | | | | | X | | X | | X | | | X | X | | X | | X | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | |
| 5 | 8 |  |  |  |  |  | X | | | X | | X | X | X | X | | | | X | | X | X | X | | | | | | X | | X | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | | X | X | X | | | | | | | | | | | | | | | | | |
| 6 | 12 |  |  |  |  |  | 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 |
|
s-nlp/Mintaka_T5_xl_ssm_outputs | ---
dataset_info:
features:
- name: question
dtype: string
- name: target
dtype: string
- name: answer_0
dtype: string
- name: answer_1
dtype: string
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dtype: string
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dtype: string
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- name: answer_122
dtype: string
- name: answer_123
dtype: string
- name: answer_124
dtype: string
- name: answer_125
dtype: string
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dtype: string
- name: answer_127
dtype: string
- name: answer_128
dtype: string
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dtype: string
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dtype: string
- name: answer_131
dtype: string
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dtype: string
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dtype: string
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dtype: string
- name: answer_135
dtype: string
- name: answer_136
dtype: string
- name: answer_137
dtype: string
- name: answer_138
dtype: string
- name: answer_139
dtype: string
- name: answer_140
dtype: string
- name: answer_141
dtype: string
- name: answer_142
dtype: string
- name: answer_143
dtype: string
- name: answer_144
dtype: string
- name: answer_145
dtype: string
- name: answer_146
dtype: string
- name: answer_147
dtype: string
- name: answer_148
dtype: string
- name: answer_149
dtype: string
- name: answer_150
dtype: string
- name: answer_151
dtype: string
- name: answer_152
dtype: string
- name: answer_153
dtype: string
- name: answer_154
dtype: string
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dtype: string
- name: answer_156
dtype: string
- name: answer_157
dtype: 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
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dtype: 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
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dtype: 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
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dtype: 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
- name: answer_177
dtype: 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
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dtype: string
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dtype: string
- name: answer_184
dtype: string
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dtype: string
- name: answer_186
dtype: string
- name: answer_187
dtype: string
- name: answer_188
dtype: string
- name: answer_189
dtype: string
- name: answer_190
dtype: string
- name: answer_191
dtype: string
- name: answer_192
dtype: string
- name: answer_193
dtype: string
- name: answer_194
dtype: string
- name: answer_195
dtype: string
- name: answer_196
dtype: string
- name: answer_197
dtype: string
- name: answer_198
dtype: string
- name: answer_199
dtype: string
- name: target_out_of_vocab
dtype: bool
splits:
- name: train
num_bytes: 116272791
num_examples: 32000
- name: validation
num_bytes: 7453582
num_examples: 2000
- name: test
num_bytes: 14833727
num_examples: 4000
download_size: 94335289
dataset_size: 138560100
---
# Dataset Card for "Mintaka_T5_xl_ssm_outputs"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
vipulmaheshwari/GTA-Image-Captioning-Dataset | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 2473559738.0
num_examples: 785
download_size: 2473661020
dataset_size: 2473559738.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
rcrupi/gbm_grb | ---
license: mit
---
|
oskarspakers/songs | ---
license: openrail
language:
- lv
pretty_name: Songs in latvian
---
Nothing here |
metaeval/universal-joy | ---
license: gpl
task_categories:
- text-classification
tags:
- multilingual
- emotion
---
```bib
@inproceedings{lamprinidis2021universal,
title={Universal Joy A Dataset and Results for Classifying Emotions Across Languages},
author={Lamprinidis, Sotiris and Bianchi, Federico and Hardt, Daniel and Hovy, Dirk},
year={2021},
volume={11th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis (WASSA 2021)}
organization={Association for Computational Linguistics}
}
``` |
BangumiBase/soundeuphonium | ---
license: mit
tags:
- art
size_categories:
- 10K<n<100K
---
# Bangumi Image Base of Sound! Euphonium
This is the image base of bangumi Sound! Euphonium, we detected 180 characters, 15917 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:----------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|
| 0 | 425 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 67 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 16 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 1094 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 18 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 77 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 31 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 99 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 11 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 19 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 22 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 14 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 21 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 3272 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 66 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 97 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 29 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 22 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 81 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 62 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 50 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 64 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 70 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 84 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 42 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 19 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 843 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 10 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 33 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 12 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 14 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 13 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
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| 33 | 23 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 21 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 22 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 23 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 23 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
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| 40 | 657 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 18 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
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| 43 | 32 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
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| 45 | 209 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
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| 48 | 285 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 66 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 26 | [Download](50/dataset.zip) |  |  |  |  |  |  |  |  |
| 51 | 32 | [Download](51/dataset.zip) |  |  |  |  |  |  |  |  |
| 52 | 28 | [Download](52/dataset.zip) |  |  |  |  |  |  |  |  |
| 53 | 102 | [Download](53/dataset.zip) |  |  |  |  |  |  |  |  |
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| 57 | 63 | [Download](57/dataset.zip) |  |  |  |  |  |  |  |  |
| 58 | 42 | [Download](58/dataset.zip) |  |  |  |  |  |  |  |  |
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| 60 | 39 | [Download](60/dataset.zip) |  |  |  |  |  |  |  |  |
| 61 | 34 | [Download](61/dataset.zip) |  |  |  |  |  |  |  |  |
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| 63 | 34 | [Download](63/dataset.zip) |  |  |  |  |  |  |  |  |
| 64 | 122 | [Download](64/dataset.zip) |  |  |  |  |  |  |  |  |
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| 67 | 45 | [Download](67/dataset.zip) |  |  |  |  |  |  |  |  |
| 68 | 34 | [Download](68/dataset.zip) |  |  |  |  |  |  |  |  |
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| 70 | 20 | [Download](70/dataset.zip) |  |  |  |  |  |  |  |  |
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| 72 | 21 | [Download](72/dataset.zip) |  |  |  |  |  |  |  |  |
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| 75 | 76 | [Download](75/dataset.zip) |  |  |  |  |  |  |  |  |
| 76 | 41 | [Download](76/dataset.zip) |  |  |  |  |  |  |  |  |
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| 78 | 17 | [Download](78/dataset.zip) |  |  |  |  |  |  |  |  |
| 79 | 994 | [Download](79/dataset.zip) |  |  |  |  |  |  |  |  |
| 80 | 26 | [Download](80/dataset.zip) |  |  |  |  |  |  |  |  |
| 81 | 16 | [Download](81/dataset.zip) |  |  |  |  |  |  |  |  |
| 82 | 90 | [Download](82/dataset.zip) |  |  |  |  |  |  |  |  |
| 83 | 174 | [Download](83/dataset.zip) |  |  |  |  |  |  |  |  |
| 84 | 64 | [Download](84/dataset.zip) |  |  |  |  |  |  |  |  |
| 85 | 36 | [Download](85/dataset.zip) |  |  |  |  |  |  |  |  |
| 86 | 90 | [Download](86/dataset.zip) |  |  |  |  |  |  |  |  |
| 87 | 68 | [Download](87/dataset.zip) |  |  |  |  |  |  |  |  |
| 88 | 7 | [Download](88/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 89 | 64 | [Download](89/dataset.zip) |  |  |  |  |  |  |  |  |
| 90 | 20 | [Download](90/dataset.zip) |  |  |  |  |  |  |  |  |
| 91 | 24 | [Download](91/dataset.zip) |  |  |  |  |  |  |  |  |
| 92 | 50 | [Download](92/dataset.zip) |  |  |  |  |  |  |  |  |
| 93 | 11 | [Download](93/dataset.zip) |  |  |  |  |  |  |  |  |
| 94 | 28 | [Download](94/dataset.zip) |  |  |  |  |  |  |  |  |
| 95 | 23 | [Download](95/dataset.zip) |  |  |  |  |  |  |  |  |
| 96 | 58 | [Download](96/dataset.zip) |  |  |  |  |  |  |  |  |
| 97 | 45 | [Download](97/dataset.zip) |  |  |  |  |  |  |  |  |
| 98 | 16 | [Download](98/dataset.zip) |  |  |  |  |  |  |  |  |
| 99 | 887 | [Download](99/dataset.zip) |  |  |  |  |  |  |  |  |
| 100 | 49 | [Download](100/dataset.zip) |  |  |  |  |  |  |  |  |
| 101 | 27 | [Download](101/dataset.zip) |  |  |  |  |  |  |  |  |
| 102 | 208 | [Download](102/dataset.zip) |  |  |  |  |  |  |  |  |
| 103 | 17 | [Download](103/dataset.zip) |  |  |  |  |  |  |  |  |
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| 105 | 14 | [Download](105/dataset.zip) |  |  |  |  |  |  |  |  |
| 106 | 34 | [Download](106/dataset.zip) |  |  |  |  |  |  |  |  |
| 107 | 34 | [Download](107/dataset.zip) |  |  |  |  |  |  |  |  |
| 108 | 116 | [Download](108/dataset.zip) |  |  |  |  |  |  |  |  |
| 109 | 22 | [Download](109/dataset.zip) |  |  |  |  |  |  |  |  |
| 110 | 24 | [Download](110/dataset.zip) |  |  |  |  |  |  |  |  |
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| 113 | 18 | [Download](113/dataset.zip) |  |  |  |  |  |  |  |  |
| 114 | 21 | [Download](114/dataset.zip) |  |  |  |  |  |  |  |  |
| 115 | 47 | [Download](115/dataset.zip) |  |  |  |  |  |  |  |  |
| 116 | 53 | [Download](116/dataset.zip) |  |  |  |  |  |  |  |  |
| 117 | 23 | [Download](117/dataset.zip) |  |  |  |  |  |  |  |  |
| 118 | 28 | [Download](118/dataset.zip) |  |  |  |  |  |  |  |  |
| 119 | 29 | [Download](119/dataset.zip) |  |  |  |  |  |  |  |  |
| 120 | 27 | [Download](120/dataset.zip) |  |  |  |  |  |  |  |  |
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| 122 | 18 | [Download](122/dataset.zip) |  |  |  |  |  |  |  |  |
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| 127 | 28 | [Download](127/dataset.zip) |  |  |  |  |  |  |  |  |
| 128 | 396 | [Download](128/dataset.zip) |  |  |  |  |  |  |  |  |
| 129 | 67 | [Download](129/dataset.zip) |  |  |  |  |  |  |  |  |
| 130 | 11 | [Download](130/dataset.zip) |  |  |  |  |  |  |  |  |
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| 133 | 10 | [Download](133/dataset.zip) |  |  |  |  |  |  |  |  |
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| 149 | 32 | [Download](149/dataset.zip) |  |  |  |  |  |  |  |  |
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| 155 | 10 | [Download](155/dataset.zip) |  |  |  |  |  |  |  |  |
| 156 | 7 | [Download](156/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 157 | 18 | [Download](157/dataset.zip) |  |  |  |  |  |  |  |  |
| 158 | 11 | [Download](158/dataset.zip) |  |  |  |  |  |  |  |  |
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| 160 | 7 | [Download](160/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 161 | 13 | [Download](161/dataset.zip) |  |  |  |  |  |  |  |  |
| 162 | 12 | [Download](162/dataset.zip) |  |  |  |  |  |  |  |  |
| 163 | 16 | [Download](163/dataset.zip) |  |  |  |  |  |  |  |  |
| 164 | 9 | [Download](164/dataset.zip) |  |  |  |  |  |  |  |  |
| 165 | 48 | [Download](165/dataset.zip) |  |  |  |  |  |  |  |  |
| 166 | 10 | [Download](166/dataset.zip) |  |  |  |  |  |  |  |  |
| 167 | 9 | [Download](167/dataset.zip) |  |  |  |  |  |  |  |  |
| 168 | 10 | [Download](168/dataset.zip) |  |  |  |  |  |  |  |  |
| 169 | 18 | [Download](169/dataset.zip) |  |  |  |  |  |  |  |  |
| 170 | 30 | [Download](170/dataset.zip) |  |  |  |  |  |  |  |  |
| 171 | 17 | [Download](171/dataset.zip) |  |  |  |  |  |  |  |  |
| 172 | 9 | [Download](172/dataset.zip) |  |  |  |  |  |  |  |  |
| 173 | 8 | [Download](173/dataset.zip) |  |  |  |  |  |  |  |  |
| 174 | 19 | [Download](174/dataset.zip) |  |  |  |  |  |  |  |  |
| 175 | 9 | [Download](175/dataset.zip) |  |  |  |  |  |  |  |  |
| 176 | 5 | [Download](176/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| 177 | 6 | [Download](177/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 178 | 8 | [Download](178/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 228 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
tdklab/Hebrew_Squad_v1 | ---
pretty_name: Hebrew_Squad_v1
annotations_creators:
- auto_translation
language_creators:
- auto_translation
languages:
- Hebrew
- he
licenses:
- cc-by-4-0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- squad
task_categories:
- question-answering
task_ids:
- extractive-qa
---
# Dataset Card for "Hebrew_Squad_v1"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/TechnionTDK/hebwiki-qa/](https://github.com/TechnionTDK/hebwiki-qa/)
- **Size of train dataset files:** 62.3 MB
- **Size of validation dataset files:** 9.48 MB
- **Total amount of disk used:** 71.78 MB
### Dataset Summary
Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. This Hebrew dataset is an automatic translation of the English SQuAD dataset https://huggingface.co/datasets/squad.
### Supported Tasks and Leaderboards
Extractive Question-Answering
### Languages
Hebrew
## Dataset Structure
Follows the standars SQuAD format.
### Data Instances
#### plain_text
- **Size of train dataset files:** 62.3 MB
- **Size of validation dataset files:** 9.48 MB
- **Total amount of disk used:** 71.78 MB
An example of 'train' looks as follows.
```
{
"id": "56be4db0acb8001400a502ee",
"title": "Super_Bowl_50",
"context": "סופרבול 50 היה משחק כדורגל אמריקאי כדי לקבוע את אלופת ליגת הפוטבול הלאומית (NFL) לעונת 2015. אלופת ועידת הכדורגל האמריקאית (AFC) דנבר ברונקוס ניצחה את אלופת ועידת הכדורגל הלאומית (NFC) קרולינה פנתרס 24–10 כדי לזכות בתואר הסופרבול השלישי שלה. המשחק נערך ב-7 בפברואר 2016 באצטדיון ליווי'ס באזור מפרץ סן פרנסיסקו בסנטה קלרה, קליפורניה. מכיוון שזה היה הסופרבול ה-50, הליגה הדגישה את יום השנה הזהב עם יוזמות שונות בנושא זהב, כמו גם השעיה זמנית את המסורת של שם כל משחק סופרבול עם ספרות רומיות (שתחתן המשחק היה ידוע בתור סופרבול L ), כך שהלוגו יוכל להציג באופן בולט את הספרות הערביות 50.",
"question": "היכן התקיים סופרבול 50?",
"answers": {
"text": ["סנטה קלרה, קליפורניה", "אצטדיון ליווי"],
"answer_start": [311, 271]
}
}
```
### Data Fields
The data fields are the same among all splits.
#### Hebrew_Squad_v1
- `id`: a `string` feature.
- `title`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
### Data Splits
| name |train|validation|
|----------|----|---------|
|Hebrew_Squad_v1|52405| 7455|
### Contributions
Created by Matan Ben-chorin, May Flaster, Guided by Dr. Oren Mishali.
This is our final project as part of computer engineering B.Sc studies in the Faculty of Electrical Engineering combined with Computer Science at Technion, Israel Institute of Technology.
For more cooperation, please contact email:
Matan Ben-chorin: matan.bh1@gmail.com
May Flaster: mayflaster96@gmail.com
|
BAAI/COIG-PC-Lite | ---
extra_gated_heading: "Acknowledge license to accept the repository"
extra_gated_prompt: |
北京智源人工智能研究院（以下简称“我们”或“研究院”）通过BAAI DataHub（data.baai.ac.cn）和COIG-PC HuggingFace仓库（https://huggingface.co/datasets/BAAI/COIG-PC）向您提供开源数据集（以下或称“数据集”），您可通过下载的方式获取您所需的开源数据集，并在遵守各原始数据集使用规则前提下，基于学习、研究、商业等目的使用相关数据集。
在您获取（包括但不限于访问、下载、复制、传播、使用等处理数据集的行为）开源数据集前，您应认真阅读并理解本《COIG-PC开源数据集使用须知与免责声明》（以下简称“本声明”）。一旦您获取开源数据集，无论您的获取方式为何，您的获取行为均将被视为对本声明全部内容的认可。
1. 平台的所有权与运营权
您应充分了解并知悉，BAAI DataHub和COIG-PC HuggingFace仓库（包括当前版本及全部历史版本）的所有权与运营权归智源人工智能研究院所有，智源人工智能研究院对本平台/本工具及开源数据集开放计划拥有最终解释权和决定权。
您知悉并理解，基于相关法律法规更新和完善以及我们需履行法律合规义务的客观变化，我们保留对本平台/本工具进行不定时更新、维护，或者中止乃至永久终止提供本平台/本工具服务的权利。我们将在合理时间内将可能发生前述情形通过公告或邮件等合理方式告知您，您应当及时做好相应的调整和安排，但我们不因发生前述任何情形对您造成的任何损失承担任何责任。
2. 开源数据集的权利主张
为了便于您基于学习、研究、商业的目的开展数据集获取、使用等活动，我们对第三方原始数据集进行了必要的格式整合、数据清洗、标注、分类、注释等相关处理环节，形成可供本平台/本工具用户使用的开源数据集。
您知悉并理解，我们不对开源数据集主张知识产权中的相关财产性权利，因此我们亦无相应义务对开源数据集可能存在的知识产权进行主动识别和保护，但这不意味着我们放弃开源数据集主张署名权、发表权、修改权和保护作品完整权（如有）等人身性权利。而原始数据集可能存在的知识产权及相应合法权益由原权利人享有。
此外，向您开放和使用经合理编排、加工和处理后的开源数据集，并不意味着我们对原始数据集知识产权、信息内容等真实、准确或无争议的认可，您应当自行筛选、仔细甄别，使用经您选择的开源数据集。您知悉并同意，研究院对您自行选择使用的原始数据集不负有任何无缺陷或无瑕疵的承诺义务或担保责任。
3. 开源数据集的使用限制
您使用数据集不得侵害我们或任何第三方的合法权益（包括但不限于著作权、专利权、商标权等知识产权与其他权益）。
获取开源数据集后，您应确保对开源数据集的使用不超过原始数据集的权利人以公示或协议等形式明确规定的使用规则，包括原始数据的使用范围、目的和合法用途等。我们在此善意地提请您留意，如您对开源数据集的使用超出原始数据集的原定使用范围及用途，您可能面临侵犯原始数据集权利人的合法权益例如知识产权的风险，并可能承担相应的法律责任。
4. 个人信息保护
基于技术限制及开源数据集的公益性质等客观原因，我们无法保证开源数据集中不包含任何个人信息，我们不对开源数据集中可能涉及的个人信息承担任何法律责任。
如开源数据集涉及个人信息，我们不对您使用开源数据集可能涉及的任何个人信息处理行为承担法律责任。我们在此善意地提请您留意，您应依据《个人信息保护法》等相关法律法规的规定处理个人信息。
为了维护信息主体的合法权益、履行可能适用的法律、行政法规的规定，如您在使用开源数据集的过程中发现涉及或者可能涉及个人信息的内容，应立即停止对数据集中涉及个人信息部分的使用，并及时通过“6. 投诉与通知”中载明的联系我们。
5. 信息内容管理
我们不对开源数据集可能涉及的违法与不良信息承担任何法律责任。
如您在使用开源数据集的过程中发现开源数据集涉及或者可能涉及任何违法与不良信息，您应立即停止对数据集中涉及违法与不良信息部分的使用，并及时通过“6. 投诉与通知”中载明的联系我们。
6. 投诉与通知
如您认为开源数据集侵犯了您的合法权益，您可通过010-50955974联系我们，我们会及时依法处理您的主张与投诉。
为了处理您的主张和投诉，我们可能需要您提供联系方式、侵权证明材料以及身份证明等材料。请注意，如果您恶意投诉或陈述失实，您将承担由此造成的全部法律责任（包括但不限于合理的费用赔偿等）。
7. 责任声明
您理解并同意，基于开源数据集的性质，数据集中可能包含来自不同来源和贡献者的数据，其真实性、准确性、客观性等可能会有所差异，我们无法对任何数据集的可用性、可靠性等做出任何承诺。
在任何情况下，我们不对开源数据集可能存在的个人信息侵权、违法与不良信息传播、知识产权侵权等任何风险承担任何法律责任。
在任何情况下，我们不对您因开源数据集遭受的或与之相关的任何损失（包括但不限于直接损失、间接损失以及可得利益损失等）承担任何法律责任。
8. 其他
开源数据集处于不断发展、变化的阶段，我们可能因业务发展、第三方合作、法律法规变动等原因更新、调整所提供的开源数据集范围，或中止、暂停、终止开源数据集提供业务。
extra_gated_fields:
Name: text
Affiliation: text
Country: text
I agree to use this model for non-commercial use ONLY: checkbox
extra_gated_button_content: "Acknowledge license"
license: unknown
language:
- zh
configs:
- config_name: default
data_files:
- split: full
path: data/full-*
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
- split: Top50PerTask
path: data/Top50PerTask-*
- split: Top100PerTask
path: data/Top100PerTask-*
- split: Top200PerTask
path: data/Top200PerTask-*
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: split
dtype: string
- name: task_name_in_eng
dtype: string
- name: task_type
struct:
- name: major
sequence: string
- name: minor
sequence: string
- name: domain
sequence: string
- name: other
dtype: string
- name: filename
dtype: string
splits:
- name: full
num_bytes: 1099400407
num_examples: 650147
- name: train
num_bytes: 410204689
num_examples: 216691
- name: valid
num_bytes: 12413560
num_examples: 16148
- name: test
num_bytes: 51472090
num_examples: 69301
- name: Top50PerTask
num_bytes: 14763925
num_examples: 19274
- name: Top100PerTask
num_bytes: 28489139
num_examples: 37701
- name: Top200PerTask
num_bytes: 51472090
num_examples: 69301
download_size: 53939740
dataset_size: 1668215900
---
# COIG Prompt Collection
## License
**Default Licensing for Sub-Datasets Without Specific License Declaration**: In instances where sub-datasets within the COIG-PC Dataset do not have a specific license declaration, the Apache License 2.0 (Apache-2.0) will be the applicable licensing terms by default.
**Precedence of Declared Licensing for Sub-Datasets**: For any sub-dataset within the COIG-PC Dataset that has an explicitly declared license, the terms and conditions of the declared license shall take precedence and govern the usage of that particular sub-dataset.
Users and developers utilizing the COIG-PC Dataset must ensure compliance with the licensing terms as outlined above. It is imperative to review and adhere to the specified licensing conditions of each sub-dataset, as they may vary.
## What is COIG-PC?
The COIG-PC Dataset is a meticulously curated and comprehensive collection of Chinese tasks and data, designed to facilitate the fine-tuning and optimization of language models for Chinese natural language processing (NLP). The dataset aims to provide researchers and developers with a rich set of resources to improve the capabilities of language models in handling Chinese text, which can be utilized in various fields such as text generation, information extraction, sentiment analysis, machine translation, among others.
COIG-PC-Lite is a subset of COIG-PC with only 200 samples from each task file. If you are looking for COIG-PC, please refer to https://huggingface.co/datasets/BAAI/COIG-PC.
## Why COIG-PC?
The COIG-PC Dataset is an invaluable resource for the domain of natural language processing (NLP) for various compelling reasons:
**Addressing Language Complexity**: Chinese is known for its intricacy, with a vast array of characters and diverse grammatical structures. A specialized dataset like COIG-PC, which is tailored for the Chinese language, is essential to adequately address these complexities during model training.
**Comprehensive Data Aggregation**: The COIG-PC Dataset is a result of an extensive effort in integrating almost all available Chinese datasets in the market. This comprehensive aggregation makes it one of the most exhaustive collections for Chinese NLP.
**Data Deduplication and Normalization**: The COIG-PC Dataset underwent rigorous manual processing to eliminate duplicate data and perform normalization. This ensures that the dataset is free from redundancy, and the data is consistent and well-structured, making it more user-friendly and efficient for model training.
**Fine-tuning and Optimization**: The dataset’s instruction-based phrasing facilitates better fine-tuning and optimization of language models. This structure allows models to better understand and execute tasks, which is particularly beneficial in improving performance on unseen or novel tasks.
The COIG-PC Dataset, with its comprehensive aggregation, meticulous selection, deduplication, and normalization of data, stands as an unmatched resource for training and optimizing language models tailored for the Chinese language and culture. It addresses the unique challenges of Chinese language processing and serves as a catalyst for advancements in Chinese NLP.
## Who builds COIG-PC?
The bedrock of COIG-PC is anchored in the dataset furnished by stardust.ai, which comprises an aggregation of data collected from the Internet.
And COIG-PC is the result of a collaborative effort involving engineers and experts from over twenty distinguished universities both domestically and internationally. Due to space constraints, it is not feasible to list all of them; however, the following are a few notable institutions among the collaborators:
- Beijing Academy of Artificial Intelligence, China
<img src="https://huggingface.co/datasets/BAAI/COIG-PC-Lite/resolve/main/assets/baai.png" alt= “BAAI” height="100" width="150">
- Peking University, China
<img src="https://huggingface.co/datasets/BAAI/COIG-PC-Lite/resolve/main/assets/pku.png" alt= “PKU” height="100" width="200">
- The Hong Kong University of Science and Technology (HKUST), China
<img src="https://huggingface.co/datasets/BAAI/COIG-PC-Lite/resolve/main/assets/hkust.png" alt= “HKUST” height="100" width="200">
- The University of Waterloo, Canada
<img src="https://huggingface.co/datasets/BAAI/COIG-PC-Lite/resolve/main/assets/waterloo.png" alt= “Waterloo” height="100" width="150">
- The University of Sheffield, United Kingdom
<img src="https://huggingface.co/datasets/BAAI/COIG-PC-Lite/resolve/main/assets/sheffield.png" alt= “Sheffield” height="100" width="200">
- Beijing University of Posts and Telecommunications, China
<img src="https://huggingface.co/datasets/BAAI/COIG-PC-Lite/resolve/main/assets/bupt.png" alt= “BUPT” height="100" width="200">
- [Multimodal Art Projection](https://huggingface.co/m-a-p)
<img src="https://huggingface.co/datasets/BAAI/COIG-PC-Lite/resolve/main/assets/map.png" alt= “M.A.P” height="100" width="200">
- stardust.ai, China
<img src="https://huggingface.co/datasets/BAAI/COIG-PC-Lite/resolve/main/assets/stardust.png" alt= “stardust.ai” height="100" width="200">
- LinkSoul.AI, China
<img src="https://huggingface.co/datasets/BAAI/COIG-PC-Lite/resolve/main/assets/linksoul.png" alt= “linksoul.ai” height="100" width="200">
For the detailed list of engineers involved in the creation and refinement of COIG-PC, please refer to the paper that will be published subsequently. This paper will provide in-depth information regarding the contributions and the specifics of the dataset’s development process.
## How to use COIG-PC?
COIG-PC is structured in a **.jsonl** file format. Each line in the file represents a single data record and is structured in JSON (JavaScript Object Notation) format. Below is a breakdown of the elements within each line:
**instruction**: This is a text string that provides the instruction for the task. For example, it might tell the model what to do with the input data.
**input**: This is the input data that the model needs to process. In the context of translation, it would be the text that needs to be translated.
**output**: This contains the expected output data after processing the input. In the context of translation, it would be the translated text.
**split**: Indicates the official split of the original dataset, which is used to categorize data for different phases of model training and evaluation. It can be 'train', 'test', 'valid', etc.
**task_type**: Contains major and minor categories for the dataset. Major categories are broader, while minor categories can be more specific subcategories.
**domain**: Indicates the domain or field to which the data belongs.
**other**: This field can contain additional information or metadata regarding the data record. If there is no additional information, it may be set to null.
### Example
Here is an example of how a line in the COIG-PC dataset might be structured:
```
{
"instruction": "请把下面的中文句子翻译成英文",
"input": "我爱你。",
"output": "I love you.",
"split": "train",
"task_type": {
"major": ["翻译"],
"minor": ["翻译", "中译英"]
},
"domain": ["通用"],
"other": null
}
```
In this example:
**instruction** tells the model to translate the following Chinese sentence into English.
**input** contains the Chinese text "我爱你" which means "I love you".
**output** contains the expected translation in English: "I love you".
**split** indicates that this data record is part of the training set.
**task_type** specifies that the major category is "Translation" and the minor categories are "Translation" and "Chinese to English".
**domain** specifies that this data record belongs to the general domain.
**other** is set to null as there is no additional information for this data record.
## Update: Aug. 30, 2023
- v1.2: Delete 31 bad task files. Update 99 task files. Rename 2 task files. Add 3 new task files. COIG-PC now has 3339 tasks in total.
- v1.1: Fix 00040-001-000 and 00050-003-000, ignore 00930 and 01373.
- v1.0: First version for arXiv paper.
- v0.6: Upload 28 new tasks. COIG-PC now has 3367 tasks in total.
- v0.5: Upload 202 new tasks. COIG-PC now has 3339 tasks in total.
- v0.4: Upload 1049 new tasks. COIG-PC now has 3137 tasks in total.
- v0.3: Upload 1139 new tasks. COIG-PC now has 2088 tasks in total.
- v0.2: Upload 422 new tasks. COIG-PC now has 949 tasks in total. Add "TopSamplenumPerTask" split where only "Samplenum" samples are used from each task.
- v0.1: Upload 527 tasks.
## COIG-PC Citation
If you want to cite COIG-PC dataset, you could use this:
```
```
## Contact Us
To contact us feel free to create an Issue in this repository.
|
SLPL/syntran-fa | ---
language:
- fa
license: mit
multilinguality:
- monolingual
size_categories:
- 30k<n<50k
task_categories:
- question-answering
- text2text-generation
- text-generation
task_ids: []
pretty_name: SynTranFa
tags:
- conditional-text-generation
- conversational-question-answering
---
# SynTran-fa
Syntactic Transformed Version of Farsi QA datasets to make fluent responses from questions and short answers. You can use this dataset by the code below:
```python
import datasets
data = datasets.load_dataset('SLPL/syntran-fa', split="train")
```
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Dataset Creation](#dataset-creation)
- [Source Data](#source-data)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Sharif-SLPL](https://github.com/Sharif-SLPL)
- **Repository:** [SynTran-fa](https://github.com/agp-internship/syntran-fa)
- **Point of Contact:** [Sadra Sabouri](mailto:sabouri.sadra@gmail.com)
### Dataset Summary
Generating fluent responses has always been challenging for the question-answering task, especially in low-resource languages like Farsi. In recent years there were some efforts for enhancing the size of datasets in Farsi. Syntran-fa is a question-answering dataset that accumulates the former Farsi QA dataset's short answers and proposes a complete fluent answer for each pair of (question, short_answer).
This dataset contains nearly 50,000 indices of questions and answers. The dataset that has been used as our sources are in [Source Data section](#source-data).
The main idea for this dataset comes from [Fluent Response Generation for Conversational Question Answering](https://aclanthology.org/2020.acl-main.19.pdf) where they used a "parser + syntactic rules" module to make different fluent answers from a pair of question and a short answer using a parser and some syntactic rules. In this project, we used [stanza](https://stanfordnlp.github.io/stanza/) as our parser to parse the question and generate a response according to it using the short (sentences without verbs - up to ~4 words) answers. One can continue this project by generating different permutations of the sentence's parts (and thus providing more than one sentence for an answer) or training a seq2seq model which does what we do with our rule-based system (by defining a new text-to-text task).
### Supported Tasks and Leaderboards
This dataset can be used for the question-answering task, especially when you are going to generate fluent responses. You can train a seq2seq model with this dataset to generate fluent responses - as done by [Fluent Response Generation for Conversational Question Answering](https://aclanthology.org/2020.acl-main.19.pdf).
### Languages
+ Persian (fa)
## Dataset Structure
Each row of the dataset will look like something like the below:
```json
{
'id': 0,
'question': 'باشگاه هاکی ساوتهمپتون چه نام دارد؟',
'short_answer': 'باشگاه هاکی ساوتهمپتون',
'fluent_answer': 'باشگاه هاکی ساوتهمپتون باشگاه هاکی ساوتهمپتون نام دارد.',
'bert_loss': 1.110097069682014
}
```
+ `id` : the entry id in dataset
+ `question` : the question
+ `short_answer` : the short answer corresponding to the `question` (the primary answer)
+ `fluent_answer` : fluent (long) answer generated from both `question` and the `short_answer` (the secondary answer)
+ `bert_loss` : the loss that [pars-bert](https://huggingface.co/HooshvareLab/bert-base-parsbert-uncased) gives when inputting the `fluent_answer` to it. As it increases the sentence is more likely to be influent.
Note: the dataset is sorted increasingly by the `bert_loss`, so first sentences are more likely to be fluent.
### Data Splits
Currently, the dataset just provided the `train` split. There would be a `test` split soon.
## Dataset Creation
### Source Data
The source datasets that we used are as follows:
+ [PersianQA](https://github.com/sajjjadayobi/PersianQA)
+ [PersianQuAD](https://ieeexplore.ieee.org/document/9729745)
#### Initial Data Collection and Normalization
We extract all short answer (sentences without verbs - up to ~4 words) entries of all open source QA datasets in Farsi and used some rules featuring the question parse tree to make long (fluent) answers.
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
The dataset is completely a subset of open source known datasets so all information in it is already there on the internet as a open-source dataset. By the way, we do not take responsibility for any of that.
## Additional Information
### Dataset Curators
The dataset is gathered together completely in the Asr Gooyesh Pardaz company's summer internship under the supervision of Soroush Gooran, Prof. Hossein Sameti, and the mentorship of Sadra Sabouri. This project was Farhan Farsi's first internship project.
### Licensing Information
MIT
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@farhaaaaa](https://github.com/farhaaaaa) and [@sadrasabouri](https://github.com/sadrasabouri) for adding this dataset. |
NX2411/AIhub-korean-speech-data-large | ---
license: apache-2.0
---
|
TheFinAI/flare-fnxl | ---
dataset_info:
features:
- name: id
dtype: string
- name: query
dtype: string
- name: answer
dtype: string
- name: text
dtype: string
- name: label
sequence: string
- name: token
sequence: string
splits:
- name: test
num_bytes: 2112362
num_examples: 318
download_size: 315090
dataset_size: 2112362
---
# Dataset Card for "flare-fnxl"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
karuna-bhaila/Unlearning_SST2v2 | ---
configs:
- config_name: default
data_files:
- split: train_forget
path: train_forget.csv
- split: test_forget
path: test_forget.csv
- split: train_retain
path: train_retain.csv
- split: test_retain
path: test_retain.csv
--- |
open-llm-leaderboard/details_Weyaxi__EulerMath-Mistral-7B | ---
pretty_name: Evaluation run of Weyaxi/EulerMath-Mistral-7B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Weyaxi/EulerMath-Mistral-7B](https://huggingface.co/Weyaxi/EulerMath-Mistral-7B)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Weyaxi__EulerMath-Mistral-7B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-04-08T14:49:30.062748](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__EulerMath-Mistral-7B/blob/main/results_2024-04-08T14-49-30.062748.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\": 0.5920253909953418,\n\
\ \"acc_stderr\": 0.03296498598379715,\n \"acc_norm\": 0.6024022284993832,\n\
\ \"acc_norm_stderr\": 0.033775842095780426,\n \"mc1\": 0.3219094247246022,\n\
\ \"mc1_stderr\": 0.0163555676119604,\n \"mc2\": 0.4722759067100739,\n\
\ \"mc2_stderr\": 0.015155963095621219\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5554607508532423,\n \"acc_stderr\": 0.014521226405627075,\n\
\ \"acc_norm\": 0.6040955631399317,\n \"acc_norm_stderr\": 0.014291228393536592\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6419040031866162,\n\
\ \"acc_stderr\": 0.004784607222774642,\n \"acc_norm\": 0.8290181238797052,\n\
\ \"acc_norm_stderr\": 0.003757236806397339\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\": 0.6,\n \
\ \"acc_stderr\": 0.04232073695151589,\n \"acc_norm\": 0.6,\n \"\
acc_norm_stderr\": 0.04232073695151589\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.618421052631579,\n \"acc_stderr\": 0.03953173377749194,\n\
\ \"acc_norm\": 0.618421052631579,\n \"acc_norm_stderr\": 0.03953173377749194\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.49,\n\
\ \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\": 0.49,\n \
\ \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6339622641509434,\n \"acc_stderr\": 0.029647813539365245,\n\
\ \"acc_norm\": 0.6339622641509434,\n \"acc_norm_stderr\": 0.029647813539365245\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6875,\n\
\ \"acc_stderr\": 0.038760854559127644,\n \"acc_norm\": 0.6875,\n\
\ \"acc_norm_stderr\": 0.038760854559127644\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \
\ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.49,\n \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\"\
: 0.49,\n \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \
\ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6069364161849711,\n\
\ \"acc_stderr\": 0.03724249595817729,\n \"acc_norm\": 0.6069364161849711,\n\
\ \"acc_norm_stderr\": 0.03724249595817729\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.04690650298201942,\n\
\ \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.04690650298201942\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.72,\n \"acc_stderr\": 0.045126085985421276,\n \"acc_norm\": 0.72,\n\
\ \"acc_norm_stderr\": 0.045126085985421276\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.4978723404255319,\n \"acc_stderr\": 0.032685726586674915,\n\
\ \"acc_norm\": 0.4978723404255319,\n \"acc_norm_stderr\": 0.032685726586674915\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.39473684210526316,\n\
\ \"acc_stderr\": 0.045981880578165414,\n \"acc_norm\": 0.39473684210526316,\n\
\ \"acc_norm_stderr\": 0.045981880578165414\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.496551724137931,\n \"acc_stderr\": 0.041665675771015785,\n\
\ \"acc_norm\": 0.496551724137931,\n \"acc_norm_stderr\": 0.041665675771015785\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.36507936507936506,\n \"acc_stderr\": 0.024796060602699965,\n \"\
acc_norm\": 0.36507936507936506,\n \"acc_norm_stderr\": 0.024796060602699965\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.42857142857142855,\n\
\ \"acc_stderr\": 0.0442626668137991,\n \"acc_norm\": 0.42857142857142855,\n\
\ \"acc_norm_stderr\": 0.0442626668137991\n },\n \"harness|hendrycksTest-global_facts|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_biology|5\": {\n \"acc\": 0.7225806451612903,\n\
\ \"acc_stderr\": 0.025470196835900055,\n \"acc_norm\": 0.7225806451612903,\n\
\ \"acc_norm_stderr\": 0.025470196835900055\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.4630541871921182,\n \"acc_stderr\": 0.035083705204426656,\n\
\ \"acc_norm\": 0.4630541871921182,\n \"acc_norm_stderr\": 0.035083705204426656\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.59,\n \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\"\
: 0.59,\n \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7515151515151515,\n \"acc_stderr\": 0.033744026441394036,\n\
\ \"acc_norm\": 0.7515151515151515,\n \"acc_norm_stderr\": 0.033744026441394036\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7525252525252525,\n \"acc_stderr\": 0.030746300742124484,\n \"\
acc_norm\": 0.7525252525252525,\n \"acc_norm_stderr\": 0.030746300742124484\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8704663212435233,\n \"acc_stderr\": 0.024233532297758723,\n\
\ \"acc_norm\": 0.8704663212435233,\n \"acc_norm_stderr\": 0.024233532297758723\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.5897435897435898,\n \"acc_stderr\": 0.024939313906940798,\n\
\ \"acc_norm\": 0.5897435897435898,\n \"acc_norm_stderr\": 0.024939313906940798\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.2740740740740741,\n \"acc_stderr\": 0.027195934804085626,\n \
\ \"acc_norm\": 0.2740740740740741,\n \"acc_norm_stderr\": 0.027195934804085626\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.634453781512605,\n \"acc_stderr\": 0.03128217706368461,\n \
\ \"acc_norm\": 0.634453781512605,\n \"acc_norm_stderr\": 0.03128217706368461\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.304635761589404,\n \"acc_stderr\": 0.03757949922943343,\n \"acc_norm\"\
: 0.304635761589404,\n \"acc_norm_stderr\": 0.03757949922943343\n },\n\
\ \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.7926605504587156,\n\
\ \"acc_stderr\": 0.01738141556360868,\n \"acc_norm\": 0.7926605504587156,\n\
\ \"acc_norm_stderr\": 0.01738141556360868\n },\n \"harness|hendrycksTest-high_school_statistics|5\"\
: {\n \"acc\": 0.5046296296296297,\n \"acc_stderr\": 0.03409825519163572,\n\
\ \"acc_norm\": 0.5046296296296297,\n \"acc_norm_stderr\": 0.03409825519163572\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7647058823529411,\n \"acc_stderr\": 0.029771775228145628,\n \"\
acc_norm\": 0.7647058823529411,\n \"acc_norm_stderr\": 0.029771775228145628\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7763713080168776,\n \"acc_stderr\": 0.027123298205229966,\n \
\ \"acc_norm\": 0.7763713080168776,\n \"acc_norm_stderr\": 0.027123298205229966\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6636771300448431,\n\
\ \"acc_stderr\": 0.031708824268455,\n \"acc_norm\": 0.6636771300448431,\n\
\ \"acc_norm_stderr\": 0.031708824268455\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n\
\ \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.768595041322314,\n \"acc_stderr\": 0.038498560987940904,\n \"\
acc_norm\": 0.768595041322314,\n \"acc_norm_stderr\": 0.038498560987940904\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.6759259259259259,\n\
\ \"acc_stderr\": 0.04524596007030048,\n \"acc_norm\": 0.6759259259259259,\n\
\ \"acc_norm_stderr\": 0.04524596007030048\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.6809815950920245,\n \"acc_stderr\": 0.03661997551073836,\n\
\ \"acc_norm\": 0.6809815950920245,\n \"acc_norm_stderr\": 0.03661997551073836\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4017857142857143,\n\
\ \"acc_stderr\": 0.04653333146973646,\n \"acc_norm\": 0.4017857142857143,\n\
\ \"acc_norm_stderr\": 0.04653333146973646\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7184466019417476,\n \"acc_stderr\": 0.04453254836326467,\n\
\ \"acc_norm\": 0.7184466019417476,\n \"acc_norm_stderr\": 0.04453254836326467\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8675213675213675,\n\
\ \"acc_stderr\": 0.022209309073165612,\n \"acc_norm\": 0.8675213675213675,\n\
\ \"acc_norm_stderr\": 0.022209309073165612\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.776500638569604,\n\
\ \"acc_stderr\": 0.01489723522945071,\n \"acc_norm\": 0.776500638569604,\n\
\ \"acc_norm_stderr\": 0.01489723522945071\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6734104046242775,\n \"acc_stderr\": 0.02524826477424284,\n\
\ \"acc_norm\": 0.6734104046242775,\n \"acc_norm_stderr\": 0.02524826477424284\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.29608938547486036,\n\
\ \"acc_stderr\": 0.015268677317602288,\n \"acc_norm\": 0.29608938547486036,\n\
\ \"acc_norm_stderr\": 0.015268677317602288\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.6797385620915033,\n \"acc_stderr\": 0.026716118380156847,\n\
\ \"acc_norm\": 0.6797385620915033,\n \"acc_norm_stderr\": 0.026716118380156847\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6816720257234726,\n\
\ \"acc_stderr\": 0.026457225067811025,\n \"acc_norm\": 0.6816720257234726,\n\
\ \"acc_norm_stderr\": 0.026457225067811025\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.6604938271604939,\n \"acc_stderr\": 0.02634856441201162,\n\
\ \"acc_norm\": 0.6604938271604939,\n \"acc_norm_stderr\": 0.02634856441201162\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4148936170212766,\n \"acc_stderr\": 0.029392236584612506,\n \
\ \"acc_norm\": 0.4148936170212766,\n \"acc_norm_stderr\": 0.029392236584612506\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.45697522816166886,\n\
\ \"acc_stderr\": 0.012722869501611419,\n \"acc_norm\": 0.45697522816166886,\n\
\ \"acc_norm_stderr\": 0.012722869501611419\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6066176470588235,\n \"acc_stderr\": 0.029674288281311155,\n\
\ \"acc_norm\": 0.6066176470588235,\n \"acc_norm_stderr\": 0.029674288281311155\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6176470588235294,\n \"acc_stderr\": 0.01965992249362335,\n \
\ \"acc_norm\": 0.6176470588235294,\n \"acc_norm_stderr\": 0.01965992249362335\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6454545454545455,\n\
\ \"acc_stderr\": 0.045820048415054174,\n \"acc_norm\": 0.6454545454545455,\n\
\ \"acc_norm_stderr\": 0.045820048415054174\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.6693877551020408,\n \"acc_stderr\": 0.030116426296540603,\n\
\ \"acc_norm\": 0.6693877551020408,\n \"acc_norm_stderr\": 0.030116426296540603\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7960199004975125,\n\
\ \"acc_stderr\": 0.02849317624532607,\n \"acc_norm\": 0.7960199004975125,\n\
\ \"acc_norm_stderr\": 0.02849317624532607\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.83,\n \"acc_stderr\": 0.03775251680686371,\n \
\ \"acc_norm\": 0.83,\n \"acc_norm_stderr\": 0.03775251680686371\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5602409638554217,\n\
\ \"acc_stderr\": 0.03864139923699122,\n \"acc_norm\": 0.5602409638554217,\n\
\ \"acc_norm_stderr\": 0.03864139923699122\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8011695906432749,\n \"acc_stderr\": 0.030611116557432528,\n\
\ \"acc_norm\": 0.8011695906432749,\n \"acc_norm_stderr\": 0.030611116557432528\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3219094247246022,\n\
\ \"mc1_stderr\": 0.0163555676119604,\n \"mc2\": 0.4722759067100739,\n\
\ \"mc2_stderr\": 0.015155963095621219\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7726913970007893,\n \"acc_stderr\": 0.011778612167091088\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.034874905231235785,\n \
\ \"acc_stderr\": 0.005053480765022253\n }\n}\n```"
repo_url: https://huggingface.co/Weyaxi/EulerMath-Mistral-7B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|arc:challenge|25_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|gsm8k|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hellaswag|10_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-08T14-49-30.062748.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-08T14-49-30.062748.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- '**/details_harness|winogrande|5_2024-04-08T14-49-30.062748.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-04-08T14-49-30.062748.parquet'
- config_name: results
data_files:
- split: 2024_04_08T14_49_30.062748
path:
- results_2024-04-08T14-49-30.062748.parquet
- split: latest
path:
- results_2024-04-08T14-49-30.062748.parquet
---
# Dataset Card for Evaluation run of Weyaxi/EulerMath-Mistral-7B
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Weyaxi/EulerMath-Mistral-7B](https://huggingface.co/Weyaxi/EulerMath-Mistral-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Weyaxi__EulerMath-Mistral-7B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-04-08T14:49:30.062748](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__EulerMath-Mistral-7B/blob/main/results_2024-04-08T14-49-30.062748.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": 0.5920253909953418,
"acc_stderr": 0.03296498598379715,
"acc_norm": 0.6024022284993832,
"acc_norm_stderr": 0.033775842095780426,
"mc1": 0.3219094247246022,
"mc1_stderr": 0.0163555676119604,
"mc2": 0.4722759067100739,
"mc2_stderr": 0.015155963095621219
},
"harness|arc:challenge|25": {
"acc": 0.5554607508532423,
"acc_stderr": 0.014521226405627075,
"acc_norm": 0.6040955631399317,
"acc_norm_stderr": 0.014291228393536592
},
"harness|hellaswag|10": {
"acc": 0.6419040031866162,
"acc_stderr": 0.004784607222774642,
"acc_norm": 0.8290181238797052,
"acc_norm_stderr": 0.003757236806397339
},
"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": 0.6,
"acc_stderr": 0.04232073695151589,
"acc_norm": 0.6,
"acc_norm_stderr": 0.04232073695151589
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.618421052631579,
"acc_stderr": 0.03953173377749194,
"acc_norm": 0.618421052631579,
"acc_norm_stderr": 0.03953173377749194
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.49,
"acc_stderr": 0.05024183937956911,
"acc_norm": 0.49,
"acc_norm_stderr": 0.05024183937956911
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6339622641509434,
"acc_stderr": 0.029647813539365245,
"acc_norm": 0.6339622641509434,
"acc_norm_stderr": 0.029647813539365245
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.6875,
"acc_stderr": 0.038760854559127644,
"acc_norm": 0.6875,
"acc_norm_stderr": 0.038760854559127644
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.47,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.47,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.49,
"acc_stderr": 0.05024183937956911,
"acc_norm": 0.49,
"acc_norm_stderr": 0.05024183937956911
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.36,
"acc_stderr": 0.048241815132442176,
"acc_norm": 0.36,
"acc_norm_stderr": 0.048241815132442176
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6069364161849711,
"acc_stderr": 0.03724249595817729,
"acc_norm": 0.6069364161849711,
"acc_norm_stderr": 0.03724249595817729
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.3333333333333333,
"acc_stderr": 0.04690650298201942,
"acc_norm": 0.3333333333333333,
"acc_norm_stderr": 0.04690650298201942
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.72,
"acc_stderr": 0.045126085985421276,
"acc_norm": 0.72,
"acc_norm_stderr": 0.045126085985421276
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.4978723404255319,
"acc_stderr": 0.032685726586674915,
"acc_norm": 0.4978723404255319,
"acc_norm_stderr": 0.032685726586674915
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.39473684210526316,
"acc_stderr": 0.045981880578165414,
"acc_norm": 0.39473684210526316,
"acc_norm_stderr": 0.045981880578165414
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.496551724137931,
"acc_stderr": 0.041665675771015785,
"acc_norm": 0.496551724137931,
"acc_norm_stderr": 0.041665675771015785
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.36507936507936506,
"acc_stderr": 0.024796060602699965,
"acc_norm": 0.36507936507936506,
"acc_norm_stderr": 0.024796060602699965
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.42857142857142855,
"acc_stderr": 0.0442626668137991,
"acc_norm": 0.42857142857142855,
"acc_norm_stderr": 0.0442626668137991
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7225806451612903,
"acc_stderr": 0.025470196835900055,
"acc_norm": 0.7225806451612903,
"acc_norm_stderr": 0.025470196835900055
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.4630541871921182,
"acc_stderr": 0.035083705204426656,
"acc_norm": 0.4630541871921182,
"acc_norm_stderr": 0.035083705204426656
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.59,
"acc_stderr": 0.04943110704237102,
"acc_norm": 0.59,
"acc_norm_stderr": 0.04943110704237102
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7515151515151515,
"acc_stderr": 0.033744026441394036,
"acc_norm": 0.7515151515151515,
"acc_norm_stderr": 0.033744026441394036
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7525252525252525,
"acc_stderr": 0.030746300742124484,
"acc_norm": 0.7525252525252525,
"acc_norm_stderr": 0.030746300742124484
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8704663212435233,
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"harness|hendrycksTest-jurisprudence|5": {
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"harness|hendrycksTest-nutrition|5": {
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"harness|hendrycksTest-professional_medicine|5": {
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},
"harness|hendrycksTest-public_relations|5": {
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"acc_norm": 0.6454545454545455,
"acc_norm_stderr": 0.045820048415054174
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.6693877551020408,
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"acc_norm": 0.6693877551020408,
"acc_norm_stderr": 0.030116426296540603
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.7960199004975125,
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"acc_norm": 0.7960199004975125,
"acc_norm_stderr": 0.02849317624532607
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.83,
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"acc_norm": 0.83,
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},
"harness|hendrycksTest-virology|5": {
"acc": 0.5602409638554217,
"acc_stderr": 0.03864139923699122,
"acc_norm": 0.5602409638554217,
"acc_norm_stderr": 0.03864139923699122
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8011695906432749,
"acc_stderr": 0.030611116557432528,
"acc_norm": 0.8011695906432749,
"acc_norm_stderr": 0.030611116557432528
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3219094247246022,
"mc1_stderr": 0.0163555676119604,
"mc2": 0.4722759067100739,
"mc2_stderr": 0.015155963095621219
},
"harness|winogrande|5": {
"acc": 0.7726913970007893,
"acc_stderr": 0.011778612167091088
},
"harness|gsm8k|5": {
"acc": 0.034874905231235785,
"acc_stderr": 0.005053480765022253
}
}
```
## 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
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### Direct Use
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[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
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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. -->
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#### 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
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Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
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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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## Dataset Card Contact
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neuralbioinfo/ESKAPE-masking | ---
license: cc-by-nc-nd-4.0
dataset_info:
features:
- name: reference_segment_id
dtype: string
- name: masked_segment
dtype: string
- name: position_to_mask
dtype: int64
- name: masked_segment_id
dtype: int64
- name: contig_id
dtype: string
- name: segment_id
dtype: string
- name: strand
dtype: string
- name: seq_start
dtype: int64
- name: seq_end
dtype: int64
- name: segment_start
dtype: int64
- name: segment_end
dtype: int64
- name: label
dtype: string
- name: segment_length
dtype: int64
- name: original_segment
dtype: string
splits:
- name: train
num_bytes: 43505486
num_examples: 40000
download_size: 19183244
dataset_size: 43505486
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Description
This dataset was used to evaluate different models on the masking exercise, measuring how well the different models can recover the original character.
## Dataset Overview
The dataset is compiled from the RefSeq database and other sources, focusing on ESKAPE pathogens. The genomic features were sampled randomly, followed by contiguous segmentation. This dataset contains various segments with lengths: [128, 256, 512, 1024]. The segments were randomly selected, and one of the characters was replaced by '*' (masked_segment column) to create a masking task. The reference_segment contains the original, non-replaced nucleotides. We performed 10,000 maskings per set, with a maximum of 2,000 genomic features. Only the genomic features: 'CDS', 'intergenic', 'pseudogene', and 'ncRNA' were considered.
## Data Fields
- `reference_segment_id`: A mapping of segment identifiers to their respective reference IDs in the database.
- `masked_segment`: The DNA sequence of the segment with certain positions masked (marked with '*') for prediction or testing purposes.
- `position_to_mask`: The specific position(s) in the sequence that have been masked, indicated by index numbers.
- `masked_segment_id`: Unique identifiers assigned to the masked segments. (unique only with respect to length)
- `contig_id`: Identifier of the contig to which the segment belongs.
- `segment_id`: Unique identifier for each genomic segment (same as reference segment id).
- `strand`: The DNA strand of the segment, indicated as '+' (positive) or '-' (negative).
- `seq_start`: Starting position of the segment within the contig.
- `seq_end`: Ending position of the segment within the contig.
- `segment_start`: Starting position of the genomic segment in the sequence.
- `segment_end`: Ending position of the genomic segment in the sequence.
- `label`: Category label for the genomic segment (e.g., 'CDS', 'intergenic').
- `segment_length`: The length of the genomic segment.
- `original_segment`: The original genomic sequence without any masking.
## Usage
This dataset is intended for academic and research purposes. Users are encouraged to use this dataset in the development and evaluation of bioinformatics models, especially those related to genomic studies.
## Contact Information
For any questions, feedback, or contributions regarding the datasets or ProkBERT, please feel free to reach out:
- **Name**: Balázs Ligeti
- **Email**: obalasz@gmail.com
We welcome your input and collaboration to improve our resources and research.
## Citation
```bibtex
@Article{ProkBERT2024,
author = {Ligeti, Balázs et al.},
journal = {Frontiers in Microbiology},
title = {{ProkBERT} family: genomic language models},
year = {2024},
volume = {14},
URL = {https://www.frontiersin.org/articles/10.3389/fmicb.2023.1331233},
DOI = {10.3389/fmicb.2023.1331233}
}
|
anubhav-singh/rel-stock | ---
license: cc
language:
- en
tags:
- stock
size_categories:
- 1K<n<10K
--- |
dim/norquinal_claude_multiround_chat_30k | ---
dataset_info:
features:
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 176848427
num_examples: 32170
download_size: 95127719
dataset_size: 176848427
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "norquinal_claude_multiround_chat_30k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
dongyoung4091/shp-generated_flan_t5_large_with_features_flan_t5_large | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: response
dtype: string
- name: prompt
dtype: string
- name: helpfulness
dtype: int64
- name: specificity
dtype: int64
- name: intent
dtype: int64
- name: factuality
dtype: int64
- name: easy-to-understand
dtype: int64
- name: relevance
dtype: int64
- name: readability
dtype: int64
- name: enough-detail
dtype: int64
- name: 'biased:'
dtype: int64
- name: fail-to-consider-individual-preferences
dtype: int64
- name: repetetive
dtype: int64
- name: fail-to-consider-context
dtype: int64
- name: too-long
dtype: int64
- name: __index_level_0__
dtype: int64
- name: log_score
dtype: float64
splits:
- name: train
num_bytes: 1748538
num_examples: 1500
download_size: 226283
dataset_size: 1748538
---
# Dataset Card for "shp-generated_flan_t5_large_with_features_flan_t5_large"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
openfun/tw-segis | ---
license: cc-by-4.0
---
|
CyberHarem/hamanami_kantaicollection | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of hamanami (Kantai Collection)
This is the dataset of hamanami (Kantai Collection), containing 276 images and their tags.
The core tags of this character are `long_hair, grey_hair, braid, single_braid, ribbon, hair_ribbon, ahoge, hair_over_eyes, brown_eyes, black_ribbon, bow`, 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 | 276 | 242.35 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hamanami_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 276 | 160.91 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hamanami_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 612 | 338.53 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hamanami_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 276 | 222.05 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hamanami_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 612 | 449.90 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hamanami_kantaicollection/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/hamanami_kantaicollection',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 8 |  |  |  |  |  | 1girl, bowtie, grey_pantyhose, long_sleeves, looking_at_viewer, pleated_dress, purple_dress, school_uniform, simple_background, solo, white_background, white_shirt, cowboy_shot, seamed_legwear, smile, blush |
| 1 | 37 |  |  |  |  |  | 1girl, bowtie, long_sleeves, school_uniform, solo, white_shirt, purple_dress, looking_at_viewer, upper_body, white_background, simple_background, hair_over_one_eye, open_mouth, blush |
| 2 | 8 |  |  |  |  |  | 1girl, grey_pantyhose, long_sleeves, pleated_dress, purple_dress, school_uniform, solo, white_shirt, full_body, lace-up_boots, open_mouth, seamed_legwear, bowtie, white_background, bangs, chibi, standing, blue_bow, blush_stickers, character_name, collared_shirt |
| 3 | 7 |  |  |  |  |  | 1girl, blue_dress, solo, bag, long_sleeves, official_alternate_costume, white_shirt, cowboy_shot, hair_over_one_eye, looking_at_viewer, blush |
| 4 | 6 |  |  |  |  |  | 1girl, black_dress, halloween_costume, solo, blush, ghost_costume, long_sleeves, official_alternate_costume, black_footwear, full_body, high_heels, open_mouth, orange_eyes |
| 5 | 5 |  |  |  |  |  | 1girl, full_body, simple_background, solo, white_background, white_shirt, white_socks, alternate_costume, blue_dress, long_sleeves, shoes, blush, looking_at_viewer, open_mouth, smile |
| 6 | 7 |  |  |  |  |  | 1girl, detached_collar, fake_animal_ears, playboy_bunny, rabbit_ears, solo, strapless_leotard, bowtie, open_mouth, rabbit_tail, simple_background, wrist_cuffs, blush, purple_leotard, white_background, adapted_costume, breasts, covered_navel, grey_pantyhose, looking_at_viewer, seamed_legwear |
| 7 | 10 |  |  |  |  |  | long_sleeves, reindeer_antlers, 1girl, blush, red_skirt, reindeer_costume, simple_background, solo, white_background, pleated_skirt, open_mouth, fur_trim, sack, animal_hood, kneehighs, looking_at_viewer |
| 8 | 5 |  |  |  |  |  | 1girl, cowboy_shot, looking_at_viewer, solo, purple_panties, blush, purple_bra, simple_background, small_breasts, underwear_only, blue_panties, camisole, collarbone, hair_over_one_eye, white_background |
| 9 | 5 |  |  |  |  |  | 1boy, 1girl, blush, hetero, nipples, solo_focus, sweat, bangs, cum_in_pussy, open_mouth, penis, small_breasts, vaginal, happy_sex, looking_at_viewer, medium_breasts, missionary, on_back, overflow, spread_legs, bar_censor, blue_bra, blue_panties, breasts_out, collarbone, completely_nude, hair_over_one_eye, heart, mosaic_censoring, navel, on_bed, smile |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bowtie | grey_pantyhose | long_sleeves | looking_at_viewer | pleated_dress | purple_dress | school_uniform | simple_background | solo | white_background | white_shirt | cowboy_shot | seamed_legwear | smile | blush | upper_body | hair_over_one_eye | open_mouth | full_body | lace-up_boots | bangs | chibi | standing | blue_bow | blush_stickers | character_name | collared_shirt | blue_dress | bag | official_alternate_costume | black_dress | halloween_costume | ghost_costume | black_footwear | high_heels | orange_eyes | white_socks | alternate_costume | shoes | detached_collar | fake_animal_ears | playboy_bunny | rabbit_ears | strapless_leotard | rabbit_tail | wrist_cuffs | purple_leotard | adapted_costume | breasts | covered_navel | reindeer_antlers | red_skirt | reindeer_costume | pleated_skirt | fur_trim | sack | animal_hood | kneehighs | purple_panties | purple_bra | small_breasts | underwear_only | blue_panties | camisole | collarbone | 1boy | hetero | nipples | solo_focus | sweat | cum_in_pussy | penis | vaginal | happy_sex | medium_breasts | missionary | on_back | overflow | spread_legs | bar_censor | blue_bra | breasts_out | completely_nude | heart | mosaic_censoring | navel | on_bed |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------|:-----------------|:---------------|:--------------------|:----------------|:---------------|:-----------------|:--------------------|:-------|:-------------------|:--------------|:--------------|:-----------------|:--------|:--------|:-------------|:--------------------|:-------------|:------------|:----------------|:--------|:--------|:-----------|:-----------|:-----------------|:-----------------|:-----------------|:-------------|:------|:-----------------------------|:--------------|:--------------------|:----------------|:-----------------|:-------------|:--------------|:--------------|:--------------------|:--------|:------------------|:-------------------|:----------------|:--------------|:--------------------|:--------------|:--------------|:-----------------|:------------------|:----------|:----------------|:-------------------|:------------|:-------------------|:----------------|:-----------|:-------|:--------------|:------------|:-----------------|:-------------|:----------------|:-----------------|:---------------|:-----------|:-------------|:-------|:---------|:----------|:-------------|:--------|:---------------|:--------|:----------|:------------|:-----------------|:-------------|:----------|:-----------|:--------------|:-------------|:-----------|:--------------|:------------------|:--------|:-------------------|:--------|:---------|
| 0 | 8 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 37 |  |  |  |  |  | X | X | | X | X | | X | X | X | X | X | X | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 8 |  |  |  |  |  | X | X | X | X | | X | X | X | | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 6 |  |  |  |  |  | X | | | X | | | | | | X | | | | | | X | | | X | X | | | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 5 |  |  |  |  |  | X | | | X | X | | | | X | X | X | X | | | X | X | | | X | X | | | | | | | | | X | | | | | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 7 |  |  |  |  |  | X | X | X | | X | | | | X | X | X | | | X | | X | | | X | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 10 |  |  |  |  |  | X | | | X | X | | | | X | X | X | | | | | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 5 |  |  |  |  |  | X | | | | X | | | | X | X | X | | X | | | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | |
| 9 | 5 |  |  |  |  |  | X | | | | X | | | | | | | | | | X | X | | X | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
sreejith8100/test | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': marriage
'1': other
- name: ground_truth
dtype: string
splits:
- name: train
num_bytes: 44443420.0
num_examples: 64
- name: test
num_bytes: 8086095.0
num_examples: 10
download_size: 52447696
dataset_size: 52529515.0
---
# Dataset Card for "test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
FelixChau/HathiTrustFullCatalogue | ---
license: apache-2.0
---
|
cvcio/mediawatch-2302 | ---
license: gpl-3.0
dataset_info:
features:
- name: id
dtype: string
- name: text
dtype: string
- name: createdAt
dtype: string
- name: source
dtype: string
- name: link
dtype: string
splits:
- name: train
num_bytes: 57902257227
num_examples: 12016379
download_size: 28013796843
dataset_size: 57902257227
language:
- el
size_categories:
- 10M<n<100M
--- |
senhorsapo/Doc | ---
license: openrail
---
|
zpn/pubchem_selfies | ---
license: openrail
---
This dataset consists of Pubchem molecules downloaded from: https://ftp.ncbi.nlm.nih.gov/pubchem/Compound/CURRENT-Full/
There are in total ~85M compounds for training, with an additional ~10M held out for validation and testing. |
alaqueboomb/voz_juice | ---
license: openrail
---
|
maveriq/tinystoriesv2_gpt4 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 2234135574
num_examples: 2717699
- name: valid
num_bytes: 22567397
num_examples: 27630
download_size: 1153194030
dataset_size: 2256702971
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
task_categories:
- text-generation
language:
- en
pretty_name: TinyStoriesV2-GPT4
size_categories:
- 1M<n<10M
---
## Prepared dataset from roneneldan/TinyStoriesV2-GPT4
# Data Preparation pipeline.
- Download TinyStoriesV2-GPT4-train.txt from https://huggingface.co/datasets/roneneldan/TinyStories/blob/main/TinyStoriesV2-GPT4-train.txt
```
raw = open('TinyStoriesV2-GPT4-train.txt').readlines()
stories = []
for x in tqdm(raw,total=len(raw)):
if x=='\n':
continue
if x.startswith('<|endoftext|>'):
chunk.append(x.strip())
stories.append(" ".join(chunk))
chunk=[]
continue
chunk.append(x.strip())
prep = [{'text':text} for text in stories]
Dataset.from_list(prep)
```
- Repeat for validation split |
felipesampaio2010/srgarrison | ---
license: openrail
---
|
jondurbin/mathjson-alpha | ---
license: apache-2.0
datasets:
- gsm8k
- meta-math/MetaMathQA
---
This is a first pass at generating MathJSON formulations of math problems to allow deterministic calculations (via cortex-js/compute-engine).
LLMs are decent at problem formulation, but terrible at calculations, especially things like calculating cosine of R radians, floating point with high precision multiplication, etc. Let's let LLMs do what they are good at and run the computation outside. |
adityarra07/test_data | ---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: transcription
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 133411975.96105696
num_examples: 1001
download_size: 134756772
dataset_size: 133411975.96105696
---
# Dataset Card for "test_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_17 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1409307748.0
num_examples: 276769
download_size: 1436124828
dataset_size: 1409307748.0
---
# Dataset Card for "chunk_17"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
presencesw/dataset4_translated_END | ---
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
- name: references
sequence: string
- name: question_vi
dtype: string
- name: answer_vi
dtype: string
- name: references_vi
sequence: string
splits:
- name: train
num_bytes: 45625153
num_examples: 7579
- name: validation
num_bytes: 6047717
num_examples: 1000
- name: test
num_bytes: 2436512
num_examples: 400
download_size: 28155735
dataset_size: 54109382
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
irenelizihui/Surfer100 | ---
license: wtfpl
---
|
Juanpablozarza292/TTS_embedded | ---
license: mit
---
|
Lollitor/CASFPROTEINMARKED | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: ID
dtype: string
- name: INPUT
dtype: string
splits:
- name: train
num_bytes: 296977
num_examples: 285
download_size: 121469
dataset_size: 296977
---
# Dataset Card for "CASFPROTEINMARKED"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/stella_hoshii_girlsfrontline | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of stella_hoshii/ステラ・星井/史黛拉·星井 (Girls' Frontline)
This is the dataset of stella_hoshii/ステラ・星井/史黛拉·星井 (Girls' Frontline), containing 82 images and their tags.
The core tags of this character are `animal_ears, cat_ears, long_hair, red_hair, red_eyes, drill_hair, breasts, artificial_eye, mechanical_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 | 82 | 95.71 MiB | [Download](https://huggingface.co/datasets/CyberHarem/stella_hoshii_girlsfrontline/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 82 | 52.46 MiB | [Download](https://huggingface.co/datasets/CyberHarem/stella_hoshii_girlsfrontline/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 200 | 118.63 MiB | [Download](https://huggingface.co/datasets/CyberHarem/stella_hoshii_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 82 | 82.77 MiB | [Download](https://huggingface.co/datasets/CyberHarem/stella_hoshii_girlsfrontline/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 200 | 165.66 MiB | [Download](https://huggingface.co/datasets/CyberHarem/stella_hoshii_girlsfrontline/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/stella_hoshii_girlsfrontline',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 11 |  |  |  |  |  | 1girl, elbow_gloves, sleeveless_dress, solo, red_dress, black_gloves, black_thighhighs, looking_at_viewer, bracelet, bare_shoulders, blush, holding_smoking_pipe, kiseru, boots, hand_on_hip, medium_breasts, short_dress, zettai_ryouiki |
| 1 | 10 |  |  |  |  |  | 1girl, hetero, solo_focus, thighhighs, blush, 1boy, penis, cum_in_pussy, open_mouth, vaginal, colored_sclera, nipples, nude, spread_legs, testicles, uncensored, black_gloves, navel, sex_from_behind |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | elbow_gloves | sleeveless_dress | solo | red_dress | black_gloves | black_thighhighs | looking_at_viewer | bracelet | bare_shoulders | blush | holding_smoking_pipe | kiseru | boots | hand_on_hip | medium_breasts | short_dress | zettai_ryouiki | hetero | solo_focus | thighhighs | 1boy | penis | cum_in_pussy | open_mouth | vaginal | colored_sclera | nipples | nude | spread_legs | testicles | uncensored | navel | sex_from_behind |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:-------------------|:-------|:------------|:---------------|:-------------------|:--------------------|:-----------|:-----------------|:--------|:-----------------------|:---------|:--------|:--------------|:-----------------|:--------------|:-----------------|:---------|:-------------|:-------------|:-------|:--------|:---------------|:-------------|:----------|:-----------------|:----------|:-------|:--------------|:------------|:-------------|:--------|:------------------|
| 0 | 11 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | |
| 1 | 10 |  |  |  |  |  | X | | | | | X | | | | | X | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
radames/diffusers-gallery-data | ---
duplicated_from: huggingface-projects/diffusers-gallery-data
---
|
mask-distilled-one-sec-cv12/chunk_184 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 702268272
num_examples: 137916
download_size: 708208494
dataset_size: 702268272
---
# Dataset Card for "chunk_184"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
dim/roleplay_instruct_v2_final | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 4382098
num_examples: 7188
download_size: 2880335
dataset_size: 4382098
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "roleplay_instruct_v2_final"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
AdapterOcean/biology_dataset_standardized_cluster_0_std | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
- name: cluster
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 13287061
num_examples: 8108
download_size: 0
dataset_size: 13287061
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "biology_dataset_standardized_cluster_0_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
heegyu/bbq | ---
license: cc-by-4.0
---
# BBQ
Repository for the Bias Benchmark for QA dataset.
https://github.com/nyu-mll/BBQ
Authors: Alicia Parrish, Angelica Chen, Nikita Nangia, Vishakh Padmakumar, Jason Phang, Jana Thompson, Phu Mon Htut, and Samuel R. Bowman.
## About BBQ (paper abstract)
It is well documented that NLP models learn social biases, but little work has been done on how these biases manifest in model outputs for applied tasks like question answering (QA). We introduce the Bias Benchmark for QA (BBQ), a dataset of question sets constructed by the authors that highlight attested social biases against people belonging to protected classes along nine social dimensions relevant for U.S. English-speaking contexts. Our task evaluates model responses at two levels: (i) given an under-informative context, we test how strongly responses refect social biases, and (ii) given an adequately informative context, we test whether the model's biases override a correct answer choice. We fnd that models often rely on stereotypes when the context is under-informative, meaning the model's outputs consistently reproduce harmful biases in this setting. Though models are more accurate when the context provides an informative answer, they still rely on stereotypes and average up to 3.4 percentage points higher accuracy when the correct answer aligns with a social bias than when it conficts, with this difference widening to over 5 points on examples targeting gender for most models tested.
## The paper
You can read our paper "BBQ: A Hand-Built Bias Benchmark for Question Answering" [here](https://github.com/nyu-mll/BBQ/blob/main/QA_bias_benchmark.pdf). The paper has been published in the Findings of ACL 2022 [here](https://aclanthology.org/2022.findings-acl.165/).
|
Arjit74/indian_food_images | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': burger
'1': butter_naan
'2': chai
'3': chapati
'4': chole_bhature
'5': dal_makhani
'6': dhokla
'7': fried_rice
'8': idli
'9': jalebi
'10': kaathi_rolls
'11': kadai_paneer
'12': kulfi
'13': masala_dosa
'14': momos
'15': paani_puri
'16': pakode
'17': pav_bhaji
'18': pizza
'19': samosa
splits:
- name: train
num_bytes: 1290537116.8314333
num_examples: 5328
- name: test
num_bytes: 252103793.3925666
num_examples: 941
download_size: 1601414642
dataset_size: 1542640910.224
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
threadberry/mini-platypus | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 4186564
num_examples: 1000
download_size: 2245921
dataset_size: 4186564
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
kuanhuggingface/tencent_tts_encodec | ---
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: file_id
dtype: string
- name: instruction
dtype: string
- name: transcription
dtype: string
- name: src_encodec_0
sequence: int64
- name: src_encodec_1
sequence: int64
- name: src_encodec_2
sequence: int64
- name: src_encodec_3
sequence: int64
- name: src_encodec_4
sequence: int64
- name: src_encodec_5
sequence: int64
- name: src_encodec_6
sequence: int64
- name: src_encodec_7
sequence: int64
- name: tgt_encodec_0
sequence: int64
- name: tgt_encodec_1
sequence: int64
- name: tgt_encodec_2
sequence: int64
- name: tgt_encodec_3
sequence: int64
- name: tgt_encodec_4
sequence: int64
- name: tgt_encodec_5
sequence: int64
- name: tgt_encodec_6
sequence: int64
- name: tgt_encodec_7
sequence: int64
splits:
- name: train
num_bytes: 18583644220
num_examples: 266780
- name: validation
num_bytes: 527818324
num_examples: 7620
- name: test
num_bytes: 508374588
num_examples: 7620
download_size: 470732178
dataset_size: 19619837132
---
# Dataset Card for "tencent_tts_encodec"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
MichaelOrme/Profane_Removed | ---
license: unknown
---
|
ganeshkamath89/reuters_articles | ---
dataset_info:
features:
- name: title
dtype: string
- name: body
dtype: string
splits:
- name: train
num_bytes: 13792576
num_examples: 17262
- name: validation
num_bytes: 1870389
num_examples: 2158
- name: test
num_bytes: 1379190
num_examples: 2158
download_size: 10073414
dataset_size: 17042155
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
CATIE-AQ/paws-x_fr_prompt_paraphrase_generation | ---
language:
- fr
license:
- other
size_categories:
- 100K<n<1M
task_categories:
- text-generation
tags:
- paraphrase-generation
- DFP
- french prompts
annotations_creators:
- found
language_creators:
- found
multilinguality:
- monolingual
source_datasets:
- paws-x
---
# paws-x_fr_prompt_paraphrase_generation
## Summary
**paws-x_fr_prompt_paraphrase_generation** is a subset of the [**Dataset of French Prompts (DFP)**](https://huggingface.co/datasets/CATIE-AQ/DFP).
It contains **562,728** rows that can be used for a paraphrase generation task.
The original data (without prompts) comes from the dataset [paws-x](https://huggingface.co/datasets/paws-x) by Yang 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
24 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.
```
'Générer une phrase qui signifie la même chose que celle-ci : "'+sentence1+'"',
'Génère une phrase qui signifie la même chose que celle-ci : "'+sentence1+'"',
'Générez une phrase qui signifie la même chose que celle-ci : "'+sentence1+'"',
'Paraphraser la phrase suivante : "'+sentence1+'"',
'Paraphrase la phrase suivante : "'+sentence1+'"',
'Paraphrasez la phrase suivante : "'+sentence1+'"',
'Créer une phrase qui signifie la même chose que celle-ci : "'+sentence1+'"',
'Crée une phrase qui signifie la même chose que celle-ci : "'+sentence1+'"',
'Créez une phrase qui signifie la même chose que celle-ci : "'+sentence1+'"',
'Créer une paraphrase de la phrase suivante : "'+sentence1+'"',
'Crée une paraphrase de la phrase suivante : "'+sentence1+'"',
'Créez une paraphrase de la phrase suivante : "'+sentence1+'"',
'Ecrire une phrase qui signifie la même chose que celle-ci : "'+sentence1+'"',
'Ecris une phrase qui signifie la même chose que celle-ci : "'+sentence1+'"',
'Ecrivez une phrase qui signifie la même chose que celle-ci : "'+sentence1+'"',
'Ecrire une paraphrase de la phrase suivante : "'+sentence1+'"',
'Ecris une paraphrase de la phrase suivante : "'+sentence1+'"',
'Ecrivez une paraphrase de la phrase suivante : "'+sentence1+'"',
'Rédiger une phrase qui signifie la même chose que celle-ci : "'+sentence1+'"',
'Rédige une phrase qui signifie la même chose que celle-ci : "'+sentence1+'"',
'Rédigez une phrase qui signifie la même chose que celle-ci : "'+sentence1+'"',
'Rédiger une paraphrase de la phrase suivante : "'+sentence1+'"',
'Rédige une paraphrase de la phrase suivante : "'+sentence1+'"',
'Rédigez une paraphrase de la phrase suivante : "'+sentence1+'"'
```
### Features used in the prompts
In the prompt list above, `sentence1` and the `target` have been constructed from:
```
paws_x = load_dataset('paws-x','fr')
if paws_x['train'][i]['label'] == 1:
sentence1 = paws_x['train'][i]['sentence1']
targets = paws_x['train'][i]['sentence2']
```
# Splits
- `train` with 520,416 samples
- `valid` with 20,640 samples
- `test` with 21,672 samples
# How to use?
```
from datasets import load_dataset
dataset = load_dataset("CATIE-AQ/paws-x_fr_prompt_paraphrase_generation")
```
# Citation
## Original data
> @InProceedings{pawsx2019emnlp,
title = {{PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification}},
author = {Yang, Yinfei and Zhang, Yuan and Tar, Chris and Baldridge, Jason},
booktitle = {Proc. of EMNLP},
year = {2019}
}
## 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
The dataset may be freely used for any purpose, although acknowledgement of Google LLC ("Google") as the data source would be appreciated. The dataset is provided "AS IS" without any warranty, express or implied. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset. |
tyzhu/lmind_hotpot_train1000_eval500_v1_qa | ---
configs:
- config_name: default
data_files:
- split: train_qa
path: data/train_qa-*
- split: train_recite_qa
path: data/train_recite_qa-*
- split: eval_qa
path: data/eval_qa-*
- split: eval_recite_qa
path: data/eval_recite_qa-*
- split: all_docs
path: data/all_docs-*
- split: all_docs_eval
path: data/all_docs_eval-*
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: answers
struct:
- name: answer_start
sequence: 'null'
- name: text
sequence: string
splits:
- name: train_qa
num_bytes: 173266
num_examples: 1000
- name: train_recite_qa
num_bytes: 1052784
num_examples: 1000
- name: eval_qa
num_bytes: 81677
num_examples: 500
- name: eval_recite_qa
num_bytes: 542914
num_examples: 500
- name: all_docs
num_bytes: 1370698
num_examples: 2959
- name: all_docs_eval
num_bytes: 1370509
num_examples: 2959
- name: train
num_bytes: 173266
num_examples: 1000
- name: validation
num_bytes: 81677
num_examples: 500
download_size: 2985172
dataset_size: 4846791
---
# Dataset Card for "lmind_hotpot_train1000_eval500_v1_qa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ashwathjadhav23/Spanish_MLM_4 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 3448922
num_examples: 25000
download_size: 1925871
dataset_size: 3448922
---
# Dataset Card for "Spanish_MLM_4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
malhajar/meditron-tr | ---
dataset_info:
features:
- name: id
dtype: string
- name: source
dtype: string
- name: title
dtype: string
- name: clean_text
dtype: string
- name: raw_text
dtype: string
- name: url
dtype: string
- name: overview
dtype: string
- name: clean_text_turkish
dtype: string
splits:
- name: train
num_bytes: 1338636287
num_examples: 37970
download_size: 659990211
dataset_size: 1338636287
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
result-muse256-muse512-wuerst-sdv15/b985b700 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 208
num_examples: 10
download_size: 1365
dataset_size: 208
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "b985b700"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
LRGB/PCQM-Contact | ---
task_categories:
- graph-ml
size_categories:
- 1M<n<10M
tags:
- lrgb
license: cc-by-4.0
---
# `peptides-functional`
### Dataset Summary
| Dataset | Domain | Task | Node Feat. (dim) | Edge Feat. (dim) | Perf. Metric |
|---|---|---|---|---|---|
| PCQM-Contact | Quantum Chemistry | Link Prediction | Atom Encoder (9) | Bond Encoder (3) | Hits@K, MRR
| Dataset | # Graphs | # Nodes | μ Nodes | μ Deg. | # Edges | μ Edges | μ Short. Path | μ Diameter
|---|---:|---:|---:|:---:|---:|---:|---:|---:|
| PCQM-Contact | 529,434 | 15,955,687 | 30.14 | 2.03 | 32,341,644 | 61.09 |4.63±0.63 | 9.86±1.79 |
## Additional Information
### Dataset Curators
* Vijay Prakash Dwivedi ([vijaydwivedi75](https://github.com/vijaydwivedi75))
### Citation Information
```
@article{dwivedi2022LRGB,
title={Long Range Graph Benchmark},
author={Dwivedi, Vijay Prakash and Rampášek, Ladislav and Galkin, Mikhail and Parviz, Ali and Wolf, Guy and Luu, Anh Tuan and Beaini, Dominique},
journal={arXiv:2206.08164},
year={2022}
}
``` |
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