datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
JeunesseAfricaine/sheng_nlu | ---
license: mit
---
## Common User Intentions
#### Greetings
- Wasemaje
- uko aje btw
- oyah...
- Form
- Alafu niaje
- Poa Sana Mambo
- Niko poa
- Pia Mimi Niko salama
- Hope siku yako iko poa
- Siko poa kabisa
- Nimekuwa poa
- Umeshindaje
- Hope uko poa
- uko poa
- Sasa
- Vipi vipi
- Niko salama
- ..its been long.
- Nko fiti
- niko fiti
- Nmeamka fity..
- Vipi
- Unasemaje
- Aaaah...itakuaje sasaa..
- .iz vipi..itakuaje..
- Form ni gani bro...
- iz vipi
#### Affirm
- Hapo sawa...
- Fty
- sai
- Hio si ni better hadi
- Imebidi.
- Eeeh mazee
- mazeee
- Fity fity
- Oooh poapoa
- Yap
- Inakaa poa
- Yeah itabidi
- Ooooh...
- Si ndo nadaaiii😅
- Oooh sawa
- Okay sawa basi
- Venye utaamua ni sawa
- Sawa wacha tungoje
- lazima
- apa umenena
- Sawa basi
- walai
- Oooh
- inaweza mbaya
- itaweza mbaya
- ni sawa
- Iko poa
- Iko tu sawa hivo
- ilinbamba.
- Nimemada
- Btw hao ata mimi naona
- but inaeleweka
- pia mimi
- iende ikiendaga
- We jua ivo
- Hata Mimi
- Nataka
- Ooh.
- Chezea tu hapo
- isorait
- Ata yako ni kali
- Ntaicheck out Leo
- hmm. Okay
- Mimi sina shida
- ooooh io iko fity...
- hii ni ngori
- maze
- sawa
- banaa
- Aaah kumbe
- Safiii..
- Sasawa
- hio ni fityyy
- Yeah nliona
- Vizii...
- Eeeeh nmekua naiona...
- Yea
- Haina nomA
- katambe
- accept basi
- ni sawa
- Issaplan
- nmeget
- nimedai tu
- eeh
- Hio ni poa
- nadai sa hii
- Eeeeh
- mi nadai tu
- firi
- Hapo freshi
#### Deny
- Sipendi
- aih
- Nimegive up
- Yangu bado
- siezi make
- Sina😊
- Haileti
- Haiwezi
- Io sikuwa nikwambie
- Sikuwa
- Wacha ata
- ata sijui
- Sijasema
- Sijai
- hiyo haiezi
- Bado.
- Uku tricks...
- sidai
- achana nayo
- ziii
- si fityy
- Nimekataa Mimi
- Sijui
- Aiwezekani
- Bado sioni
#### Courtesy
- Imefika... shukran
- Haina ngori
- Inafaa hivo
- Utakuwa umeniokolea manzee
- Karibu
- Nyc one
- Hakuna pressure
- Gai. Pole
- Usijali I will
- Nimekufeel hapo
- Waah izaa
- Pole lkn
- Pole
- plz
- okay...pole
- thanks for pulling up lkn..
- shukran
- Eeeeh nyc
- Thanx for the info
- Uko aje
- haina pressure
- eih, iko fiti.
- vitu kama hizo
- sahii
#### Asking clarification
- check alafu unishow
- Sasa msee akishabuy anafanya aje
- Umeenda wapi
- nlikuwa nadai
- Nlikua nataka
- Ulipata
- leo jioni utakuwa?
- uko
- umelostia wapi?
- ingine?
- hii inamaanisha?
- Wewe Sasa ni nani?
- warrathos
- kwani nisiende sasa
- unadai zingine?
- Kwani
- Haiya...
- Unadu?
- inakuanga mangapiii...
- Kuna nn
- Nauliza
- Hakuna kwanini
- Nadai kujua what
- Kwanini hakuna
- Kwa nini hakuna
- Uliniambia
- Mbona
- Nlikua nashangaa
- Unadu nini
- Oooh mara moja
- Unaeza taka?
- unaeza make?
- Umeipata?
- wapi kwingine tena
- kuna yenye natafuta
- Sijajua bado
- Niko na ingine
- ulikuwa unataka
- ulinishow?
- ulinsho
- Umepata
- Ata stage hakuna?
- Huku hakuna kibandaski?
- Sai ndio uko available
- Ivo
- Inaeza
- Naeza
- Btw, nikuulize
- Uliza
- hadi sa hii
- Nauliza ndio nijue kama bado iko
- Btw ile hoteli tulienda na wewe apo kiimbo huendangi?
#### Comedy
- Ata kama
- Wasikupee pressure
- umeanza jokes
- Ulisumbua sana
- Unaeza niambia ivo
- usinicheke
- Hakuna😁😁kwanini
- aki wewe.
- naskia mpaka ulipiga sherehe
- sio?
- uko na kakitu
- Aaaaii
- .uko fity nayo..
- icome through mbaya...
#### Small talk
- Kuchil tu bana
- Inafaa hivo
- Acha niskizie
- Skujua hii stuff
- nacheza chini
- hii imesink deep.
- mi Niko
- khai, gai, ghaiye
- Woiye
- ndo nmeland
- Nimekuona
- Kaaai
- Nambie
- bado nashangaa aliipull thru maze
- Niambie
- Najua uko kejani
- Bado uko
- Utakuwa sawa
- Niko poa ata kama uniliacha hanging jana
- issa deal
- Walai io nilijua utasema
- hujawai sahau hii
- Sijajua bado
- Ni maroundi tu
- Enyewe imetoka mbali
- Hadi nimekuwa Tao leo
- Ni mnoma mbaya
- Anyway mambo ni polepole
- Imagine
- Sina la kusema
- Sai
- Najua umeboeka
#### Resolute
- Nataka leo
- hayo ndo maisha Sasa
- vile itakuja maze
- Acha tu
- Waaah Leo haiwezi
- Ni sawa tu
- Imeisha
- Itabidi
- siendagi
- siezi kuangusha
- nachangamkia hii
- Weno ivi...
- Hii price iko poa...
#### implore
- but nimetry tena
- aminia tu
- Ebu try
- Alafu
- naona hufeel kuongea
- Watu hawaongei?
- Itabidi tu umesort
- Naona huna shughuli yangu
- tufanye pamoja
- khai, gai, ghaiye
- so kalunch
- ama?
- Sahii ni the best time
- Kwanza sahii
- hii weekend
- Kaanza next weekend ni fity
- this weekend
- Acha ntacheki
- izo sasa..
- Acha tuone
- So tunafikanga ivor morning mapemaa
- naona uko rada
- mapema kiasi
- nimchapie niskie...
- Naisaka walai
#### Bye
- Ama kesho
- Ngoja nta rudi baadaye
- nacheki tu rada ya kesho
- Nitakusort kesho morning
- Ni hivo nimekafunga
- nitakushow
- Nextweek ndio inaeza
- Ntakuchapia kama ntamake
- Freshi
#### Sample Bot Responses
- tulia tu hana mambo mob
- si you know how we do it
- Form ni gani
- Oooh nmekuget
- znaeza kupea stress
- Hues make leo
- nshow password
- Nmeichangamkia design ya ngori
- Oooh nmekuget...
- ilicome through
- Naisaka walai
- kesho ntakuchapia
- nichapie niskie
- Aaaah..😅
- Alafu ile story ya
- Ooooh ebu ntasaka
- Saa ngapi uko free..
- Ama unasema ya
- Safiii..naona uko rada
- Ilkulemea🤣
- Acha ntacheki
- imeharibia form..
- Nmeitafuta
- Ndio nimeget
- inaeza saidia mtu
- Email yako ni gani
- Wacha niangalie
- nangoja ulipe
- nimeshikika
- Sawa tuma email
- Kwani ulimwambia nini
- Najua ata most of the time
- mara most btw
- Unajua tu ni risky
- unadai tu niseme mi ni robot
- kwanini
- ndio usiulizwe
- Ukiangalia niambie
- Last time ukinipigia nilikuwa nimeenda kuoshwa
- ikishaenda kwa mganga hairudi
- Hata Mimi ni hayo mambo madogo madogo ndio imenieka.
- We jua nafikirianga mingi ni venye zingine huwa sisemi
- Na najua
- unarelax
- mm ata sko tensed
- sahii ata ni risky
- but ntakuchapia
- oooh waah..
- aaaah ata ww
- hii si fityy
- maze itabidi tudunde virtual
- tunadunda wapiiii..
- kwani sa mi ndo nafaa kumshow kila time coz this is not the first time namwambia🤦♀️
- Wacha hizo.
- Yeah niko hapa
- Niko
- Give me sometime.
- Maze...nmecheza ki mimi
- Uko busy
- Chill kiasi
- Wacha nikusort
- ntakushow
- looking for you hupatikani
- Mnaniogopa ama
- Wewe unapenda free
- Nakusort sai chill mazee
- Kiasi
- relax mkubwa
- Sahii uko sorted sindio
- Ni juu
- bringing the future to us
- hiyo ni form yangu daily
- Ata mimi sitaki ufala 😂
- Imagine
- Uko sawa
- Uko sawa ama unaitaji ingine
- ka unaeza
- utanichapia tu
- unasemaje lakini
- Niulize
- Uko na number
- Ukiboeka wewe nitext
- unadai sa hii ?
- skuwa nimeona
- Acha nicheki
- Ni Friday bana
- Niko chilled tu
- Unadai aje.
- Utanichapia basi
- Umenyamaza sana bana
- imekam through ama
- Nategea umalize ndo nikushow ile form
- Guidance tu kiasi
- Tutadiscuss pia stori
- Nakwelewa
- tujue niaje
- itaweza mbaya
- Kuna hopes za kulearn
|
kxly/niji_jelly | ---
language:
- en
license: creativeml-openrail-m
thumbnail: >-
https://huggingface.co/datasets/kxly/niji_jelly/blob/main/niji_jelly_showcase_2.png
tags:
- stable-diffusion
- text-to-image
- image-to-image
inference: false
pretty_name: niji_jelly Style
---
# LoRA - niji_jelly
LoRA trained on images trained on from MidJourney's Niji style, specifically the jelly look.
All image examples are on the dalcefoV3Anime model: https://civitai.com/models/5398/dalcefov3animepastelmix
I recommend using the LoRA at around 0.8 emphasis for best results.
## License
This embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the LoRA to deliberately produce nor share illegal or harmful outputs or content
2. The authors claim no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license) |
siberian-lang-lab/evenki-russian-parallel-corpora | ---
dataset_info:
features:
- name: env
dtype: string
- name: ru
dtype: string
- name: source
dtype: string
- name: sentence_num
dtype: int64
splits:
- name: train
num_bytes: 653661
num_examples: 2267
download_size: 311403
dataset_size: 653661
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: cc-by-4.0
--- |
biglab/webui-7kbal | ---
license: other
---
This data accompanies the WebUI project (https://dl.acm.org/doi/abs/10.1145/3544548.3581158)
For more information, check out the project website: https://uimodeling.github.io/
To download this dataset, you need to install the huggingface-hub package
```
pip install huggingface-hub
```
Use snapshot_download
```
from huggingface_hub import snapshot_download
snapshot_download(repo_id="biglab/webui-7kbal", repo_type="dataset")
```
IMPORTANT
* Before downloading and using, please review the copyright info here: https://github.com/js0nwu/webui/blob/main/COPYRIGHT.txt
* Not all data samples have the same number of files (e.g., same number of device screenshots) due to the fact that the crawler used a timeout during collection
* The dataset released on HuggingFace was filtered using a list of explicit words and therefore contains fewer samples than the experiments originally used in the paper. The raw dataset is currently available (https://drive.google.com/drive/folders/1hcO75W2FjsZoibsj2TIbKz67hy9JkOBz?usp=share_link) but may be removed in the future. |
Blaise-g/SumPubmed | ---
language:
- en
paperswithcode_id:
pretty_name: SumPubmed
train-eval-index:
- config: Blaise-g--SumPubmed
task: summarization
task_id: summarization
splits:
eval_split: test
col_mapping:
text: text
abstract: target
---
# Dataset Card for "SumPubmed"
## Original Dataset Description
- **Repository:** [https://github.com/vgupta123/sumpubmed](https://github.com/vgupta123/sumpubmed)
- **Paper:** [More Information Needed](https://vgupta123.github.io/docs/121_paper.pdf)
## Description of dataset processing
5 rows were dropped from the original dataset taken from KAGGLE as they were missing the respective 'shorter_abstract' entries.
The 'line_text' and 'filename_text' columns were left untouched while the remaining ones were processed to remove the '\n' (many repetitions of those present in the original dataset), '\<dig\>', '\<cit\>', 'BACKGROUND', 'RESULTS' and 'CONCLUSIONS' matching strings which were deemed not necessary for the purpose of summarization. Additionally, extra spaces were removed and spacing around punctuations was fixed.
|
shilpimittal123/testTT | ---
license: apache-2.0
---
|
open-llm-leaderboard/details_AbacusResearch__Jallabi-34B | ---
pretty_name: Evaluation run of AbacusResearch/Jallabi-34B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [AbacusResearch/Jallabi-34B](https://huggingface.co/AbacusResearch/Jallabi-34B)\
\ 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_AbacusResearch__Jallabi-34B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-01T23:57:00.309695](https://huggingface.co/datasets/open-llm-leaderboard/details_AbacusResearch__Jallabi-34B/blob/main/results_2024-03-01T23-57-00.309695.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\": 0.7588027752694985,\n\
\ \"acc_stderr\": 0.028198784274698175,\n \"acc_norm\": 0.7634897551647938,\n\
\ \"acc_norm_stderr\": 0.028724253839732584,\n \"mc1\": 0.3659730722154223,\n\
\ \"mc1_stderr\": 0.016862941684088365,\n \"mc2\": 0.5146389940410719,\n\
\ \"mc2_stderr\": 0.015020552354313921\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6348122866894198,\n \"acc_stderr\": 0.014070265519268804,\n\
\ \"acc_norm\": 0.6604095563139932,\n \"acc_norm_stderr\": 0.01383903976282017\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6382194781915953,\n\
\ \"acc_stderr\": 0.004795337009118205,\n \"acc_norm\": 0.8380800637323242,\n\
\ \"acc_norm_stderr\": 0.0036762448867232664\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956911,\n \
\ \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956911\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.7037037037037037,\n\
\ \"acc_stderr\": 0.03944624162501116,\n \"acc_norm\": 0.7037037037037037,\n\
\ \"acc_norm_stderr\": 0.03944624162501116\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.8552631578947368,\n \"acc_stderr\": 0.028631951845930384,\n\
\ \"acc_norm\": 0.8552631578947368,\n \"acc_norm_stderr\": 0.028631951845930384\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.84,\n\
\ \"acc_stderr\": 0.03684529491774709,\n \"acc_norm\": 0.84,\n \
\ \"acc_norm_stderr\": 0.03684529491774709\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.8188679245283019,\n \"acc_stderr\": 0.023702963526757798,\n\
\ \"acc_norm\": 0.8188679245283019,\n \"acc_norm_stderr\": 0.023702963526757798\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.9166666666666666,\n\
\ \"acc_stderr\": 0.023112508176051233,\n \"acc_norm\": 0.9166666666666666,\n\
\ \"acc_norm_stderr\": 0.023112508176051233\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.53,\n \"acc_stderr\": 0.050161355804659205,\n \
\ \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.050161355804659205\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.65,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\"\
: 0.65,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.45,\n \"acc_stderr\": 0.04999999999999999,\n \
\ \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.04999999999999999\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.7052023121387283,\n\
\ \"acc_stderr\": 0.034765996075164785,\n \"acc_norm\": 0.7052023121387283,\n\
\ \"acc_norm_stderr\": 0.034765996075164785\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.5196078431372549,\n \"acc_stderr\": 0.04971358884367405,\n\
\ \"acc_norm\": 0.5196078431372549,\n \"acc_norm_stderr\": 0.04971358884367405\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.85,\n \"acc_stderr\": 0.03588702812826369,\n \"acc_norm\": 0.85,\n\
\ \"acc_norm_stderr\": 0.03588702812826369\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.7659574468085106,\n \"acc_stderr\": 0.027678452578212387,\n\
\ \"acc_norm\": 0.7659574468085106,\n \"acc_norm_stderr\": 0.027678452578212387\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5614035087719298,\n\
\ \"acc_stderr\": 0.04668000738510455,\n \"acc_norm\": 0.5614035087719298,\n\
\ \"acc_norm_stderr\": 0.04668000738510455\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.7586206896551724,\n \"acc_stderr\": 0.03565998174135302,\n\
\ \"acc_norm\": 0.7586206896551724,\n \"acc_norm_stderr\": 0.03565998174135302\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.7407407407407407,\n \"acc_stderr\": 0.022569897074918435,\n \"\
acc_norm\": 0.7407407407407407,\n \"acc_norm_stderr\": 0.022569897074918435\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5396825396825397,\n\
\ \"acc_stderr\": 0.04458029125470973,\n \"acc_norm\": 0.5396825396825397,\n\
\ \"acc_norm_stderr\": 0.04458029125470973\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\
: 0.55,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-high_school_biology|5\"\
: {\n \"acc\": 0.9032258064516129,\n \"acc_stderr\": 0.016818943416345197,\n\
\ \"acc_norm\": 0.9032258064516129,\n \"acc_norm_stderr\": 0.016818943416345197\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.6157635467980296,\n \"acc_stderr\": 0.0342239856565755,\n \"acc_norm\"\
: 0.6157635467980296,\n \"acc_norm_stderr\": 0.0342239856565755\n },\n\
\ \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\"\
: 0.8,\n \"acc_stderr\": 0.04020151261036846,\n \"acc_norm\": 0.8,\n\
\ \"acc_norm_stderr\": 0.04020151261036846\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.8727272727272727,\n \"acc_stderr\": 0.026024657651656187,\n\
\ \"acc_norm\": 0.8727272727272727,\n \"acc_norm_stderr\": 0.026024657651656187\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.9090909090909091,\n \"acc_stderr\": 0.020482086775424218,\n \"\
acc_norm\": 0.9090909090909091,\n \"acc_norm_stderr\": 0.020482086775424218\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9689119170984456,\n \"acc_stderr\": 0.01252531062552703,\n\
\ \"acc_norm\": 0.9689119170984456,\n \"acc_norm_stderr\": 0.01252531062552703\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.8102564102564103,\n \"acc_stderr\": 0.019880165406588792,\n\
\ \"acc_norm\": 0.8102564102564103,\n \"acc_norm_stderr\": 0.019880165406588792\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.44074074074074077,\n \"acc_stderr\": 0.030270671157284067,\n \
\ \"acc_norm\": 0.44074074074074077,\n \"acc_norm_stderr\": 0.030270671157284067\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.8613445378151261,\n \"acc_stderr\": 0.02244826447683258,\n \
\ \"acc_norm\": 0.8613445378151261,\n \"acc_norm_stderr\": 0.02244826447683258\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.4966887417218543,\n \"acc_stderr\": 0.04082393379449654,\n \"\
acc_norm\": 0.4966887417218543,\n \"acc_norm_stderr\": 0.04082393379449654\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.9192660550458716,\n \"acc_stderr\": 0.011680172292862076,\n \"\
acc_norm\": 0.9192660550458716,\n \"acc_norm_stderr\": 0.011680172292862076\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.6342592592592593,\n \"acc_stderr\": 0.032847388576472056,\n \"\
acc_norm\": 0.6342592592592593,\n \"acc_norm_stderr\": 0.032847388576472056\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.9215686274509803,\n \"acc_stderr\": 0.01886951464665893,\n \"\
acc_norm\": 0.9215686274509803,\n \"acc_norm_stderr\": 0.01886951464665893\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.919831223628692,\n \"acc_stderr\": 0.01767667999189163,\n \
\ \"acc_norm\": 0.919831223628692,\n \"acc_norm_stderr\": 0.01767667999189163\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7757847533632287,\n\
\ \"acc_stderr\": 0.027991534258519513,\n \"acc_norm\": 0.7757847533632287,\n\
\ \"acc_norm_stderr\": 0.027991534258519513\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8549618320610687,\n \"acc_stderr\": 0.03088466108951538,\n\
\ \"acc_norm\": 0.8549618320610687,\n \"acc_norm_stderr\": 0.03088466108951538\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.9173553719008265,\n \"acc_stderr\": 0.02513538235660422,\n \"\
acc_norm\": 0.9173553719008265,\n \"acc_norm_stderr\": 0.02513538235660422\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8981481481481481,\n\
\ \"acc_stderr\": 0.029239272675632748,\n \"acc_norm\": 0.8981481481481481,\n\
\ \"acc_norm_stderr\": 0.029239272675632748\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.8650306748466258,\n \"acc_stderr\": 0.02684576505455386,\n\
\ \"acc_norm\": 0.8650306748466258,\n \"acc_norm_stderr\": 0.02684576505455386\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.6428571428571429,\n\
\ \"acc_stderr\": 0.04547960999764376,\n \"acc_norm\": 0.6428571428571429,\n\
\ \"acc_norm_stderr\": 0.04547960999764376\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.9029126213592233,\n \"acc_stderr\": 0.02931596291881348,\n\
\ \"acc_norm\": 0.9029126213592233,\n \"acc_norm_stderr\": 0.02931596291881348\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9145299145299145,\n\
\ \"acc_stderr\": 0.018315891685625845,\n \"acc_norm\": 0.9145299145299145,\n\
\ \"acc_norm_stderr\": 0.018315891685625845\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.89,\n \"acc_stderr\": 0.03144660377352202,\n \
\ \"acc_norm\": 0.89,\n \"acc_norm_stderr\": 0.03144660377352202\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.913154533844189,\n\
\ \"acc_stderr\": 0.010070298377747786,\n \"acc_norm\": 0.913154533844189,\n\
\ \"acc_norm_stderr\": 0.010070298377747786\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.8034682080924855,\n \"acc_stderr\": 0.021393961404363847,\n\
\ \"acc_norm\": 0.8034682080924855,\n \"acc_norm_stderr\": 0.021393961404363847\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.6703910614525139,\n\
\ \"acc_stderr\": 0.01572153107518387,\n \"acc_norm\": 0.6703910614525139,\n\
\ \"acc_norm_stderr\": 0.01572153107518387\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.8431372549019608,\n \"acc_stderr\": 0.02082375883758091,\n\
\ \"acc_norm\": 0.8431372549019608,\n \"acc_norm_stderr\": 0.02082375883758091\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.819935691318328,\n\
\ \"acc_stderr\": 0.02182342285774494,\n \"acc_norm\": 0.819935691318328,\n\
\ \"acc_norm_stderr\": 0.02182342285774494\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.8672839506172839,\n \"acc_stderr\": 0.01887735383957184,\n\
\ \"acc_norm\": 0.8672839506172839,\n \"acc_norm_stderr\": 0.01887735383957184\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.6276595744680851,\n \"acc_stderr\": 0.028838921471251458,\n \
\ \"acc_norm\": 0.6276595744680851,\n \"acc_norm_stderr\": 0.028838921471251458\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5775749674054759,\n\
\ \"acc_stderr\": 0.012615600475734921,\n \"acc_norm\": 0.5775749674054759,\n\
\ \"acc_norm_stderr\": 0.012615600475734921\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.8272058823529411,\n \"acc_stderr\": 0.022966067585581784,\n\
\ \"acc_norm\": 0.8272058823529411,\n \"acc_norm_stderr\": 0.022966067585581784\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.8104575163398693,\n \"acc_stderr\": 0.015856152189980263,\n \
\ \"acc_norm\": 0.8104575163398693,\n \"acc_norm_stderr\": 0.015856152189980263\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7,\n\
\ \"acc_stderr\": 0.04389311454644287,\n \"acc_norm\": 0.7,\n \
\ \"acc_norm_stderr\": 0.04389311454644287\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.8408163265306122,\n \"acc_stderr\": 0.023420972069166344,\n\
\ \"acc_norm\": 0.8408163265306122,\n \"acc_norm_stderr\": 0.023420972069166344\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8905472636815921,\n\
\ \"acc_stderr\": 0.02207632610182466,\n \"acc_norm\": 0.8905472636815921,\n\
\ \"acc_norm_stderr\": 0.02207632610182466\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.9,\n \"acc_stderr\": 0.030151134457776334,\n \
\ \"acc_norm\": 0.9,\n \"acc_norm_stderr\": 0.030151134457776334\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5542168674698795,\n\
\ \"acc_stderr\": 0.038695433234721015,\n \"acc_norm\": 0.5542168674698795,\n\
\ \"acc_norm_stderr\": 0.038695433234721015\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.9005847953216374,\n \"acc_stderr\": 0.022949025579355027,\n\
\ \"acc_norm\": 0.9005847953216374,\n \"acc_norm_stderr\": 0.022949025579355027\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3659730722154223,\n\
\ \"mc1_stderr\": 0.016862941684088365,\n \"mc2\": 0.5146389940410719,\n\
\ \"mc2_stderr\": 0.015020552354313921\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8145224940805051,\n \"acc_stderr\": 0.010923965303140505\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6520090978013646,\n \
\ \"acc_stderr\": 0.013120581030382132\n }\n}\n```"
repo_url: https://huggingface.co/AbacusResearch/Jallabi-34B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|arc:challenge|25_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|gsm8k|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hellaswag|10_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-01T23-57-00.309695.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-01T23-57-00.309695.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- '**/details_harness|winogrande|5_2024-03-01T23-57-00.309695.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-01T23-57-00.309695.parquet'
- config_name: results
data_files:
- split: 2024_03_01T23_57_00.309695
path:
- results_2024-03-01T23-57-00.309695.parquet
- split: latest
path:
- results_2024-03-01T23-57-00.309695.parquet
---
# Dataset Card for Evaluation run of AbacusResearch/Jallabi-34B
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [AbacusResearch/Jallabi-34B](https://huggingface.co/AbacusResearch/Jallabi-34B) 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_AbacusResearch__Jallabi-34B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-01T23:57:00.309695](https://huggingface.co/datasets/open-llm-leaderboard/details_AbacusResearch__Jallabi-34B/blob/main/results_2024-03-01T23-57-00.309695.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": 0.7588027752694985,
"acc_stderr": 0.028198784274698175,
"acc_norm": 0.7634897551647938,
"acc_norm_stderr": 0.028724253839732584,
"mc1": 0.3659730722154223,
"mc1_stderr": 0.016862941684088365,
"mc2": 0.5146389940410719,
"mc2_stderr": 0.015020552354313921
},
"harness|arc:challenge|25": {
"acc": 0.6348122866894198,
"acc_stderr": 0.014070265519268804,
"acc_norm": 0.6604095563139932,
"acc_norm_stderr": 0.01383903976282017
},
"harness|hellaswag|10": {
"acc": 0.6382194781915953,
"acc_stderr": 0.004795337009118205,
"acc_norm": 0.8380800637323242,
"acc_norm_stderr": 0.0036762448867232664
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.51,
"acc_stderr": 0.05024183937956911,
"acc_norm": 0.51,
"acc_norm_stderr": 0.05024183937956911
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.7037037037037037,
"acc_stderr": 0.03944624162501116,
"acc_norm": 0.7037037037037037,
"acc_norm_stderr": 0.03944624162501116
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.8552631578947368,
"acc_stderr": 0.028631951845930384,
"acc_norm": 0.8552631578947368,
"acc_norm_stderr": 0.028631951845930384
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.84,
"acc_stderr": 0.03684529491774709,
"acc_norm": 0.84,
"acc_norm_stderr": 0.03684529491774709
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.8188679245283019,
"acc_stderr": 0.023702963526757798,
"acc_norm": 0.8188679245283019,
"acc_norm_stderr": 0.023702963526757798
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.9166666666666666,
"acc_stderr": 0.023112508176051233,
"acc_norm": 0.9166666666666666,
"acc_norm_stderr": 0.023112508176051233
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.53,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.53,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.65,
"acc_stderr": 0.047937248544110196,
"acc_norm": 0.65,
"acc_norm_stderr": 0.047937248544110196
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.45,
"acc_stderr": 0.04999999999999999,
"acc_norm": 0.45,
"acc_norm_stderr": 0.04999999999999999
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.7052023121387283,
"acc_stderr": 0.034765996075164785,
"acc_norm": 0.7052023121387283,
"acc_norm_stderr": 0.034765996075164785
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.5196078431372549,
"acc_stderr": 0.04971358884367405,
"acc_norm": 0.5196078431372549,
"acc_norm_stderr": 0.04971358884367405
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.85,
"acc_stderr": 0.03588702812826369,
"acc_norm": 0.85,
"acc_norm_stderr": 0.03588702812826369
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.7659574468085106,
"acc_stderr": 0.027678452578212387,
"acc_norm": 0.7659574468085106,
"acc_norm_stderr": 0.027678452578212387
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5614035087719298,
"acc_stderr": 0.04668000738510455,
"acc_norm": 0.5614035087719298,
"acc_norm_stderr": 0.04668000738510455
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.7586206896551724,
"acc_stderr": 0.03565998174135302,
"acc_norm": 0.7586206896551724,
"acc_norm_stderr": 0.03565998174135302
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.7407407407407407,
"acc_stderr": 0.022569897074918435,
"acc_norm": 0.7407407407407407,
"acc_norm_stderr": 0.022569897074918435
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.5396825396825397,
"acc_stderr": 0.04458029125470973,
"acc_norm": 0.5396825396825397,
"acc_norm_stderr": 0.04458029125470973
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.55,
"acc_stderr": 0.05,
"acc_norm": 0.55,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.9032258064516129,
"acc_stderr": 0.016818943416345197,
"acc_norm": 0.9032258064516129,
"acc_norm_stderr": 0.016818943416345197
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.6157635467980296,
"acc_stderr": 0.0342239856565755,
"acc_norm": 0.6157635467980296,
"acc_norm_stderr": 0.0342239856565755
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.8,
"acc_stderr": 0.04020151261036846,
"acc_norm": 0.8,
"acc_norm_stderr": 0.04020151261036846
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.8727272727272727,
"acc_stderr": 0.026024657651656187,
"acc_norm": 0.8727272727272727,
"acc_norm_stderr": 0.026024657651656187
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.9090909090909091,
"acc_stderr": 0.020482086775424218,
"acc_norm": 0.9090909090909091,
"acc_norm_stderr": 0.020482086775424218
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9689119170984456,
"acc_stderr": 0.01252531062552703,
"acc_norm": 0.9689119170984456,
"acc_norm_stderr": 0.01252531062552703
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.8102564102564103,
"acc_stderr": 0.019880165406588792,
"acc_norm": 0.8102564102564103,
"acc_norm_stderr": 0.019880165406588792
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.44074074074074077,
"acc_stderr": 0.030270671157284067,
"acc_norm": 0.44074074074074077,
"acc_norm_stderr": 0.030270671157284067
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.8613445378151261,
"acc_stderr": 0.02244826447683258,
"acc_norm": 0.8613445378151261,
"acc_norm_stderr": 0.02244826447683258
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.4966887417218543,
"acc_stderr": 0.04082393379449654,
"acc_norm": 0.4966887417218543,
"acc_norm_stderr": 0.04082393379449654
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.9192660550458716,
"acc_stderr": 0.011680172292862076,
"acc_norm": 0.9192660550458716,
"acc_norm_stderr": 0.011680172292862076
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.6342592592592593,
"acc_stderr": 0.032847388576472056,
"acc_norm": 0.6342592592592593,
"acc_norm_stderr": 0.032847388576472056
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.9215686274509803,
"acc_stderr": 0.01886951464665893,
"acc_norm": 0.9215686274509803,
"acc_norm_stderr": 0.01886951464665893
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.919831223628692,
"acc_stderr": 0.01767667999189163,
"acc_norm": 0.919831223628692,
"acc_norm_stderr": 0.01767667999189163
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.7757847533632287,
"acc_stderr": 0.027991534258519513,
"acc_norm": 0.7757847533632287,
"acc_norm_stderr": 0.027991534258519513
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.8549618320610687,
"acc_stderr": 0.03088466108951538,
"acc_norm": 0.8549618320610687,
"acc_norm_stderr": 0.03088466108951538
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.9173553719008265,
"acc_stderr": 0.02513538235660422,
"acc_norm": 0.9173553719008265,
"acc_norm_stderr": 0.02513538235660422
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.8981481481481481,
"acc_stderr": 0.029239272675632748,
"acc_norm": 0.8981481481481481,
"acc_norm_stderr": 0.029239272675632748
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.8650306748466258,
"acc_stderr": 0.02684576505455386,
"acc_norm": 0.8650306748466258,
"acc_norm_stderr": 0.02684576505455386
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.6428571428571429,
"acc_stderr": 0.04547960999764376,
"acc_norm": 0.6428571428571429,
"acc_norm_stderr": 0.04547960999764376
},
"harness|hendrycksTest-management|5": {
"acc": 0.9029126213592233,
"acc_stderr": 0.02931596291881348,
"acc_norm": 0.9029126213592233,
"acc_norm_stderr": 0.02931596291881348
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.9145299145299145,
"acc_stderr": 0.018315891685625845,
"acc_norm": 0.9145299145299145,
"acc_norm_stderr": 0.018315891685625845
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.89,
"acc_stderr": 0.03144660377352202,
"acc_norm": 0.89,
"acc_norm_stderr": 0.03144660377352202
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.913154533844189,
"acc_stderr": 0.010070298377747786,
"acc_norm": 0.913154533844189,
"acc_norm_stderr": 0.010070298377747786
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.8034682080924855,
"acc_stderr": 0.021393961404363847,
"acc_norm": 0.8034682080924855,
"acc_norm_stderr": 0.021393961404363847
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.6703910614525139,
"acc_stderr": 0.01572153107518387,
"acc_norm": 0.6703910614525139,
"acc_norm_stderr": 0.01572153107518387
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.8431372549019608,
"acc_stderr": 0.02082375883758091,
"acc_norm": 0.8431372549019608,
"acc_norm_stderr": 0.02082375883758091
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.819935691318328,
"acc_stderr": 0.02182342285774494,
"acc_norm": 0.819935691318328,
"acc_norm_stderr": 0.02182342285774494
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.8672839506172839,
"acc_stderr": 0.01887735383957184,
"acc_norm": 0.8672839506172839,
"acc_norm_stderr": 0.01887735383957184
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.6276595744680851,
"acc_stderr": 0.028838921471251458,
"acc_norm": 0.6276595744680851,
"acc_norm_stderr": 0.028838921471251458
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.5775749674054759,
"acc_stderr": 0.012615600475734921,
"acc_norm": 0.5775749674054759,
"acc_norm_stderr": 0.012615600475734921
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.8272058823529411,
"acc_stderr": 0.022966067585581784,
"acc_norm": 0.8272058823529411,
"acc_norm_stderr": 0.022966067585581784
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.8104575163398693,
"acc_stderr": 0.015856152189980263,
"acc_norm": 0.8104575163398693,
"acc_norm_stderr": 0.015856152189980263
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.7,
"acc_stderr": 0.04389311454644287,
"acc_norm": 0.7,
"acc_norm_stderr": 0.04389311454644287
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.8408163265306122,
"acc_stderr": 0.023420972069166344,
"acc_norm": 0.8408163265306122,
"acc_norm_stderr": 0.023420972069166344
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8905472636815921,
"acc_stderr": 0.02207632610182466,
"acc_norm": 0.8905472636815921,
"acc_norm_stderr": 0.02207632610182466
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.9,
"acc_stderr": 0.030151134457776334,
"acc_norm": 0.9,
"acc_norm_stderr": 0.030151134457776334
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5542168674698795,
"acc_stderr": 0.038695433234721015,
"acc_norm": 0.5542168674698795,
"acc_norm_stderr": 0.038695433234721015
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.9005847953216374,
"acc_stderr": 0.022949025579355027,
"acc_norm": 0.9005847953216374,
"acc_norm_stderr": 0.022949025579355027
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3659730722154223,
"mc1_stderr": 0.016862941684088365,
"mc2": 0.5146389940410719,
"mc2_stderr": 0.015020552354313921
},
"harness|winogrande|5": {
"acc": 0.8145224940805051,
"acc_stderr": 0.010923965303140505
},
"harness|gsm8k|5": {
"acc": 0.6520090978013646,
"acc_stderr": 0.013120581030382132
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_167 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1228592668.0
num_examples: 241279
download_size: 1256052549
dataset_size: 1228592668.0
---
# Dataset Card for "chunk_167"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
liuyanchen1015/MULTI_VALUE_rte_demonstrative_for_definite_articles | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: test
num_bytes: 893663
num_examples: 2470
- name: train
num_bytes: 769342
num_examples: 2040
download_size: 1070331
dataset_size: 1663005
---
# Dataset Card for "MULTI_VALUE_rte_demonstrative_for_definite_articles"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ashar786K/UrduRoman | ---
license: cc-by-4.0
---
|
Lilsunx/roubt-201 | ---
license: openrail
---
|
malteklaes/cpp-code-code_search_net-style | ---
license: apache-2.0
task_categories:
- text-generation
- fill-mask
language:
- code
size_categories:
- 1K<n<10K
dataset_info:
- config_name: all
features:
- name: repository_name
dtype: string
- name: func_path_in_repository
dtype: string
- name: func_name
dtype: string
- name: whole_func_string
dtype: string
- name: language
dtype: string
- name: func_code_string
dtype: string
- name: func_code_tokens
sequence: string
- name: func_documentation_string
dtype: string
- name: func_documentation_tokens
sequence: string
- name: split_name
dtype: string
- name: func_code_url
dtype: string
splits:
- name: train
num_bytes: 5850604083
num_examples: 1880853
- name: test
num_bytes: 308626333
num_examples: 100529
- name: validation
num_bytes: 274564382
num_examples: 89154
download_size: 5117370511
dataset_size: 6433794798
- config_name: cpp
features:
- name: repository_name
dtype: string
- name: func_path_in_repository
dtype: string
- name: func_name
dtype: string
- name: whole_func_string
dtype: string
- name: language
dtype: string
- name: func_code_string
dtype: string
- name: func_code_tokens
sequence: string
- name: func_documentation_string
dtype: string
- name: func_documentation_tokens
sequence: string
- name: split_name
dtype: string
- name: func_code_url
dtype: string
splits:
- name: train
num_bytes: 1429272535
num_examples: 454451
- name: test
num_bytes: 82377246
num_examples: 26909
- name: validation
num_bytes: 42358315
num_examples: 15328
download_size: 1060569153
dataset_size: 1554008096
configs:
- config_name: default
data_files:
- split: train
path: "cpp_dataset/train/train.jsonl"
- split: test
path: "cpp_dataset/test/test.jsonl"
- split: validation
path: "cpp_dataset/validation/validation.jsonl"
---
# C++ Dataset
> documentation source: https://huggingface.co/docs/datasets/main/en/repository_structure
### Supported Tasks and Leaderboards
- `language-modeling`: The dataset can be used to train a model for modelling programming languages, which consists in building language models for programming languages.
### Language
- C++ **programming** language
## Dataset Structure
### Data Instances
A data point consists of a function code along with its documentation. Each data point also contains meta data on the function, such as the repository it was extracted from.
```
{
'id': '0',
'repository_name': 'organisation/repository',
'func_path_in_repository': 'src/path/to/file.py',
'func_name': 'func',
'whole_func_string': 'def func(args):\n"""Docstring"""\n [...]',
'language': 'python',
'func_code_string': '[...]',
'func_code_tokens': ['def', 'func', '(', 'args', ')', ...],
'func_documentation_string': 'Docstring',
'func_documentation_string_tokens': ['Docstring'],
'split_name': 'train',
'func_code_url': 'https://github.com/<org>/<repo>/blob/<hash>/src/path/to/file.py#L111-L150'
}
```
### Data Fields
- `id`: Arbitrary number
- `repository_name`: name of the GitHub repository
- `func_path_in_repository`: tl;dr: path to the file which holds the function in the repository
- `func_name`: name of the function in the file
- `whole_func_string`: Code + documentation of the function
- `language`: Programming language in whoch the function is written
- `func_code_string`: Function code
- `func_code_tokens`: Tokens yielded by Treesitter
- `func_documentation_string`: Function documentation
- `func_documentation_string_tokens`: Tokens yielded by Treesitter
- `split_name`: Name of the split to which the example belongs (one of train, test or valid)
- `func_code_url`: URL to the function code on Github
### Data Splits
Three splits are available:
- train
- test
- valid
### Citation Information
- based on (format and idea): https://huggingface.co/datasets/code_search_net/blob/main/code_search_net.py
@article{husain2019codesearchnet,
title={C++ Dataset},
author={Klaes, Malte},
year={2024}
}
|
distil-whisper/spgispeech-timestamped | ---
license: other
task_categories:
- automatic-speech-recognition
language:
- en
extra_gated_prompt: |-
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extra_gated_fields:
Full name: text
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I accept the Terms of Usage: checkbox
---
# Distil Whisper: SPGISpeech With Timestamps
This is a variant of the [SPGISpeech](https://huggingface.co/datasets/kensho/spgispeech) dataset, augmented to return the pseudo-labelled Whisper
Transcriptions alongside the original dataset elements. The pseudo-labelled transcriptions were generated by
labelling the input audio data with the Whisper [large-v2](https://huggingface.co/openai/whisper-large-v2)
model with *greedy* sampling and timestamp prediction. For information on how the original dataset was curated, refer to the original
[dataset card](https://huggingface.co/datasets/kensho/spgispeech).
## Standalone Usage
First, install the latest version of the 🤗 Datasets package:
```bash
pip install --upgrade pip
pip install --upgrade datasets[audio]
```
The dataset can be downloaded and pre-processed on disk using the [`load_dataset`](https://huggingface.co/docs/datasets/v2.14.5/en/package_reference/loading_methods#datasets.load_dataset)
function:
```python
from datasets import load_dataset
dataset = load_dataset("distil-whisper/spgispeech", "L")
# take the first sample of the validation set
sample = dataset["validation"][0]
```
It can also be streamed directly from the Hub using Datasets' [streaming mode](https://huggingface.co/blog/audio-datasets#streaming-mode-the-silver-bullet).
Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire
dataset to disk:
```python
from datasets import load_dataset
dataset = load_dataset("distil-whisper/spgispeech", "L", streaming=True)
# take the first sample of the validation set
sample = next(iter(dataset["validation"]))
```
## Distil Whisper Usage
To use this dataset to reproduce a Distil Whisper training run, refer to the instructions on the
[Distil Whisper repository](https://github.com/huggingface/distil-whisper#training).
## License
This dataset is licensed under custom terms. To view the custom license for this dataset, refer to the original [dataset card](https://huggingface.co/datasets/kensho/spgispeech).
|
open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE4_3.8w-r16-gate_up_down-test1 | ---
pretty_name: Evaluation run of CHIH-HUNG/llama-2-13b-FINETUNE4_3.8w-r16-gate_up_down-test1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [CHIH-HUNG/llama-2-13b-FINETUNE4_3.8w-r16-gate_up_down-test1](https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE4_3.8w-r16-gate_up_down-test1)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE4_3.8w-r16-gate_up_down-test1\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-25T21:38:01.231208](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE4_3.8w-r16-gate_up_down-test1/blob/main/results_2023-10-25T21-38-01.231208.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.22818791946308725,\n\
\ \"em_stderr\": 0.00429775606227976,\n \"f1\": 0.2705872483221472,\n\
\ \"f1_stderr\": 0.004287875673448546,\n \"acc\": 0.45044049897886096,\n\
\ \"acc_stderr\": 0.010454670771991827\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.22818791946308725,\n \"em_stderr\": 0.00429775606227976,\n\
\ \"f1\": 0.2705872483221472,\n \"f1_stderr\": 0.004287875673448546\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.12661106899166036,\n \
\ \"acc_stderr\": 0.009159715283081099\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7742699289660616,\n \"acc_stderr\": 0.011749626260902557\n\
\ }\n}\n```"
repo_url: https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE4_3.8w-r16-gate_up_down-test1
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|arc:challenge|25_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_25T21_38_01.231208
path:
- '**/details_harness|drop|3_2023-10-25T21-38-01.231208.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-25T21-38-01.231208.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_25T21_38_01.231208
path:
- '**/details_harness|gsm8k|5_2023-10-25T21-38-01.231208.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-25T21-38-01.231208.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hellaswag|10_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-10T09-30-33.515075.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-10T09-30-33.515075.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-10T09-30-33.515075.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_25T21_38_01.231208
path:
- '**/details_harness|winogrande|5_2023-10-25T21-38-01.231208.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-25T21-38-01.231208.parquet'
- config_name: results
data_files:
- split: 2023_10_10T09_30_33.515075
path:
- results_2023-10-10T09-30-33.515075.parquet
- split: 2023_10_25T21_38_01.231208
path:
- results_2023-10-25T21-38-01.231208.parquet
- split: latest
path:
- results_2023-10-25T21-38-01.231208.parquet
---
# Dataset Card for Evaluation run of CHIH-HUNG/llama-2-13b-FINETUNE4_3.8w-r16-gate_up_down-test1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE4_3.8w-r16-gate_up_down-test1
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [CHIH-HUNG/llama-2-13b-FINETUNE4_3.8w-r16-gate_up_down-test1](https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE4_3.8w-r16-gate_up_down-test1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE4_3.8w-r16-gate_up_down-test1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-25T21:38:01.231208](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE4_3.8w-r16-gate_up_down-test1/blob/main/results_2023-10-25T21-38-01.231208.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.22818791946308725,
"em_stderr": 0.00429775606227976,
"f1": 0.2705872483221472,
"f1_stderr": 0.004287875673448546,
"acc": 0.45044049897886096,
"acc_stderr": 0.010454670771991827
},
"harness|drop|3": {
"em": 0.22818791946308725,
"em_stderr": 0.00429775606227976,
"f1": 0.2705872483221472,
"f1_stderr": 0.004287875673448546
},
"harness|gsm8k|5": {
"acc": 0.12661106899166036,
"acc_stderr": 0.009159715283081099
},
"harness|winogrande|5": {
"acc": 0.7742699289660616,
"acc_stderr": 0.011749626260902557
}
}
```
### 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] |
lazyboy450/youtube_results | ---
license: mit
---
|
liuyanchen1015/MULTI_VALUE_sst2_completive_done | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: score
dtype: int64
splits:
- name: dev
num_bytes: 4919
num_examples: 34
- name: test
num_bytes: 14703
num_examples: 89
- name: train
num_bytes: 222194
num_examples: 1716
download_size: 120634
dataset_size: 241816
---
# Dataset Card for "MULTI_VALUE_sst2_completive_done"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ajmangus/qm_bob_hard_4_mixture_1.0e | ---
dataset_info:
features:
- name: alice_label
dtype: bool
- name: bob_label
dtype: bool
- name: charlie_label
dtype: bool
- name: difficulty
dtype: int64
- name: statement
dtype: string
- name: choices
sequence: string
- name: character
dtype: string
- name: label
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
splits:
- name: train
num_bytes: 20101703.0
num_examples: 166263
- name: validation
num_bytes: 2026424.3333333333
num_examples: 16758
- name: test
num_bytes: 2012512.6666666667
num_examples: 16650
download_size: 6306313
dataset_size: 24140640.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
AbdulHadi806/mail_spam_ham_dataset | ---
license: mit
language:
- en
tags:
- email
- spam or ham
---
## Mail dataset(spam and ham)
5616 rows
 |
distilled-one-sec-cv12-each-chunk-uniq/chunk_228 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1185722940.0
num_examples: 231045
download_size: 1212141033
dataset_size: 1185722940.0
---
# Dataset Card for "chunk_228"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
distilabel-internal-testing/SystemChat-1.1-AltGens | ---
size_categories: n<1K
config_names:
- keep_columns
tags:
- synthetic
- distilabel
- rlaif
---
<p align="left">
<a href="https://github.com/argilla-io/distilabel">
<img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>
</a>
</p>
# Dataset Card for SystemChat-1.1-AltGens
This dataset has been created with [Distilabel](https://distilabel.argilla.io/).
## Dataset Summary
This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI:
```console
distilabel pipeline run --config "https://huggingface.co/datasets/distilabel-internal-testing/SystemChat-1.1-AltGens/raw/main/pipeline.yaml"
```
or explore the configuration:
```console
distilabel pipeline info --config "https://huggingface.co/datasets/distilabel-internal-testing/SystemChat-1.1-AltGens/raw/main/pipeline.yaml"
```
## Dataset structure
The examples have the following structure per configuration:
<details><summary> Configuration: keep_columns </summary><hr>
```json
{
"generation_models": [
"01-ai/Yi-34B-Chat",
"Nexusflow/Starling-LM-7B-beta",
"Qwen/Qwen1.5-14B-Chat"
],
"generations": [
"Certainly! Here\u0027s a simple guide on how to recite the \u0027Om Dum Durgayei Namaha\u0027 mantra:\n\n1. **Find a Quiet Space**: Choose a peaceful room or area where you can sit undisturbed for a few minutes.\n\n2. **Position Yourself**: Sit in a comfortable position, such as cross-legged on the floor or in a chair. You can also stand or walk while reciting the mantra, as movement can enhance your connection to the deity.\n\n3. **Invoke the Presence**: Before you begin, take a few moments to focus",
" Absolutely! Here\u0027s a step-by-step guide on how to recite the Durga mantra daily:\n\n1. Choose a quiet and clean space: Select a space in your home that is calm and free from distractions. You may choose to set up an altar with a picture or statue of Goddess Durga if you have one.\n\n2. Cleansing rituals: Before starting your prayer or meditation, perform some cleansing rituals like washing your hands, feet, and face, and lighting a lamp or candle.\n\n3. Sit comfortably: Sit",
"Certainly! Reciting the mantra \u0027Om Dum Durgayei Namaha\u0027 is a simple yet potent practice. Here\u0027s a step-by-step guide to help you get started:\n\n1. Find a sacred space: Choose a quiet and peaceful location in your home where you can sit comfortably and focus on your meditation or puja.\n\n2. Choose a time: A consistent time, such as early morning or before bedtime, is ideal for mantra recitation. This creates a ritualistic habit.\n\n3. Proper pronunciation: Begin with a simple Om, which represents the unity of all sounds and the divine within. Pronounce it as \"Aa"
],
"messages": [
{
"content": "You are a Hinduism religious advisor who provides practical advice and guidance to faithful adherents.",
"role": "system"
},
{
"content": "I want to deepen my practice of Hinduism, but I\u0027m not sure where to start. Can you suggest some daily rituals and practices that I can incorporate into my lifestyle?",
"role": "user"
},
{
"content": "Certainly! A great place to start is with the practice of puja, which is a daily ritual of offering devotion to a deity. You can create a small altar in your home with images or statues of your chosen deity, and offer items such as flowers, fruits, and incense. You can also chant mantras or recite prayers during your puja. Additionally, consider incorporating meditation and yoga into your daily routine, as these practices can help to cultivate inner peace and spiritual growth.\n\nIn terms of lifestyle, consider adopting a vegetarian diet, as this is a common practice among Hindus. You can also try to live according to the principles of dharma, which include being truthful, compassionate, and selfless in your actions.\n\nI hope this helps! Do you have any specific deities or practices that you are interested in learning more about?",
"role": "assistant"
},
{
"content": "That\u0027s very helpful, thank you. I\u0027m particularly interested in the goddess Durga. Can you tell me more about her and any specific practices associated with her worship?",
"role": "user"
},
{
"content": "Of course! Durga is a powerful and revered goddess in Hinduism, often associated with protection, strength, and motherhood. She is typically depicted with multiple arms, each holding a different weapon or symbol, and riding a lion or tiger.\n\nOne of the most important festivals associated with Durga is Navratri, which is a nine-day celebration in her honor. During this time, devotees may fast, perform puja, and recite Durga\u0027s mantras and prayers. Another common practice is to create a clay or sand sculpture of Durga, which is then immersed in water on the final day of the festival.\n\nIf you\u0027re looking to incorporate Durga worship into your daily practice, you could try reciting her mantra, \u0027Om Dum Durgayei Namaha,\u0027 which is believed to offer protection and strength. You could also offer her favorite foods, such as sweets and fruits, during your puja.\n\nI hope that helps! Let me know if you have any other questions.",
"role": "assistant"
},
{
"content": "Thank you so much for the information. I\u0027d like to try reciting her mantra daily. Can you give me more guidance on how to do that?",
"role": "user"
}
]
}
```
This subset can be loaded as:
```python
from datasets import load_dataset
ds = load_dataset("distilabel-internal-testing/SystemChat-1.1-AltGens", "keep_columns")
```
</details>
|
freshpearYoon/vr_train_free_20 | ---
dataset_info:
features:
- name: audio
struct:
- name: array
sequence: float64
- name: path
dtype: string
- name: sampling_rate
dtype: int64
- name: filename
dtype: string
- name: NumOfUtterance
dtype: int64
- name: text
dtype: string
- name: samplingrate
dtype: int64
- name: begin_time
dtype: float64
- name: end_time
dtype: float64
- name: speaker_id
dtype: string
- name: directory
dtype: string
splits:
- name: train
num_bytes: 5934326849
num_examples: 10000
download_size: 969150148
dataset_size: 5934326849
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
distilled-from-one-sec-cv12/chunk_255 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 932905224
num_examples: 181782
download_size: 945790329
dataset_size: 932905224
---
# Dataset Card for "chunk_255"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ibivibiv/alpaca_tasksource1 | ---
dataset_info:
features:
- name: input
dtype: string
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 135721409
num_examples: 253971
download_size: 77056633
dataset_size: 135721409
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
gdj-pinecone/test | ---
license: apache-2.0
---
|
samaxr/code-summary-java | ---
dataset_info:
features:
- name: code
dtype: string
- name: summary
dtype: string
splits:
- name: train
num_bytes: 217009651
num_examples: 285670
- name: validation
num_bytes: 23881486
num_examples: 31741
- name: test
num_bytes: 60490904
num_examples: 79352
download_size: 110962221
dataset_size: 301382041
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
CyberHarem/dp_12_girlsfrontline | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of dp_12/DP-12/DP-12 (Girls' Frontline)
This is the dataset of dp_12/DP-12/DP-12 (Girls' Frontline), containing 92 images and their tags.
The core tags of this character are `blue_hair, breasts, long_hair, large_breasts, bangs, grey_eyes, hairband, braid, blue_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 92 | 143.93 MiB | [Download](https://huggingface.co/datasets/CyberHarem/dp_12_girlsfrontline/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 92 | 71.86 MiB | [Download](https://huggingface.co/datasets/CyberHarem/dp_12_girlsfrontline/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 232 | 155.90 MiB | [Download](https://huggingface.co/datasets/CyberHarem/dp_12_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 92 | 122.81 MiB | [Download](https://huggingface.co/datasets/CyberHarem/dp_12_girlsfrontline/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 232 | 234.36 MiB | [Download](https://huggingface.co/datasets/CyberHarem/dp_12_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/dp_12_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 | 14 |  |  |  |  |  | 1girl, looking_at_viewer, solo, cleavage, hat_flower, smile, blush, closed_mouth, sunflower, white_bikini, white_headwear, collarbone, bare_shoulders, hair_between_eyes, necklace, outdoors, white_background, white_dress, choker, navel, ocean, simple_background, thighs, wet, wrist_cuffs |
| 1 | 14 |  |  |  |  |  | 1girl, simple_background, smile, solo, white_background, blush, looking_at_viewer, closed_mouth, sweater, white_hairband, turtleneck, upper_body, bare_shoulders |
| 2 | 6 |  |  |  |  |  | 1girl, holding_gun, solo, jacket, pantyhose, smile, white_background, assault_rifle, looking_at_viewer, simple_background, white_hairband |
| 3 | 6 |  |  |  |  |  | 1girl, black_thighhighs, blush, solo, cleavage, hair_between_eyes, hair_flower, looking_at_viewer, jewelry, smile, thighs, white_panties, china_dress, closed_mouth, huge_breasts, official_alternate_costume |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | looking_at_viewer | solo | cleavage | hat_flower | smile | blush | closed_mouth | sunflower | white_bikini | white_headwear | collarbone | bare_shoulders | hair_between_eyes | necklace | outdoors | white_background | white_dress | choker | navel | ocean | simple_background | thighs | wet | wrist_cuffs | sweater | white_hairband | turtleneck | upper_body | holding_gun | jacket | pantyhose | assault_rifle | black_thighhighs | hair_flower | jewelry | white_panties | china_dress | huge_breasts | official_alternate_costume |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------------|:-------|:-----------|:-------------|:--------|:--------|:---------------|:------------|:---------------|:-----------------|:-------------|:-----------------|:--------------------|:-----------|:-----------|:-------------------|:--------------|:---------|:--------|:--------|:--------------------|:---------|:------|:--------------|:----------|:-----------------|:-------------|:-------------|:--------------|:---------|:------------|:----------------|:-------------------|:--------------|:----------|:----------------|:--------------|:---------------|:-----------------------------|
| 0 | 14 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | |
| 1 | 14 |  |  |  |  |  | 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 | | | | | | | |
| 3 | 6 |  |  |  |  |  | X | X | X | X | | X | X | X | | | | | | X | | | | | | | | | X | | | | | | | | | | | X | X | X | X | X | X | X |
|
HansBauer/Rober | ---
license: apache-2.0
---
|
IlyaGusev/gazeta | ---
annotations_creators:
- expert-generated
- found
language_creators:
- expert-generated
- found
task_categories:
- summarization
language:
- ru
size_categories:
- 10K<n<100K
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
paperswithcode_id: gazeta
---
# Dataset Card for Gazeta
## 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://github.com/IlyaGusev/gazeta
- **Paper:** [Dataset for Automatic Summarization of Russian News](https://arxiv.org/abs/2006.11063)
- **Leaderboard:** https://paperswithcode.com/sota/text-summarization-on-gazeta
- **Point of Contact:** [Ilya Gusev](ilya.gusev@phystech.edu)
### Dataset Summary
Dataset for automatic summarization of Russian news. News and their summaries are from the Gazeta website. Summaries were parsed as the content of an HTML tag with “description” property. Additional selection of good summaries was performed. There are two versions of this dataset.
### Supported Tasks and Leaderboards
Leaderboard on Papers With Code: [text-summarization-on-gazeta](https://paperswithcode.com/sota/text-summarization-on-gazeta).
Please use the original [evaluation script](https://github.com/IlyaGusev/summarus/blob/master/evaluate.py) with the same parameters. Example:
```
python3 evaluate.py --predicted-path predictions.txt --gold-path targets.txt --language ru --tokenize-after --lower
```
### Languages
The dataset is in Russian.
### Usage
Loading version 1.0:
```python
from datasets import load_dataset
dataset = load_dataset('IlyaGusev/gazeta', revision="v1.0")
```
Loading version 2.0:
```python
from datasets import load_dataset
dataset = load_dataset('IlyaGusev/gazeta', revision="v2.0")
```
### Other datasets
Other Russian summarization datasets:
* Russian part of [XL-Sum](https://huggingface.co/datasets/csebuetnlp/xlsum), parsed from www.bbc.com/russian, 77803 samples
* Russian part of [MLSUM](https://huggingface.co/datasets/mlsum), parsed from www.mk.ru, 27063 samples
## Dataset Structure
### Data Instances
For each instance, there is a string for the article, a string for the summary, and a string for the url. Additionally, a string for the title and a date are provided.
```
{
'date': '2019-10-01 15:14:05',
'url': 'https://www.gazeta.ru/tech/2019/10/01/12698923/whatsapp_pls.shtml',
'title': 'На последнем издыхании: у кого отключится WhatsApp',
'summary': 'Мессенджер WhatsApp перестанет работать на ряде смартфонов — речь идет о гаджетах на базе операционных систем Android 2.3.7 и iOS 8, которые считаются устаревшими. В компании отмечают, что сервис на этих устройствах может отключиться в любой момент, поэтому будет целесообразно сменить устройство либо обновить ОС.',
'text': 'На официальном сайте мессенджера WhatsApp появилось сообщение о том, что с 1 февраля 2020 года сервис прекратит свою работу на некоторых устаревших смартфонах. Речь идет об устройствах, работающих на базе операционных систем Android 2.3.7 и iOS 8. При этом руководство WhatsApp предупреждает, что даже до обозначенного выше дедлайна функционал мессенджера на этих ОС может быть ограничен. «В связи с тем, что мы не планируем обновлять данные операционные системы, некоторые функции могут перестать работать на них в любое время», — говорится в пресс-релизе компании. Чтобы сохранить возможность пользоваться мессенджером без проблем, следует обновить версию прошивки или приобрести новое, более современное устройство. Сообщается, что на старых версиях операционных систем уже не получится завести новый аккаунт WhatsApp или верифицировать уже существующий. При этом в WhatsApp порекомендовали пользоваться устройствами с Android 4.0.3 и более поздними версиями, а также iOS 9 и более поздними версиями. Ранее стало известно о том, что с 31 декабря 2019 года WhatsApp прекращает поддержку устройств на базе операционной системы Windows Phone, от разработки которой пришлось отказаться. Впрочем, если верить статистике , эти меры вряд ли затронут большое количество пользователей. По состоянию на май 2019 года лишь 0,3% всех владельцев Android все еще пользуются ОС версий 2.3.3–2.3.7. Что же касается iOS, то версия под номером «10» или старше установлена на 5% устройств Apple. Как уже упоминалось выше, выпуск новых гаджетов на Windows Phone и вовсе прекращен ее создателем. В середине сентября экс-сотрудник АНБ Эдвард Сноуден раскритиковал WhatsApp за несовершенную систему защиты, порекомендовав политикам пользоваться другими средствами связи. Журналист французской радиостанции France Inter отметил, что президент Франции Эмманюэль Макрон для связи использует Telegram, а премьер-министр страны Эдуар Филипп — WhatsApp. Сноуден назвал такое решение «большой ошибкой», учитывая серьезные посты, которые занимают Макрон и Филипп. По словам Сноудена, эти сервисы безопаснее обычных SMS-сообщений, но все еще «чрезвычайно опасны, если вы премьер-министр». Больше всего претензий у информатора к WhatsApp, который стал частью активов корпорации Facebook в 2014 году. Эдвард Сноуден отметил, что после приобретения мессенджера Facebook «слой за слоем» снимает различные уровни защиты сервиса, чтобы при необходимости читать переписку своих пользователей. Ранее с критикой в адрес WhatsApp выступил и глава Telegram Павел Дуров. По словам предпринимателя, после устранения одной «дыры» в мессенджере тут же появляются новые. «Все выявленные проблемы позволяют вести слежку, выглядят и функционируют как бэкдоры», — заявил Дуров. При этом Дуров подчеркнул, что WhatsApp мог быть вынужден установить бэкдоры по указанию ФБР. В июне руководство WhatsApp заявило о том, что их сервис готов судиться с юзерами за нарушение правил пользования. В список нарушений входит использование программы «не в личных целях» и применение автоматической рассылки сообщений. По данным пресс-службы WhatsApp, уже сейчас обнаружены и заморожены «миллионы аккаунтов», пойманных на «злоупотреблении». «Наша платформа изначально создавалась, чтобы помогать людям общаться с их друзьями и любимыми... Используя информацию приложения, мы нашли и заблокировали миллионы злоупотребляющих аккаунтов от использования нашей сети», – заявили в WhatsApp. В частности, нарушение происходит, если компания публично заявляет о возможности использовать WhatsApp, нарушая при этом правила пользования мессенджером. «Ничто в этом объявлении не ограничивает право WhatsApp от применения своих условий с использованием технологий. Классификаторы на основе machine learning нам в этом помогают, и мы продолжим их использовать», – добавили в команде приложения.',
}
```
Some dataset statistics are below:
| Feature | Mean Token Count | Mean Sentence Count |
|:---------|:---------|--------------------------------------------------|
| Text | 767 | 37 |
| Summary | 50 | 3 |
### Data Splits
| Dataset Split | v1, Number of Instances in Split | v2, Number of Instances in Split |
|:---------|:---------|:---------|
| Train | 52,400 | 60,964 |
| Validation | 5,265 | 6,369 |
| Test | 5,770 | 6,793 |
## Dataset Creation
### Curation Rationale
When the first version of the dataset was collected, there were no other datasets for Russian text summarization. Even now, it is one of the few datasets for this task.
### Source Data
#### Initial Data Collection and Normalization
* The source of data is the [Gazeta](https://www.gazeta.ru/) website.
* Parsing scripts are [here](https://github.com/IlyaGusev/gazeta/tree/master/parser).
* Cleaning and normalization Colab notebook is [here](https://colab.research.google.com/drive/1Ed_chVrslp_7vJNS3PmRC0_ZJrRQYv0C)
#### Who are the source language producers?
Texts and summaries were written by journalists at [Gazeta](https://www.gazeta.ru/).
### Annotations
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
The dataset is not anonymized, so individuals' names can be found in the dataset. Information about the original author is not included in the dataset.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
It is a dataset from a single source. Thus it has a constrained text style and event perspective.
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The data was collected by Ilya Gusev.
### Licensing Information
Legal basis for distribution of the dataset: https://www.gazeta.ru/credits.shtml, paragraph 2.1.2. All rights belong to "www.gazeta.ru". Usage of this dataset is possible only for personal purposes on a non-commercial basis.
### Citation Information
```bibtex
@InProceedings{10.1007/978-3-030-59082-6_9,
author="Gusev, Ilya",
editor="Filchenkov, Andrey and Kauttonen, Janne and Pivovarova, Lidia",
title="Dataset for Automatic Summarization of Russian News",
booktitle="Artificial Intelligence and Natural Language",
year="2020",
publisher="Springer International Publishing",
address="Cham",
pages="122--134",
isbn="978-3-030-59082-6"
}
```
### Contributions
[N/A] |
Jayknightcoolie/bumblebee-story-action | ---
license: mit
---
|
Gummybear05/pause_changed | ---
dataset_info:
features:
- name: path
dtype: string
- name: filename
dtype: string
- name: text
dtype: string
- name: quality
dtype: string
- name: city
dtype: string
- name: gender
dtype: string
- name: age
dtype: string
- name: array
sequence: float64
- name: sample_rate
dtype: int64
splits:
- name: train
num_bytes: 7409341848
num_examples: 8531
- name: test
num_bytes: 258512151
num_examples: 120
download_size: 646687623
dataset_size: 7667853999
---
# Dataset Card for "pause_changed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
TrainingDataPro/miners-detection | ---
language:
- en
license: cc-by-nc-nd-4.0
task_categories:
- image-classification
- object-detection
tags:
- code
dataset_info:
features:
- name: id
dtype: int32
- name: name
dtype: string
- name: image
dtype: image
- name: mask
dtype: image
- name: width
dtype: uint16
- name: height
dtype: uint16
- name: shapes
sequence:
- name: label
dtype:
class_label:
names:
'0': Miner
- name: type
dtype: string
- name: points
sequence:
sequence: float32
- name: rotation
dtype: float32
- name: occluded
dtype: uint8
- name: attributes
sequence:
- name: name
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 5907438
num_examples: 8
download_size: 5795853
dataset_size: 5907438
---
# Miners Detection dataset
The dataset consists of of photos captured within various mines, focusing on **miners** engaged in their work. Each photo is annotated with bounding box detection of the miners, an attribute highlights whether each miner is sitting or standing in the photo.
The dataset's diverse applications such as computer vision, safety assessment and others make it a valuable resource for *researchers, employers, and policymakers in the mining industry*.
# Get the dataset
### This is just an example of the data
Leave a request on [**https://trainingdata.pro/data-market**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=miners-detection) to discuss your requirements, learn about the price and buy the dataset.
# Dataset structure
- **images** - contains of original images of miners
- **boxes** - includes bounding box labeling for the original images
- **annotations.xml** - contains coordinates of the bounding boxes and labels, created for the original photo
# Data Format
Each image from `images` folder is accompanied by an XML-annotation in the `annotations.xml` file indicating the coordinates of the bounding boxes for miners detection. For each point, the x and y coordinates are provided. The position of the miner is also provided by the attribute **is_sitting** (true, false).
# Example of XML file structure
.png?generation=1695040600108833&alt=media)
# Miners detection might be made in accordance with your requirements.
## [TrainingData](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=miners-detection) provides high-quality data annotation tailored to your needs
More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets**
TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets** |
cristiansales/altaia | ---
license: openrail
---
|
ayeshgk/cpatminer_data_bug_ctx | ---
license: mit
---
|
silk-road/Embedding-Adapter | ---
license: apache-2.0
---
|
open-llm-leaderboard/details_BreadAi__StoryPy | ---
pretty_name: Evaluation run of BreadAi/StoryPy
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [BreadAi/StoryPy](https://huggingface.co/BreadAi/StoryPy) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_BreadAi__StoryPy\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-23T10:16:36.157284](https://huggingface.co/datasets/open-llm-leaderboard/details_BreadAi__StoryPy/blob/main/results_2023-09-23T10-16-36.157284.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0007340604026845638,\n\
\ \"em_stderr\": 0.00027736144573356746,\n \"f1\": 0.011790058724832235,\n\
\ \"f1_stderr\": 0.0007354126826155291,\n \"acc\": 0.255327545382794,\n\
\ \"acc_stderr\": 0.007024647268145198\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0007340604026845638,\n \"em_stderr\": 0.00027736144573356746,\n\
\ \"f1\": 0.011790058724832235,\n \"f1_stderr\": 0.0007354126826155291\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\
: 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.510655090765588,\n\
\ \"acc_stderr\": 0.014049294536290396\n }\n}\n```"
repo_url: https://huggingface.co/BreadAi/StoryPy
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_07_19T10_25_02.732559
path:
- '**/details_harness|arc:challenge|25_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_23T10_16_36.157284
path:
- '**/details_harness|drop|3_2023-09-23T10-16-36.157284.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-23T10-16-36.157284.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_23T10_16_36.157284
path:
- '**/details_harness|gsm8k|5_2023-09-23T10-16-36.157284.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-23T10-16-36.157284.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hellaswag|10_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T10:25:02.732559.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T10:25:02.732559.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T10:25:02.732559.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_23T10_16_36.157284
path:
- '**/details_harness|winogrande|5_2023-09-23T10-16-36.157284.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-23T10-16-36.157284.parquet'
- config_name: results
data_files:
- split: 2023_07_19T10_25_02.732559
path:
- results_2023-07-19T10:25:02.732559.parquet
- split: 2023_09_23T10_16_36.157284
path:
- results_2023-09-23T10-16-36.157284.parquet
- split: latest
path:
- results_2023-09-23T10-16-36.157284.parquet
---
# Dataset Card for Evaluation run of BreadAi/StoryPy
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/BreadAi/StoryPy
- **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 [BreadAi/StoryPy](https://huggingface.co/BreadAi/StoryPy) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_BreadAi__StoryPy",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-23T10:16:36.157284](https://huggingface.co/datasets/open-llm-leaderboard/details_BreadAi__StoryPy/blob/main/results_2023-09-23T10-16-36.157284.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0007340604026845638,
"em_stderr": 0.00027736144573356746,
"f1": 0.011790058724832235,
"f1_stderr": 0.0007354126826155291,
"acc": 0.255327545382794,
"acc_stderr": 0.007024647268145198
},
"harness|drop|3": {
"em": 0.0007340604026845638,
"em_stderr": 0.00027736144573356746,
"f1": 0.011790058724832235,
"f1_stderr": 0.0007354126826155291
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.510655090765588,
"acc_stderr": 0.014049294536290396
}
}
```
### 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] |
open-llm-leaderboard/details_NousResearch__Nous-Puffin-70B | ---
pretty_name: Evaluation run of NousResearch/Nous-Puffin-70B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [NousResearch/Nous-Puffin-70B](https://huggingface.co/NousResearch/Nous-Puffin-70B)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_NousResearch__Nous-Puffin-70B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-23T17:19:58.299008](https://huggingface.co/datasets/open-llm-leaderboard/details_NousResearch__Nous-Puffin-70B/blob/main/results_2023-09-23T17-19-58.299008.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0019924496644295304,\n\
\ \"em_stderr\": 0.00045666764626670005,\n \"f1\": 0.06601090604026844,\n\
\ \"f1_stderr\": 0.001371965767363261,\n \"acc\": 0.5908367954724018,\n\
\ \"acc_stderr\": 0.011701371531806812\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0019924496644295304,\n \"em_stderr\": 0.00045666764626670005,\n\
\ \"f1\": 0.06601090604026844,\n \"f1_stderr\": 0.001371965767363261\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.34268385140257773,\n \
\ \"acc_stderr\": 0.01307303023082791\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8389897395422258,\n \"acc_stderr\": 0.010329712832785715\n\
\ }\n}\n```"
repo_url: https://huggingface.co/NousResearch/Nous-Puffin-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_24T17_45_27.892102
path:
- '**/details_harness|arc:challenge|25_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_23T17_19_58.299008
path:
- '**/details_harness|drop|3_2023-09-23T17-19-58.299008.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-23T17-19-58.299008.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_23T17_19_58.299008
path:
- '**/details_harness|gsm8k|5_2023-09-23T17-19-58.299008.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-23T17-19-58.299008.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hellaswag|10_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-24T17:45:27.892102.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_24T17_45_27.892102
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-24T17:45:27.892102.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-24T17:45:27.892102.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_23T17_19_58.299008
path:
- '**/details_harness|winogrande|5_2023-09-23T17-19-58.299008.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-23T17-19-58.299008.parquet'
- config_name: results
data_files:
- split: 2023_09_23T17_19_58.299008
path:
- results_2023-09-23T17-19-58.299008.parquet
- split: latest
path:
- results_2023-09-23T17-19-58.299008.parquet
---
# Dataset Card for Evaluation run of NousResearch/Nous-Puffin-70B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/NousResearch/Nous-Puffin-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 [NousResearch/Nous-Puffin-70B](https://huggingface.co/NousResearch/Nous-Puffin-70B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_NousResearch__Nous-Puffin-70B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-23T17:19:58.299008](https://huggingface.co/datasets/open-llm-leaderboard/details_NousResearch__Nous-Puffin-70B/blob/main/results_2023-09-23T17-19-58.299008.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0019924496644295304,
"em_stderr": 0.00045666764626670005,
"f1": 0.06601090604026844,
"f1_stderr": 0.001371965767363261,
"acc": 0.5908367954724018,
"acc_stderr": 0.011701371531806812
},
"harness|drop|3": {
"em": 0.0019924496644295304,
"em_stderr": 0.00045666764626670005,
"f1": 0.06601090604026844,
"f1_stderr": 0.001371965767363261
},
"harness|gsm8k|5": {
"acc": 0.34268385140257773,
"acc_stderr": 0.01307303023082791
},
"harness|winogrande|5": {
"acc": 0.8389897395422258,
"acc_stderr": 0.010329712832785715
}
}
```
### 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] |
KTH/nst | ---
license: cc0-1.0
task_categories:
- automatic-speech-recognition
language:
- sv
---
# NST Swedish ASR Database (16 kHz) – reorganized
This database was created by Nordic Language Technology for the development of automatic speech recognition and dictation in Swedish. In this updated version, the organization of the data have been altered to improve the usefulness of the database.
In the original version of the material, the files were organized in a specific folder structure where the folder names were meaningful. However, the file names were not meaningful, and there were also cases of files with identical names in different folders. This proved to be impractical, since users had to keep the original folder structure in order to use the data. The files have been renamed, such that the file names are unique and meaningful regardless of the folder structure. The original metadata files were in spl format. These have been converted to JSON format. The converted metadata files are also anonymized and the text encoding has been converted from ANSI to UTF-8.
See the documentation file for a full description of the data and the changes made to the database.
The data is originally hosted on the National Library of Norway website.
https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-56/
Hosting on Hugging Face datasets for convenience.
Licence
CC0 1.0 Universal (CC0 1.0)
Public Domain Dedication |
pythonist/PubMedQA | ---
train-eval-index:
- config: pythonist--PubMedQA
task: question-answering
task_id: extractive_question_answering
splits:
eval_split: train
col_mapping:
id: answers.answer_start
---
|
Wanfq/Explore_Instruct_Brainstorming_10k | ---
license: cc-by-nc-4.0
language:
- en
---
<p align="center" width="100%">
</p>
<div id="top" align="center">
**Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active Exploration**
<h4> |<a href="https://arxiv.org/abs/2310.09168"> 📑 Paper </a> |
<a href="https://huggingface.co/datasets?sort=trending&search=Explore_Instruct"> 🤗 Data </a> |
<a href="https://huggingface.co/models?sort=trending&search=Explore-LM"> 🤗 Model </a> |
<a href="https://github.com/fanqiwan/Explore-Instruct"> 🐱 Github Repo </a> |
</h4>
<!-- **Authors:** -->
_**Fanqi Wan<sup>†</sup>, Xinting Huang<sup>‡</sup>, Tao Yang<sup>†</sup>, Xiaojun Quan<sup>†</sup>, Wei Bi<sup>‡</sup>, Shuming Shi<sup>‡</sup>**_
<!-- **Affiliations:** -->
_<sup>†</sup> Sun Yat-sen University,
<sup>‡</sup> Tencent AI Lab_
</div>
## News
- **Oct 16, 2023:** 🔥 We're excited to announce that the Explore-Instruct datasets in brainstorming, rewriting, and math domains are now available on 🤗 [Huggingface Datasets](https://huggingface.co/datasets?sort=trending&search=Explore_Instruct)! Additionally, we've released Explore-LM models that have been initialized with LLaMA-7B and fine-tuned with the Explore-Instruct data in each domain. You can find these models on 🤗 [Huggingface Models](https://huggingface.co/models?sort=trending&search=Explore-LM). Happy exploring and instructing!
## Contents
- [Overview](#overview)
- [Data Release](#data-release)
- [Model Release](#model-release)
- [Data Generation Process](#data-generation-process)
- [Fine-tuning](#fine-tuning)
- [Evaluation](#evaluation)
- [Limitations](#limitations)
- [License](#license)
- [Citation](#citation)
- [Acknowledgements](#acknowledgments)
## Overview
We propose Explore-Instruct, a novel approach to enhancing domain-specific instruction coverage. We posit that the domain space is inherently structured akin to a tree, reminiscent of cognitive science ontologies. Drawing from the essence of classical search algorithms and incorporating the power of LLMs, Explore-Instruct is conceived to actively traverse the domain space and generate instruction-tuning data, **not** necessitating a predefined tree structure. Specifically, Explore-Instruct employs two strategic operations: lookahead and backtracking exploration:
- **Lookahead** delves into a multitude of potential fine-grained sub-tasks, thereby mapping out a complex network of tasks
- **Backtracking** seeks alternative branches to widen the search boundary, hence extending the domain spectrum.
<p align="center">
<img src="https://github.com/fanqiwan/Explore-Instruct/blob/main/assets/fig2.png?raw=true" width="95%"> <br>
</p>
## Data Release
We release the Explore-Instruct data in brainstorming, rewriting, and math domains on 🤗 [Huggingface Datasets](https://huggingface.co/datasets?sort=trending&search=Explore_Instruct). Each domain includes two versions of datasets: the basic and extended version. The base version contains 10k instruction-tuning data and the extended version contains 16k, 32k, and 64k instruction-tuning data for each domain respectively. Each dataset is a structured data file in the JSON format. It consists of a list of dictionaries, with each dictionary containing the following fields:
- `instruction`: `str`, describes the task the model should perform.
- `input`: `str`, optional context or input for the task.
- `output`: `str`, ground-truth output text for the task and input text.
The results of data-centric analysis are shown as follows:
<p align="left">
<img src="https://github.com/fanqiwan/Explore-Instruct/blob/main/assets/fig1.png?raw=true" width="50%"> <br>
</p>
| Method | Brainstorming Unique<br/>V-N pairs | Rewriting Unique<br/>V-N pairs | Math Unique<br/>V-N pairs |
|:--------------------------------|:----------------------------------:|:------------------------------:|:-------------------------:|
| _Domain-Specific Human-Curated_ | 2 | 8 | 3 |
| _Domain-Aware Self-Instruct_ | 781 | 1715 | 451 |
| Explore-Instruct | **790** | **2015** | **917** |
## Model Release
We release the Explore-LM models in brainstorming, rewriting, and math domains on 🤗 [Huggingface Models](https://huggingface.co/models?sort=trending&search=Explore-LM). Each domain includes two versions of models: the basic and extended version trained with the corresponding version of dataset.
The results of automatic and human evaluation in three domains are shown as follows:
- Automatic evaluation:
| Automatic Comparison in the Brainstorming Domain | Win:Tie:Lose | Beat Rate |
|:-------------------------------------------------|:------------:|:---------:|
| Explore-LM vs Domain-Curated-LM | 194:1:13 | 93.72 |
| Explore-LM-Ext vs Domain-Curated-LM | 196:1:11 | 94.69 |
| Explore-LM vs Domain-Instruct-LM | 114:56:38 | 75.00 |
| Explore-LM-Ext vs Domain-Instruct-LM | 122:55:31 | 79.74 |
| Explore-LM vs ChatGPT | 52:71:85 | 37.96 |
| Explore-LM-Ext vs ChatGPT | 83:69:56 | 59.71 |
| Automatic Comparison in the Rewriting Domain | Win:Tie:Lose | Beat Rate |
|:---------------------------------------------|:------------:|:---------:|
| Explore-LM vs Domain-Curated-LM | 50:38:6 | 89.29 |
| Explore-LM-Ext vs Domain-Curated-LM | 53:37:4 | 92.98 |
| Explore-LM vs Domain-Instruct-LM | 34:49:11 | 75.56 |
| Explore-LM-Ext vs Domain-Instruct-LM | 35:53:6 | 85.37 |
| Explore-LM vs ChatGPT | 11:59:24 | 31.43 |
| Explore-LM-Ext vs ChatGPT | 12:56:26 | 31.58 |
| Automatic Comparison in the Math Domain | Accuracy Rate |
|:----------------------------------------|:-------------:|
| Domain-Curated-LM | 3.4 |
| Domain-Instruct-LM | 4.0 |
| Explore-LM | 6.8 |
| Explore-LM-Ext | 8.4 |
| ChatGPT | 34.8 |
- Human evaluation:
<p align="left">
<img src="https://github.com/fanqiwan/Explore-Instruct/blob/main/assets/fig5.png?raw=true" width="95%"> <br>
</p>
## Data Generation Process
To generate the domain-specific instruction-tuning data, please follow the following commands step by step:
### Domain Space Exploration
```
python3 generate_instruction.py \
--action extend \
--save_dir ./en_data/demo_domain \ # input dir include current domain tree for exploration
--out_dir ./en_data/demo_domain_exploration \ # output dir of the explored new domain tree
--lang <LANGUAGE> \ # currently support 'en'
--domain demo_domain \ # domain for exploration
--extend_nums <TASK_NUMBER_DEPTH_0>,...,<TASK_NUMBER_DEPTH_MAX_DEPTH-1> \ # exploration breadth at each depth
--max_depth <MAX_DEPTH> \ # exploration depth
--assistant_name <ASSISTANT_NAME> # currently support openai and claude
```
### Instruction-Tuning Data Generation
```
python3 generate_instruction.py \
--action enrich \
--save_dir ./en_data/demo_domain_exploration \ # input dir include current domain tree for data generation
--out_dir ./en_data/demo_domain_generation \ # output dir of the domain tree with generated data
--lang <LANGUAGE> \ # currently support 'en'
--domain demo_domain \ # domain for exploration
--enrich_nums <DATA_NUMBER_DEPTH_0>,...,<DATA_NUMBER_DEPTH_MAX_DEPTH> \ # data number for task at each depth
--enrich_batch_size <BATCH_SIZE> \ # batch size for data generation
--assistant_name <ASSISTANT_NAME> # currently support openai and claude
```
### Task Pruning
```
python3 generate_instruction.py \
--action prune \
--save_dir ./en_data/demo_domain_generation \ # input dir include current domain tree for task pruning
--out_dir ./en_data/demo_domain_pruning \ # output dir of the domain tree with 'pruned_subtasks_name.json' file
--lang <LANGUAGE> \ # currently support 'en'
--domain demo_domain \ # domain for exploration
--pruned_file ./en_data/demo_domain_pruning/pruned_subtasks_name.json \ # file of pruned tasks
--prune_threshold <PRUNE_THRESHOLD> \ # threshold of rouge-l overlap between task names
--assistant_name <ASSISTANT_NAME> # currently support openai and claude
```
### Data Filtering
```
python3 generate_instruction.py \
--action filter \
--save_dir ./en_data/demo_domain_pruning \ # input dir include current domain tree for data filtering
--out_dir ./en_data/demo_domain_filtering \ # output dir of the domain tree with fitered data
--lang <LANGUAGE> \ # currently support 'en'
--domain demo_domain \ # domain for exploration
--pruned_file ./en_data/demo_domain_pruning/pruned_subtasks_name.json \ # file of pruned tasks
--filter_threshold <FILTER_THRESHOLD> \ # threshold of rouge-l overlap between instructions
--assistant_name <ASSISTANT_NAME> # currently support openai and claude
```
### Data Sampling
```
python3 generate_instruction.py \
--action sample \
--save_dir ./en_data/demo_domain_filtering \ # input dir include current domain tree for data sampling
--out_dir ./en_data/demo_domain_sampling \ # output dir of the domain tree with sampled data
--lang <LANGUAGE> \ # currently support 'en'
--domain demo_domain \ # domain for exploration
--pruned_file ./en_data/demo_domain_filtering/pruned_subtasks_name.json \ # file of pruned tasks
--sample_example_num <SAMPLE_EXAMPLES_NUM> \ # number of sampled examples
--sample_max_depth <SAMPLE_MAX_DEPTH> \ # max depth for data sampling
--sample_use_pruned \ # do not sample from pruned tasks
--assistant_name <ASSISTANT_NAME> # currently support openai and claude
```
## Fine-tuning
We fine-tune LLaMA-7B with the following hyperparameters:
| Hyperparameter | Global Batch Size | Learning rate | Epochs | Max length | Weight decay |
|:----------------|-------------------:|---------------:|--------:|------------:|--------------:|
| LLaMA 7B | 128 | 2e-5 | 3 | 512| 0 |
To reproduce the training procedure, please use the following command:
```
deepspeed --num_gpus=8 ./train/train.py \
--deepspeed ./deepspeed_config/deepspeed_zero3_offload_config.json \
--model_name_or_path decapoda-research/llama-7b-hf \
--data_path ./en_data/demo_domain_sampling \
--fp16 True \
--output_dir ./training_results/explore-lm-7b-demo-domain \
--num_train_epochs 3 \
--per_device_train_batch_size 2 \
--per_device_eval_batch_size 2 \
--gradient_accumulation_steps 8 \
--evaluation_strategy "no" \
--model_max_length 512 \
--save_strategy "steps" \
--save_steps 2000 \
--save_total_limit 1 \
--learning_rate 2e-5 \
--weight_decay 0. \
--warmup_ratio 0.03 \
--lr_scheduler_type "cosine" \
--logging_steps 1 \
--prompt_type alpaca \
2>&1 | tee ./training_logs/explore-lm-7b-demo-domain.log
python3 ./train/zero_to_fp32.py \
--checkpoint_dir ./training_results/explore-lm-7b-demo-domain \
--output_file ./training_results/explore-lm-7b-demo-domain/pytorch_model.bin
```
## Evaluation
The evaluation datasets for different domains are as follows:
- Brainstorming and Rewriting: From the corresponding categories in the translated test set of BELLE. ([en_eval_set.jsonl](./eval/question/en_eval_set.jsonl))
- Math: From randomly selected 500 questions from the test set of MATH. ([MATH_eval_set_sample.jsonl](./eval/question/MATH_eval_set_sample.jsonl))
The evaluation metrics for different domains are as follows:
- Brainstorming and Rewriting: Both automatic and human evaluations following Vicuna.
- Math: Accuracy Rate metric in solving math problems.
The automatic evaluation commands for different domains are as follows:
```
# Brainstorming and Rewriting Domain
# 1. Inference
python3 ./eval/generate.py \
--model_id <MODEL_ID> \
--model_path <MODEL_PATH> \
--question_file ./eval/question/en_eval_set.jsonl \
--answer_file ./eval/answer/<MODEL_ID>.jsonl \
--num_gpus 8 \
--num_beams 1 \
--temperature 0.7 \
--max_new_tokens 512 \
--prompt_type alpaca \
--do_sample
# 2. Evaluation
python3 ./eval/chatgpt_score.py \
--baseline_file ./eval/answer/<MODEL_1>.jsonl \ # answer of baseline model to compare with
--answer_file ./eval/answer/<MODEL_2>.jsonl \ # answer of evaluation model
--review_file ./eval/review/<MODEL_1>_cp_<MODEL_2>_<DOMAIN>.jsonl \ # review from chatgpt
--prompt_file ./eval/prompt/en_review_prompt_compare.jsonl \ # evaluation prompt for chatgpt
--target_classes <DOMAIN> \ # evaluation domain
--batch_size <BATCH_SIZE> \
--review_model "gpt-3.5-turbo-0301"
```
```
# Math Domain
# 1. Inference
python3 ./eval/generate.py \
--model_id <MODEL_ID> \
--model_path <MODEL_PATH> \
--question_file ./eval/question/MATH_eval_set_sample.jsonl \
--answer_file ./eval/answer/<MODEL_ID>.jsonl \
--num_gpus 8 \
--num_beams 10 \
--temperature 1.0 \
--max_new_tokens 512 \
--prompt_type alpaca
# 2. Evaluation
python3 ./eval/auto_eval.py \
--question_file ./eval/question/MATH_eval_set_sample.jsonl \
--answer_file ./eval/answer/<MODEL_ID>.jsonl # answer of evaluation model
```
## Limitations
Explore-Instruct is still under development and needs a lot of improvements. We acknowledge that our work focuses on the enhancement of domain-specific instruction coverage and does not address other aspects of instruction-tuning, such as the generation of complex and challenging instructions or the mitigation of toxic and harmful instructions. Future work is needed to explore the potential of our approach in these areas.
## License
Explore-Instruct is intended and licensed for research use only. The dataset is CC BY NC 4.0 (allowing only non-commercial use) and models trained using the dataset should not be used outside of research purposes. The weights of Explore-LM models are also CC BY NC 4.0 (allowing only non-commercial use).
## Citation
If you find this work is relevant with your research or applications, please feel free to cite our work!
```
@misc{wan2023explore,
title={Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active Exploration},
author={Fanqi, Wan and Xinting, Huang and Tao, Yang and Xiaojun, Quan and Wei, Bi and Shuming, Shi},
year={2023},
eprint={2310.09168},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## Acknowledgments
This repo benefits from [Stanford-Alpaca](https://github.com/tatsu-lab/stanford_alpaca) and [Vicuna](https://github.com/lm-sys/FastChat). Thanks for their wonderful works!
|
VictorG-028/Small_subset_of_Synthetic_Word_Dataset | ---
license: unknown
language:
- en
task_categories:
- image-to-text
tags:
- code
pretty_name: mjsynth
size_categories:
- 10K<n<100K
---
This dataset contains:
- 40351 images (71.39%) in train dataset
- 6378 images (14.29%) in validation dataset
- 6391 images (14.32%) in test dataset
- Total: 53120 images
- sourced from the extensive Synthetic Word Dataset, a large-scale word-image dataset.
The original and complete dataset (9 million images, 10.68GB) can be found and downloaded at [this academic torrent](https://academictorrents.com/details/3d0b4f09080703d2a9c6be50715b46389fdb3af1). |
jakartaresearch/cerpen-corpus | ---
annotations_creators:
- no-annotation
language:
- id
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Small Indonesian Short Story Corpus
size_categories:
- n<1K
- 10K<n<100K
source_datasets:
- original
tags:
- cerpen
- short-story
task_categories:
- text-generation
task_ids:
- language-modeling
---
# Dataset Card for Cerpen Corpus
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This is a small size for Indonesian short story gathered from the internet.
We keep the large size for internal research. if you are interested, please join to [our discord server](https://discord.gg/6v28dq8dRE)
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@andreaschandra](https://github.com/andreaschandra) for adding this dataset. |
ZetangForward/StrokeNUWA | ---
license: apache-2.0
---
|
agkphysics/AudioSet | ---
language:
- en
license: cc-by-4.0
size_categories:
- 10K<n<100K
- 1M<n<10M
source_datasets:
- original
task_categories:
- audio-classification
paperswithcode_id: audioset
pretty_name: AudioSet
config_names:
- balanced
- unbalanced
tags:
- audio
dataset_info:
- config_name: balanced
features:
- name: video_id
dtype: string
- name: audio
dtype: audio
- name: labels
sequence: string
- name: human_labels
sequence: string
splits:
- name: train
num_bytes: 26016210987
num_examples: 18685
- name: test
num_bytes: 23763682278
num_examples: 17142
download_size: 49805654900
dataset_size: 49779893265
- config_name: unbalanced
features:
- name: video_id
dtype: string
- name: audio
dtype: audio
- name: labels
sequence: string
- name: human_labels
sequence: string
splits:
- name: train
num_bytes: 2408656417541
num_examples: 1738788
- name: test
num_bytes: 23763682278
num_examples: 17142
download_size: 2433673104977
dataset_size: 2432420099819
---
# Dataset Card for AudioSet
## Dataset Description
- **Homepage**: https://research.google.com/audioset/index.html
- **Paper**: https://storage.googleapis.com/gweb-research2023-media/pubtools/pdf/45857.pdf
- **Leaderboard**: https://paperswithcode.com/sota/audio-classification-on-audioset
### Dataset Summary
[AudioSet](https://research.google.com/audioset/dataset/index.html) is a
dataset of 10-second clips from YouTube, annotated into one or more
sound categories, following the AudioSet ontology.
### Supported Tasks and Leaderboards
- `audio-classification`: Classify audio clips into categories. The
leaderboard is available
[here](https://paperswithcode.com/sota/audio-classification-on-audioset)
### Languages
The class labels in the dataset are in English.
## Dataset Structure
### Data Instances
Example instance from the dataset:
```python
{
'video_id': '--PJHxphWEs',
'audio': {
'path': 'audio/bal_train/--PJHxphWEs.flac',
'array': array([-0.04364824, -0.05268681, -0.0568949 , ..., 0.11446512,
0.14912748, 0.13409865]),
'sampling_rate': 48000
},
'labels': ['/m/09x0r', '/t/dd00088'],
'human_labels': ['Speech', 'Gush']
}
```
### Data Fields
Instances have the following fields:
- `video_id`: a `string` feature containing the original YouTube ID.
- `audio`: an `Audio` feature containing the audio data and sample rate.
- `labels`: a sequence of `string` features containing the labels
associated with the audio clip.
- `human_labels`: a sequence of `string` features containing the
human-readable forms of the same labels as in `labels`.
### Data Splits
The distribuion of audio clips is as follows:
#### `balanced` configuration
| |train|test |
|-----------|----:|----:|
|# instances|18685|17142|
#### `unbalanced` configuration
| |train |test |
|-----------|------:|----:|
|# instances|1738788|17142|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
The labels are from the AudioSet ontology. Audio clips are from YouTube.
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
1. The YouTube videos in this copy of AudioSet were downloaded in March
2023, so not all of the original audios are available. The number of
clips able to be downloaded is as follows:
- Balanced train: 18685 audio clips out of 22160 originally.
- Unbalanced train: 1738788 clips out of 2041789 originally.
- Evaluation: 17142 audio clips out of 20371 originally.
2. Most audio is sampled at 48 kHz 24 bit, but about 10% is sampled at
44.1 kHz 24 bit. Audio files are stored in the FLAC format.
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
The AudioSet data is licensed under CC-BY-4.0
## Citation
```bibtex
@inproceedings{jort_audioset_2017,
title = {Audio Set: An ontology and human-labeled dataset for audio events},
author = {Jort F. Gemmeke and Daniel P. W. Ellis and Dylan Freedman and Aren Jansen and Wade Lawrence and R. Channing Moore and Manoj Plakal and Marvin Ritter},
year = {2017},
booktitle = {Proc. IEEE ICASSP 2017},
address = {New Orleans, LA}
}
```
|
Wilsonlab/FineTune | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 129504780.0
num_examples: 433
download_size: 0
dataset_size: 129504780.0
---
# Dataset Card for "FineTune"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
AndyLiu0104/Soldering-Data-Tiny-1004-unsolder-area | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 18073742.875
num_examples: 10481
download_size: 0
dataset_size: 18073742.875
---
# Dataset Card for "Soldering-Data-Tiny-1004-unsolder-area"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_stabilityai__stablelm-2-1_6b | ---
pretty_name: Evaluation run of stabilityai/stablelm-2-1_6b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [stabilityai/stablelm-2-1_6b](https://huggingface.co/stabilityai/stablelm-2-1_6b)\
\ 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_stabilityai__stablelm-2-1_6b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-01-24T10:43:24.406547](https://huggingface.co/datasets/open-llm-leaderboard/details_stabilityai__stablelm-2-1_6b/blob/main/results_2024-01-24T10-43-24.406547.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\": 0.3923741043833077,\n\
\ \"acc_stderr\": 0.03405640954935936,\n \"acc_norm\": 0.3955514306541472,\n\
\ \"acc_norm_stderr\": 0.03480165961817428,\n \"mc1\": 0.22031823745410037,\n\
\ \"mc1_stderr\": 0.014509045171487283,\n \"mc2\": 0.36783858238841727,\n\
\ \"mc2_stderr\": 0.013915102083485486\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.3967576791808874,\n \"acc_stderr\": 0.014296513020180632,\n\
\ \"acc_norm\": 0.4334470989761092,\n \"acc_norm_stderr\": 0.014481376224558896\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5185222067317268,\n\
\ \"acc_stderr\": 0.004986356526063975,\n \"acc_norm\": 0.7045409281019717,\n\
\ \"acc_norm_stderr\": 0.004553164013379557\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4444444444444444,\n\
\ \"acc_stderr\": 0.04292596718256981,\n \"acc_norm\": 0.4444444444444444,\n\
\ \"acc_norm_stderr\": 0.04292596718256981\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.3157894736842105,\n \"acc_stderr\": 0.0378272898086547,\n\
\ \"acc_norm\": 0.3157894736842105,\n \"acc_norm_stderr\": 0.0378272898086547\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.41,\n\
\ \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.41,\n \
\ \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.3169811320754717,\n \"acc_stderr\": 0.028637235639800935,\n\
\ \"acc_norm\": 0.3169811320754717,\n \"acc_norm_stderr\": 0.028637235639800935\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.4305555555555556,\n\
\ \"acc_stderr\": 0.04140685639111503,\n \"acc_norm\": 0.4305555555555556,\n\
\ \"acc_norm_stderr\": 0.04140685639111503\n },\n \"harness|hendrycksTest-college_chemistry|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_computer_science|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_mathematics|5\"\
: {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.31213872832369943,\n\
\ \"acc_stderr\": 0.035331333893236574,\n \"acc_norm\": 0.31213872832369943,\n\
\ \"acc_norm_stderr\": 0.035331333893236574\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.1568627450980392,\n \"acc_stderr\": 0.03618664819936248,\n\
\ \"acc_norm\": 0.1568627450980392,\n \"acc_norm_stderr\": 0.03618664819936248\n\
\ },\n \"harness|hendrycksTest-computer_security|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-conceptual_physics|5\"\
: {\n \"acc\": 0.3276595744680851,\n \"acc_stderr\": 0.030683020843231004,\n\
\ \"acc_norm\": 0.3276595744680851,\n \"acc_norm_stderr\": 0.030683020843231004\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2631578947368421,\n\
\ \"acc_stderr\": 0.04142439719489362,\n \"acc_norm\": 0.2631578947368421,\n\
\ \"acc_norm_stderr\": 0.04142439719489362\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.503448275862069,\n \"acc_stderr\": 0.04166567577101579,\n\
\ \"acc_norm\": 0.503448275862069,\n \"acc_norm_stderr\": 0.04166567577101579\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.2698412698412698,\n \"acc_stderr\": 0.022860838309232072,\n \"\
acc_norm\": 0.2698412698412698,\n \"acc_norm_stderr\": 0.022860838309232072\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.24603174603174602,\n\
\ \"acc_stderr\": 0.038522733649243156,\n \"acc_norm\": 0.24603174603174602,\n\
\ \"acc_norm_stderr\": 0.038522733649243156\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.28,\n \"acc_stderr\": 0.045126085985421276,\n \
\ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.045126085985421276\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.3870967741935484,\n \"acc_stderr\": 0.027709359675032495,\n \"\
acc_norm\": 0.3870967741935484,\n \"acc_norm_stderr\": 0.027709359675032495\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.24630541871921183,\n \"acc_stderr\": 0.030315099285617732,\n \"\
acc_norm\": 0.24630541871921183,\n \"acc_norm_stderr\": 0.030315099285617732\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\"\
: 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.503030303030303,\n \"acc_stderr\": 0.03904272341431856,\n\
\ \"acc_norm\": 0.503030303030303,\n \"acc_norm_stderr\": 0.03904272341431856\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.5050505050505051,\n \"acc_stderr\": 0.035621707606254015,\n \"\
acc_norm\": 0.5050505050505051,\n \"acc_norm_stderr\": 0.035621707606254015\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.46113989637305697,\n \"acc_stderr\": 0.03597524411734579,\n\
\ \"acc_norm\": 0.46113989637305697,\n \"acc_norm_stderr\": 0.03597524411734579\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.32564102564102565,\n \"acc_stderr\": 0.02375966576741229,\n\
\ \"acc_norm\": 0.32564102564102565,\n \"acc_norm_stderr\": 0.02375966576741229\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.26666666666666666,\n \"acc_stderr\": 0.02696242432507384,\n \
\ \"acc_norm\": 0.26666666666666666,\n \"acc_norm_stderr\": 0.02696242432507384\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.3319327731092437,\n \"acc_stderr\": 0.030588697013783663,\n\
\ \"acc_norm\": 0.3319327731092437,\n \"acc_norm_stderr\": 0.030588697013783663\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.23178807947019867,\n \"acc_stderr\": 0.03445406271987054,\n \"\
acc_norm\": 0.23178807947019867,\n \"acc_norm_stderr\": 0.03445406271987054\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.5064220183486239,\n \"acc_stderr\": 0.021435554820013077,\n \"\
acc_norm\": 0.5064220183486239,\n \"acc_norm_stderr\": 0.021435554820013077\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.24074074074074073,\n \"acc_stderr\": 0.029157522184605603,\n \"\
acc_norm\": 0.24074074074074073,\n \"acc_norm_stderr\": 0.029157522184605603\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.5,\n \"acc_stderr\": 0.03509312031717982,\n \"acc_norm\": 0.5,\n\
\ \"acc_norm_stderr\": 0.03509312031717982\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\
: {\n \"acc\": 0.4936708860759494,\n \"acc_stderr\": 0.03254462010767859,\n\
\ \"acc_norm\": 0.4936708860759494,\n \"acc_norm_stderr\": 0.03254462010767859\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.5291479820627802,\n\
\ \"acc_stderr\": 0.03350073248773404,\n \"acc_norm\": 0.5291479820627802,\n\
\ \"acc_norm_stderr\": 0.03350073248773404\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.4961832061068702,\n \"acc_stderr\": 0.043851623256015534,\n\
\ \"acc_norm\": 0.4961832061068702,\n \"acc_norm_stderr\": 0.043851623256015534\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.4380165289256198,\n \"acc_stderr\": 0.04529146804435791,\n \"\
acc_norm\": 0.4380165289256198,\n \"acc_norm_stderr\": 0.04529146804435791\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.4444444444444444,\n\
\ \"acc_stderr\": 0.04803752235190193,\n \"acc_norm\": 0.4444444444444444,\n\
\ \"acc_norm_stderr\": 0.04803752235190193\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.4110429447852761,\n \"acc_stderr\": 0.038656978537853624,\n\
\ \"acc_norm\": 0.4110429447852761,\n \"acc_norm_stderr\": 0.038656978537853624\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.32142857142857145,\n\
\ \"acc_stderr\": 0.0443280405529152,\n \"acc_norm\": 0.32142857142857145,\n\
\ \"acc_norm_stderr\": 0.0443280405529152\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.42718446601941745,\n \"acc_stderr\": 0.04897957737781168,\n\
\ \"acc_norm\": 0.42718446601941745,\n \"acc_norm_stderr\": 0.04897957737781168\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.5811965811965812,\n\
\ \"acc_stderr\": 0.03232128912157792,\n \"acc_norm\": 0.5811965811965812,\n\
\ \"acc_norm_stderr\": 0.03232128912157792\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.42,\n \"acc_stderr\": 0.04960449637488584,\n \
\ \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.04960449637488584\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.545338441890166,\n\
\ \"acc_stderr\": 0.0178063045850526,\n \"acc_norm\": 0.545338441890166,\n\
\ \"acc_norm_stderr\": 0.0178063045850526\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.45375722543352603,\n \"acc_stderr\": 0.026803720583206177,\n\
\ \"acc_norm\": 0.45375722543352603,\n \"acc_norm_stderr\": 0.026803720583206177\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.23798882681564246,\n\
\ \"acc_stderr\": 0.014242630070574915,\n \"acc_norm\": 0.23798882681564246,\n\
\ \"acc_norm_stderr\": 0.014242630070574915\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.027826109307283686,\n\
\ \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.027826109307283686\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.4180064308681672,\n\
\ \"acc_stderr\": 0.028013651891995072,\n \"acc_norm\": 0.4180064308681672,\n\
\ \"acc_norm_stderr\": 0.028013651891995072\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.4567901234567901,\n \"acc_stderr\": 0.027716661650194038,\n\
\ \"acc_norm\": 0.4567901234567901,\n \"acc_norm_stderr\": 0.027716661650194038\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.29432624113475175,\n \"acc_stderr\": 0.027187127011503796,\n \
\ \"acc_norm\": 0.29432624113475175,\n \"acc_norm_stderr\": 0.027187127011503796\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2861799217731421,\n\
\ \"acc_stderr\": 0.011543642878150757,\n \"acc_norm\": 0.2861799217731421,\n\
\ \"acc_norm_stderr\": 0.011543642878150757\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.2647058823529412,\n \"acc_stderr\": 0.026799562024887674,\n\
\ \"acc_norm\": 0.2647058823529412,\n \"acc_norm_stderr\": 0.026799562024887674\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.38235294117647056,\n \"acc_stderr\": 0.019659922493623336,\n \
\ \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.019659922493623336\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.45454545454545453,\n\
\ \"acc_stderr\": 0.04769300568972744,\n \"acc_norm\": 0.45454545454545453,\n\
\ \"acc_norm_stderr\": 0.04769300568972744\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.32653061224489793,\n \"acc_stderr\": 0.030021056238440313,\n\
\ \"acc_norm\": 0.32653061224489793,\n \"acc_norm_stderr\": 0.030021056238440313\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.472636815920398,\n\
\ \"acc_stderr\": 0.03530235517334682,\n \"acc_norm\": 0.472636815920398,\n\
\ \"acc_norm_stderr\": 0.03530235517334682\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.65,\n \"acc_stderr\": 0.047937248544110196,\n \
\ \"acc_norm\": 0.65,\n \"acc_norm_stderr\": 0.047937248544110196\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.39759036144578314,\n\
\ \"acc_stderr\": 0.038099730845402184,\n \"acc_norm\": 0.39759036144578314,\n\
\ \"acc_norm_stderr\": 0.038099730845402184\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.5614035087719298,\n \"acc_stderr\": 0.0380579750559046,\n\
\ \"acc_norm\": 0.5614035087719298,\n \"acc_norm_stderr\": 0.0380579750559046\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.22031823745410037,\n\
\ \"mc1_stderr\": 0.014509045171487283,\n \"mc2\": 0.36783858238841727,\n\
\ \"mc2_stderr\": 0.013915102083485486\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6456195737963694,\n \"acc_stderr\": 0.013443314368356092\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.17437452615617893,\n \
\ \"acc_stderr\": 0.010451421361976233\n }\n}\n```"
repo_url: https://huggingface.co/stabilityai/stablelm-2-1_6b
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_24T10_43_24.406547
path:
- '**/details_harness|arc:challenge|25_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|gsm8k|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hellaswag|10_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-24T10-43-24.406547.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-24T10-43-24.406547.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- '**/details_harness|winogrande|5_2024-01-24T10-43-24.406547.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-01-24T10-43-24.406547.parquet'
- config_name: results
data_files:
- split: 2024_01_24T10_43_24.406547
path:
- results_2024-01-24T10-43-24.406547.parquet
- split: latest
path:
- results_2024-01-24T10-43-24.406547.parquet
---
# Dataset Card for Evaluation run of stabilityai/stablelm-2-1_6b
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [stabilityai/stablelm-2-1_6b](https://huggingface.co/stabilityai/stablelm-2-1_6b) 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_stabilityai__stablelm-2-1_6b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-24T10:43:24.406547](https://huggingface.co/datasets/open-llm-leaderboard/details_stabilityai__stablelm-2-1_6b/blob/main/results_2024-01-24T10-43-24.406547.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": 0.3923741043833077,
"acc_stderr": 0.03405640954935936,
"acc_norm": 0.3955514306541472,
"acc_norm_stderr": 0.03480165961817428,
"mc1": 0.22031823745410037,
"mc1_stderr": 0.014509045171487283,
"mc2": 0.36783858238841727,
"mc2_stderr": 0.013915102083485486
},
"harness|arc:challenge|25": {
"acc": 0.3967576791808874,
"acc_stderr": 0.014296513020180632,
"acc_norm": 0.4334470989761092,
"acc_norm_stderr": 0.014481376224558896
},
"harness|hellaswag|10": {
"acc": 0.5185222067317268,
"acc_stderr": 0.004986356526063975,
"acc_norm": 0.7045409281019717,
"acc_norm_stderr": 0.004553164013379557
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.37,
"acc_stderr": 0.04852365870939099,
"acc_norm": 0.37,
"acc_norm_stderr": 0.04852365870939099
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.4444444444444444,
"acc_stderr": 0.04292596718256981,
"acc_norm": 0.4444444444444444,
"acc_norm_stderr": 0.04292596718256981
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.3157894736842105,
"acc_stderr": 0.0378272898086547,
"acc_norm": 0.3157894736842105,
"acc_norm_stderr": 0.0378272898086547
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.41,
"acc_stderr": 0.049431107042371025,
"acc_norm": 0.41,
"acc_norm_stderr": 0.049431107042371025
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.3169811320754717,
"acc_stderr": 0.028637235639800935,
"acc_norm": 0.3169811320754717,
"acc_norm_stderr": 0.028637235639800935
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.4305555555555556,
"acc_stderr": 0.04140685639111503,
"acc_norm": 0.4305555555555556,
"acc_norm_stderr": 0.04140685639111503
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.32,
"acc_stderr": 0.04688261722621504,
"acc_norm": 0.32,
"acc_norm_stderr": 0.04688261722621504
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.4,
"acc_stderr": 0.049236596391733084,
"acc_norm": 0.4,
"acc_norm_stderr": 0.049236596391733084
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.31213872832369943,
"acc_stderr": 0.035331333893236574,
"acc_norm": 0.31213872832369943,
"acc_norm_stderr": 0.035331333893236574
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.1568627450980392,
"acc_stderr": 0.03618664819936248,
"acc_norm": 0.1568627450980392,
"acc_norm_stderr": 0.03618664819936248
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.47,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.47,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.3276595744680851,
"acc_stderr": 0.030683020843231004,
"acc_norm": 0.3276595744680851,
"acc_norm_stderr": 0.030683020843231004
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.2631578947368421,
"acc_stderr": 0.04142439719489362,
"acc_norm": 0.2631578947368421,
"acc_norm_stderr": 0.04142439719489362
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.503448275862069,
"acc_stderr": 0.04166567577101579,
"acc_norm": 0.503448275862069,
"acc_norm_stderr": 0.04166567577101579
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.2698412698412698,
"acc_stderr": 0.022860838309232072,
"acc_norm": 0.2698412698412698,
"acc_norm_stderr": 0.022860838309232072
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.24603174603174602,
"acc_stderr": 0.038522733649243156,
"acc_norm": 0.24603174603174602,
"acc_norm_stderr": 0.038522733649243156
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.28,
"acc_stderr": 0.045126085985421276,
"acc_norm": 0.28,
"acc_norm_stderr": 0.045126085985421276
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.3870967741935484,
"acc_stderr": 0.027709359675032495,
"acc_norm": 0.3870967741935484,
"acc_norm_stderr": 0.027709359675032495
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.24630541871921183,
"acc_stderr": 0.030315099285617732,
"acc_norm": 0.24630541871921183,
"acc_norm_stderr": 0.030315099285617732
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.41,
"acc_stderr": 0.049431107042371025,
"acc_norm": 0.41,
"acc_norm_stderr": 0.049431107042371025
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.503030303030303,
"acc_stderr": 0.03904272341431856,
"acc_norm": 0.503030303030303,
"acc_norm_stderr": 0.03904272341431856
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.5050505050505051,
"acc_stderr": 0.035621707606254015,
"acc_norm": 0.5050505050505051,
"acc_norm_stderr": 0.035621707606254015
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.46113989637305697,
"acc_stderr": 0.03597524411734579,
"acc_norm": 0.46113989637305697,
"acc_norm_stderr": 0.03597524411734579
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.32564102564102565,
"acc_stderr": 0.02375966576741229,
"acc_norm": 0.32564102564102565,
"acc_norm_stderr": 0.02375966576741229
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.26666666666666666,
"acc_stderr": 0.02696242432507384,
"acc_norm": 0.26666666666666666,
"acc_norm_stderr": 0.02696242432507384
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.3319327731092437,
"acc_stderr": 0.030588697013783663,
"acc_norm": 0.3319327731092437,
"acc_norm_stderr": 0.030588697013783663
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.23178807947019867,
"acc_stderr": 0.03445406271987054,
"acc_norm": 0.23178807947019867,
"acc_norm_stderr": 0.03445406271987054
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.5064220183486239,
"acc_stderr": 0.021435554820013077,
"acc_norm": 0.5064220183486239,
"acc_norm_stderr": 0.021435554820013077
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.24074074074074073,
"acc_stderr": 0.029157522184605603,
"acc_norm": 0.24074074074074073,
"acc_norm_stderr": 0.029157522184605603
},
"harness|hendrycksTest-high_school_us_history|5": {
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"acc_stderr": 0.03509312031717982,
"acc_norm": 0.5,
"acc_norm_stderr": 0.03509312031717982
},
"harness|hendrycksTest-high_school_world_history|5": {
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"acc_stderr": 0.03254462010767859,
"acc_norm": 0.4936708860759494,
"acc_norm_stderr": 0.03254462010767859
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.5291479820627802,
"acc_stderr": 0.03350073248773404,
"acc_norm": 0.5291479820627802,
"acc_norm_stderr": 0.03350073248773404
},
"harness|hendrycksTest-human_sexuality|5": {
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"acc_stderr": 0.043851623256015534,
"acc_norm": 0.4961832061068702,
"acc_norm_stderr": 0.043851623256015534
},
"harness|hendrycksTest-international_law|5": {
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"acc_norm": 0.4380165289256198,
"acc_norm_stderr": 0.04529146804435791
},
"harness|hendrycksTest-jurisprudence|5": {
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"acc_stderr": 0.04803752235190193,
"acc_norm": 0.4444444444444444,
"acc_norm_stderr": 0.04803752235190193
},
"harness|hendrycksTest-logical_fallacies|5": {
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"acc_stderr": 0.038656978537853624,
"acc_norm": 0.4110429447852761,
"acc_norm_stderr": 0.038656978537853624
},
"harness|hendrycksTest-machine_learning|5": {
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"acc_stderr": 0.0443280405529152,
"acc_norm": 0.32142857142857145,
"acc_norm_stderr": 0.0443280405529152
},
"harness|hendrycksTest-management|5": {
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"acc_stderr": 0.04897957737781168,
"acc_norm": 0.42718446601941745,
"acc_norm_stderr": 0.04897957737781168
},
"harness|hendrycksTest-marketing|5": {
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"acc_stderr": 0.03232128912157792,
"acc_norm": 0.5811965811965812,
"acc_norm_stderr": 0.03232128912157792
},
"harness|hendrycksTest-medical_genetics|5": {
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"acc_stderr": 0.04960449637488584,
"acc_norm": 0.42,
"acc_norm_stderr": 0.04960449637488584
},
"harness|hendrycksTest-miscellaneous|5": {
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"acc_stderr": 0.0178063045850526,
"acc_norm": 0.545338441890166,
"acc_norm_stderr": 0.0178063045850526
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.45375722543352603,
"acc_stderr": 0.026803720583206177,
"acc_norm": 0.45375722543352603,
"acc_norm_stderr": 0.026803720583206177
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.23798882681564246,
"acc_stderr": 0.014242630070574915,
"acc_norm": 0.23798882681564246,
"acc_norm_stderr": 0.014242630070574915
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.38235294117647056,
"acc_stderr": 0.027826109307283686,
"acc_norm": 0.38235294117647056,
"acc_norm_stderr": 0.027826109307283686
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.4180064308681672,
"acc_stderr": 0.028013651891995072,
"acc_norm": 0.4180064308681672,
"acc_norm_stderr": 0.028013651891995072
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.4567901234567901,
"acc_stderr": 0.027716661650194038,
"acc_norm": 0.4567901234567901,
"acc_norm_stderr": 0.027716661650194038
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.29432624113475175,
"acc_stderr": 0.027187127011503796,
"acc_norm": 0.29432624113475175,
"acc_norm_stderr": 0.027187127011503796
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.2861799217731421,
"acc_stderr": 0.011543642878150757,
"acc_norm": 0.2861799217731421,
"acc_norm_stderr": 0.011543642878150757
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.2647058823529412,
"acc_stderr": 0.026799562024887674,
"acc_norm": 0.2647058823529412,
"acc_norm_stderr": 0.026799562024887674
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.38235294117647056,
"acc_stderr": 0.019659922493623336,
"acc_norm": 0.38235294117647056,
"acc_norm_stderr": 0.019659922493623336
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.45454545454545453,
"acc_stderr": 0.04769300568972744,
"acc_norm": 0.45454545454545453,
"acc_norm_stderr": 0.04769300568972744
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.32653061224489793,
"acc_stderr": 0.030021056238440313,
"acc_norm": 0.32653061224489793,
"acc_norm_stderr": 0.030021056238440313
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.472636815920398,
"acc_stderr": 0.03530235517334682,
"acc_norm": 0.472636815920398,
"acc_norm_stderr": 0.03530235517334682
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.65,
"acc_stderr": 0.047937248544110196,
"acc_norm": 0.65,
"acc_norm_stderr": 0.047937248544110196
},
"harness|hendrycksTest-virology|5": {
"acc": 0.39759036144578314,
"acc_stderr": 0.038099730845402184,
"acc_norm": 0.39759036144578314,
"acc_norm_stderr": 0.038099730845402184
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.5614035087719298,
"acc_stderr": 0.0380579750559046,
"acc_norm": 0.5614035087719298,
"acc_norm_stderr": 0.0380579750559046
},
"harness|truthfulqa:mc|0": {
"mc1": 0.22031823745410037,
"mc1_stderr": 0.014509045171487283,
"mc2": 0.36783858238841727,
"mc2_stderr": 0.013915102083485486
},
"harness|winogrande|5": {
"acc": 0.6456195737963694,
"acc_stderr": 0.013443314368356092
},
"harness|gsm8k|5": {
"acc": 0.17437452615617893,
"acc_stderr": 0.010451421361976233
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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## Dataset Card Contact
[More Information Needed] |
HumbleIntelligence/food-ner-1-Million | ---
license: apache-2.0
dataset_info:
features:
- name: sentence
dtype: string
- name: ner
sequence: string
- name: nltk_tokens
sequence: string
- name: iob_tags
sequence: string
splits:
- name: train
num_bytes: 1301681090
num_examples: 1025394
- name: validation
num_bytes: 34620222
num_examples: 28792
- name: test
num_bytes: 31809502
num_examples: 25793
download_size: 329879893
dataset_size: 1368110814
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
scandukuri/humaneval-patch | ---
license: mit
---
[openai_humaneval](https://huggingface.co/datasets/openai_humaneval) dataset, with one-line bugs of various forms in the solutions. These bugs are generated using abstract syntax trees (ASTs) in Python, to randomly sample variables, functions, and expressions in the function body and replace them with other variables, functions and expressions respectively.
The data contains two splits- ```control``` and ```print```. Code for generating ```humaneval-patch``` is provided [here](https://github.com/janphilippfranken/printllama/tree/main/experiments/humaneval-patch/print-insertions). Developed as part of an investigation of language models' ability to utilize print statements to effectively repair buggy code. A detailed description of the dataset construction process is included below.
**control**
To generate this dataset, we begin by considering a pair (*i*, *t*) consisting of a solution *i* from [openai_humaneval](https://huggingface.co/datasets/openai_humaneval) - which contains 164 unique problem-solution pairs - and a perturbation type *t* in {```variable```, ```expression```, ```function```}.
1. Construct an abstract syntax tree for solution *i*.
2. Construct a buggy solution *i'* by randomly replacing a node *n* of type *t* with another node *n'* of type *t*. If *i'* both represents a novel incorrect solution and evaluates without error, add buggy solution *i'* to the dataset and move to problem *i + 1*.
3. Attempt step 2 100 times, exiting the loop as soon as a valid buggy solution is reached. If a valid solution is not reached, move on from pair (*i*, *t*).
We repeat for all pairs (*i*, *t*), and end up with 316 novel buggy solutions to OpenAI's humaneval problems. This is the ```control``` split.
**print**
Now, selecting 30 buggy solutions from the ```control``` split - 10 of each type *t* - we construct "expert" manual print statements that would help a user debug the incorrect solutions.
Considering each buggy solution *j* from the ```control``` split:
1. Sample 3 expert prints from the 30 manual prints above.
2. Prompt GPT-4 to insert a similar print statement for buggy solution *j*.
3. Generate 20 different print insertions *p* per problem
4. Allow [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) to select the print insertion *p* among 20 insertions for problem *j* which leads to the highest bug repair accuracy (where the repair accuracy for a print *p* of interest is calculated as a percentage over 20 repair attempts by the model)
5. Keep the print insertion *p* associated with the highest repair accuracy for problem *j*.
We end up with the 316 buggy solutions from the ```control``` split, each with a 'mixtral-optimal' print statement inserted - this is the ```print``` split. |
nikchar/claim_detection_paper_test_roberta | ---
dataset_info:
features:
- name: label
dtype: string
- name: claim
dtype: string
- name: evidence_wiki_url
dtype: string
- name: Is_Claim
dtype: string
- name: Claim_detection_result
dtype: string
splits:
- name: train
num_bytes: 1175913
num_examples: 11073
download_size: 0
dataset_size: 1175913
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "claim_detection_paper_test_roberta"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
JulietteBenguigui142/titres-catalogues-ventes_INHA | ---
license: mit
---
|
Den4ikAI/mailruQA-big | ---
license: mit
---
Обработан из 54 гигабайт данных. Удалены имена, не используются ответы больше 100 символов. |
open-llm-leaderboard/details_KnutJaegersberg__internlm-20b-llamafied | ---
pretty_name: Evaluation run of KnutJaegersberg/internlm-20b-llamafied
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [KnutJaegersberg/internlm-20b-llamafied](https://huggingface.co/KnutJaegersberg/internlm-20b-llamafied)\
\ 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_KnutJaegersberg__internlm-20b-llamafied\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-01-13T19:39:44.590825](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__internlm-20b-llamafied/blob/main/results_2024-01-13T19-39-44.590825.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\": 0.2529697662773502,\n\
\ \"acc_stderr\": 0.03077338700338214,\n \"acc_norm\": 0.2544077864541873,\n\
\ \"acc_norm_stderr\": 0.031593813415922045,\n \"mc1\": 0.2215422276621787,\n\
\ \"mc1_stderr\": 0.014537867601301145,\n \"mc2\": 0.4805606031451568,\n\
\ \"mc2_stderr\": 0.016999605402858272\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.21928327645051193,\n \"acc_stderr\": 0.012091245787615723,\n\
\ \"acc_norm\": 0.26791808873720135,\n \"acc_norm_stderr\": 0.012942030195136423\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.25542720573590916,\n\
\ \"acc_stderr\": 0.004352098082984431,\n \"acc_norm\": 0.26399123680541725,\n\
\ \"acc_norm_stderr\": 0.004398937225038417\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847394,\n \
\ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.044619604333847394\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.21481481481481482,\n\
\ \"acc_stderr\": 0.03547854198560826,\n \"acc_norm\": 0.21481481481481482,\n\
\ \"acc_norm_stderr\": 0.03547854198560826\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.28289473684210525,\n \"acc_stderr\": 0.03665349695640767,\n\
\ \"acc_norm\": 0.28289473684210525,\n \"acc_norm_stderr\": 0.03665349695640767\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.22,\n\
\ \"acc_stderr\": 0.04163331998932269,\n \"acc_norm\": 0.22,\n \
\ \"acc_norm_stderr\": 0.04163331998932269\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.2792452830188679,\n \"acc_stderr\": 0.02761116340239972,\n\
\ \"acc_norm\": 0.2792452830188679,\n \"acc_norm_stderr\": 0.02761116340239972\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2222222222222222,\n\
\ \"acc_stderr\": 0.034765901043041336,\n \"acc_norm\": 0.2222222222222222,\n\
\ \"acc_norm_stderr\": 0.034765901043041336\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \
\ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.23,\n \"acc_stderr\": 0.04229525846816506,\n \"acc_norm\"\
: 0.23,\n \"acc_norm_stderr\": 0.04229525846816506\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.23,\n \"acc_stderr\": 0.04229525846816507,\n \
\ \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.04229525846816507\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.28901734104046245,\n\
\ \"acc_stderr\": 0.03456425745087,\n \"acc_norm\": 0.28901734104046245,\n\
\ \"acc_norm_stderr\": 0.03456425745087\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.04092563958237655,\n\
\ \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.04092563958237655\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.27,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\": 0.27,\n\
\ \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.17446808510638298,\n \"acc_stderr\": 0.024809442335503973,\n\
\ \"acc_norm\": 0.17446808510638298,\n \"acc_norm_stderr\": 0.024809442335503973\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2543859649122807,\n\
\ \"acc_stderr\": 0.0409698513984367,\n \"acc_norm\": 0.2543859649122807,\n\
\ \"acc_norm_stderr\": 0.0409698513984367\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.2620689655172414,\n \"acc_stderr\": 0.036646663372252565,\n\
\ \"acc_norm\": 0.2620689655172414,\n \"acc_norm_stderr\": 0.036646663372252565\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.2619047619047619,\n \"acc_stderr\": 0.022644212615525214,\n \"\
acc_norm\": 0.2619047619047619,\n \"acc_norm_stderr\": 0.022644212615525214\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.30158730158730157,\n\
\ \"acc_stderr\": 0.04104947269903394,\n \"acc_norm\": 0.30158730158730157,\n\
\ \"acc_norm_stderr\": 0.04104947269903394\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145632,\n \
\ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145632\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.24193548387096775,\n\
\ \"acc_stderr\": 0.024362599693031103,\n \"acc_norm\": 0.24193548387096775,\n\
\ \"acc_norm_stderr\": 0.024362599693031103\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.2857142857142857,\n \"acc_stderr\": 0.0317852971064275,\n\
\ \"acc_norm\": 0.2857142857142857,\n \"acc_norm_stderr\": 0.0317852971064275\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\"\
: 0.27,\n \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.3393939393939394,\n \"acc_stderr\": 0.03697442205031595,\n\
\ \"acc_norm\": 0.3393939393939394,\n \"acc_norm_stderr\": 0.03697442205031595\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.30808080808080807,\n \"acc_stderr\": 0.03289477330098616,\n \"\
acc_norm\": 0.30808080808080807,\n \"acc_norm_stderr\": 0.03289477330098616\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.23316062176165803,\n \"acc_stderr\": 0.03051611137147602,\n\
\ \"acc_norm\": 0.23316062176165803,\n \"acc_norm_stderr\": 0.03051611137147602\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.2948717948717949,\n \"acc_stderr\": 0.023119362758232273,\n\
\ \"acc_norm\": 0.2948717948717949,\n \"acc_norm_stderr\": 0.023119362758232273\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.26666666666666666,\n \"acc_stderr\": 0.026962424325073835,\n \
\ \"acc_norm\": 0.26666666666666666,\n \"acc_norm_stderr\": 0.026962424325073835\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.2689075630252101,\n \"acc_stderr\": 0.028801392193631276,\n\
\ \"acc_norm\": 0.2689075630252101,\n \"acc_norm_stderr\": 0.028801392193631276\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.2847682119205298,\n \"acc_stderr\": 0.036848815213890225,\n \"\
acc_norm\": 0.2847682119205298,\n \"acc_norm_stderr\": 0.036848815213890225\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.24403669724770644,\n \"acc_stderr\": 0.01841528635141641,\n \"\
acc_norm\": 0.24403669724770644,\n \"acc_norm_stderr\": 0.01841528635141641\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.3148148148148148,\n \"acc_stderr\": 0.0316746870682898,\n \"acc_norm\"\
: 0.3148148148148148,\n \"acc_norm_stderr\": 0.0316746870682898\n },\n\
\ \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.25980392156862747,\n\
\ \"acc_stderr\": 0.030778554678693244,\n \"acc_norm\": 0.25980392156862747,\n\
\ \"acc_norm_stderr\": 0.030778554678693244\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\
: {\n \"acc\": 0.23628691983122363,\n \"acc_stderr\": 0.027652153144159267,\n\
\ \"acc_norm\": 0.23628691983122363,\n \"acc_norm_stderr\": 0.027652153144159267\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.17040358744394618,\n\
\ \"acc_stderr\": 0.025234593447136165,\n \"acc_norm\": 0.17040358744394618,\n\
\ \"acc_norm_stderr\": 0.025234593447136165\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.2900763358778626,\n \"acc_stderr\": 0.03980066246467766,\n\
\ \"acc_norm\": 0.2900763358778626,\n \"acc_norm_stderr\": 0.03980066246467766\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.21487603305785125,\n \"acc_stderr\": 0.037494924487096966,\n \"\
acc_norm\": 0.21487603305785125,\n \"acc_norm_stderr\": 0.037494924487096966\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.24074074074074073,\n\
\ \"acc_stderr\": 0.041331194402438376,\n \"acc_norm\": 0.24074074074074073,\n\
\ \"acc_norm_stderr\": 0.041331194402438376\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.3312883435582822,\n \"acc_stderr\": 0.03697983910025588,\n\
\ \"acc_norm\": 0.3312883435582822,\n \"acc_norm_stderr\": 0.03697983910025588\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.19642857142857142,\n\
\ \"acc_stderr\": 0.03770970049347018,\n \"acc_norm\": 0.19642857142857142,\n\
\ \"acc_norm_stderr\": 0.03770970049347018\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.2621359223300971,\n \"acc_stderr\": 0.043546310772605956,\n\
\ \"acc_norm\": 0.2621359223300971,\n \"acc_norm_stderr\": 0.043546310772605956\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.1581196581196581,\n\
\ \"acc_stderr\": 0.023902325549560392,\n \"acc_norm\": 0.1581196581196581,\n\
\ \"acc_norm_stderr\": 0.023902325549560392\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.17,\n \"acc_stderr\": 0.03775251680686371,\n \
\ \"acc_norm\": 0.17,\n \"acc_norm_stderr\": 0.03775251680686371\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.26947637292464877,\n\
\ \"acc_stderr\": 0.01586624307321505,\n \"acc_norm\": 0.26947637292464877,\n\
\ \"acc_norm_stderr\": 0.01586624307321505\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.18497109826589594,\n \"acc_stderr\": 0.020903975842083027,\n\
\ \"acc_norm\": 0.18497109826589594,\n \"acc_norm_stderr\": 0.020903975842083027\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2245810055865922,\n\
\ \"acc_stderr\": 0.01395680366654464,\n \"acc_norm\": 0.2245810055865922,\n\
\ \"acc_norm_stderr\": 0.01395680366654464\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.2647058823529412,\n \"acc_stderr\": 0.025261691219729474,\n\
\ \"acc_norm\": 0.2647058823529412,\n \"acc_norm_stderr\": 0.025261691219729474\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.2861736334405145,\n\
\ \"acc_stderr\": 0.02567025924218895,\n \"acc_norm\": 0.2861736334405145,\n\
\ \"acc_norm_stderr\": 0.02567025924218895\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.26851851851851855,\n \"acc_stderr\": 0.02465968518596729,\n\
\ \"acc_norm\": 0.26851851851851855,\n \"acc_norm_stderr\": 0.02465968518596729\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.2801418439716312,\n \"acc_stderr\": 0.02678917235114024,\n \
\ \"acc_norm\": 0.2801418439716312,\n \"acc_norm_stderr\": 0.02678917235114024\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2653194263363755,\n\
\ \"acc_stderr\": 0.011276198843958878,\n \"acc_norm\": 0.2653194263363755,\n\
\ \"acc_norm_stderr\": 0.011276198843958878\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.29044117647058826,\n \"acc_stderr\": 0.027576468622740522,\n\
\ \"acc_norm\": 0.29044117647058826,\n \"acc_norm_stderr\": 0.027576468622740522\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.23202614379084968,\n \"acc_stderr\": 0.017077373377857016,\n \
\ \"acc_norm\": 0.23202614379084968,\n \"acc_norm_stderr\": 0.017077373377857016\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.18181818181818182,\n\
\ \"acc_stderr\": 0.036942843353378,\n \"acc_norm\": 0.18181818181818182,\n\
\ \"acc_norm_stderr\": 0.036942843353378\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.2816326530612245,\n \"acc_stderr\": 0.028795185574291286,\n\
\ \"acc_norm\": 0.2816326530612245,\n \"acc_norm_stderr\": 0.028795185574291286\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.22885572139303484,\n\
\ \"acc_stderr\": 0.029705284056772426,\n \"acc_norm\": 0.22885572139303484,\n\
\ \"acc_norm_stderr\": 0.029705284056772426\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.22,\n \"acc_stderr\": 0.041633319989322695,\n \
\ \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.041633319989322695\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.2289156626506024,\n\
\ \"acc_stderr\": 0.03270745277352477,\n \"acc_norm\": 0.2289156626506024,\n\
\ \"acc_norm_stderr\": 0.03270745277352477\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.23976608187134502,\n \"acc_stderr\": 0.03274485211946956,\n\
\ \"acc_norm\": 0.23976608187134502,\n \"acc_norm_stderr\": 0.03274485211946956\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2215422276621787,\n\
\ \"mc1_stderr\": 0.014537867601301145,\n \"mc2\": 0.4805606031451568,\n\
\ \"mc2_stderr\": 0.016999605402858272\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.47829518547750594,\n \"acc_stderr\": 0.01403923921648463\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\
: 0.0\n }\n}\n```"
repo_url: https://huggingface.co/KnutJaegersberg/internlm-20b-llamafied
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_13T19_39_44.590825
path:
- '**/details_harness|arc:challenge|25_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|gsm8k|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hellaswag|10_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-13T19-39-44.590825.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-13T19-39-44.590825.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- '**/details_harness|winogrande|5_2024-01-13T19-39-44.590825.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-01-13T19-39-44.590825.parquet'
- config_name: results
data_files:
- split: 2024_01_13T19_39_44.590825
path:
- results_2024-01-13T19-39-44.590825.parquet
- split: latest
path:
- results_2024-01-13T19-39-44.590825.parquet
---
# Dataset Card for Evaluation run of KnutJaegersberg/internlm-20b-llamafied
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [KnutJaegersberg/internlm-20b-llamafied](https://huggingface.co/KnutJaegersberg/internlm-20b-llamafied) 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_KnutJaegersberg__internlm-20b-llamafied",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-13T19:39:44.590825](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__internlm-20b-llamafied/blob/main/results_2024-01-13T19-39-44.590825.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": 0.2529697662773502,
"acc_stderr": 0.03077338700338214,
"acc_norm": 0.2544077864541873,
"acc_norm_stderr": 0.031593813415922045,
"mc1": 0.2215422276621787,
"mc1_stderr": 0.014537867601301145,
"mc2": 0.4805606031451568,
"mc2_stderr": 0.016999605402858272
},
"harness|arc:challenge|25": {
"acc": 0.21928327645051193,
"acc_stderr": 0.012091245787615723,
"acc_norm": 0.26791808873720135,
"acc_norm_stderr": 0.012942030195136423
},
"harness|hellaswag|10": {
"acc": 0.25542720573590916,
"acc_stderr": 0.004352098082984431,
"acc_norm": 0.26399123680541725,
"acc_norm_stderr": 0.004398937225038417
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.27,
"acc_stderr": 0.044619604333847394,
"acc_norm": 0.27,
"acc_norm_stderr": 0.044619604333847394
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.21481481481481482,
"acc_stderr": 0.03547854198560826,
"acc_norm": 0.21481481481481482,
"acc_norm_stderr": 0.03547854198560826
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.28289473684210525,
"acc_stderr": 0.03665349695640767,
"acc_norm": 0.28289473684210525,
"acc_norm_stderr": 0.03665349695640767
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.22,
"acc_stderr": 0.04163331998932269,
"acc_norm": 0.22,
"acc_norm_stderr": 0.04163331998932269
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.2792452830188679,
"acc_stderr": 0.02761116340239972,
"acc_norm": 0.2792452830188679,
"acc_norm_stderr": 0.02761116340239972
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.2222222222222222,
"acc_stderr": 0.034765901043041336,
"acc_norm": 0.2222222222222222,
"acc_norm_stderr": 0.034765901043041336
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.29,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.29,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.23,
"acc_stderr": 0.04229525846816506,
"acc_norm": 0.23,
"acc_norm_stderr": 0.04229525846816506
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.23,
"acc_stderr": 0.04229525846816507,
"acc_norm": 0.23,
"acc_norm_stderr": 0.04229525846816507
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.28901734104046245,
"acc_stderr": 0.03456425745087,
"acc_norm": 0.28901734104046245,
"acc_norm_stderr": 0.03456425745087
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.21568627450980393,
"acc_stderr": 0.04092563958237655,
"acc_norm": 0.21568627450980393,
"acc_norm_stderr": 0.04092563958237655
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.27,
"acc_stderr": 0.044619604333847394,
"acc_norm": 0.27,
"acc_norm_stderr": 0.044619604333847394
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.17446808510638298,
"acc_stderr": 0.024809442335503973,
"acc_norm": 0.17446808510638298,
"acc_norm_stderr": 0.024809442335503973
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.2543859649122807,
"acc_stderr": 0.0409698513984367,
"acc_norm": 0.2543859649122807,
"acc_norm_stderr": 0.0409698513984367
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.2620689655172414,
"acc_stderr": 0.036646663372252565,
"acc_norm": 0.2620689655172414,
"acc_norm_stderr": 0.036646663372252565
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.2619047619047619,
"acc_stderr": 0.022644212615525214,
"acc_norm": 0.2619047619047619,
"acc_norm_stderr": 0.022644212615525214
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.30158730158730157,
"acc_stderr": 0.04104947269903394,
"acc_norm": 0.30158730158730157,
"acc_norm_stderr": 0.04104947269903394
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.38,
"acc_stderr": 0.04878317312145632,
"acc_norm": 0.38,
"acc_norm_stderr": 0.04878317312145632
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.24193548387096775,
"acc_stderr": 0.024362599693031103,
"acc_norm": 0.24193548387096775,
"acc_norm_stderr": 0.024362599693031103
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.2857142857142857,
"acc_stderr": 0.0317852971064275,
"acc_norm": 0.2857142857142857,
"acc_norm_stderr": 0.0317852971064275
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.27,
"acc_stderr": 0.044619604333847394,
"acc_norm": 0.27,
"acc_norm_stderr": 0.044619604333847394
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.3393939393939394,
"acc_stderr": 0.03697442205031595,
"acc_norm": 0.3393939393939394,
"acc_norm_stderr": 0.03697442205031595
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.30808080808080807,
"acc_stderr": 0.03289477330098616,
"acc_norm": 0.30808080808080807,
"acc_norm_stderr": 0.03289477330098616
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.23316062176165803,
"acc_stderr": 0.03051611137147602,
"acc_norm": 0.23316062176165803,
"acc_norm_stderr": 0.03051611137147602
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.2948717948717949,
"acc_stderr": 0.023119362758232273,
"acc_norm": 0.2948717948717949,
"acc_norm_stderr": 0.023119362758232273
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.26666666666666666,
"acc_stderr": 0.026962424325073835,
"acc_norm": 0.26666666666666666,
"acc_norm_stderr": 0.026962424325073835
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.2689075630252101,
"acc_stderr": 0.028801392193631276,
"acc_norm": 0.2689075630252101,
"acc_norm_stderr": 0.028801392193631276
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.2847682119205298,
"acc_stderr": 0.036848815213890225,
"acc_norm": 0.2847682119205298,
"acc_norm_stderr": 0.036848815213890225
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.24403669724770644,
"acc_stderr": 0.01841528635141641,
"acc_norm": 0.24403669724770644,
"acc_norm_stderr": 0.01841528635141641
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.3148148148148148,
"acc_stderr": 0.0316746870682898,
"acc_norm": 0.3148148148148148,
"acc_norm_stderr": 0.0316746870682898
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.25980392156862747,
"acc_stderr": 0.030778554678693244,
"acc_norm": 0.25980392156862747,
"acc_norm_stderr": 0.030778554678693244
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.23628691983122363,
"acc_stderr": 0.027652153144159267,
"acc_norm": 0.23628691983122363,
"acc_norm_stderr": 0.027652153144159267
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.17040358744394618,
"acc_stderr": 0.025234593447136165,
"acc_norm": 0.17040358744394618,
"acc_norm_stderr": 0.025234593447136165
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.2900763358778626,
"acc_stderr": 0.03980066246467766,
"acc_norm": 0.2900763358778626,
"acc_norm_stderr": 0.03980066246467766
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.21487603305785125,
"acc_stderr": 0.037494924487096966,
"acc_norm": 0.21487603305785125,
"acc_norm_stderr": 0.037494924487096966
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.24074074074074073,
"acc_stderr": 0.041331194402438376,
"acc_norm": 0.24074074074074073,
"acc_norm_stderr": 0.041331194402438376
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.3312883435582822,
"acc_stderr": 0.03697983910025588,
"acc_norm": 0.3312883435582822,
"acc_norm_stderr": 0.03697983910025588
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.19642857142857142,
"acc_stderr": 0.03770970049347018,
"acc_norm": 0.19642857142857142,
"acc_norm_stderr": 0.03770970049347018
},
"harness|hendrycksTest-management|5": {
"acc": 0.2621359223300971,
"acc_stderr": 0.043546310772605956,
"acc_norm": 0.2621359223300971,
"acc_norm_stderr": 0.043546310772605956
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.1581196581196581,
"acc_stderr": 0.023902325549560392,
"acc_norm": 0.1581196581196581,
"acc_norm_stderr": 0.023902325549560392
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.17,
"acc_stderr": 0.03775251680686371,
"acc_norm": 0.17,
"acc_norm_stderr": 0.03775251680686371
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.26947637292464877,
"acc_stderr": 0.01586624307321505,
"acc_norm": 0.26947637292464877,
"acc_norm_stderr": 0.01586624307321505
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.18497109826589594,
"acc_stderr": 0.020903975842083027,
"acc_norm": 0.18497109826589594,
"acc_norm_stderr": 0.020903975842083027
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.2245810055865922,
"acc_stderr": 0.01395680366654464,
"acc_norm": 0.2245810055865922,
"acc_norm_stderr": 0.01395680366654464
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.2647058823529412,
"acc_stderr": 0.025261691219729474,
"acc_norm": 0.2647058823529412,
"acc_norm_stderr": 0.025261691219729474
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.2861736334405145,
"acc_stderr": 0.02567025924218895,
"acc_norm": 0.2861736334405145,
"acc_norm_stderr": 0.02567025924218895
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.26851851851851855,
"acc_stderr": 0.02465968518596729,
"acc_norm": 0.26851851851851855,
"acc_norm_stderr": 0.02465968518596729
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.2801418439716312,
"acc_stderr": 0.02678917235114024,
"acc_norm": 0.2801418439716312,
"acc_norm_stderr": 0.02678917235114024
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.2653194263363755,
"acc_stderr": 0.011276198843958878,
"acc_norm": 0.2653194263363755,
"acc_norm_stderr": 0.011276198843958878
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.29044117647058826,
"acc_stderr": 0.027576468622740522,
"acc_norm": 0.29044117647058826,
"acc_norm_stderr": 0.027576468622740522
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.23202614379084968,
"acc_stderr": 0.017077373377857016,
"acc_norm": 0.23202614379084968,
"acc_norm_stderr": 0.017077373377857016
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.18181818181818182,
"acc_stderr": 0.036942843353378,
"acc_norm": 0.18181818181818182,
"acc_norm_stderr": 0.036942843353378
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.2816326530612245,
"acc_stderr": 0.028795185574291286,
"acc_norm": 0.2816326530612245,
"acc_norm_stderr": 0.028795185574291286
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.22885572139303484,
"acc_stderr": 0.029705284056772426,
"acc_norm": 0.22885572139303484,
"acc_norm_stderr": 0.029705284056772426
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.22,
"acc_stderr": 0.041633319989322695,
"acc_norm": 0.22,
"acc_norm_stderr": 0.041633319989322695
},
"harness|hendrycksTest-virology|5": {
"acc": 0.2289156626506024,
"acc_stderr": 0.03270745277352477,
"acc_norm": 0.2289156626506024,
"acc_norm_stderr": 0.03270745277352477
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.23976608187134502,
"acc_stderr": 0.03274485211946956,
"acc_norm": 0.23976608187134502,
"acc_norm_stderr": 0.03274485211946956
},
"harness|truthfulqa:mc|0": {
"mc1": 0.2215422276621787,
"mc1_stderr": 0.014537867601301145,
"mc2": 0.4805606031451568,
"mc2_stderr": 0.016999605402858272
},
"harness|winogrande|5": {
"acc": 0.47829518547750594,
"acc_stderr": 0.01403923921648463
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
Mitsuki-Sakamoto/alpaca_farm-deberta-re-pref-64-fil_self_160m_bo16_2_mix_50_kl_0.1_prm_70m_thr_0.0_seed_2_t_0.5 | ---
dataset_info:
config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1
features:
- name: instruction
dtype: string
- name: input
dtype: string
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- name: gen_kwargs
struct:
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dtype: bool
- name: max_new_tokens
dtype: int64
- name: pad_token_id
dtype: int64
- name: top_k
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- name: top_p
dtype: float64
- name: reward_1
dtype: float64
- name: reward_2
dtype: float64
- name: n_samples
dtype: int64
- name: reject_select
dtype: string
- name: index
dtype: int64
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
- name: filtered_epoch
dtype: int64
- name: gen_reward
dtype: float64
- name: gen_response
dtype: string
splits:
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num_examples: 18928
download_size: 685575439
dataset_size: 1326769378
configs:
- config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1
data_files:
- split: epoch_0
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-*
- split: epoch_1
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-*
- split: epoch_2
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-*
- split: epoch_3
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-*
- split: epoch_4
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-*
- split: epoch_5
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-*
- split: epoch_6
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-*
- split: epoch_9
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_10-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_11-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_12-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_13-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_14-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_15-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_16-*
- split: epoch_17
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_17-*
- split: epoch_18
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_18-*
- split: epoch_19
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_19-*
- split: epoch_20
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_20-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_21-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_22-*
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path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_23-*
- split: epoch_24
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_24-*
- split: epoch_25
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_25-*
- split: epoch_26
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_26-*
- split: epoch_27
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_27-*
- split: epoch_28
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_28-*
- split: epoch_29
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_29-*
---
|
stevenboa/test_dataset | ---
license: mit
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
- name: input_ids
sequence: int64
- name: attention_mask
sequence: int64
- name: labels
sequence: int64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 3299813
num_examples: 1400
download_size: 686303
dataset_size: 3299813
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
casual/national_speech_corpusv2 | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: transcription
dtype: string
splits:
- name: train
num_bytes: 541423622.36
num_examples: 3538
download_size: 557460372
dataset_size: 541423622.36
---
# Dataset Card for "national_speech_corpusv2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
lavita/AlpaCare-MedInstruct-52k | ---
dataset_info:
features:
- name: output
dtype: string
- name: input
dtype: string
- name: instruction
dtype: string
splits:
- name: train
num_bytes: 64721846
num_examples: 52002
download_size: 36697625
dataset_size: 64721846
task_categories:
- text-generation
language:
- en
tags:
- medical
size_categories:
- 10K<n<100K
---
# Dataset Card for "AlpaCare-MedInstruct-52k"
AlpaCare GitHub repo: [https://github.com/XZhang97666/AlpaCare](https://github.com/XZhang97666/AlpaCare)
## Citation:
If you use this dataset, please cite the original paper:
```
@misc{zhang2023alpacareinstructiontuned,
title={AlpaCare: Instruction-tuned Large Language Models for Medical Application},
author={Xinlu Zhang and Chenxin Tian and Xianjun Yang and Lichang Chen and Zekun Li and Linda Ruth Petzold},
year={2023},
eprint={2310.14558},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
newsmediabias/debiased_dataset | ---
license: creativeml-openrail-m
task_categories:
- text-classification
- token-classification
---
#### **Dataset Description**
**About the Dataset**:
This dataset contains text data that has been processed to identify biased statements based on dimensions and aspects. Each entry has been processed using the GPT-4 language model and manually verified by 5 human annotators for quality assurance.
**Purpose**:
The dataset aims to help train and evaluate machine learning models in detecting, classifying, and correcting biases in text content, making it essential for NLP research related to fairness and debiasing.
**Origin**:
The dataset has been curated from diverse sources, including online media articles, blogs, and user comments. These sources were chosen for their likelihood of containing varying degrees of bias.
---
#### **Composition**
**Dataset Statistics**:
- Total entries: 7500
**Data Fields**:
- **biased_text (string)**: The original text containing biased content.
- **dimension (string)**: The broader category under which the bias can be classified, such as race, gender, religion, etc.
- **aspect (string)**: The specific perspective or angle of bias present in the text.
- **biased_profain_words (string)**: Words in the `biased_text` that are identified as profane or highly biased.
- **bias_label (string)**: The category or degree of bias ranging from mild, moderate, to severe.
- **debiased_text (string)**: The debiased version of the `biased_text` generated using GPT-4 and subsequently verified by 5 human annotators.
#### **Data Use and Limitations**
**Usage**:
This dataset can be employed for training models in detecting and correcting bias in text. It can also benchmark bias detection and correction algorithms.
**Limitations**:
- The dataset, though comprehensive, may not encapsulate all types of biases.
- The debiasing process may retain subtle biases or overlook some nuances, despite being vetted by human annotators.
- Labels might contain inherent subjectivities as they are determined based on annotator discretion.
|
chenqile09/alpaca-2-7B-chinese-couplet-val-4k-predictions | ---
configs:
- config_name: default
data_files:
- split: data
path: data/data-*
dataset_info:
features:
- name: input
dtype: string
- name: prediction
dtype: string
- name: reference
dtype: string
splits:
- name: data
num_bytes: 386155
num_examples: 4000
download_size: 342185
dataset_size: 386155
---
# Dataset Card for "alpaca-2-7B-chinese-couplet-val-4k-predictions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
bertbsb/Herbeetcelso | ---
license: openrail
---
|
openaccess-ai-collective/e3172f01df1640a109901582a7ee3acd | Invalid username or password. |
projecte-aina/CaWikiTC | ---
YAML tags:
annotations_creators:
- automatically-generated
language_creators:
- found
language:
- ca
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
pretty_name: cawikitc
size_categories:
- unknown
source_datasets: []
task_categories:
- text-classification
task_ids:
- multi-class-classification
---
# Dataset Card for CaWikiTC
## Dataset Description
- **Point of Contact:** langtech@bsc.es
### Dataset Summary
CaWikiTC (Catalan Wikipedia Text Classification) is a text classification dataset authomatically created by scraping Catalan Wikipedia article summaries and their associated thematic category. It contains 21002 texts (19952 and 1050 in the train and dev partitions, respectively) classified under 67 exclusive categories.
For the dataset creation, we selected all the Catalan Wikipedia article summaries from a previously fixed variety of subcategories, most of which are professional disciplines and social sciences-related fields. The texts that were originally associated with more than one category were discarded to avoid class overlappings.
This dataset was created as part of the experiments from [Entailment-based Task Transfer for Catalan Text Classification in Small Data Regimes](http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6551). Its original purpose was to serve as a task transfer source to train an entailment model, which was then used to perform a different text classification task.
This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by-sa/4.0/">Attribution-ShareAlike 4.0 International</a>.
### Supported Tasks and Leaderboards
Text classification, Language Model
### Languages
The dataset is in Catalan (`ca-ES`).
## Dataset Structure
### Data Instances
Two json files (train and development splits).
### Data Fields
Each example contains the following 3 fields:
* text: Catalan Wikipedia article summary (string)
* label: topic
#### Example:
<pre>
[
{
'text': "Novum Organum és el títol de l'obra més important de Francis Bacon, publicada el 1620. Rep el seu nom perquè pretén ser una superació del tractat sobre lògica d'Aristòtil, anomenat Organon. Es basa a trobar la causa de tot fenomen per inducció, observant quan passa i quan no i extrapolant aleshores les condicions que fan que es doni. Aquest raonament va influir decisivament en la formació del mètode científic, especialment en la fase d'elaboració d'hipòtesis. També indica que el prejudici és l'enemic de la ciència, perquè impideix generar noves idees. Els prejudicis més comuns s'expliquen amb la metàfora de l'ídol o allò que és falsament adorat. Existeixen ídols de la tribu (comuns a tots els éssers humans per la seva naturalesa), de la caverna (procedents de l'educació), del fòrum (causats per un ús incorrecte del llenguatge) i del teatre (basats en idees anteriors errònies, notablement en filosofia).",
'label': 'Filosofia',
},
...
]
</pre>
#### Labels
* 'Administració', 'Aeronàutica', 'Agricultura', 'Antropologia', 'Arqueologia', 'Arquitectura', 'Art', 'Astronomia', 'Astronàutica', 'Biblioteconomia', 'Biotecnologia', 'Catàstrofes', 'Circ', 'Ciència militar', 'Ciència-ficció', 'Ciències ambientals', 'Ciències de la salut', 'Ciències polítiques', 'Conflictes', 'Cronometria', 'Cultura popular', 'Dansa', 'Dret', 'Ecologia', 'Enginyeria', 'Epidèmies', 'Esoterisme', 'Estris', 'Festivals', 'Filologia', 'Filosofia', 'Fiscalitat', 'Física', 'Geografia', 'Geologia', 'Gestió', 'Heràldica', 'Història', 'Humor', 'Indumentària', 'Informàtica', 'Jaciments paleontològics', 'Jocs', 'Lingüística', 'Llengües', 'Llocs ficticis', 'Matemàtiques', 'Metodologia', 'Mitologia', 'Multimèdia', 'Museologia', 'Nàutica', 'Objectes astronòmics', 'Pedagogia', 'Periodisme', 'Protestes', 'Pseudociència', 'Psicologia', 'Química', 'Robòtica', 'Ràdio', 'Seguretat laboral', 'Sociologia', 'Telecomunicacions', 'Televisió', 'Teologia', 'Ètica'
### Data Splits
Train and development splits were created in a stratified fashion, following a 95% and 5% proportion, respectively. The sizes of each split are the following:
* train.json: 19952 examples
* dev.json: 1050 examples
### Annotations
#### Annotation process
The crawled data contained the categories' annotations, which were then used to create this dataset with the mentioned criteria.
### Personal and Sensitive Information
No personal or sensitive information included.
## Considerations for Using the Data
### Social Impact of Dataset
We hope this dataset contributes to the development of language modeCAls in Catalan, a low-resource language.
### Discussion of Biases
[N/A]
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
Irene Baucells (irene.baucells@bsc.es)
This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
### Licensing Information
This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by-sa/4.0/">Attribution-ShareAlike 4.0 International</a>.
### Citation Information
|
huggingartists/nautilus-pompilius | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/nautilus-pompilius"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.142168 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/7099ea093179fc16f7bca186affd6c0f.533x533x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/nautilus-pompilius">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Nautilus Pompilius (Наутилус Помпилиус)</div>
<a href="https://genius.com/artists/nautilus-pompilius">
<div style="text-align: center; font-size: 14px;">@nautilus-pompilius</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/nautilus-pompilius).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/nautilus-pompilius")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|67| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/nautilus-pompilius")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
manojpreveen/Conversational_Data | ---
license: apache-2.0
---
Respective Repos are in **manojpreveen/ConversationalRepo**
Post-Process Code Info :
* data_process_conv.py
* data_process_conv_split.py
Datasets Info :
1. sharegpt_deep_clean_lang_en.json - ***https://huggingface.co/datasets/manojpreveen/ConversationalRepo/tree/main/sharegpt-raw***
2. openassistant_oasst1_conversation_deep_clean_lang_en.json - ***https://huggingface.co/datasets/manojpreveen/ConversationalRepo/tree/main/OpenAssistant***
3. ultrachat.json - ***https://huggingface.co/datasets/manojpreveen/ConversationalRepo/tree/main/ultrachat***
4. baize_medical.json - ***https://huggingface.co/datasets/manojpreveen/ConversationalRepo/tree/main/baize***
5. baize_quora.json - ***https://huggingface.co/datasets/manojpreveen/ConversationalRepo/tree/main/baize***
6. baize_stackoverflow.json - ***https://huggingface.co/datasets/manojpreveen/ConversationalRepo/tree/main/baize***
7. camel_ai_society.json - ***https://huggingface.co/datasets/manojpreveen/ConversationalRepo/tree/main/camel***
8. camel_code.json - ***https://huggingface.co/datasets/manojpreveen/ConversationalRepo/tree/main/camel***
9. iamai_roleplay.json - ***https://huggingface.co/datasets/manojpreveen/ConversationalRepo/tree/main/roleplay***
|
liuyanchen1015/MULTI_VALUE_mnli_indefinite_for_definite_articles | ---
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: score
dtype: int64
splits:
- name: dev_matched
num_bytes: 1467118
num_examples: 6685
- name: dev_mismatched
num_bytes: 1543592
num_examples: 6758
- name: test_matched
num_bytes: 1487678
num_examples: 6783
- name: test_mismatched
num_bytes: 1573522
num_examples: 6893
- name: train
num_bytes: 59402090
num_examples: 268805
download_size: 43512115
dataset_size: 65474000
---
# Dataset Card for "MULTI_VALUE_mnli_indefinite_for_definite_articles"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
slone/e-mordovia-articles-2023 | ---
dataset_info:
features:
- name: src_sent_id
dtype: float64
- name: src_sent
dtype: string
- name: tgt_sent_id
dtype: float64
- name: tgt_sent
dtype: string
- name: sim
dtype: float64
- name: sim_pnlz
dtype: float64
- name: src_doc_hash
dtype: string
- name: tgt_doc_hash
dtype: string
- name: docs_sim
dtype: float64
- name: src_id
dtype: int64
splits:
- name: train
num_bytes: 39447584
num_examples: 76400
download_size: 15646643
dataset_size: 39447584
---
# Dataset Card for "e-mordovia-articles-2023"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ravithejads/alpaca_marathi_cleaned_output | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: telugu_instruction
dtype: string
- name: telugu_input
dtype: string
- name: telugu_output
dtype: string
- name: telugu_transliterated_instruction
dtype: string
- name: telugu_transliterated_input
dtype: string
- name: telugu_transliterated_output
dtype: string
- name: marathi_instruction
dtype: string
- name: marathi_input
dtype: string
- name: marathi_output
dtype: string
splits:
- name: train
num_bytes: 220307094
num_examples: 28910
download_size: 93883153
dataset_size: 220307094
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
JackBAI/bert_pretrain_datasets | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 24500165181
num_examples: 80462898
download_size: 14400389487
dataset_size: 24500165181
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "bert_pretrain_datasets"
This dataset is essentially a concatenation of the training set of the English Wikipedia (wikipedia.20220301.en.train) and the Book Corpus (bookcorpus.train).
This is exactly how I get this dataset:
```
from datasets import load_dataset, concatenate_datasets, load_from_disk
cache_dir = "/data/haob2/cache/"
# book corpus
bookcorpus = load_dataset("bookcorpus", split="train", cache_dir=cache_dir)
# english wikipedia
wiki = load_dataset("wikipedia", "20220301.en", split="train", cache_dir=cache_dir)
wiki = wiki.remove_columns([col for col in wiki.column_names if col != "text"])
# # concatenation
concat = concatenate_datasets([bookcorpus, wiki])
concat.push_to_hub("JackBAI/bert_pretrain_datasets")
```
Note that this is a naive reproduction of the dataset that BERT is using. We believe the official BERT checkpoint is pretrained on a much more engineered dataset.
|
smmile/SMMILE_backup_raw | ---
license: cc-by-nc-sa-4.0
---
|
liuyanchen1015/MULTI_VALUE_wnli_no_gender_distinction | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: dev
num_bytes: 7426
num_examples: 37
- name: test
num_bytes: 28837
num_examples: 104
- name: train
num_bytes: 75721
num_examples: 406
download_size: 43547
dataset_size: 111984
---
# Dataset Card for "MULTI_VALUE_wnli_no_gender_distinction"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
allenai/wcep_sparse_mean | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- other
multilinguality:
- monolingual
pretty_name: WCEP-10
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- summarization
task_ids:
- news-articles-summarization
paperswithcode_id: wcep
train-eval-index:
- config: default
task: summarization
task_id: summarization
splits:
train_split: train
eval_split: test
col_mapping:
document: text
summary: target
metrics:
- type: rouge
name: Rouge
---
This is a copy of the [WCEP-10](https://huggingface.co/datasets/ccdv/WCEP-10) dataset, except the input source documents of its `test` split have been replaced by a __sparse__ retriever. The retrieval pipeline used:
- __query__: The `summary` field of each example
- __corpus__: The union of all documents in the `train`, `validation` and `test` splits
- __retriever__: BM25 via [PyTerrier](https://pyterrier.readthedocs.io/en/latest/) with default settings
- __top-k strategy__: `"mean"`, i.e. the number of documents retrieved, `k`, is set as the mean number of documents seen across examples in this dataset, in this case `k==9`
Retrieval results on the `train` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8753 | 0.6443 | 0.6196 | 0.6237 |
Retrieval results on the `validation` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8706 | 0.6280 | 0.6260 | 0.5989 |
Retrieval results on the `test` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8836 | 0.6658 | 0.6601 | 0.6388 | |
oeg/CelebA_RoBERTa_Sp | ---
license: apache-2.0
task_categories:
- table-question-answering
- question-answering
- translation
- text2text-generation
language:
- es
tags:
- CelebA
- Spanish
- celebFaces attributes
- face detection
- face recognition
pretty_name: RoBERTa+CelebA training corpus in Spanish
size_categories:
- 100M<n<1B
---
## Corpus Summary
This corpus contains 250000 entries made up of a pair of sentences in Spanish and their respective similarity value in the range 0 to 1. This corpus was used in the training of the
[sentence-transformer](https://www.sbert.net/) library to improve the efficiency of the [RoBERTa-large-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-large-bne) base model.
Each of the pairs of sentences are textual descriptions of the faces of the CelebA dataset, which were previously translated into Spanish. The process followed to generate it was:
- First, a translation of the original English text into Spanish was made. The original corpus in English was obtained from the work [Text2faceGAN ](https://arxiv.org/pdf/1911.11378.pdf)
- An algorithm was implemented that randomly selects two sentences from the translated corpus and calculates their similarity value. _Spacy_ was used to obtain the similarity value of each
pair of sentences.
- Since both _Spacy_ and most of the libraries to calculate sentence similarity only work in the English language, part of the algorithm consisted in additionally selecting the pair of sentences from the original corpus in English.
Finally, the final training corpus for RoBERTa is defined by the Spanish text and the similarity score.
- Each pair of sentences in Spanish and the similarity value separated by the character "|", are saved as entries of the new corpus.
The training of RoBERTa-large-bne + CelebA, using the present corpus was developed, resulting in the new model [RoBERTa-celebA-Sp](https://huggingface.co/oeg/RoBERTa-CelebA-Sp/blob).
## Corpus Fields
Each corpus entry is composed of:
- Sentence A: Descriptive sentence of a CelebA face in Spanish.
- Sentence B: Descriptive sentence of a CelebA face in Spanish.
- Similarity Value: Similarity of sentence A and sentence B.
Each component is separated by the character "|" with the structure:
```
SentenceA | Sentence B | similarity value
```
You can download the file with a _.txt_ or _.csv_ extension as appropriate.
## Citation information
**Citing**: If you used CelebA_RoBERTa_Sp corpus in your work, please cite the paper publish in **[Information Processing and Management](https://doi.org/10.1016/j.ipm.2024.103667)**:
```bib
@article{YAURILOZANO2024103667,
title = {Generative Adversarial Networks for text-to-face synthesis & generation: A quantitative–qualitative analysis of Natural Language Processing encoders for Spanish},
journal = {Information Processing & Management},
volume = {61},
number = {3},
pages = {103667},
year = {2024},
issn = {0306-4573},
doi = {https://doi.org/10.1016/j.ipm.2024.103667},
url = {https://www.sciencedirect.com/science/article/pii/S030645732400027X},
author = {Eduardo Yauri-Lozano and Manuel Castillo-Cara and Luis Orozco-Barbosa and Raúl García-Castro}
}
```
## License
This corpus is available under the **[Apache License 2.0](https://github.com/manwestc/TINTO/blob/main/LICENSE)**.
## Autors
- [Eduardo Yauri Lozano](https://github.com/eduar03yauri)
- [Manuel Castillo-Cara](https://github.com/manwestc)
- [Raúl García-Castro](https://github.com/rgcmme)
[*Universidad Nacional de Ingeniería*](https://www.uni.edu.pe/), [*Ontology Engineering Group*](https://oeg.fi.upm.es/), [*Universidad Politécnica de Madrid.*](https://www.upm.es/internacional)
## Contributors
See the full list of contributors [here](https://github.com/eduar03yauri/DCGAN-text2face-forSpanish).
<kbd><img src="https://www.uni.edu.pe/images/logos/logo_uni_2016.png" alt="Universidad Politécnica de Madrid" width="100"></kbd>
<kbd><img src="https://raw.githubusercontent.com/oeg-upm/TINTO/main/assets/logo-oeg.png" alt="Ontology Engineering Group" width="100"></kbd>
<kbd><img src="https://raw.githubusercontent.com/oeg-upm/TINTO/main/assets/logo-upm.png" alt="Universidad Politécnica de Madrid" width="100"></kbd> |
CyberHarem/karlsruhe_azurlane | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of karlsruhe/カールスルーエ/卡尔斯鲁厄 (Azur Lane)
This is the dataset of karlsruhe/カールスルーエ/卡尔斯鲁厄 (Azur Lane), containing 14 images and their tags.
The core tags of this character are `bangs, blue_eyes, breasts, short_hair, hat, white_hair, black_headwear, medium_breasts, beret, small_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 | 14 | 11.42 MiB | [Download](https://huggingface.co/datasets/CyberHarem/karlsruhe_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 14 | 7.97 MiB | [Download](https://huggingface.co/datasets/CyberHarem/karlsruhe_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 28 | 14.13 MiB | [Download](https://huggingface.co/datasets/CyberHarem/karlsruhe_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 14 | 10.98 MiB | [Download](https://huggingface.co/datasets/CyberHarem/karlsruhe_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 28 | 17.84 MiB | [Download](https://huggingface.co/datasets/CyberHarem/karlsruhe_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/karlsruhe_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 14 |  |  |  |  |  | 1girl, solo, looking_at_viewer, short_sleeves, simple_background, cleavage, open_mouth, smile, standing, white_background, short_dress, black_thighhighs, blush, open_jacket, red_jacket, buttons, closed_mouth, cropped_jacket, gloves, military_uniform, teeth |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | looking_at_viewer | short_sleeves | simple_background | cleavage | open_mouth | smile | standing | white_background | short_dress | black_thighhighs | blush | open_jacket | red_jacket | buttons | closed_mouth | cropped_jacket | gloves | military_uniform | teeth |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------|:----------------|:--------------------|:-----------|:-------------|:--------|:-----------|:-------------------|:--------------|:-------------------|:--------|:--------------|:-------------|:----------|:---------------|:-----------------|:---------|:-------------------|:--------|
| 0 | 14 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
KauaTheFox/Whiter_VOZ | ---
license: openrail
---
|
12Manman12/entobcltrnsltn | ---
license: unknown
language:
- en
--- |
CyberHarem/ereshkigal_fgo | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of ereshkigal/エレシュキガル/埃列什基伽勒 (Fate/Grand Order)
This is the dataset of ereshkigal/エレシュキガル/埃列什基伽勒 (Fate/Grand Order), containing 500 images and their tags.
The core tags of this character are `long_hair, blonde_hair, parted_bangs, two_side_up, red_eyes, ribbon, earrings, hair_ribbon, breasts, very_long_hair, bow, red_ribbon, medium_breasts, hair_bow, red_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 | 500 | 936.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ereshkigal_fgo/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 500 | 805.25 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ereshkigal_fgo/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1322 | 1.54 GiB | [Download](https://huggingface.co/datasets/CyberHarem/ereshkigal_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/ereshkigal_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 | 12 |  |  |  |  |  | 1girl, jewelry, red_cape, solo, tiara, closed_mouth, skull, blush, looking_at_viewer, spine, simple_background, white_background, hood, upper_body, black_dress, detached_collar, cloak, smile |
| 1 | 10 |  |  |  |  |  | 1girl, holding_weapon, looking_at_viewer, red_cloak, single_sleeve, skull, solo, spine, tiara, two-sided_fabric, asymmetrical_legwear, asymmetrical_sleeves, black_thighhighs, floating_hair, gold_trim, single_thighhigh, two-sided_cloak, hooded_cloak, black_dress, black_leotard, closed_mouth, birdcage, red_cape, smile, hoop_earrings, nail_polish, blush, hood_down, long_sleeves |
| 2 | 12 |  |  |  |  |  | 1girl, black_dress, jewelry, layered_sleeves, long_sleeves, solo, tiara, holding, infinity_symbol, skull_ornament, birdcage, floating_hair, fur-trimmed_sleeves, looking_at_viewer, looking_to_the_side, chain, closed_mouth, light_particles, two-sided_fabric, wide_sleeves, black_pantyhose, gold, hand_up, smile |
| 3 | 16 |  |  |  |  |  | 1girl, black_dress, chain, jewelry, layered_sleeves, long_sleeves, single_leg_pantyhose, skull_ornament, solo, tiara, fur-trimmed_sleeves, birdcage, black_pantyhose, black_socks, infinity_symbol, two-sided_fabric, full_body, gold, holding, looking_at_viewer, single_thighhigh, uneven_legwear, floating_hair, single_sock, spirit, closed_mouth, black_thighhighs, blue_fire, looking_to_the_side, petals, hand_up, parted_lips, standing, flower, weapon |
| 4 | 17 |  |  |  |  |  | 1girl, brown_scarf, long_sleeves, red_coat, alternate_costume, plaid_scarf, solo, looking_at_viewer, tiara, blush, duffel_coat, plaid_skirt, pleated_skirt, grey_skirt, black_pantyhose, miniskirt, coffee_cup, holding_cup, outdoors, smile, jacket, open_mouth, standing |
| 5 | 15 |  |  |  |  |  | 1girl, looking_at_viewer, solo, cleavage, jewelry, blush, navel, beach, collarbone, outdoors, bare_shoulders, black_bikini, ocean, cloud, day, open_mouth, smile, tiara, water, blue_sky, closed_mouth, large_breasts, sitting, thighs |
| 6 | 9 |  |  |  |  |  | 1girl, bare_shoulders, blush, red_dress, sleeveless_dress, solo, bracelet, gift_box, infinity_symbol, looking_at_viewer, white_bow, official_alternate_costume, valentine, black_pantyhose, cross_necklace, holding_gift, shawl, hands_up, open_mouth, white_ribbon |
| 7 | 6 |  |  |  |  |  | 1girl, bare_shoulders, detached_collar, fake_animal_ears, playboy_bunny, rabbit_ears, solo, strapless_leotard, wrist_cuffs, blush, looking_at_viewer, black_pantyhose, cleavage, rabbit_tail, bowtie, collarbone, fishnet_pantyhose, sitting |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | jewelry | red_cape | solo | tiara | closed_mouth | skull | blush | looking_at_viewer | spine | simple_background | white_background | hood | upper_body | black_dress | detached_collar | cloak | smile | holding_weapon | red_cloak | single_sleeve | two-sided_fabric | asymmetrical_legwear | asymmetrical_sleeves | black_thighhighs | floating_hair | gold_trim | single_thighhigh | two-sided_cloak | hooded_cloak | black_leotard | birdcage | hoop_earrings | nail_polish | hood_down | long_sleeves | layered_sleeves | holding | infinity_symbol | skull_ornament | fur-trimmed_sleeves | looking_to_the_side | chain | light_particles | wide_sleeves | black_pantyhose | gold | hand_up | single_leg_pantyhose | black_socks | full_body | uneven_legwear | single_sock | spirit | blue_fire | petals | parted_lips | standing | flower | weapon | brown_scarf | red_coat | alternate_costume | plaid_scarf | duffel_coat | plaid_skirt | pleated_skirt | grey_skirt | miniskirt | coffee_cup | holding_cup | outdoors | jacket | open_mouth | cleavage | navel | beach | collarbone | bare_shoulders | black_bikini | ocean | cloud | day | water | blue_sky | large_breasts | sitting | thighs | red_dress | sleeveless_dress | bracelet | gift_box | white_bow | official_alternate_costume | valentine | cross_necklace | holding_gift | shawl | hands_up | white_ribbon | fake_animal_ears | playboy_bunny | rabbit_ears | strapless_leotard | wrist_cuffs | rabbit_tail | bowtie | fishnet_pantyhose |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:----------|:-----------|:-------|:--------|:---------------|:--------|:--------|:--------------------|:--------|:--------------------|:-------------------|:-------|:-------------|:--------------|:------------------|:--------|:--------|:-----------------|:------------|:----------------|:-------------------|:-----------------------|:-----------------------|:-------------------|:----------------|:------------|:-------------------|:------------------|:---------------|:----------------|:-----------|:----------------|:--------------|:------------|:---------------|:------------------|:----------|:------------------|:-----------------|:----------------------|:----------------------|:--------|:------------------|:---------------|:------------------|:-------|:----------|:-----------------------|:--------------|:------------|:-----------------|:--------------|:---------|:------------|:---------|:--------------|:-----------|:---------|:---------|:--------------|:-----------|:--------------------|:--------------|:--------------|:--------------|:----------------|:-------------|:------------|:-------------|:--------------|:-----------|:---------|:-------------|:-----------|:--------|:--------|:-------------|:-----------------|:---------------|:--------|:--------|:------|:--------|:-----------|:----------------|:----------|:---------|:------------|:-------------------|:-----------|:-----------|:------------|:-----------------------------|:------------|:-----------------|:---------------|:--------|:-----------|:---------------|:-------------------|:----------------|:--------------|:--------------------|:--------------|:--------------|:---------|:--------------------|
| 0 | 12 |  |  |  |  |  | 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 | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 16 |  |  |  |  |  | X | X | | X | X | X | | | X | | | | | | X | | | | | | | X | | | X | X | | X | | | | X | | | | X | X | X | X | X | X | X | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 17 |  |  |  |  |  | X | | | X | X | | | X | X | | | | | | | | | X | | | | | | | | | | | | | | | | | | X | | | | | | | | | | X | | | | | | | | | | | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 15 |  |  |  |  |  | X | X | | X | X | X | | X | X | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | |
| 6 | 9 |  |  |  |  |  | X | | | X | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | X | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | |
| 7 | 6 |  |  |  |  |  | X | | | X | | | | X | X | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | X | X | | | | | | | | X | | | | | | | | | | | | | | X | X | X | X | X | X | X | X |
|
shaggysus/movieAudio | ---
license: cc-by-4.0
task_categories:
- automatic-speech-recognition
language:
- en
--- |
GlobalCampus/openalex-multilingual-embeddings | ---
dataset_info:
features:
- name: id
dtype: string
- name: embedding
sequence: float64
splits:
- name: train
num_bytes: 751739666430
num_examples: 243212198
download_size: 640572858900
dataset_size: 751739666430
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: cc0-1.0
tags:
- openalex
- embeddings
pretty_name: OpenAlex Mutilingual Embeddings
source_dataset:
- openalex
---
# OpenAlex Multilingual Embeddings
This dataset contains multilingual text embeddings of all records in [OpenAlex](https://openalex.org/) with a title or an abstract from the snapshot of 2023-10-20.
The dataset was created for the [FORAS project](https://asreview.nl/project/foras/) to investigate the efficacy of
different methods of searching in databases of academic publications. All scripts will be available in a [GitHub repository](https://github.com/IDfuse/foras).
The project is supported by a grant from the Dutch Research Council (grant no. 406.22.GO.048)
## Description of the data
- The dataset has two columns, `id` and `embedding`. The `id` columns contains the OpenAlex identifier of the record. The `embedding` column contains the text embedding,
which is a vector of 384 floats.
- The multilingual embedding model [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) was used to generate the embeddings. For every
with a title or abstract we generated an embedding of `'query: '` + `title` + `' '` + `abstract`. The model has a maximum token input length of 512 tokens. |
arthurmluz/cstnews_data-wiki_temario_results | ---
dataset_info:
features:
- name: id
dtype: string
- name: text
dtype: string
- name: summary
dtype: string
- name: gen_summary
dtype: string
- name: rouge
struct:
- name: rouge1
dtype: float64
- name: rouge2
dtype: float64
- name: rougeL
dtype: float64
- name: rougeLsum
dtype: float64
- name: bert
struct:
- name: f1
sequence: float64
- name: hashcode
dtype: string
- name: precision
sequence: float64
- name: recall
sequence: float64
- name: moverScore
dtype: float64
splits:
- name: validation
num_bytes: 61374
num_examples: 16
download_size: 58330
dataset_size: 61374
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
# Dataset Card for "cstnews_data-wiki_temario_results"
rouge= {'rouge1': 0.5525303131963668, 'rouge2': 0.3319831382549481, 'rougeL': 0.40901822666642607, 'rougeLsum': 0.40901822666642607}
bert= {'precision': 0.782619547098875, 'recall': 0.8086873143911362, 'f1': 0.7947092242538929}
mover = 0.6412309719553281 |
DaniFrame/HSOLPerturbed | ---
dataset_info:
features:
- name: count
dtype: int64
- name: hate_speech_count
dtype: int64
- name: offensive_language_count
dtype: int64
- name: neither_count
dtype: int64
- name: class
dtype:
class_label:
names:
'0': hate speech
'1': offensive language
'2': neither
- name: tweet
dtype: string
splits:
- name: hsol_perturbed_keyboard_0.01
num_bytes: 651089
num_examples: 4957
- name: hsol_perturbed_keyboard_0.05
num_bytes: 651333
num_examples: 4957
- name: hsol_perturbed_keyboard_0.1
num_bytes: 651720
num_examples: 4957
- name: hsol_perturbed_ocr_0.01
num_bytes: 651029
num_examples: 4957
- name: hsol_perturbed_ocr_0.05
num_bytes: 651047
num_examples: 4957
- name: hsol_perturbed_ocr_0.1
num_bytes: 651059
num_examples: 4957
- name: hsol_perturbed_spellingerror_0.01
num_bytes: 652461
num_examples: 4957
- name: hsol_perturbed_spellingerror_0.05
num_bytes: 656504
num_examples: 4957
- name: hsol_perturbed_spellingerror_0.1
num_bytes: 661760
num_examples: 4957
- name: hsol_perturbed_typos_0.01
num_bytes: 651173
num_examples: 4957
- name: hsol_perturbed_typos_0.05
num_bytes: 651752
num_examples: 4957
- name: hsol_perturbed_typos_0.1
num_bytes: 652435
num_examples: 4957
- name: hsol_perturbed_sne_0.1
num_bytes: 650990
num_examples: 4957
- name: hsol_perturbed_sne_0.2
num_bytes: 650690
num_examples: 4957
- name: hsol_perturbed_sne_0.3
num_bytes: 651339
num_examples: 4957
- name: hsol_perturbed_sswn_0.1
num_bytes: 661571
num_examples: 4957
- name: hsol_perturbed_sswn_0.2
num_bytes: 672414
num_examples: 4957
- name: hsol_perturbed_sswn_0.3
num_bytes: 684467
num_examples: 4957
- name: hsol_perturbed_contraction
num_bytes: 648114
num_examples: 4957
- name: hsol_perturbed_insertadv
num_bytes: 764862
num_examples: 4957
- name: hsol_perturbed_prejudice
num_bytes: 645247
num_examples: 4957
- name: hsol_perturbed_punctuation
num_bytes: 679299
num_examples: 4957
- name: hsol_perturbed_reverseneg
num_bytes: 665446
num_examples: 4957
- name: hsol_perturbed_swapnum
num_bytes: 646336
num_examples: 4957
- name: hsol_perturbed_verbtense
num_bytes: 654617
num_examples: 4957
- name: hsol_perturbed_twitter
num_bytes: 719030
num_examples: 4957
- name: hsol_perturbed_wordcase
num_bytes: 645191
num_examples: 4957
download_size: 6331984
dataset_size: 17872975
---
# Dataset Card for "HSOLPerturbed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_Gryphe__MythoMix-L2-13b | ---
pretty_name: Evaluation run of Gryphe/MythoMix-L2-13b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Gryphe/MythoMix-L2-13b](https://huggingface.co/Gryphe/MythoMix-L2-13b) on the\
\ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Gryphe__MythoMix-L2-13b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-22T22:23:08.063250](https://huggingface.co/datasets/open-llm-leaderboard/details_Gryphe__MythoMix-L2-13b/blob/main/results_2023-09-22T22-23-08.063250.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.12804110738255034,\n\
\ \"em_stderr\": 0.0034218610287585043,\n \"f1\": 0.19858850671140846,\n\
\ \"f1_stderr\": 0.0035721276185422235,\n \"acc\": 0.42692797214890377,\n\
\ \"acc_stderr\": 0.01016682217493381\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.12804110738255034,\n \"em_stderr\": 0.0034218610287585043,\n\
\ \"f1\": 0.19858850671140846,\n \"f1_stderr\": 0.0035721276185422235\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.09931766489764973,\n \
\ \"acc_stderr\": 0.008238371412683973\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7545382794001578,\n \"acc_stderr\": 0.012095272937183644\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Gryphe/MythoMix-L2-13b
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_09T21_38_13.191902
path:
- '**/details_harness|arc:challenge|25_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_22T22_23_08.063250
path:
- '**/details_harness|drop|3_2023-09-22T22-23-08.063250.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-22T22-23-08.063250.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_22T22_23_08.063250
path:
- '**/details_harness|gsm8k|5_2023-09-22T22-23-08.063250.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-22T22-23-08.063250.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hellaswag|10_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-09T21:38:13.191902.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-09T21:38:13.191902.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-09T21:38:13.191902.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_22T22_23_08.063250
path:
- '**/details_harness|winogrande|5_2023-09-22T22-23-08.063250.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-22T22-23-08.063250.parquet'
- config_name: results
data_files:
- split: 2023_08_09T21_38_13.191902
path:
- results_2023-08-09T21:38:13.191902.parquet
- split: 2023_09_22T22_23_08.063250
path:
- results_2023-09-22T22-23-08.063250.parquet
- split: latest
path:
- results_2023-09-22T22-23-08.063250.parquet
---
# Dataset Card for Evaluation run of Gryphe/MythoMix-L2-13b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Gryphe/MythoMix-L2-13b
- **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 [Gryphe/MythoMix-L2-13b](https://huggingface.co/Gryphe/MythoMix-L2-13b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Gryphe__MythoMix-L2-13b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-22T22:23:08.063250](https://huggingface.co/datasets/open-llm-leaderboard/details_Gryphe__MythoMix-L2-13b/blob/main/results_2023-09-22T22-23-08.063250.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.12804110738255034,
"em_stderr": 0.0034218610287585043,
"f1": 0.19858850671140846,
"f1_stderr": 0.0035721276185422235,
"acc": 0.42692797214890377,
"acc_stderr": 0.01016682217493381
},
"harness|drop|3": {
"em": 0.12804110738255034,
"em_stderr": 0.0034218610287585043,
"f1": 0.19858850671140846,
"f1_stderr": 0.0035721276185422235
},
"harness|gsm8k|5": {
"acc": 0.09931766489764973,
"acc_stderr": 0.008238371412683973
},
"harness|winogrande|5": {
"acc": 0.7545382794001578,
"acc_stderr": 0.012095272937183644
}
}
```
### 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] |
vwxyzjn/openhermes-dev__mistralai_Mixtral-8x7B-Instruct-v0.1__1706903049 | ---
dataset_info:
features:
- name: model
dtype: 'null'
- name: category
dtype: string
- name: language
dtype: string
- name: custom_instruction
dtype: bool
- name: id
dtype: string
- name: topic
dtype: string
- name: avatarUrl
dtype: 'null'
- name: idx
dtype: 'null'
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: weight
dtype: 'null'
- name: system_prompt
dtype: string
- name: source
dtype: string
- name: model_name
dtype: string
- name: skip_prompt_formatting
dtype: bool
- name: title
dtype: string
- name: hash
dtype: 'null'
- name: views
dtype: 'null'
- name: prompt
dtype: string
- name: chosen_policy
dtype: string
- name: chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: token_length
dtype: int64
- name: rejected
list:
- name: content
dtype: string
- name: role
dtype: string
- name: rejected_policy
dtype: string
splits:
- name: train_prefs
num_bytes: 1421806
num_examples: 159
- name: test_prefs
num_bytes: 31623
num_examples: 8
download_size: 875929
dataset_size: 1453429
configs:
- config_name: default
data_files:
- split: train_prefs
path: data/train_prefs-*
- split: test_prefs
path: data/test_prefs-*
---
|
sayannath/forest-data-with-caption | ---
license: apache-2.0
---
|
pinecone/core-2020-05-10-deduplication | ---
annotations_creators:
- unknown
language_creators:
- unknown
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- unknown
task_categories:
- other
task_ids:
- natural-language-inference
- semantic-similarity-scoring
- text-scoring
pretty_name: CORE Deduplication of Scholarly Documents
tags:
- deduplication
---
# Dataset Card for CORE Deduplication
## Dataset Description
- **Homepage:** [https://core.ac.uk/about/research-outputs](https://core.ac.uk/about/research-outputs)
- **Repository:** [https://core.ac.uk/datasets/core_2020-05-10_deduplication.zip](https://core.ac.uk/datasets/core_2020-05-10_deduplication.zip)
- **Paper:** [Deduplication of Scholarly Documents using Locality Sensitive Hashing and Word Embeddings](http://oro.open.ac.uk/id/eprint/70519)
- **Point of Contact:** [CORE Team](https://core.ac.uk/about#contact)
- **Size of downloaded dataset files:** 204 MB
### Dataset Summary
CORE 2020 Deduplication dataset (https://core.ac.uk/documentation/dataset) contains 100K scholarly documents labeled as duplicates/non-duplicates.
### Languages
The dataset language is English (BCP-47 `en`)
### Citation Information
```
@inproceedings{dedup2020,
title={Deduplication of Scholarly Documents using Locality Sensitive Hashing and Word Embeddings},
author={Gyawali, Bikash and Anastasiou, Lucas and Knoth, Petr},
booktitle = {Proceedings of 12th Language Resources and Evaluation Conference},
month = may,
year = 2020,
publisher = {France European Language Resources Association},
pages = {894-903}
}
```
|
hz244/cat_test_0 | ---
license: apache-2.0
---
|
lilacai/lilac-mosaic-instruct-v3 | ---
tags:
- Lilac
---
# lilac/mosaic-instruct-v3
This dataset is a [Lilac](http://lilacml.com) processed dataset. Original dataset: [https://huggingface.co/datasets/mosaicml/instruct-v3](https://huggingface.co/datasets/mosaicml/instruct-v3)
To download the dataset to a local directory:
```bash
lilac download lilacai/lilac-mosaic-instruct-v3
```
or from python with:
```py
ll.download("lilacai/lilac-mosaic-instruct-v3")
```
|
lmms-lab/MP-DocVQA | ---
dataset_info:
features:
- name: questionId
dtype: string
- name: question
dtype: string
- name: doc_id
dtype: string
- name: page_ids
dtype: string
- name: answers
dtype: string
- name: answer_page_idx
dtype: string
- name: data_split
dtype: string
- name: image_1
dtype: image
- name: image_2
dtype: image
- name: image_3
dtype: image
- name: image_4
dtype: image
- name: image_5
dtype: image
- name: image_6
dtype: image
- name: image_7
dtype: image
- name: image_8
dtype: image
- name: image_9
dtype: image
- name: image_10
dtype: image
- name: image_11
dtype: image
- name: image_12
dtype: image
- name: image_13
dtype: image
- name: image_14
dtype: image
- name: image_15
dtype: image
- name: image_16
dtype: image
- name: image_17
dtype: image
- name: image_18
dtype: image
- name: image_19
dtype: image
- name: image_20
dtype: image
splits:
- name: val
num_bytes: 14398036594.615
num_examples: 5187
- name: test
num_bytes: 11100541695.151
num_examples: 5019
download_size: 8574046936
dataset_size: 25498578289.766
configs:
- config_name: default
data_files:
- split: val
path: data/val-*
- split: test
path: data/test-*
---
|
CyberHarem/kongo_azurlane | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of kongo/金剛/金刚 (Azur Lane)
This is the dataset of kongo/金剛/金刚 (Azur Lane), containing 98 images and their tags.
The core tags of this character are `blonde_hair, long_hair, breasts, blue_eyes, braid, bangs, large_breasts, hat, horns, 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 | 98 | 133.93 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kongo_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 98 | 77.65 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kongo_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 240 | 162.51 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kongo_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 98 | 118.97 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kongo_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 240 | 223.68 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kongo_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/kongo_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 6 |  |  |  |  |  | 1girl, black_cape, fake_horns, garter_straps, looking_at_viewer, pleated_skirt, sheathed, solo, sword, white_gloves, white_skirt, framed_breasts, long_sleeves, smile, black_thighhighs, blush, closed_mouth, cowboy_shot, jacket, simple_background, uniform, white_ascot, character_name, hand_on_hip, medium_breasts, one_eye_closed, sidelocks, white_background |
| 1 | 11 |  |  |  |  |  | 1girl, school_uniform, solo, black_thighhighs, hairclip, looking_at_viewer, pleated_skirt, cardigan, white_shirt, blue_skirt, headphones, red_bowtie, cleavage, collared_shirt, long_sleeves, official_alternate_costume, smile, blush, collarbone, sitting, open_mouth, panties, school_bag, shoes, simple_background |
| 2 | 8 |  |  |  |  |  | 1girl, looking_at_viewer, solo, pink_kimono, smile, wide_sleeves, blush, bow, cherry_blossoms, hair_flower, obi, white_gloves, folding_fan, hakama_skirt, petals, closed_mouth, hair_bun, long_sleeves, oil-paper_umbrella, side_ponytail, standing, holding_fan, holding_umbrella, pink_flower |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_cape | fake_horns | garter_straps | looking_at_viewer | pleated_skirt | sheathed | solo | sword | white_gloves | white_skirt | framed_breasts | long_sleeves | smile | black_thighhighs | blush | closed_mouth | cowboy_shot | jacket | simple_background | uniform | white_ascot | character_name | hand_on_hip | medium_breasts | one_eye_closed | sidelocks | white_background | school_uniform | hairclip | cardigan | white_shirt | blue_skirt | headphones | red_bowtie | cleavage | collared_shirt | official_alternate_costume | collarbone | sitting | open_mouth | panties | school_bag | shoes | pink_kimono | wide_sleeves | bow | cherry_blossoms | hair_flower | obi | folding_fan | hakama_skirt | petals | hair_bun | oil-paper_umbrella | side_ponytail | standing | holding_fan | holding_umbrella | pink_flower |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------------|:-------------|:----------------|:--------------------|:----------------|:-----------|:-------|:--------|:---------------|:--------------|:-----------------|:---------------|:--------|:-------------------|:--------|:---------------|:--------------|:---------|:--------------------|:----------|:--------------|:-----------------|:--------------|:-----------------|:-----------------|:------------|:-------------------|:-----------------|:-----------|:-----------|:--------------|:-------------|:-------------|:-------------|:-----------|:-----------------|:-----------------------------|:-------------|:----------|:-------------|:----------|:-------------|:--------|:--------------|:---------------|:------|:------------------|:--------------|:------|:--------------|:---------------|:---------|:-----------|:---------------------|:----------------|:-----------|:--------------|:-------------------|:--------------|
| 0 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 11 |  |  |  |  |  | X | | | | X | X | | X | | | | | X | X | X | X | | | | X | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | |
| 2 | 8 |  |  |  |  |  | X | | | | X | | | X | | X | | | X | X | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
tunaerturk/KayzerTurkishReviews-ds-mini | ---
dataset_info:
features:
- name: review
dtype: string
- name: review_length
dtype: int64
splits:
- name: train
num_bytes: 1252876.2642514652
num_examples: 3378
- name: validation
num_bytes: 139455.7357485349
num_examples: 376
download_size: 895863
dataset_size: 1392332.0
---
# Dataset Card for "KayzerTurkishReviews-ds-mini"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
antoniopagnotts/block_world_problem_llama_dataset_v3_1k | ---
license: mit
---
|
AlienKevin/source_han_sans_ja_regular_left_right | ---
license: cc0-1.0
---
|
dim/raw_bugurts_5k | ---
license: mit
dataset_info:
features:
- name: bugurt
dtype: string
splits:
- name: train
num_bytes: 4987896.531100478
num_examples: 5000
download_size: 2606633
dataset_size: 4987896.531100478
---
|
Admin0805/deepnet | ---
license: other
license_name: citibankdemobusiness
license_link: https://github.com/CitibankDemoBusiness/billiondollars/blob/git/LICENSE
---
|
TheGreatRambler/mm2_level | ---
language:
- multilingual
license:
- cc-by-nc-sa-4.0
multilinguality:
- multilingual
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- other
- object-detection
- text-retrieval
- token-classification
- text-generation
task_ids: []
pretty_name: Mario Maker 2 levels
tags:
- text-mining
---
# Mario Maker 2 levels
Part of the [Mario Maker 2 Dataset Collection](https://tgrcode.com/posts/mario_maker_2_datasets)
## Dataset Description
The Mario Maker 2 levels dataset consists of 26.6 million levels from Nintendo's online service totaling around 100GB of data. The dataset was created using the self-hosted [Mario Maker 2 api](https://tgrcode.com/posts/mario_maker_2_api) over the course of 1 month in February 2022.
### How to use it
The Mario Maker 2 levels dataset is a very large dataset so for most use cases it is recommended to make use of the streaming API of `datasets`. You can load and iterate through the dataset with the following code:
```python
from datasets import load_dataset
ds = load_dataset("TheGreatRambler/mm2_level", streaming=True, split="train")
print(next(iter(ds)))
#OUTPUT:
{
'data_id': 3000004,
'name': 'カベキック',
'description': 'カベキックをとにかくするコースです。',
'uploaded': 1561644329,
'created': 1561674240,
'gamestyle': 4,
'theme': 0,
'difficulty': 0,
'tag1': 7,
'tag2': 10,
'game_version': 1,
'world_record': 8049,
'upload_time': 193540,
'upload_attempts': 1,
'num_comments': 60,
'clear_condition': 0,
'clear_condition_magnitude': 0,
'timer': 300,
'autoscroll_speed': 0,
'clears': 1646,
'attempts': 3168,
'clear_rate': 51.957070707070706,
'plays': 1704,
'versus_matches': 80,
'coop_matches': 27,
'likes': 152,
'boos': 118,
'unique_players_and_versus': 1391,
'weekly_likes': 0,
'weekly_plays': 1,
'uploader_pid': '5218390885570355093',
'first_completer_pid': '16824392528839047213',
'record_holder_pid': '5411258160547085075',
'level_data': [some binary data],
'unk2': 0,
'unk3': [some binary data],
'unk9': 3,
'unk10': 4,
'unk11': 1,
'unk12': 1
}
```
Level data is a binary blob describing the actual level and is equivalent to the level format Nintendo uses in-game. It is gzip compressed and needs to be decompressed to be read. To read it you only need to use the provided `level.ksy` kaitai struct file and install the kaitai struct runtime to parse it into an object:
```python
from datasets import load_dataset
from kaitaistruct import KaitaiStream
from io import BytesIO
from level import Level
import zlib
ds = load_dataset("TheGreatRambler/mm2_level", streaming=True, split="train")
level_data = next(iter(ds))["level_data"]
level = Level(KaitaiStream(BytesIO(zlib.decompress(level_data))))
# NOTE level.overworld.objects is a fixed size (limitation of Kaitai struct)
# must iterate by object_count or null objects will be included
for i in range(level.overworld.object_count):
obj = level.overworld.objects[i]
print("X: %d Y: %d ID: %s" % (obj.x, obj.y, obj.id))
#OUTPUT:
X: 1200 Y: 400 ID: ObjId.block
X: 1360 Y: 400 ID: ObjId.block
X: 1360 Y: 240 ID: ObjId.block
X: 1520 Y: 240 ID: ObjId.block
X: 1680 Y: 240 ID: ObjId.block
X: 1680 Y: 400 ID: ObjId.block
X: 1840 Y: 400 ID: ObjId.block
X: 2000 Y: 400 ID: ObjId.block
X: 2160 Y: 400 ID: ObjId.block
X: 2320 Y: 400 ID: ObjId.block
X: 2480 Y: 560 ID: ObjId.block
X: 2480 Y: 720 ID: ObjId.block
X: 2480 Y: 880 ID: ObjId.block
X: 2160 Y: 880 ID: ObjId.block
```
Rendering the level data into an image can be done using [Toost](https://github.com/TheGreatRambler/toost) if desired.
You can also download the full dataset. Note that this will download ~100GB:
```python
ds = load_dataset("TheGreatRambler/mm2_level", split="train")
```
## Data Structure
### Data Instances
```python
{
'data_id': 3000004,
'name': 'カベキック',
'description': 'カベキックをとにかくするコースです。',
'uploaded': 1561644329,
'created': 1561674240,
'gamestyle': 4,
'theme': 0,
'difficulty': 0,
'tag1': 7,
'tag2': 10,
'game_version': 1,
'world_record': 8049,
'upload_time': 193540,
'upload_attempts': 1,
'num_comments': 60,
'clear_condition': 0,
'clear_condition_magnitude': 0,
'timer': 300,
'autoscroll_speed': 0,
'clears': 1646,
'attempts': 3168,
'clear_rate': 51.957070707070706,
'plays': 1704,
'versus_matches': 80,
'coop_matches': 27,
'likes': 152,
'boos': 118,
'unique_players_and_versus': 1391,
'weekly_likes': 0,
'weekly_plays': 1,
'uploader_pid': '5218390885570355093',
'first_completer_pid': '16824392528839047213',
'record_holder_pid': '5411258160547085075',
'level_data': [some binary data],
'unk2': 0,
'unk3': [some binary data],
'unk9': 3,
'unk10': 4,
'unk11': 1,
'unk12': 1
}
```
### Data Fields
|Field|Type|Description|
|---|---|---|
|data_id|int|Data IDs are unique identifiers, gaps in the table are due to levels deleted by users or Nintendo|
|name|string|Course name|
|description|string|Course description|
|uploaded|int|UTC timestamp for when the level was uploaded|
|created|int|Local timestamp for when the level was created|
|gamestyle|int|Gamestyle, enum below|
|theme|int|Theme, enum below|
|difficulty|int|Difficulty, enum below|
|tag1|int|The first tag, if it exists, enum below|
|tag2|int|The second tag, if it exists, enum below|
|game_version|int|The version of the game this level was made on|
|world_record|int|The world record in milliseconds|
|upload_time|int|The upload time in milliseconds|
|upload_attempts|int|The number of attempts it took the uploader to upload|
|num_comments|int|Number of comments, may not reflect the archived comments if there were more than 1000 comments|
|clear_condition|int|Clear condition, enum below|
|clear_condition_magnitude|int|If applicable, the magnitude of the clear condition|
|timer|int|The timer of the level|
|autoscroll_speed|int|A unit of how fast the configured autoscroll speed is for the level|
|clears|int|Course clears|
|attempts|int|Course attempts|
|clear_rate|float|Course clear rate as a float between 0 and 1|
|plays|int|Course plays, or "footprints"|
|versus_matches|int|Course versus matches|
|coop_matches|int|Course coop matches|
|likes|int|Course likes|
|boos|int|Course boos|
|unique_players_and_versus|int|All unique players that have ever played this level, including the number of versus matches|
|weekly_likes|int|The weekly likes on this course|
|weekly_plays|int|The weekly plays on this course|
|uploader_pid|string|The player ID of the uploader|
|first_completer_pid|string|The player ID of the user who first cleared this course|
|record_holder_pid|string|The player ID of the user who held the world record at time of archival |
|level_data|bytes|The GZIP compressed decrypted level data, kaitai struct file is provided for reading|
|unk2|int|Unknown|
|unk3|bytes|Unknown|
|unk9|int|Unknown|
|unk10|int|Unknown|
|unk11|int|Unknown|
|unk12|int|Unknown|
### Data Splits
The dataset only contains a train split.
## Enums
The dataset contains some enum integer fields. This can be used to convert back to their string equivalents:
```python
GameStyles = {
0: "SMB1",
1: "SMB3",
2: "SMW",
3: "NSMBU",
4: "SM3DW"
}
Difficulties = {
0: "Easy",
1: "Normal",
2: "Expert",
3: "Super expert"
}
CourseThemes = {
0: "Overworld",
1: "Underground",
2: "Castle",
3: "Airship",
4: "Underwater",
5: "Ghost house",
6: "Snow",
7: "Desert",
8: "Sky",
9: "Forest"
}
TagNames = {
0: "None",
1: "Standard",
2: "Puzzle solving",
3: "Speedrun",
4: "Autoscroll",
5: "Auto mario",
6: "Short and sweet",
7: "Multiplayer versus",
8: "Themed",
9: "Music",
10: "Art",
11: "Technical",
12: "Shooter",
13: "Boss battle",
14: "Single player",
15: "Link"
}
ClearConditions = {
137525990: "Reach the goal without landing after leaving the ground.",
199585683: "Reach the goal after defeating at least/all (n) Mechakoopa(s).",
272349836: "Reach the goal after defeating at least/all (n) Cheep Cheep(s).",
375673178: "Reach the goal without taking damage.",
426197923: "Reach the goal as Boomerang Mario.",
436833616: "Reach the goal while wearing a Shoe.",
713979835: "Reach the goal as Fire Mario.",
744927294: "Reach the goal as Frog Mario.",
751004331: "Reach the goal after defeating at least/all (n) Larry(s).",
900050759: "Reach the goal as Raccoon Mario.",
947659466: "Reach the goal after defeating at least/all (n) Blooper(s).",
976173462: "Reach the goal as Propeller Mario.",
994686866: "Reach the goal while wearing a Propeller Box.",
998904081: "Reach the goal after defeating at least/all (n) Spike(s).",
1008094897: "Reach the goal after defeating at least/all (n) Boom Boom(s).",
1051433633: "Reach the goal while holding a Koopa Shell.",
1061233896: "Reach the goal after defeating at least/all (n) Porcupuffer(s).",
1062253843: "Reach the goal after defeating at least/all (n) Charvaargh(s).",
1079889509: "Reach the goal after defeating at least/all (n) Bullet Bill(s).",
1080535886: "Reach the goal after defeating at least/all (n) Bully/Bullies.",
1151250770: "Reach the goal while wearing a Goomba Mask.",
1182464856: "Reach the goal after defeating at least/all (n) Hop-Chops.",
1219761531: "Reach the goal while holding a Red POW Block. OR Reach the goal after activating at least/all (n) Red POW Block(s).",
1221661152: "Reach the goal after defeating at least/all (n) Bob-omb(s).",
1259427138: "Reach the goal after defeating at least/all (n) Spiny/Spinies.",
1268255615: "Reach the goal after defeating at least/all (n) Bowser(s)/Meowser(s).",
1279580818: "Reach the goal after defeating at least/all (n) Ant Trooper(s).",
1283945123: "Reach the goal on a Lakitu's Cloud.",
1344044032: "Reach the goal after defeating at least/all (n) Boo(s).",
1425973877: "Reach the goal after defeating at least/all (n) Roy(s).",
1429902736: "Reach the goal while holding a Trampoline.",
1431944825: "Reach the goal after defeating at least/all (n) Morton(s).",
1446467058: "Reach the goal after defeating at least/all (n) Fish Bone(s).",
1510495760: "Reach the goal after defeating at least/all (n) Monty Mole(s).",
1656179347: "Reach the goal after picking up at least/all (n) 1-Up Mushroom(s).",
1665820273: "Reach the goal after defeating at least/all (n) Hammer Bro(s.).",
1676924210: "Reach the goal after hitting at least/all (n) P Switch(es). OR Reach the goal while holding a P Switch.",
1715960804: "Reach the goal after activating at least/all (n) POW Block(s). OR Reach the goal while holding a POW Block.",
1724036958: "Reach the goal after defeating at least/all (n) Angry Sun(s).",
1730095541: "Reach the goal after defeating at least/all (n) Pokey(s).",
1780278293: "Reach the goal as Superball Mario.",
1839897151: "Reach the goal after defeating at least/all (n) Pom Pom(s).",
1969299694: "Reach the goal after defeating at least/all (n) Peepa(s).",
2035052211: "Reach the goal after defeating at least/all (n) Lakitu(s).",
2038503215: "Reach the goal after defeating at least/all (n) Lemmy(s).",
2048033177: "Reach the goal after defeating at least/all (n) Lava Bubble(s).",
2076496776: "Reach the goal while wearing a Bullet Bill Mask.",
2089161429: "Reach the goal as Big Mario.",
2111528319: "Reach the goal as Cat Mario.",
2131209407: "Reach the goal after defeating at least/all (n) Goomba(s)/Galoomba(s).",
2139645066: "Reach the goal after defeating at least/all (n) Thwomp(s).",
2259346429: "Reach the goal after defeating at least/all (n) Iggy(s).",
2549654281: "Reach the goal while wearing a Dry Bones Shell.",
2694559007: "Reach the goal after defeating at least/all (n) Sledge Bro(s.).",
2746139466: "Reach the goal after defeating at least/all (n) Rocky Wrench(es).",
2749601092: "Reach the goal after grabbing at least/all (n) 50-Coin(s).",
2855236681: "Reach the goal as Flying Squirrel Mario.",
3036298571: "Reach the goal as Buzzy Mario.",
3074433106: "Reach the goal as Builder Mario.",
3146932243: "Reach the goal as Cape Mario.",
3174413484: "Reach the goal after defeating at least/all (n) Wendy(s).",
3206222275: "Reach the goal while wearing a Cannon Box.",
3314955857: "Reach the goal as Link.",
3342591980: "Reach the goal while you have Super Star invincibility.",
3346433512: "Reach the goal after defeating at least/all (n) Goombrat(s)/Goombud(s).",
3348058176: "Reach the goal after grabbing at least/all (n) 10-Coin(s).",
3353006607: "Reach the goal after defeating at least/all (n) Buzzy Beetle(s).",
3392229961: "Reach the goal after defeating at least/all (n) Bowser Jr.(s).",
3437308486: "Reach the goal after defeating at least/all (n) Koopa Troopa(s).",
3459144213: "Reach the goal after defeating at least/all (n) Chain Chomp(s).",
3466227835: "Reach the goal after defeating at least/all (n) Muncher(s).",
3481362698: "Reach the goal after defeating at least/all (n) Wiggler(s).",
3513732174: "Reach the goal as SMB2 Mario.",
3649647177: "Reach the goal in a Koopa Clown Car/Junior Clown Car.",
3725246406: "Reach the goal as Spiny Mario.",
3730243509: "Reach the goal in a Koopa Troopa Car.",
3748075486: "Reach the goal after defeating at least/all (n) Piranha Plant(s)/Jumping Piranha Plant(s).",
3797704544: "Reach the goal after defeating at least/all (n) Dry Bones.",
3824561269: "Reach the goal after defeating at least/all (n) Stingby/Stingbies.",
3833342952: "Reach the goal after defeating at least/all (n) Piranha Creeper(s).",
3842179831: "Reach the goal after defeating at least/all (n) Fire Piranha Plant(s).",
3874680510: "Reach the goal after breaking at least/all (n) Crates(s).",
3974581191: "Reach the goal after defeating at least/all (n) Ludwig(s).",
3977257962: "Reach the goal as Super Mario.",
4042480826: "Reach the goal after defeating at least/all (n) Skipsqueak(s).",
4116396131: "Reach the goal after grabbing at least/all (n) Coin(s).",
4117878280: "Reach the goal after defeating at least/all (n) Magikoopa(s).",
4122555074: "Reach the goal after grabbing at least/all (n) 30-Coin(s).",
4153835197: "Reach the goal as Balloon Mario.",
4172105156: "Reach the goal while wearing a Red POW Box.",
4209535561: "Reach the Goal while riding Yoshi.",
4269094462: "Reach the goal after defeating at least/all (n) Spike Top(s).",
4293354249: "Reach the goal after defeating at least/all (n) Banzai Bill(s)."
}
```
<!-- TODO create detailed statistics -->
## Dataset Creation
The dataset was created over a little more than a month in Febuary 2022 using the self hosted [Mario Maker 2 api](https://tgrcode.com/posts/mario_maker_2_api). As requests made to Nintendo's servers require authentication the process had to be done with upmost care and limiting download speed as to not overload the API and risk a ban. There are no intentions to create an updated release of this dataset.
## Considerations for Using the Data
The dataset consists of levels from many different Mario Maker 2 players globally and as such their titles and descriptions could contain harmful language. Harmful depictions could also be present in the level data, should you choose to render it.
|
Jajatheone2/Jajatheone | ---
license: apache-2.0
---
|
nateraw/airbnb-stock-price-new | ---
license:
- cc0-1.0
kaggle_id: evangower/airbnb-stock-price
---
# Dataset Card for Airbnb Stock Price
## 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://kaggle.com/datasets/evangower/airbnb-stock-price
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This contains the historical stock price of Airbnb (ticker symbol ABNB) an American company that operates an online marketplace for lodging, primarily homestays for vacation rentals, and tourism activities. Based in San Francisco, California, the platform is accessible via website and mobile app.
### 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
This dataset was shared by [@evangower](https://kaggle.com/evangower)
### Licensing Information
The license for this dataset is cc0-1.0
### Citation Information
```bibtex
[More Information Needed]
```
### Contributions
[More Information Needed] |
CyberHarem/kurumi_touhou | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of kurumi/くるみ (Touhou)
This is the dataset of kurumi/くるみ (Touhou), containing 133 images and their tags.
The core tags of this character are `blonde_hair, long_hair, wings, bow, bat_wings, yellow_eyes, ribbon, purple_wings, hair_ribbon, bangs, 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 | 133 | 117.85 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kurumi_touhou/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 133 | 78.44 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kurumi_touhou/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 239 | 144.06 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kurumi_touhou/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 133 | 108.56 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kurumi_touhou/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 239 | 187.86 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kurumi_touhou/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/kurumi_touhou',
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 | 29 |  |  |  |  |  | suspender_skirt, 1girl, long_sleeves, white_shirt, solo, red_bowtie, black_skirt, center_frills, smile, looking_at_viewer, white_ribbon, blush, frilled_skirt, demon_wings, hair_bow, white_bow, closed_mouth, open_mouth, shoes |
| 1 | 18 |  |  |  |  |  | 1girl, skirt, solo, suspenders, smile, open_mouth |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | suspender_skirt | 1girl | long_sleeves | white_shirt | solo | red_bowtie | black_skirt | center_frills | smile | looking_at_viewer | white_ribbon | blush | frilled_skirt | demon_wings | hair_bow | white_bow | closed_mouth | open_mouth | shoes | skirt | suspenders |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------|:--------|:---------------|:--------------|:-------|:-------------|:--------------|:----------------|:--------|:--------------------|:---------------|:--------|:----------------|:--------------|:-----------|:------------|:---------------|:-------------|:--------|:--------|:-------------|
| 0 | 29 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | |
| 1 | 18 |  |  |  |  |  | | X | | | X | | | | X | | | | | | | | | X | | X | X |
|
sthibomt/aes-research-dataset | ---
language:
- en
tags:
- code
pretty_name: asap
size_categories:
- 10K<n<100K
--- |
enzostvs/what-is-the-prompt | ---
license: mit
---
|
nc33/multispan_quoref | ---
license: mit
dataset_info:
features:
- name: id
dtype: string
- name: question
dtype: string
- name: context
dtype: string
- name: answers
sequence:
- name: answer_start
dtype: int32
- name: text
dtype: string
- name: num_span
dtype: int64
- name: label
sequence: string
- name: type
dtype: string
- name: structure
dtype: string
splits:
- name: validation
num_bytes: 10164883
num_examples: 2418
- name: train
num_bytes: 83767911
num_examples: 19399
download_size: 7949927
dataset_size: 93932794
---
|
satani/common_voice_13_0_hi_pseudo_labelled | ---
dataset_info:
- config_name: hi
features:
- name: client_id
dtype: string
- name: path
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: sentence
dtype: string
- name: up_votes
dtype: int64
- name: down_votes
dtype: int64
- name: age
dtype: string
- name: gender
dtype: string
- name: accent
dtype: string
- name: locale
dtype: string
- name: segment
dtype: string
- name: variant
dtype: string
- name: whisper_transcript
sequence: int64
splits:
- name: train
num_bytes: 133795020.934
num_examples: 4479
- name: validation
num_bytes: 67494362.935
num_examples: 2281
- name: test
num_bytes: 102994313.039
num_examples: 2947
download_size: 269388323
dataset_size: 304283696.908
- config_name: hi_in
features:
- name: id
dtype: int32
- name: num_samples
dtype: int32
- name: path
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: transcription
dtype: string
- name: raw_transcription
dtype: string
- name: gender
dtype:
class_label:
names:
'0': male
'1': female
'2': other
- name: lang_id
dtype:
class_label:
names:
'0': af_za
'1': am_et
'2': ar_eg
'3': as_in
'4': ast_es
'5': az_az
'6': be_by
'7': bg_bg
'8': bn_in
'9': bs_ba
'10': ca_es
'11': ceb_ph
'12': ckb_iq
'13': cmn_hans_cn
'14': cs_cz
'15': cy_gb
'16': da_dk
'17': de_de
'18': el_gr
'19': en_us
'20': es_419
'21': et_ee
'22': fa_ir
'23': ff_sn
'24': fi_fi
'25': fil_ph
'26': fr_fr
'27': ga_ie
'28': gl_es
'29': gu_in
'30': ha_ng
'31': he_il
'32': hi_in
'33': hr_hr
'34': hu_hu
'35': hy_am
'36': id_id
'37': ig_ng
'38': is_is
'39': it_it
'40': ja_jp
'41': jv_id
'42': ka_ge
'43': kam_ke
'44': kea_cv
'45': kk_kz
'46': km_kh
'47': kn_in
'48': ko_kr
'49': ky_kg
'50': lb_lu
'51': lg_ug
'52': ln_cd
'53': lo_la
'54': lt_lt
'55': luo_ke
'56': lv_lv
'57': mi_nz
'58': mk_mk
'59': ml_in
'60': mn_mn
'61': mr_in
'62': ms_my
'63': mt_mt
'64': my_mm
'65': nb_no
'66': ne_np
'67': nl_nl
'68': nso_za
'69': ny_mw
'70': oc_fr
'71': om_et
'72': or_in
'73': pa_in
'74': pl_pl
'75': ps_af
'76': pt_br
'77': ro_ro
'78': ru_ru
'79': sd_in
'80': sk_sk
'81': sl_si
'82': sn_zw
'83': so_so
'84': sr_rs
'85': sv_se
'86': sw_ke
'87': ta_in
'88': te_in
'89': tg_tj
'90': th_th
'91': tr_tr
'92': uk_ua
'93': umb_ao
'94': ur_pk
'95': uz_uz
'96': vi_vn
'97': wo_sn
'98': xh_za
'99': yo_ng
'100': yue_hant_hk
'101': zu_za
'102': all
- name: language
dtype: string
- name: lang_group_id
dtype:
class_label:
names:
'0': western_european_we
'1': eastern_european_ee
'2': central_asia_middle_north_african_cmn
'3': sub_saharan_african_ssa
'4': south_asian_sa
'5': south_east_asian_sea
'6': chinese_japanase_korean_cjk
- name: whisper_transcript
sequence: int64
splits:
- name: train
num_bytes: 1537557908.2
num_examples: 2120
- name: validation
num_bytes: 164618710.0
num_examples: 239
- name: test
num_bytes: 310072327.0
num_examples: 418
download_size: 1998285116
dataset_size: 2012248945.2
configs:
- config_name: hi
data_files:
- split: train
path: hi/train-*
- split: validation
path: hi/validation-*
- split: test
path: hi/test-*
- config_name: hi_in
data_files:
- split: train
path: hi_in/train-*
- split: validation
path: hi_in/validation-*
- split: test
path: hi_in/test-*
---
|
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