id stringlengths 2 115 | author stringlengths 2 42 ⌀ | last_modified timestamp[us, tz=UTC] | downloads int64 0 8.87M | likes int64 0 3.84k | paperswithcode_id stringlengths 2 45 ⌀ | tags list | lastModified timestamp[us, tz=UTC] | createdAt stringlengths 24 24 | key stringclasses 1 value | created timestamp[us] | card stringlengths 1 1.01M | embedding list | library_name stringclasses 21 values | pipeline_tag stringclasses 27 values | mask_token null | card_data null | widget_data null | model_index null | config null | transformers_info null | spaces null | safetensors null | transformersInfo null | modelId stringlengths 5 111 ⌀ | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
tyzhu/find_last_sent_train_50_eval_40 | tyzhu | 2023-11-08T06:31:40Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T06:31:40Z | 2023-11-08T06:29:41.000Z | 2023-11-08T06:29:41 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: title
dtype: string
- name: context
dtype: string
splits:
- name: train
num_bytes: 180593
num_examples: 140
- name: validation
num_bytes: 39604
num_examples: 40
download_size: 0
dataset_size: 220197
---
# Dataset Card for "find_last_sent_train_50_eval_40"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.4636705815792084,
-0.053982146084308624,
0.39901989698410034,
0.28286927938461304,
-0.09107440710067749,
0.10144593566656113,
0.13625292479991913,
0.2238321751356125,
0.6329540610313416,
0.39440539479255676,
-0.8158881664276123,
-0.7473084330558777,
-0.4267062544822693,
-0.1695759445428... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
linxin020826/dehazing_test | linxin020826 | 2023-11-08T06:47:38Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T06:47:38Z | 2023-11-08T06:37:21.000Z | 2023-11-08T06:37:21 | Entry not found | [
-0.32276472449302673,
-0.22568407654762268,
0.8622258901596069,
0.4346148371696472,
-0.5282984972000122,
0.7012965679168701,
0.7915717363357544,
0.07618629932403564,
0.7746022939682007,
0.2563222646713257,
-0.785281777381897,
-0.22573848068714142,
-0.9104482531547546,
0.5715669393539429,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
aigeek0x0/alpaca-gpt4-1000 | aigeek0x0 | 2023-11-15T00:37:55Z | 0 | 1 | null | [
"license:cc-by-nc-4.0",
"region:us"
] | 2023-11-15T00:37:55Z | 2023-11-08T07:11:45.000Z | 2023-11-08T07:11:45 | ---
license: cc-by-nc-4.0
---
The dataset consists of 1000 randomly selected rows from the original [alpaca-gpt4](https://huggingface.co/datasets/vicgalle/alpaca-gpt4) dataset. | [
-0.8627439737319946,
-0.7070332169532776,
0.4449124038219452,
0.2871515154838562,
-0.20424368977546692,
-0.43649667501449585,
0.5413141846656799,
-0.2858302593231201,
1.1002793312072754,
0.9063655138015747,
-0.9082609415054321,
-0.28802430629730225,
-0.29344967007637024,
0.1374115347862243... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
KETI-AIR/kor_anli | KETI-AIR | 2023-11-15T01:18:06Z | 0 | 0 | anli | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"task_ids:multi-input-text-classification",
"size_categories:100K<n<1M",
"language:ko",
"license:cc-by-nc-4.0",
"region:us"
] | 2023-11-15T01:18:06Z | 2023-11-08T07:39:41.000Z | 2023-11-08T07:39:41 | ---
language:
- ko
license:
- cc-by-nc-4.0
size_categories:
- 100K<n<1M
task_categories:
- text-classification
task_ids:
- natural-language-inference
- multi-input-text-classification
paperswithcode_id: anli
pretty_name: Adversarial NLI
dataset_info:
features:
- name: data_index_by_user
dtype: int32
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
- name: reason
dtype: string
splits:
- name: train_r1
num_bytes: 8505556
num_examples: 16946
- name: train_r2
num_bytes: 22521662
num_examples: 45460
- name: train_r3
num_bytes: 48605206
num_examples: 100459
- name: dev_r1
num_bytes: 628891
num_examples: 1000
- name: dev_r2
num_bytes: 613763
num_examples: 1000
- name: dev_r3
num_bytes: 740840
num_examples: 1200
- name: test_r1
num_bytes: 626555
num_examples: 1000
- name: test_r2
num_bytes: 633241
num_examples: 1000
- name: test_r3
num_bytes: 736887
num_examples: 1200
download_size: 23386318
dataset_size: 83612601
---
# Dataset Card for anli
## Licensing Information
[cc-4 Attribution-NonCommercial](https://github.com/facebookresearch/anli/blob/main/LICENSE)
## Source Data Citation INformation
```
@InProceedings{nie2019adversarial,
title={Adversarial NLI: A New Benchmark for Natural Language Understanding},
author={Nie, Yixin
and Williams, Adina
and Dinan, Emily
and Bansal, Mohit
and Weston, Jason
and Kiela, Douwe},
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
year = "2020",
publisher = "Association for Computational Linguistics",
} | [
-0.27917343378067017,
-0.8347379565238953,
0.1181754618883133,
0.20840021967887878,
0.04289272800087929,
0.060922227799892426,
-0.24956457316875458,
-0.42447125911712646,
0.09586293995380402,
0.3098617494106293,
-0.6741060018539429,
-0.5948660373687744,
-0.557064950466156,
0.32905715703964... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Nexdata/Non-safety_and_inductive_Prompt_data | Nexdata | 2023-11-22T09:37:42Z | 0 | 0 | null | [
"task_categories:text-generation",
"task_categories:zero-shot-classification",
"language:zh",
"region:us"
] | 2023-11-22T09:37:42Z | 2023-11-08T08:02:36.000Z | 2023-11-08T08:02:36 | ---
task_categories:
- text-generation
- zero-shot-classification
language:
- zh
---
# Dataset Card for Nexdata/Non-safety_and_inductive_Prompt_data
## Description
Non-safety and inductive Prompt data, about 500,000 in total, this dataset can be used for tasks such as LLM training, chatgpt
For more details, please refer to the link: https://www.nexdata.ai/datasets/1349?source=Huggingface
# Specifications
## Data content
Non-safety and inductive Prompt data
## Data size
About 500,000
## Collecting type
volience,politically sensitive,bad language,porn,induce
## Collecting method
written by professional annotators
## Storage format
txt
## Language
Chinese
# Licensing Information
Commercial License | [
-0.5084230899810791,
-0.6425987482070923,
-0.0484524741768837,
0.47560325264930725,
-0.27764004468917847,
-0.3342131972312927,
0.07448650896549225,
-0.26676368713378906,
0.3685642182826996,
0.5287967920303345,
-0.752196192741394,
-0.8015486001968384,
-0.34048327803611755,
0.190940439701080... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Nexdata/Unsupervised_Text_Data_For_Literary_Subjects | Nexdata | 2023-11-10T07:21:54Z | 0 | 0 | null | [
"task_categories:conversational",
"language:zh",
"region:us"
] | 2023-11-10T07:21:54Z | 2023-11-08T08:05:07.000Z | 2023-11-08T08:05:07 | ---
task_categories:
- conversational
language:
- zh
---
# Dataset Card for Nexdata/Unsupervised_Text_Data_For_Literary_Subjects
## Description
Subjects content data, about 1T in total; each piece of subjects' content contains title,text,author,date,subject,keyword; this dataset can be used for tasks such as LLM training, chatgpt
For more details, please refer to the link: https://www.nexdata.ai/datasets/1310?source=Huggingface
# Specifications
## Data content
News content data，about 79 subjects
## Data size
About 1TB
## Data fields
Text data with title,text,author,date,subject,keyword
## Collection method
Using keywords to retrieve data from massive databases, and the keywords are the subject and keyword
## Storage format
json
## Language
Chinese
# Licensing Information
Commercial License | [
-0.18958882987499237,
-0.5649231672286987,
0.25833022594451904,
0.3336004614830017,
-0.45565715432167053,
-0.1582212895154953,
-0.1730203777551651,
-0.015924960374832153,
0.41572675108909607,
0.9916139841079712,
-0.6327949166297913,
-0.9969897866249084,
-0.4722239375114441,
0.0695911273360... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Nexdata/Multi-Round_Interpersonal_Dialogues_Text_Data | Nexdata | 2023-11-10T07:19:51Z | 0 | 0 | null | [
"language:zh",
"region:us"
] | 2023-11-10T07:19:51Z | 2023-11-08T08:08:11.000Z | 2023-11-08T08:08:11 | ---
language:
- zh
---
# Dataset Card for Nexdata/Multi-Round_Interpersonal_Dialogues_Text_Data
## Description
This database is the interactive text corpus of real users on the mobile phone. The database itself has been desensitized to ensure of no private information of the user's (A and B are the codes to replace the sender and receiver, and sensitive information such as cellphone number and user name are replaced with '* * *'). This database can be used for tasks such as natural language understanding.
For more details, please refer to the link: https://www.nexdata.ai/datasets/150?source=Huggingface
# Specifications
## Data content
text corpus of multi-round interpersonal dialogues in the real world.
## Data size
830,276 groups.
## Collecting period
the year 2,015
## Storage format
txt
## Language
Chinese
## Applications
semantic parsing of multi-round dialogues in smart customer service and intelligent interaction scenarios.
# Licensing Information
Commercial License | [
-0.5557376742362976,
-1.0586371421813965,
0.1111125573515892,
0.6112616062164307,
-0.10395451635122299,
-0.004649848211556673,
-0.1408703476190567,
-0.4683052599430084,
0.5297184586524963,
0.5732030272483826,
-0.7734461426734924,
-0.6460506319999695,
-0.21635273098945618,
0.224368020892143... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Nexdata/Chinese_Medical_Question_Answering_Data | Nexdata | 2023-11-10T07:18:08Z | 0 | 3 | null | [
"task_categories:question-answering",
"language:zh",
"region:us"
] | 2023-11-10T07:18:08Z | 2023-11-08T08:10:28.000Z | 2023-11-08T08:10:28 | ---
task_categories:
- question-answering
language:
- zh
---
# Dataset Card for Nexdata/Chinese_Medical_Question_Answering_Data
## Description
The data contains 203,029 groups Chinese question answering data between doctors and patients of different diseases.
For more details, please refer to the link: https://www.nexdata.ai/datasets/1086?source=Huggingface
# Specifications
## Data content
203,029 Chinese question & answering data between doctors and patients;
## Storage format
json
## Language
Chinese
## Collection time
May 2,020
## Application scenario
intelligence medical diagnosis; question answering
# Licensing Information
Commercial License | [
-0.37287744879722595,
-0.7882391214370728,
0.4231880009174347,
0.3217333257198334,
-0.020753979682922363,
-0.282550185918808,
0.24348269402980804,
-0.484256774187088,
0.6140000224113464,
0.6517589092254639,
-0.7783190608024597,
-0.7699757218360901,
-0.42706263065338135,
0.15121254324913025... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Nexdata/Minnan_Dialect_Pronunciation_Dictionary | Nexdata | 2023-11-10T07:17:04Z | 0 | 0 | null | [
"task_categories:automatic-speech-recognition",
"region:us"
] | 2023-11-10T07:17:04Z | 2023-11-08T08:11:55.000Z | 2023-11-08T08:11:55 | ---
task_categories:
- automatic-speech-recognition
---
# Dataset Card for Nexdata/Minnan_Dialect_Pronunciation_Dictionary
## Description
Each entry consists of three parts: words, pinyin, and tones. The dictionary can be used to provide pronunciation reference for sound recording personnel, research, and development of pronunciation recognition technology, etc.
For more details, please refer to the link: https://www.nexdata.ai/datasets/51?source=Huggingface
# Specifications
## Format
txt
## Content
87,166 Minnan dialect words and corresponding phonetic symbols.
## Language
Minnan dialect
## Application scenario
speech recognition
# Licensing Information
Commercial License | [
-0.5946226716041565,
-0.42012810707092285,
0.17083704471588135,
0.3872952163219452,
-0.47650423645973206,
-0.428372859954834,
-0.1278214007616043,
-0.2803901731967926,
0.9025518894195557,
0.7129786610603333,
-0.5906749963760376,
-0.7729912400245667,
-0.18519563972949982,
0.4938161969184875... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Nexdata/Cantonese_Pronunciation_Dictionary | Nexdata | 2023-11-10T07:17:36Z | 0 | 0 | null | [
"task_categories:automatic-speech-recognition",
"region:us"
] | 2023-11-10T07:17:36Z | 2023-11-08T08:13:30.000Z | 2023-11-08T08:13:30 | ---
task_categories:
- automatic-speech-recognition
---
# Dataset Card for Nexdata/Cantonese_Pronunciation_Dictionary
## Description
This pronunciation dictionary collects words with dialect characteristics in Guangdong cantonese regions. Each entry consists of three parts: words, pinyin and tones. The dictionary can be used to provide pronunciation reference for sound recording personnel, research and development of pronunciation recognition technology, etc.
For more details, please refer to the link: https://www.nexdata.ai/datasets/55?source=Huggingface
# Specifications
## Data Size
80,279 entries
## Content
Cantonese Pronunciation Dictionary collects Cantonese vocabulary and pronunciation
# Licensing Information
Commercial License | [
-0.4319288730621338,
-0.4251648485660553,
0.04208402708172798,
0.5710606575012207,
-0.23806564509868622,
-0.45514944195747375,
0.17045855522155762,
-0.2144360989332199,
0.7350354790687561,
0.7110684514045715,
-0.4735703766345978,
-0.647758424282074,
-0.2229524403810501,
0.28044813871383667... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Nexdata/Mandarin_Pronunciation_Dictionary | Nexdata | 2023-11-10T07:16:45Z | 0 | 0 | null | [
"task_categories:automatic-speech-recognition",
"language:zh",
"region:us"
] | 2023-11-10T07:16:45Z | 2023-11-08T08:14:47.000Z | 2023-11-08T08:14:47 | ---
task_categories:
- automatic-speech-recognition
language:
- zh
---
# Dataset Card for Nexdata/Mandarin_Pronunciation_Dictionary
## Description
The data contains 570,060 entries. All words and pronunciations are produced by linguists. It can be used in the research and development of Mandarin ASR technology.
For more details, please refer to the link: https://www.nexdata.ai/datasets/1094?source=Huggingface
# Specifications
## Format
TXT
## Data content
570,060 Mandarin words and corresponding pinyin
## Language
Mandarin
## Application scenario
speech recognition
# Licensing Information
Commercial License | [
-0.3149147033691406,
-0.27657297253608704,
-0.19719427824020386,
0.42900022864341736,
-0.06993706524372101,
-0.30304205417633057,
-0.18508145213127136,
-0.29012733697891235,
0.4014412760734558,
0.5408499836921692,
-0.3799806535243988,
-0.6260371804237366,
-0.2353665679693222,
0.32223105430... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
k8tems/splats | k8tems | 2023-11-08T08:16:41Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T08:16:41Z | 2023-11-08T08:16:05.000Z | 2023-11-08T08:16:05 | Entry not found | [
-0.32276472449302673,
-0.22568407654762268,
0.8622258901596069,
0.4346148371696472,
-0.5282984972000122,
0.7012965679168701,
0.7915717363357544,
0.07618629932403564,
0.7746022939682007,
0.2563222646713257,
-0.785281777381897,
-0.22573848068714142,
-0.9104482531547546,
0.5715669393539429,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Nexdata/Japanese_Pronunciation_Dictionary | Nexdata | 2023-11-10T07:16:29Z | 0 | 0 | null | [
"task_categories:automatic-speech-recognition",
"language:ja",
"region:us"
] | 2023-11-10T07:16:29Z | 2023-11-08T08:16:14.000Z | 2023-11-08T08:16:14 | ---
task_categories:
- automatic-speech-recognition
language:
- ja
---
# Dataset Card for Nexdata/Japanese_Pronunciation_Dictionary
## Description
The data contains 101,702 entries. All words and pronunciations are produced by Japanese linguists. It can be used in the research and development of Japanese ASR technology.
For more details, please refer to the link: https://www.nexdata.ai/datasets/1088?source=Huggingface
# Specifications
## Format
TXT
## Data content
101,702 Japanese words and corresponding hiragana characters
## Language
Japanese
## Application scenario
speech recognition
# Licensing Information
Commercial License | [
-0.5960499048233032,
-0.47216665744781494,
0.18507647514343262,
0.23875382542610168,
-0.022891134023666382,
-0.20807980000972748,
-0.12255805730819702,
-0.3261622190475464,
0.5558441877365112,
0.7143965363502502,
-0.6298812031745911,
-0.7219082117080688,
-0.5036940574645996,
0.387434244155... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Nexdata/English_Pronunciation_Dictionary | Nexdata | 2023-11-10T07:16:06Z | 0 | 0 | null | [
"task_categories:automatic-speech-recognition",
"language:en",
"region:us"
] | 2023-11-10T07:16:06Z | 2023-11-08T08:17:32.000Z | 2023-11-08T08:17:32 | ---
task_categories:
- automatic-speech-recognition
language:
- en
---
# Dataset Card for Nexdata/English_Pronunciation_Dictionary
## Description
The data contains 500,113 entries. All words and pronunciations are produced by English linguists. It can be used in the research and development of English ASR technology.
For more details, please refer to the link: https://www.nexdata.ai/datasets/1095?source=Huggingface
# Specifications
## Format
txt
## Data content
500,113 English words and corresponding phonetic transcriptions
## Language
English
## Application scenario
speech recognition
# Licensing Information
Commercial License | [
-0.5344926714897156,
-0.31909099221229553,
-0.04408960044384003,
0.3471442461013794,
0.12374008446931839,
-0.17424780130386353,
-0.11194409430027008,
-0.38298967480659485,
0.6051002144813538,
0.5158416032791138,
-0.4468185007572174,
-0.6438552141189575,
-0.48819422721862793,
0.476295590400... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Nexdata/Korean_Pronunciation_Dictionary | Nexdata | 2023-11-10T07:16:18Z | 0 | 0 | null | [
"task_categories:automatic-speech-recognition",
"language:ko",
"region:us"
] | 2023-11-10T07:16:18Z | 2023-11-08T08:18:47.000Z | 2023-11-08T08:18:47 | ---
task_categories:
- automatic-speech-recognition
language:
- ko
---
# Dataset Card for Nexdata/Korean_Pronunciation_Dictionary
## Description
The data contains 444,202 entries. All words and pronunciations are produced by Korean linguists. It can be used in the research and development of Korean ASR technology.
For more details, please refer to the link: https://www.nexdata.ai/datasets/1221?source=Huggingface
# Specifications
## Format
txt
## Content
444,202 Korean words and corresponding phonetic symbols.
## Language
Korean
## Application scenarios
speech recognition, speech synthesis.
# Licensing Information
Commercial License | [
-0.479617714881897,
-0.13003137707710266,
0.13260327279567719,
0.5196365118026733,
-0.03472582995891571,
-0.008845601230859756,
-0.11018628627061844,
-0.2783331871032715,
0.5127196311950684,
0.7008427381515503,
-0.6575319766998291,
-0.837530255317688,
-0.420219361782074,
0.4293049275875091... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
nlplabtdtu/wikihow-processes-vi | nlplabtdtu | 2023-11-08T08:53:12Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T08:53:12Z | 2023-11-08T08:47:45.000Z | 2023-11-08T08:47:45 | Entry not found | [
-0.32276472449302673,
-0.22568407654762268,
0.8622258901596069,
0.4346148371696472,
-0.5282984972000122,
0.7012965679168701,
0.7915717363357544,
0.07618629932403564,
0.7746022939682007,
0.2563222646713257,
-0.785281777381897,
-0.22573848068714142,
-0.9104482531547546,
0.5715669393539429,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
yuancj/character-profiles-romance-prepare | yuancj | 2023-11-08T08:56:34Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T08:56:34Z | 2023-11-08T08:54:21.000Z | 2023-11-08T08:54:21 | ---
dataset_info:
features: []
splits:
- name: train
num_bytes: 0
num_examples: 0
download_size: 0
dataset_size: 0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "character-profiles-romance-prepare"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.5061955451965332,
-0.39656758308410645,
0.25191330909729004,
0.488713800907135,
0.09980081766843796,
0.022285880520939827,
0.2858847677707672,
-0.03535621985793114,
1.0202980041503906,
0.7108550071716309,
-1.051865577697754,
-0.7663849592208862,
-0.18095582723617554,
-0.0215177871286869... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
fate7/weather4cast | fate7 | 2023-11-08T09:10:33Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-08T09:10:33Z | 2023-11-08T09:10:33.000Z | 2023-11-08T09:10:33 | ---
license: apache-2.0
---
| [
-0.12853392958641052,
-0.18616779148578644,
0.6529127955436707,
0.49436280131340027,
-0.19319361448287964,
0.23607419431209564,
0.36072003841400146,
0.050563063472509384,
0.579365611076355,
0.7400140762329102,
-0.6508104205131531,
-0.23783954977989197,
-0.7102249264717102,
-0.0478260256350... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
mhenrichsen/creator | mhenrichsen | 2023-11-08T09:20:26Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T09:20:26Z | 2023-11-08T09:20:23.000Z | 2023-11-08T09:20:23 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
splits:
- name: train
num_bytes: 375555
num_examples: 1000
download_size: 11378
dataset_size: 375555
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "creator"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.588806688785553,
-0.35239022970199585,
0.2331690639257431,
0.2412286251783371,
-0.15815867483615875,
0.10401222854852676,
0.1708575189113617,
-0.15405461192131042,
0.9312933087348938,
0.5600684881210327,
-0.8718087673187256,
-0.5909435153007507,
-0.7145982980728149,
-0.15810388326644897... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
elzooz/Amod_mental_health_counseling_conversations | elzooz | 2023-11-08T09:27:51Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-08T09:27:51Z | 2023-11-08T09:27:51.000Z | 2023-11-08T09:27:51 | ---
license: openrail
---
| [
-0.12853392958641052,
-0.18616779148578644,
0.6529127955436707,
0.49436280131340027,
-0.19319361448287964,
0.23607419431209564,
0.36072003841400146,
0.050563063472509384,
0.579365611076355,
0.7400140762329102,
-0.6508104205131531,
-0.23783954977989197,
-0.7102249264717102,
-0.0478260256350... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
baixue6269/character-profiles-romance-output | baixue6269 | 2023-11-08T10:00:39Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T10:00:39Z | 2023-11-08T09:28:13.000Z | 2023-11-08T09:28:13 | ---
dataset_info:
features:
- name: name
dtype: string
- name: categories
sequence: string
- name: personalities
sequence: string
- name: description
dtype: string
- name: conversation
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 103050
num_examples: 10
download_size: 67798
dataset_size: 103050
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "character-profiles-romance-output"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.45209038257598877,
-0.387479305267334,
0.23499053716659546,
0.2828691303730011,
-0.013331077992916107,
0.02942688949406147,
0.16504186391830444,
-0.053221505135297775,
1.1118868589401245,
0.7320383787155151,
-1.1390290260314941,
-0.9179519414901733,
-0.3474879860877991,
-0.1302829235792... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
yuancj/character-profiles-romance-prepare2 | yuancj | 2023-11-08T11:25:53Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T11:25:53Z | 2023-11-08T09:34:07.000Z | 2023-11-08T09:34:07 | ---
dataset_info:
features:
- name: name
dtype: string
- name: categories
sequence: string
- name: personalities
sequence: string
- name: description
dtype: string
- name: conversation
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 20485
num_examples: 2
download_size: 30036
dataset_size: 20485
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "character-profiles-romance-prepare2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.38730111718177795,
-0.3518264591693878,
0.2578680217266083,
0.5174130797386169,
0.026587575674057007,
-0.020008591935038567,
0.32584312558174133,
-0.1085728257894516,
0.8953464031219482,
0.6888488531112671,
-0.9588369727134705,
-0.6355290412902832,
-0.2959051728248596,
-0.05939191579818... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ianjayyuson/solo_pic | ianjayyuson | 2023-11-08T20:45:15Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T20:45:15Z | 2023-11-08T09:51:35.000Z | 2023-11-08T09:51:35 | Entry not found | [
-0.32276472449302673,
-0.22568407654762268,
0.8622258901596069,
0.4346148371696472,
-0.5282984972000122,
0.7012965679168701,
0.7915717363357544,
0.07618629932403564,
0.7746022939682007,
0.2563222646713257,
-0.785281777381897,
-0.22573848068714142,
-0.9104482531547546,
0.5715669393539429,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
albertvillanova/wiki | albertvillanova | 2023-11-08T09:56:09Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T09:56:09Z | 2023-11-08T09:56:09.000Z | 2023-11-08T09:56:09 | Entry not found | [
-0.32276472449302673,
-0.22568407654762268,
0.8622258901596069,
0.4346148371696472,
-0.5282984972000122,
0.7012965679168701,
0.7915717363357544,
0.07618629932403564,
0.7746022939682007,
0.2563222646713257,
-0.785281777381897,
-0.22573848068714142,
-0.9104482531547546,
0.5715669393539429,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
CarrotzRule123/crawl-apu.edu | CarrotzRule123 | 2023-11-08T09:58:19Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T09:58:19Z | 2023-11-08T09:56:10.000Z | 2023-11-08T09:56:10 | Entry not found | [
-0.32276472449302673,
-0.22568407654762268,
0.8622258901596069,
0.4346148371696472,
-0.5282984972000122,
0.7012965679168701,
0.7915717363357544,
0.07618629932403564,
0.7746022939682007,
0.2563222646713257,
-0.785281777381897,
-0.22573848068714142,
-0.9104482531547546,
0.5715669393539429,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
arieg/bw_spec_cls_80 | arieg | 2023-11-08T09:58:53Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T09:58:53Z | 2023-11-08T09:58:43.000Z | 2023-11-08T09:58:43 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': '10'
'1': '1039'
'2': '1040'
'3': '1082'
'4': '1083'
'5': '1102'
'6': '1193'
'7': '1195'
'8': '1196'
'9': '1197'
'10': '1270'
'11': '1276'
'12': '1277'
'13': '1278'
'14': '140'
'15': '141'
'16': '1417'
'17': '1427'
'18': '1443'
'19': '1482'
'20': '1510'
'21': '1544'
'22': '1642'
'23': '1644'
'24': '1649'
'25': '1661'
'26': '1663'
'27': '1666'
'28': '1673'
'29': '1680'
'30': '1681'
'31': '1682'
'32': '1683'
'33': '1684'
'34': '1685'
'35': '190'
'36': '193'
'37': '194'
'38': '197'
'39': '2'
'40': '200'
'41': '203'
'42': '204'
'43': '207'
'44': '210'
'45': '211'
'46': '212'
'47': '213'
'48': '255'
'49': '256'
'50': '368'
'51': '424'
'52': '5'
'53': '534'
'54': '540'
'55': '546'
'56': '574'
'57': '615'
'58': '620'
'59': '621'
'60': '625'
'61': '666'
'62': '667'
'63': '676'
'64': '694'
'65': '695'
'66': '714'
'67': '715'
'68': '716'
'69': '718'
'70': '777'
'71': '814'
'72': '821'
'73': '822'
'74': '825'
'75': '853'
'76': '897'
'77': '995'
'78': '997'
'79': '998'
splits:
- name: train
num_bytes: 89716674.4
num_examples: 1600
download_size: 87975685
dataset_size: 89716674.4
---
# Dataset Card for "bw_spec_cls_80"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.7281789183616638,
-0.1844707578420639,
0.1933465152978897,
0.2655416429042816,
-0.2622053921222687,
-0.16420036554336548,
0.0011206622002646327,
-0.2817683517932892,
0.56434166431427,
0.5333073735237122,
-0.7624777555465698,
-0.8276515603065491,
-0.5609703063964844,
-0.18455566465854645... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
AdamMashaka/MCV | AdamMashaka | 2023-11-08T09:59:05Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-08T09:59:05Z | 2023-11-08T09:59:05.000Z | 2023-11-08T09:59:05 | ---
license: apache-2.0
---
| [
-0.12853392958641052,
-0.18616779148578644,
0.6529127955436707,
0.49436280131340027,
-0.19319361448287964,
0.23607419431209564,
0.36072003841400146,
0.050563063472509384,
0.579365611076355,
0.7400140762329102,
-0.6508104205131531,
-0.23783954977989197,
-0.7102249264717102,
-0.0478260256350... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
faizalnf1800/bangs_under_sidepart_hairstyle_anime_woman | faizalnf1800 | 2023-11-08T09:59:42Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T09:59:42Z | 2023-11-08T09:59:42.000Z | 2023-11-08T09:59:42 | Entry not found | [
-0.32276472449302673,
-0.22568407654762268,
0.8622258901596069,
0.4346148371696472,
-0.5282984972000122,
0.7012965679168701,
0.7915717363357544,
0.07618629932403564,
0.7746022939682007,
0.2563222646713257,
-0.785281777381897,
-0.22573848068714142,
-0.9104482531547546,
0.5715669393539429,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ldp72/test2 | ldp72 | 2023-11-08T10:03:13Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T10:03:13Z | 2023-11-08T10:03:13.000Z | 2023-11-08T10:03:13 | Entry not found | [
-0.32276472449302673,
-0.22568407654762268,
0.8622258901596069,
0.4346148371696472,
-0.5282984972000122,
0.7012965679168701,
0.7915717363357544,
0.07618629932403564,
0.7746022939682007,
0.2563222646713257,
-0.785281777381897,
-0.22573848068714142,
-0.9104482531547546,
0.5715669393539429,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Nexdata/English-Japanese_Parallel_Corpus_Data | Nexdata | 2023-11-10T07:15:01Z | 0 | 0 | null | [
"task_categories:translation",
"language:en",
"language:ja",
"region:us"
] | 2023-11-10T07:15:01Z | 2023-11-08T10:47:40.000Z | 2023-11-08T10:47:40 | ---
task_categories:
- translation
language:
- en
- ja
---
# Dataset Card for Nexdata/English-Japanese_Parallel_Corpus_Data
## Description
The 850,000 English Japanese Parallel Corpus Data is a bilingual text is stored in text format. It covers multiple fields such as tourism, medical treatment, daily life, news, etc. average English sentence 23 words. The data desensitization and quality checking had been done. It can be used as a basic corpus for text data analysis in fields such as machine translation.rn
For more details, please refer to the link: https://www.nexdata.ai/datasets/1186?source=Huggingface
# Specifications
## Storage format
TXT
## Data content
English-Japanese Parallel Corpus Data
## Data size
0.85 million pairs of English-Japanese Parallel Corpus Data. The English sentences contain 23 words on average.
## Language
English, Japanese
## Accuracy rate
90%
## Application scenario
machine translation
# Licensing Information
Commercial License | [
-0.5336967706680298,
-0.7732139825820923,
0.2942643463611603,
0.47255244851112366,
-0.22684618830680847,
-0.03784451261162758,
-0.28727179765701294,
-0.3985368311405182,
0.6121654510498047,
0.5709323287010193,
-0.39591801166534424,
-0.8885242938995361,
-0.6680867671966553,
0.61915260553359... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Nexdata/Japanese-English_Parallel_Corpus_Data | Nexdata | 2023-11-10T07:13:07Z | 0 | 0 | null | [
"task_categories:translation",
"language:ja",
"region:us"
] | 2023-11-10T07:13:07Z | 2023-11-08T10:50:47.000Z | 2023-11-08T10:50:47 | ---
task_categories:
- translation
language:
- ja
---
# Dataset Card for Nexdata/Japanese-English_Parallel_Corpus_Data
## Description
Japanese and English parallel corpus, 380,000 groups in total; excluded political, porn, personal information and other sensitive vocabulary; it can be a base corpus for text-based data analysis, used in machine translation and other fields.
For more details, please refer to the link: https://www.nexdata.ai/datasets/153?source=Huggingface
# Specifications
## Storage format
TXT
## Data content
Japanese-English Parallel Corpus Data
## Data size
0.38 million pairs of Japanese-English Parallel Corpus Data
## Language
Japanese, English
## Application scenario
machine translation
# Licensing Information
Commercial License | [
-0.6740300059318542,
-0.6633209586143494,
0.22113542258739471,
0.5079109072685242,
-0.10094387829303741,
0.10973440855741501,
-0.2096778303384781,
-0.3438951075077057,
0.6777833104133606,
0.6587461233139038,
-0.5216996073722839,
-0.9483186602592468,
-0.7899399995803833,
0.39067569375038147... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Nexdata/Traditional_Chinese_Oral_Message_Data | Nexdata | 2023-11-10T07:12:42Z | 0 | 0 | null | [
"task_categories:conversational",
"language:zh",
"region:us"
] | 2023-11-10T07:12:42Z | 2023-11-08T10:53:38.000Z | 2023-11-08T10:53:38 | ---
task_categories:
- conversational
language:
- zh
---
# Dataset Card for Nexdata/Traditional_Chinese_Oral_Message_Data
## Description
Traditional Chinese SMS corpus, 10 million in total, real traditional Chinese spoken language text data; only contains text messages; the content is stored in txt format; the data set can be used for natural language understanding and related tasks.
For more details, please refer to the link: https://www.nexdata.ai/datasets/182?source=Huggingface
# Specifications
## Data content
Traditional Chinese SMS corpus text data
## Data size
10 million
## Collecting period
The year 2,014
## Storage format
txt
## Language
Chinese
# Licensing Information
Commercial License | [
-0.26548731327056885,
-0.8830503821372986,
0.08002397418022156,
0.6956434845924377,
-0.4018808901309967,
-0.22650201618671417,
-0.3466719388961792,
-0.5704298615455627,
0.5488253831863403,
0.7320136427879333,
-0.4948602318763733,
-0.764754593372345,
-0.2980794906616211,
0.17195414006710052... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Nexdata/Cantonese_Script_Data | Nexdata | 2023-11-10T07:11:31Z | 0 | 0 | null | [
"region:us"
] | 2023-11-10T07:11:31Z | 2023-11-08T10:55:02.000Z | 2023-11-08T10:55:02 |
---
# Dataset Card for Nexdata/Cantonese_Script_Data
## Description
Cantonese textual data, 82 million pieces in total; data is collected from Cantonese script text; data set can be used for natural language understanding, knowledge base construction and other tasks.
For more details, please refer to the link: https://www.nexdata.ai/datasets/188?source=Huggingface
# Specifications
## Data content
82 million Cantonese script texts
## Data size
82 million Cantonese script texts
## Collecting period
The year 2,015
## Storage format
txt
## Language
Cantonese
# Licensing Information
Commercial License | [
-0.2546437978744507,
-0.8996290564537048,
0.09971364587545395,
0.7376828193664551,
-0.20647494494915009,
-0.3699195086956024,
0.1490148901939392,
-0.37430378794670105,
0.5158123970031738,
1.0221343040466309,
-0.47692322731018066,
-0.7941981554031372,
-0.5513010025024414,
0.2514381408691406... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Nexdata/English_Intention_Annotation_Data_in_Interactive_Scenes | Nexdata | 2023-11-10T07:09:51Z | 0 | 0 | null | [
"task_categories:text-classification",
"language:en",
"region:us"
] | 2023-11-10T07:09:51Z | 2023-11-08T10:56:30.000Z | 2023-11-08T10:56:30 | ---
task_categories:
- text-classification
language:
- en
---
# Dataset Card for Nexdata/English_Intention_Annotation_Data_in_Interactive_Scenes
## Description
84,516 Sentences - English Intention Annotation Data in Interactive Scenes, annotated with intent classes, including slot and slot value information; the intent field includes music, weather, date, schedule, home equipment, etc.; it is applied to intent recognition research and related fields.
For more details, please refer to the link: https://www.nexdata.ai/datasets/1154?source=Huggingface
# Specifications
## Content
intent-type single sentence annotation data
## ContentLabel Content
Manually write sentences with corresponding intentions and make intent annotations
## Storage Format
Excel
## Language
English
## Data Size
84,516 Sentences
## Application scenario
intention understanding in speech interaction
# Licensing Information
Commercial License | [
-0.45407724380493164,
-0.8646690845489502,
0.4575542211532593,
0.6555711627006531,
-0.14459481835365295,
-0.7861083149909973,
-0.10659831017255783,
-0.4676748812198639,
0.5130786299705505,
0.6416625380516052,
-0.841221272945404,
-0.78299880027771,
-0.2514168620109558,
-0.03585488721728325,... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Nexdata/Chinese-Japanese_Parallel_Corpus_Data | Nexdata | 2023-11-10T07:23:13Z | 0 | 0 | null | [
"task_categories:translation",
"language:ja",
"language:zh",
"region:us"
] | 2023-11-10T07:23:13Z | 2023-11-08T11:00:00.000Z | 2023-11-08T11:00:00 | ---
task_categories:
- translation
language:
- ja
- zh
---
# Dataset Card for Nexdata/Chinese-Japanese_Parallel_Corpus_Data
## Description
9.83 Million Pairs of Sentences - Chinese-Japanese Parallel Corpus Data be stored in txt format. It covers multiple fields including general, IT, news, patent, and international engine. The data desensitization and quality checking had been done. It can be used as a basic corpus for text data analysis in fields such as machine translation.
For more details, please refer to the link: https://www.nexdata.ai/datasets/1069?source=Huggingface
# Specifications
## Format
TXT
## Data content
Chinese-Japanese parallel corpus
## Data size
9.83 million pairs of Chinese-Japanese Parallel Corpus Data.
## Language
Chinese, Japanese
## Applications
machine translation
## Accuracy rate
90%
# Licensing Information
Commercial License | [
-0.36474961042404175,
-0.7301474809646606,
0.2772642970085144,
0.5708712935447693,
-0.2804536521434784,
-0.09303124994039536,
-0.28300073742866516,
-0.27963787317276,
0.4854159653186798,
0.5929646492004395,
-0.33080241084098816,
-0.841505765914917,
-0.5573911666870117,
0.3945731818675995,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Nexdata/Chinese-Korean_Parallel_Corpus_Data | Nexdata | 2023-11-10T07:05:57Z | 0 | 0 | null | [
"task_categories:translation",
"language:ko",
"region:us"
] | 2023-11-10T07:05:57Z | 2023-11-08T11:01:33.000Z | 2023-11-08T11:01:33 | ---
task_categories:
- translation
language:
- ko
---
# Dataset Card for Nexdata/Chinese-Korean_Parallel_Corpus_Data
## Description
12,820,000 sets of parallel translation corpus between China and Korea, which are stored in txt files. It covers many fields including spoken language, traveling, news, and finance. Data cleaning, desensitization, and quality inspection have been carried out. It can be used as the basic corpus database in the text data files as well as used in machine translation.
For more details, please refer to the link: https://www.nexdata.ai/datasets/1200?source=Huggingface
# Specifications
## Storage format
TXT
## Data content
Chinese-Korean Parallel Corpus Data
## Data size
12.82 million pairs of Chinese-Korean Parallel Corpus Data. The Chinese sentences contain 25.7 characters on average.
## Language
Chinese, Korean
## Accuracy rate
90%
## Application scenario
machine translation
# Licensing Information
Commercial License | [
-0.3457546830177307,
-0.4416629374027252,
0.35630443692207336,
0.6355734467506409,
-0.20918498933315277,
-0.049037933349609375,
-0.34900641441345215,
-0.36398574709892273,
0.4218003749847412,
0.5628919005393982,
-0.290702223777771,
-0.907639741897583,
-0.47596150636672974,
0.39487645030021... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
faizalnf1800/web_novel_cover_image | faizalnf1800 | 2023-11-08T11:07:09Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T11:07:09Z | 2023-11-08T11:07:09.000Z | 2023-11-08T11:07:09 | Entry not found | [
-0.3227649927139282,
-0.225684255361557,
0.862226128578186,
0.43461498618125916,
-0.5282987952232361,
0.7012963891029358,
0.7915717363357544,
0.07618629932403564,
0.7746025919914246,
0.2563219666481018,
-0.7852816581726074,
-0.2257382869720459,
-0.9104480743408203,
0.5715669393539429,
-0... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
MyRebRIc/tomas | MyRebRIc | 2023-11-08T11:11:18Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-08T11:11:18Z | 2023-11-08T11:07:53.000Z | 2023-11-08T11:07:53 | ---
license: openrail
---
| [
-0.12853367626667023,
-0.18616794049739838,
0.6529126763343811,
0.4943627417087555,
-0.19319313764572144,
0.23607443273067474,
0.36071979999542236,
0.05056338757276535,
0.5793654322624207,
0.7400138974189758,
-0.6508103013038635,
-0.23783987760543823,
-0.710224986076355,
-0.047825977206230... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Rezuwan/Parrot60_Dataset | Rezuwan | 2023-11-08T14:20:27Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-08T14:20:27Z | 2023-11-08T11:11:18.000Z | 2023-11-08T11:11:18 | ---
license: apache-2.0
---
| [
-0.12853367626667023,
-0.18616794049739838,
0.6529126763343811,
0.4943627417087555,
-0.19319313764572144,
0.23607443273067474,
0.36071979999542236,
0.05056338757276535,
0.5793654322624207,
0.7400138974189758,
-0.6508103013038635,
-0.23783987760543823,
-0.710224986076355,
-0.047825977206230... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
gttsehu/basque_parlament_1 | gttsehu | 2023-11-08T11:32:29Z | 0 | 0 | null | [
"license:cc0-1.0",
"region:us"
] | 2023-11-08T11:32:29Z | 2023-11-08T11:32:29.000Z | 2023-11-08T11:32:29 | ---
license: cc0-1.0
---
| [
-0.12853367626667023,
-0.18616794049739838,
0.6529126763343811,
0.4943627417087555,
-0.19319313764572144,
0.23607443273067474,
0.36071979999542236,
0.05056338757276535,
0.5793654322624207,
0.7400138974189758,
-0.6508103013038635,
-0.23783987760543823,
-0.710224986076355,
-0.047825977206230... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
sushi009/hf_test_repo | sushi009 | 2023-11-08T11:46:24Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T11:46:24Z | 2023-11-08T11:46:24.000Z | 2023-11-08T11:46:24 | Entry not found | [
-0.3227645754814148,
-0.22568479180335999,
0.8622263669967651,
0.43461522459983826,
-0.52829909324646,
0.7012971639633179,
0.7915719747543335,
0.07618614286184311,
0.774603009223938,
0.2563217282295227,
-0.7852813005447388,
-0.22573819756507874,
-0.9104475975036621,
0.5715674161911011,
-... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Martin7412/Azure | Martin7412 | 2023-11-08T11:58:35Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T11:58:35Z | 2023-11-08T11:57:08.000Z | 2023-11-08T11:57:08 | Types of Resource Locks
Shared vs. Exclusive Locks
In Azure, shared locks, or read-only locks, allow authorized users to view a <a href="https://dumpsmedia.com/education/an-azure-resource-can-have-multiple-delete-locks/">An Azure Resource can have Multiple Delete Locksl</a> resource but prevent modifications. Exclusive locks restrict all types of operations on the resource, ensuring complete protection against any changes.
Read-Only Locks
Read-only locks are commonly used to prevent accidental alterations. They enable viewing but prohibit modifications to the resource.
Delete Locks
Delete locks play a critical role in preventing accidental deletions. Azure resources support multiple delete locks, ensuring an additional layer of security.
Importance of Delete Locks
Delete locks offer a failsafe against unintentional removal of critical resources. Having multiple delete locks adds further assurance and control, minimizing the risk of inadvertent data loss or service disruptions.
Implementing Multiple Delete Locks
Azure allows the implementation of multiple delete locks on a single resource, providing enhanced protection. Users can set up multiple delete locks <a href="https://dumpsmedia.com/education/an-azure-resource-can-have-multiple-delete-locks/">An Azure Resource can have Multiple Delete Locksl</a> to fortify resource safety.
Click Here For More Details>>>>>>>https://dumpsmedia.com/education/an-azure-resource-can-have-multiple-delete-locks/ | [
-0.3607025444507599,
-0.41062307357788086,
0.24396905303001404,
0.1653117537498474,
-0.22003969550132751,
0.6521841883659363,
0.1921481192111969,
-0.5665655136108398,
0.09307257831096649,
0.6056234836578369,
-0.5633797645568848,
-0.31680625677108765,
-0.528927206993103,
0.21302486956119537... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
open-llm-leaderboard/details_Weyaxi__OpenOrca-Nebula-7B_public | open-llm-leaderboard | 2023-11-08T12:01:52Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T12:01:52Z | 2023-11-08T12:00:48.000Z | 2023-11-08T12:00:48 | ---
pretty_name: Evaluation run of Weyaxi/OpenOrca-Nebula-7B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Weyaxi/OpenOrca-Nebula-7B](https://huggingface.co/Weyaxi/OpenOrca-Nebula-7B)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 61 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Weyaxi__OpenOrca-Nebula-7B_public\"\
,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\
\nThese are the [latest results from run 2023-11-08T11:58:02.317093](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__OpenOrca-Nebula-7B_public/blob/main/results_2023-11-08T11-58-02.317093.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\": 0.5781344309327976,\n\
\ \"acc_stderr\": 0.03435050067075012,\n \"acc_norm\": 0.581933273042423,\n\
\ \"acc_norm_stderr\": 0.03433158518593753,\n \"mc1\": 0.3684210526315789,\n\
\ \"mc1_stderr\": 0.016886551261046046,\n \"mc2\": 0.531795789007015,\n\
\ \"mc2_stderr\": 0.015539765760842488\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.552901023890785,\n \"acc_stderr\": 0.014529380160526848,\n\
\ \"acc_norm\": 0.5870307167235495,\n \"acc_norm_stderr\": 0.014388344935398326\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6283608842859988,\n\
\ \"acc_stderr\": 0.004822550638450896,\n \"acc_norm\": 0.8183628759211312,\n\
\ \"acc_norm_stderr\": 0.0038475722596364257\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5777777777777777,\n\
\ \"acc_stderr\": 0.04266763404099582,\n \"acc_norm\": 0.5777777777777777,\n\
\ \"acc_norm_stderr\": 0.04266763404099582\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.5986842105263158,\n \"acc_stderr\": 0.03988903703336284,\n\
\ \"acc_norm\": 0.5986842105263158,\n \"acc_norm_stderr\": 0.03988903703336284\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.52,\n\
\ \"acc_stderr\": 0.05021167315686779,\n \"acc_norm\": 0.52,\n \
\ \"acc_norm_stderr\": 0.05021167315686779\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.5773584905660377,\n \"acc_stderr\": 0.03040233144576954,\n\
\ \"acc_norm\": 0.5773584905660377,\n \"acc_norm_stderr\": 0.03040233144576954\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6597222222222222,\n\
\ \"acc_stderr\": 0.039621355734862175,\n \"acc_norm\": 0.6597222222222222,\n\
\ \"acc_norm_stderr\": 0.039621355734862175\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \
\ \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\"\
: 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \
\ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6127167630057804,\n\
\ \"acc_stderr\": 0.03714325906302065,\n \"acc_norm\": 0.6127167630057804,\n\
\ \"acc_norm_stderr\": 0.03714325906302065\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.3235294117647059,\n \"acc_stderr\": 0.046550104113196177,\n\
\ \"acc_norm\": 0.3235294117647059,\n \"acc_norm_stderr\": 0.046550104113196177\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.68,\n \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.68,\n\
\ \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.4595744680851064,\n \"acc_stderr\": 0.03257901482099835,\n\
\ \"acc_norm\": 0.4595744680851064,\n \"acc_norm_stderr\": 0.03257901482099835\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.39473684210526316,\n\
\ \"acc_stderr\": 0.045981880578165414,\n \"acc_norm\": 0.39473684210526316,\n\
\ \"acc_norm_stderr\": 0.045981880578165414\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5310344827586206,\n \"acc_stderr\": 0.04158632762097828,\n\
\ \"acc_norm\": 0.5310344827586206,\n \"acc_norm_stderr\": 0.04158632762097828\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.41798941798941797,\n \"acc_stderr\": 0.02540255550326091,\n \"\
acc_norm\": 0.41798941798941797,\n \"acc_norm_stderr\": 0.02540255550326091\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.36507936507936506,\n\
\ \"acc_stderr\": 0.04306241259127153,\n \"acc_norm\": 0.36507936507936506,\n\
\ \"acc_norm_stderr\": 0.04306241259127153\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252605,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252605\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7129032258064516,\n\
\ \"acc_stderr\": 0.025736542745594528,\n \"acc_norm\": 0.7129032258064516,\n\
\ \"acc_norm_stderr\": 0.025736542745594528\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.4729064039408867,\n \"acc_stderr\": 0.03512819077876106,\n\
\ \"acc_norm\": 0.4729064039408867,\n \"acc_norm_stderr\": 0.03512819077876106\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.6,\n \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\"\
: 0.6,\n \"acc_norm_stderr\": 0.049236596391733084\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7151515151515152,\n \"acc_stderr\": 0.0352439084451178,\n\
\ \"acc_norm\": 0.7151515151515152,\n \"acc_norm_stderr\": 0.0352439084451178\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7626262626262627,\n \"acc_stderr\": 0.0303137105381989,\n \"acc_norm\"\
: 0.7626262626262627,\n \"acc_norm_stderr\": 0.0303137105381989\n },\n\
\ \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \
\ \"acc\": 0.7823834196891192,\n \"acc_stderr\": 0.029778663037752954,\n\
\ \"acc_norm\": 0.7823834196891192,\n \"acc_norm_stderr\": 0.029778663037752954\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.5769230769230769,\n \"acc_stderr\": 0.025049197876042345,\n\
\ \"acc_norm\": 0.5769230769230769,\n \"acc_norm_stderr\": 0.025049197876042345\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3037037037037037,\n \"acc_stderr\": 0.028037929969114986,\n \
\ \"acc_norm\": 0.3037037037037037,\n \"acc_norm_stderr\": 0.028037929969114986\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.5630252100840336,\n \"acc_stderr\": 0.032219436365661956,\n\
\ \"acc_norm\": 0.5630252100840336,\n \"acc_norm_stderr\": 0.032219436365661956\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.37748344370860926,\n \"acc_stderr\": 0.0395802723112157,\n \"\
acc_norm\": 0.37748344370860926,\n \"acc_norm_stderr\": 0.0395802723112157\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.7798165137614679,\n \"acc_stderr\": 0.017765978652327562,\n \"\
acc_norm\": 0.7798165137614679,\n \"acc_norm_stderr\": 0.017765978652327562\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.39814814814814814,\n \"acc_stderr\": 0.033384734032074016,\n \"\
acc_norm\": 0.39814814814814814,\n \"acc_norm_stderr\": 0.033384734032074016\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7450980392156863,\n \"acc_stderr\": 0.03058759135160425,\n \"\
acc_norm\": 0.7450980392156863,\n \"acc_norm_stderr\": 0.03058759135160425\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7848101265822784,\n \"acc_stderr\": 0.026750826994676166,\n \
\ \"acc_norm\": 0.7848101265822784,\n \"acc_norm_stderr\": 0.026750826994676166\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6412556053811659,\n\
\ \"acc_stderr\": 0.03219079200419995,\n \"acc_norm\": 0.6412556053811659,\n\
\ \"acc_norm_stderr\": 0.03219079200419995\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7022900763358778,\n \"acc_stderr\": 0.04010358942462203,\n\
\ \"acc_norm\": 0.7022900763358778,\n \"acc_norm_stderr\": 0.04010358942462203\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.71900826446281,\n \"acc_stderr\": 0.04103203830514511,\n \"acc_norm\"\
: 0.71900826446281,\n \"acc_norm_stderr\": 0.04103203830514511\n },\n\
\ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.6481481481481481,\n\
\ \"acc_stderr\": 0.046166311118017125,\n \"acc_norm\": 0.6481481481481481,\n\
\ \"acc_norm_stderr\": 0.046166311118017125\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.6687116564417178,\n \"acc_stderr\": 0.03697983910025588,\n\
\ \"acc_norm\": 0.6687116564417178,\n \"acc_norm_stderr\": 0.03697983910025588\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.375,\n\
\ \"acc_stderr\": 0.04595091388086298,\n \"acc_norm\": 0.375,\n \
\ \"acc_norm_stderr\": 0.04595091388086298\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.040580420156460344,\n\
\ \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.040580420156460344\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8162393162393162,\n\
\ \"acc_stderr\": 0.025372139671722933,\n \"acc_norm\": 0.8162393162393162,\n\
\ \"acc_norm_stderr\": 0.025372139671722933\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542129,\n \
\ \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.04512608598542129\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7739463601532567,\n\
\ \"acc_stderr\": 0.014957458504335833,\n \"acc_norm\": 0.7739463601532567,\n\
\ \"acc_norm_stderr\": 0.014957458504335833\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.615606936416185,\n \"acc_stderr\": 0.026189666966272035,\n\
\ \"acc_norm\": 0.615606936416185,\n \"acc_norm_stderr\": 0.026189666966272035\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3553072625698324,\n\
\ \"acc_stderr\": 0.01600698993480319,\n \"acc_norm\": 0.3553072625698324,\n\
\ \"acc_norm_stderr\": 0.01600698993480319\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.630718954248366,\n \"acc_stderr\": 0.02763417668960266,\n\
\ \"acc_norm\": 0.630718954248366,\n \"acc_norm_stderr\": 0.02763417668960266\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6463022508038585,\n\
\ \"acc_stderr\": 0.02715520810320086,\n \"acc_norm\": 0.6463022508038585,\n\
\ \"acc_norm_stderr\": 0.02715520810320086\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.6759259259259259,\n \"acc_stderr\": 0.02604176620271716,\n\
\ \"acc_norm\": 0.6759259259259259,\n \"acc_norm_stderr\": 0.02604176620271716\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.42907801418439717,\n \"acc_stderr\": 0.02952591430255856,\n \
\ \"acc_norm\": 0.42907801418439717,\n \"acc_norm_stderr\": 0.02952591430255856\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4302477183833116,\n\
\ \"acc_stderr\": 0.012645361435115233,\n \"acc_norm\": 0.4302477183833116,\n\
\ \"acc_norm_stderr\": 0.012645361435115233\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.5220588235294118,\n \"acc_stderr\": 0.03034326422421352,\n\
\ \"acc_norm\": 0.5220588235294118,\n \"acc_norm_stderr\": 0.03034326422421352\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.5980392156862745,\n \"acc_stderr\": 0.01983517648437539,\n \
\ \"acc_norm\": 0.5980392156862745,\n \"acc_norm_stderr\": 0.01983517648437539\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6454545454545455,\n\
\ \"acc_stderr\": 0.04582004841505417,\n \"acc_norm\": 0.6454545454545455,\n\
\ \"acc_norm_stderr\": 0.04582004841505417\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.5265306122448979,\n \"acc_stderr\": 0.03196412734523272,\n\
\ \"acc_norm\": 0.5265306122448979,\n \"acc_norm_stderr\": 0.03196412734523272\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7711442786069652,\n\
\ \"acc_stderr\": 0.029705284056772432,\n \"acc_norm\": 0.7711442786069652,\n\
\ \"acc_norm_stderr\": 0.029705284056772432\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \
\ \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4578313253012048,\n\
\ \"acc_stderr\": 0.0387862677100236,\n \"acc_norm\": 0.4578313253012048,\n\
\ \"acc_norm_stderr\": 0.0387862677100236\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.7602339181286549,\n \"acc_stderr\": 0.032744852119469564,\n\
\ \"acc_norm\": 0.7602339181286549,\n \"acc_norm_stderr\": 0.032744852119469564\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3684210526315789,\n\
\ \"mc1_stderr\": 0.016886551261046046,\n \"mc2\": 0.531795789007015,\n\
\ \"mc2_stderr\": 0.015539765760842488\n }\n}\n```"
repo_url: https://huggingface.co/Weyaxi/OpenOrca-Nebula-7B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|arc:challenge|25_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hellaswag|10_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-11-08T11-58-02.317093.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-management|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-virology|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-08T11-58-02.317093.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- '**/details_harness|truthfulqa:mc|0_2023-11-08T11-58-02.317093.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-11-08T11-58-02.317093.parquet'
- config_name: results
data_files:
- split: 2023_11_08T11_58_02.317093
path:
- results_2023-11-08T11-58-02.317093.parquet
- split: latest
path:
- results_2023-11-08T11-58-02.317093.parquet
---
# Dataset Card for Evaluation run of Weyaxi/OpenOrca-Nebula-7B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Weyaxi/OpenOrca-Nebula-7B
- **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 [Weyaxi/OpenOrca-Nebula-7B](https://huggingface.co/Weyaxi/OpenOrca-Nebula-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Weyaxi__OpenOrca-Nebula-7B_public",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-08T11:58:02.317093](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__OpenOrca-Nebula-7B_public/blob/main/results_2023-11-08T11-58-02.317093.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": 0.5781344309327976,
"acc_stderr": 0.03435050067075012,
"acc_norm": 0.581933273042423,
"acc_norm_stderr": 0.03433158518593753,
"mc1": 0.3684210526315789,
"mc1_stderr": 0.016886551261046046,
"mc2": 0.531795789007015,
"mc2_stderr": 0.015539765760842488
},
"harness|arc:challenge|25": {
"acc": 0.552901023890785,
"acc_stderr": 0.014529380160526848,
"acc_norm": 0.5870307167235495,
"acc_norm_stderr": 0.014388344935398326
},
"harness|hellaswag|10": {
"acc": 0.6283608842859988,
"acc_stderr": 0.004822550638450896,
"acc_norm": 0.8183628759211312,
"acc_norm_stderr": 0.0038475722596364257
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.32,
"acc_stderr": 0.04688261722621504,
"acc_norm": 0.32,
"acc_norm_stderr": 0.04688261722621504
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5777777777777777,
"acc_stderr": 0.04266763404099582,
"acc_norm": 0.5777777777777777,
"acc_norm_stderr": 0.04266763404099582
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.5986842105263158,
"acc_stderr": 0.03988903703336284,
"acc_norm": 0.5986842105263158,
"acc_norm_stderr": 0.03988903703336284
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.52,
"acc_stderr": 0.05021167315686779,
"acc_norm": 0.52,
"acc_norm_stderr": 0.05021167315686779
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.5773584905660377,
"acc_stderr": 0.03040233144576954,
"acc_norm": 0.5773584905660377,
"acc_norm_stderr": 0.03040233144576954
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.6597222222222222,
"acc_stderr": 0.039621355734862175,
"acc_norm": 0.6597222222222222,
"acc_norm_stderr": 0.039621355734862175
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.42,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.42,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.48,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.48,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.38,
"acc_stderr": 0.048783173121456316,
"acc_norm": 0.38,
"acc_norm_stderr": 0.048783173121456316
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6127167630057804,
"acc_stderr": 0.03714325906302065,
"acc_norm": 0.6127167630057804,
"acc_norm_stderr": 0.03714325906302065
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.3235294117647059,
"acc_stderr": 0.046550104113196177,
"acc_norm": 0.3235294117647059,
"acc_norm_stderr": 0.046550104113196177
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.68,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.68,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.4595744680851064,
"acc_stderr": 0.03257901482099835,
"acc_norm": 0.4595744680851064,
"acc_norm_stderr": 0.03257901482099835
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.39473684210526316,
"acc_stderr": 0.045981880578165414,
"acc_norm": 0.39473684210526316,
"acc_norm_stderr": 0.045981880578165414
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5310344827586206,
"acc_stderr": 0.04158632762097828,
"acc_norm": 0.5310344827586206,
"acc_norm_stderr": 0.04158632762097828
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.41798941798941797,
"acc_stderr": 0.02540255550326091,
"acc_norm": 0.41798941798941797,
"acc_norm_stderr": 0.02540255550326091
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.36507936507936506,
"acc_stderr": 0.04306241259127153,
"acc_norm": 0.36507936507936506,
"acc_norm_stderr": 0.04306241259127153
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.33,
"acc_stderr": 0.04725815626252605,
"acc_norm": 0.33,
"acc_norm_stderr": 0.04725815626252605
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7129032258064516,
"acc_stderr": 0.025736542745594528,
"acc_norm": 0.7129032258064516,
"acc_norm_stderr": 0.025736542745594528
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.4729064039408867,
"acc_stderr": 0.03512819077876106,
"acc_norm": 0.4729064039408867,
"acc_norm_stderr": 0.03512819077876106
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.6,
"acc_stderr": 0.049236596391733084,
"acc_norm": 0.6,
"acc_norm_stderr": 0.049236596391733084
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7151515151515152,
"acc_stderr": 0.0352439084451178,
"acc_norm": 0.7151515151515152,
"acc_norm_stderr": 0.0352439084451178
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7626262626262627,
"acc_stderr": 0.0303137105381989,
"acc_norm": 0.7626262626262627,
"acc_norm_stderr": 0.0303137105381989
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.7823834196891192,
"acc_stderr": 0.029778663037752954,
"acc_norm": 0.7823834196891192,
"acc_norm_stderr": 0.029778663037752954
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.5769230769230769,
"acc_stderr": 0.025049197876042345,
"acc_norm": 0.5769230769230769,
"acc_norm_stderr": 0.025049197876042345
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.3037037037037037,
"acc_stderr": 0.028037929969114986,
"acc_norm": 0.3037037037037037,
"acc_norm_stderr": 0.028037929969114986
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.5630252100840336,
"acc_stderr": 0.032219436365661956,
"acc_norm": 0.5630252100840336,
"acc_norm_stderr": 0.032219436365661956
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.37748344370860926,
"acc_stderr": 0.0395802723112157,
"acc_norm": 0.37748344370860926,
"acc_norm_stderr": 0.0395802723112157
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.7798165137614679,
"acc_stderr": 0.017765978652327562,
"acc_norm": 0.7798165137614679,
"acc_norm_stderr": 0.017765978652327562
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.39814814814814814,
"acc_stderr": 0.033384734032074016,
"acc_norm": 0.39814814814814814,
"acc_norm_stderr": 0.033384734032074016
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7450980392156863,
"acc_stderr": 0.03058759135160425,
"acc_norm": 0.7450980392156863,
"acc_norm_stderr": 0.03058759135160425
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7848101265822784,
"acc_stderr": 0.026750826994676166,
"acc_norm": 0.7848101265822784,
"acc_norm_stderr": 0.026750826994676166
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6412556053811659,
"acc_stderr": 0.03219079200419995,
"acc_norm": 0.6412556053811659,
"acc_norm_stderr": 0.03219079200419995
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7022900763358778,
"acc_stderr": 0.04010358942462203,
"acc_norm": 0.7022900763358778,
"acc_norm_stderr": 0.04010358942462203
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.71900826446281,
"acc_stderr": 0.04103203830514511,
"acc_norm": 0.71900826446281,
"acc_norm_stderr": 0.04103203830514511
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.6481481481481481,
"acc_stderr": 0.046166311118017125,
"acc_norm": 0.6481481481481481,
"acc_norm_stderr": 0.046166311118017125
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.6687116564417178,
"acc_stderr": 0.03697983910025588,
"acc_norm": 0.6687116564417178,
"acc_norm_stderr": 0.03697983910025588
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.375,
"acc_stderr": 0.04595091388086298,
"acc_norm": 0.375,
"acc_norm_stderr": 0.04595091388086298
},
"harness|hendrycksTest-management|5": {
"acc": 0.7864077669902912,
"acc_stderr": 0.040580420156460344,
"acc_norm": 0.7864077669902912,
"acc_norm_stderr": 0.040580420156460344
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8162393162393162,
"acc_stderr": 0.025372139671722933,
"acc_norm": 0.8162393162393162,
"acc_norm_stderr": 0.025372139671722933
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.72,
"acc_stderr": 0.04512608598542129,
"acc_norm": 0.72,
"acc_norm_stderr": 0.04512608598542129
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.7739463601532567,
"acc_stderr": 0.014957458504335833,
"acc_norm": 0.7739463601532567,
"acc_norm_stderr": 0.014957458504335833
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.615606936416185,
"acc_stderr": 0.026189666966272035,
"acc_norm": 0.615606936416185,
"acc_norm_stderr": 0.026189666966272035
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.3553072625698324,
"acc_stderr": 0.01600698993480319,
"acc_norm": 0.3553072625698324,
"acc_norm_stderr": 0.01600698993480319
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.630718954248366,
"acc_stderr": 0.02763417668960266,
"acc_norm": 0.630718954248366,
"acc_norm_stderr": 0.02763417668960266
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.6463022508038585,
"acc_stderr": 0.02715520810320086,
"acc_norm": 0.6463022508038585,
"acc_norm_stderr": 0.02715520810320086
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.6759259259259259,
"acc_stderr": 0.02604176620271716,
"acc_norm": 0.6759259259259259,
"acc_norm_stderr": 0.02604176620271716
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.42907801418439717,
"acc_stderr": 0.02952591430255856,
"acc_norm": 0.42907801418439717,
"acc_norm_stderr": 0.02952591430255856
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4302477183833116,
"acc_stderr": 0.012645361435115233,
"acc_norm": 0.4302477183833116,
"acc_norm_stderr": 0.012645361435115233
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.5220588235294118,
"acc_stderr": 0.03034326422421352,
"acc_norm": 0.5220588235294118,
"acc_norm_stderr": 0.03034326422421352
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.5980392156862745,
"acc_stderr": 0.01983517648437539,
"acc_norm": 0.5980392156862745,
"acc_norm_stderr": 0.01983517648437539
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6454545454545455,
"acc_stderr": 0.04582004841505417,
"acc_norm": 0.6454545454545455,
"acc_norm_stderr": 0.04582004841505417
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.5265306122448979,
"acc_stderr": 0.03196412734523272,
"acc_norm": 0.5265306122448979,
"acc_norm_stderr": 0.03196412734523272
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.7711442786069652,
"acc_stderr": 0.029705284056772432,
"acc_norm": 0.7711442786069652,
"acc_norm_stderr": 0.029705284056772432
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.79,
"acc_stderr": 0.040936018074033256,
"acc_norm": 0.79,
"acc_norm_stderr": 0.040936018074033256
},
"harness|hendrycksTest-virology|5": {
"acc": 0.4578313253012048,
"acc_stderr": 0.0387862677100236,
"acc_norm": 0.4578313253012048,
"acc_norm_stderr": 0.0387862677100236
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.7602339181286549,
"acc_stderr": 0.032744852119469564,
"acc_norm": 0.7602339181286549,
"acc_norm_stderr": 0.032744852119469564
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3684210526315789,
"mc1_stderr": 0.016886551261046046,
"mc2": 0.531795789007015,
"mc2_stderr": 0.015539765760842488
}
}
```
### 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] | [
-0.7301012873649597,
-0.8451433181762695,
0.2815612554550171,
0.19767087697982788,
-0.18912255764007568,
-0.0749097615480423,
0.031257204711437225,
-0.22665652632713318,
0.5999518632888794,
-0.03262026607990265,
-0.45544466376304626,
-0.7243497371673584,
-0.43413907289505005,
0.22836242616... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
open-llm-leaderboard/details_pankajmathur__Mistral-7B-model_45k6e2e4_public | open-llm-leaderboard | 2023-11-08T12:04:33Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T12:04:33Z | 2023-11-08T12:03:34.000Z | 2023-11-08T12:03:34 | ---
pretty_name: Evaluation run of pankajmathur/Mistral-7B-model_45k6e2e4
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [pankajmathur/Mistral-7B-model_45k6e2e4](https://huggingface.co/pankajmathur/Mistral-7B-model_45k6e2e4)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 61 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the 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_pankajmathur__Mistral-7B-model_45k6e2e4_public\"\
,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\
\nThese are the [latest results from run 2023-11-08T12:00:55.074514](https://huggingface.co/datasets/open-llm-leaderboard/details_pankajmathur__Mistral-7B-model_45k6e2e4_public/blob/main/results_2023-11-08T12-00-55.074514.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\": 0.23184197178254598,\n\
\ \"acc_stderr\": 0.030693965962788314,\n \"acc_norm\": 0.23241967500614574,\n\
\ \"acc_norm_stderr\": 0.030706854185546608,\n \"mc1\": 0.24357405140758873,\n\
\ \"mc1_stderr\": 0.01502635482491078,\n \"mc2\": 0.5084657838244592,\n\
\ \"mc2_stderr\": 0.016201328114036084\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.2022184300341297,\n \"acc_stderr\": 0.011737454431872104,\n\
\ \"acc_norm\": 0.2431740614334471,\n \"acc_norm_stderr\": 0.01253655414458709\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.2577175861382195,\n\
\ \"acc_stderr\": 0.004364838000335622,\n \"acc_norm\": 0.2508464449312886,\n\
\ \"acc_norm_stderr\": 0.0043261434303600976\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.17,\n \"acc_stderr\": 0.0377525168068637,\n \
\ \"acc_norm\": 0.17,\n \"acc_norm_stderr\": 0.0377525168068637\n },\n\
\ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.18518518518518517,\n\
\ \"acc_stderr\": 0.03355677216313142,\n \"acc_norm\": 0.18518518518518517,\n\
\ \"acc_norm_stderr\": 0.03355677216313142\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.17763157894736842,\n \"acc_stderr\": 0.031103182383123398,\n\
\ \"acc_norm\": 0.17763157894736842,\n \"acc_norm_stderr\": 0.031103182383123398\n\
\ },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\"\
: {\n \"acc\": 0.21509433962264152,\n \"acc_stderr\": 0.02528839450289137,\n\
\ \"acc_norm\": 0.21509433962264152,\n \"acc_norm_stderr\": 0.02528839450289137\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2569444444444444,\n\
\ \"acc_stderr\": 0.03653946969442099,\n \"acc_norm\": 0.2569444444444444,\n\
\ \"acc_norm_stderr\": 0.03653946969442099\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909284,\n \
\ \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909284\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.26,\n \"acc_stderr\": 0.0440844002276808,\n \"acc_norm\": 0.26,\n\
\ \"acc_norm_stderr\": 0.0440844002276808\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.18,\n \"acc_stderr\": 0.03861229196653695,\n \
\ \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.03861229196653695\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.20809248554913296,\n\
\ \"acc_stderr\": 0.030952890217749874,\n \"acc_norm\": 0.20809248554913296,\n\
\ \"acc_norm_stderr\": 0.030952890217749874\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.04092563958237654,\n\
\ \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.04092563958237654\n\
\ },\n \"harness|hendrycksTest-computer_security|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-conceptual_physics|5\"\
: {\n \"acc\": 0.26382978723404255,\n \"acc_stderr\": 0.028809989854102973,\n\
\ \"acc_norm\": 0.26382978723404255,\n \"acc_norm_stderr\": 0.028809989854102973\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.23684210526315788,\n\
\ \"acc_stderr\": 0.039994238792813365,\n \"acc_norm\": 0.23684210526315788,\n\
\ \"acc_norm_stderr\": 0.039994238792813365\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.23448275862068965,\n \"acc_stderr\": 0.035306258743465914,\n\
\ \"acc_norm\": 0.23448275862068965,\n \"acc_norm_stderr\": 0.035306258743465914\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.24867724867724866,\n \"acc_stderr\": 0.022261817692400175,\n \"\
acc_norm\": 0.24867724867724866,\n \"acc_norm_stderr\": 0.022261817692400175\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\": 0.18,\n \"acc_stderr\": 0.038612291966536934,\n \
\ \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.038612291966536934\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.1774193548387097,\n \"acc_stderr\": 0.02173254068932927,\n \"\
acc_norm\": 0.1774193548387097,\n \"acc_norm_stderr\": 0.02173254068932927\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.15270935960591134,\n \"acc_stderr\": 0.02530890453938062,\n \"\
acc_norm\": 0.15270935960591134,\n \"acc_norm_stderr\": 0.02530890453938062\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.27,\n \"acc_stderr\": 0.0446196043338474,\n \"acc_norm\"\
: 0.27,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03225078108306289,\n\
\ \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03225078108306289\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.17676767676767677,\n \"acc_stderr\": 0.027178752639044915,\n \"\
acc_norm\": 0.17676767676767677,\n \"acc_norm_stderr\": 0.027178752639044915\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.19689119170984457,\n \"acc_stderr\": 0.028697873971860664,\n\
\ \"acc_norm\": 0.19689119170984457,\n \"acc_norm_stderr\": 0.028697873971860664\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.20512820512820512,\n \"acc_stderr\": 0.02047323317355198,\n\
\ \"acc_norm\": 0.20512820512820512,\n \"acc_norm_stderr\": 0.02047323317355198\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.2074074074074074,\n \"acc_stderr\": 0.024720713193952165,\n \
\ \"acc_norm\": 0.2074074074074074,\n \"acc_norm_stderr\": 0.024720713193952165\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.21008403361344538,\n \"acc_stderr\": 0.026461398717471874,\n\
\ \"acc_norm\": 0.21008403361344538,\n \"acc_norm_stderr\": 0.026461398717471874\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.18543046357615894,\n \"acc_stderr\": 0.03173284384294285,\n \"\
acc_norm\": 0.18543046357615894,\n \"acc_norm_stderr\": 0.03173284384294285\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.1926605504587156,\n \"acc_stderr\": 0.016909276884936094,\n \"\
acc_norm\": 0.1926605504587156,\n \"acc_norm_stderr\": 0.016909276884936094\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.1527777777777778,\n \"acc_stderr\": 0.024536326026134224,\n \"\
acc_norm\": 0.1527777777777778,\n \"acc_norm_stderr\": 0.024536326026134224\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.25,\n \"acc_stderr\": 0.03039153369274154,\n \"acc_norm\": 0.25,\n\
\ \"acc_norm_stderr\": 0.03039153369274154\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\
: {\n \"acc\": 0.270042194092827,\n \"acc_stderr\": 0.028900721906293426,\n\
\ \"acc_norm\": 0.270042194092827,\n \"acc_norm_stderr\": 0.028900721906293426\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.31390134529147984,\n\
\ \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.31390134529147984,\n\
\ \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.2595419847328244,\n \"acc_stderr\": 0.03844876139785271,\n\
\ \"acc_norm\": 0.2595419847328244,\n \"acc_norm_stderr\": 0.03844876139785271\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.2396694214876033,\n \"acc_stderr\": 0.03896878985070417,\n \"\
acc_norm\": 0.2396694214876033,\n \"acc_norm_stderr\": 0.03896878985070417\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.25925925925925924,\n\
\ \"acc_stderr\": 0.042365112580946336,\n \"acc_norm\": 0.25925925925925924,\n\
\ \"acc_norm_stderr\": 0.042365112580946336\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.22085889570552147,\n \"acc_stderr\": 0.032591773927421776,\n\
\ \"acc_norm\": 0.22085889570552147,\n \"acc_norm_stderr\": 0.032591773927421776\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.33035714285714285,\n\
\ \"acc_stderr\": 0.04464285714285713,\n \"acc_norm\": 0.33035714285714285,\n\
\ \"acc_norm_stderr\": 0.04464285714285713\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.17475728155339806,\n \"acc_stderr\": 0.037601780060266224,\n\
\ \"acc_norm\": 0.17475728155339806,\n \"acc_norm_stderr\": 0.037601780060266224\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.2905982905982906,\n\
\ \"acc_stderr\": 0.02974504857267404,\n \"acc_norm\": 0.2905982905982906,\n\
\ \"acc_norm_stderr\": 0.02974504857267404\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.23754789272030652,\n\
\ \"acc_stderr\": 0.015218733046150193,\n \"acc_norm\": 0.23754789272030652,\n\
\ \"acc_norm_stderr\": 0.015218733046150193\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.24855491329479767,\n \"acc_stderr\": 0.023267528432100174,\n\
\ \"acc_norm\": 0.24855491329479767,\n \"acc_norm_stderr\": 0.023267528432100174\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\": 0.22549019607843138,\n \"acc_stderr\": 0.023929155517351284,\n\
\ \"acc_norm\": 0.22549019607843138,\n \"acc_norm_stderr\": 0.023929155517351284\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.1864951768488746,\n\
\ \"acc_stderr\": 0.02212243977248077,\n \"acc_norm\": 0.1864951768488746,\n\
\ \"acc_norm_stderr\": 0.02212243977248077\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.21604938271604937,\n \"acc_stderr\": 0.022899162918445806,\n\
\ \"acc_norm\": 0.21604938271604937,\n \"acc_norm_stderr\": 0.022899162918445806\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.23404255319148937,\n \"acc_stderr\": 0.025257861359432414,\n \
\ \"acc_norm\": 0.23404255319148937,\n \"acc_norm_stderr\": 0.025257861359432414\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2457627118644068,\n\
\ \"acc_stderr\": 0.010996156635142692,\n \"acc_norm\": 0.2457627118644068,\n\
\ \"acc_norm_stderr\": 0.010996156635142692\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.18382352941176472,\n \"acc_stderr\": 0.023529242185193106,\n\
\ \"acc_norm\": 0.18382352941176472,\n \"acc_norm_stderr\": 0.023529242185193106\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.25,\n \"acc_stderr\": 0.01751781884501444,\n \"acc_norm\"\
: 0.25,\n \"acc_norm_stderr\": 0.01751781884501444\n },\n \"harness|hendrycksTest-public_relations|5\"\
: {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03955932861795833,\n\
\ \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03955932861795833\n\
\ },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.18775510204081633,\n\
\ \"acc_stderr\": 0.02500025603954621,\n \"acc_norm\": 0.18775510204081633,\n\
\ \"acc_norm_stderr\": 0.02500025603954621\n },\n \"harness|hendrycksTest-sociology|5\"\
: {\n \"acc\": 0.24378109452736318,\n \"acc_stderr\": 0.03036049015401465,\n\
\ \"acc_norm\": 0.24378109452736318,\n \"acc_norm_stderr\": 0.03036049015401465\n\
\ },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\":\
\ 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.28,\n\
\ \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-virology|5\"\
: {\n \"acc\": 0.28313253012048195,\n \"acc_stderr\": 0.03507295431370518,\n\
\ \"acc_norm\": 0.28313253012048195,\n \"acc_norm_stderr\": 0.03507295431370518\n\
\ },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.3216374269005848,\n\
\ \"acc_stderr\": 0.03582529442573122,\n \"acc_norm\": 0.3216374269005848,\n\
\ \"acc_norm_stderr\": 0.03582529442573122\n },\n \"harness|truthfulqa:mc|0\"\
: {\n \"mc1\": 0.24357405140758873,\n \"mc1_stderr\": 0.01502635482491078,\n\
\ \"mc2\": 0.5084657838244592,\n \"mc2_stderr\": 0.016201328114036084\n\
\ }\n}\n```"
repo_url: https://huggingface.co/pankajmathur/Mistral-7B-model_45k6e2e4
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|arc:challenge|25_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hellaswag|10_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-11-08T12-00-55.074514.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-management|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-virology|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-08T12-00-55.074514.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- '**/details_harness|truthfulqa:mc|0_2023-11-08T12-00-55.074514.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-11-08T12-00-55.074514.parquet'
- config_name: results
data_files:
- split: 2023_11_08T12_00_55.074514
path:
- results_2023-11-08T12-00-55.074514.parquet
- split: latest
path:
- results_2023-11-08T12-00-55.074514.parquet
---
# Dataset Card for Evaluation run of pankajmathur/Mistral-7B-model_45k6e2e4
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/pankajmathur/Mistral-7B-model_45k6e2e4
- **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 [pankajmathur/Mistral-7B-model_45k6e2e4](https://huggingface.co/pankajmathur/Mistral-7B-model_45k6e2e4) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the 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_pankajmathur__Mistral-7B-model_45k6e2e4_public",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-08T12:00:55.074514](https://huggingface.co/datasets/open-llm-leaderboard/details_pankajmathur__Mistral-7B-model_45k6e2e4_public/blob/main/results_2023-11-08T12-00-55.074514.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": 0.23184197178254598,
"acc_stderr": 0.030693965962788314,
"acc_norm": 0.23241967500614574,
"acc_norm_stderr": 0.030706854185546608,
"mc1": 0.24357405140758873,
"mc1_stderr": 0.01502635482491078,
"mc2": 0.5084657838244592,
"mc2_stderr": 0.016201328114036084
},
"harness|arc:challenge|25": {
"acc": 0.2022184300341297,
"acc_stderr": 0.011737454431872104,
"acc_norm": 0.2431740614334471,
"acc_norm_stderr": 0.01253655414458709
},
"harness|hellaswag|10": {
"acc": 0.2577175861382195,
"acc_stderr": 0.004364838000335622,
"acc_norm": 0.2508464449312886,
"acc_norm_stderr": 0.0043261434303600976
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.17,
"acc_stderr": 0.0377525168068637,
"acc_norm": 0.17,
"acc_norm_stderr": 0.0377525168068637
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.18518518518518517,
"acc_stderr": 0.03355677216313142,
"acc_norm": 0.18518518518518517,
"acc_norm_stderr": 0.03355677216313142
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.17763157894736842,
"acc_stderr": 0.031103182383123398,
"acc_norm": 0.17763157894736842,
"acc_norm_stderr": 0.031103182383123398
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.21509433962264152,
"acc_stderr": 0.02528839450289137,
"acc_norm": 0.21509433962264152,
"acc_norm_stderr": 0.02528839450289137
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.2569444444444444,
"acc_stderr": 0.03653946969442099,
"acc_norm": 0.2569444444444444,
"acc_norm_stderr": 0.03653946969442099
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.24,
"acc_stderr": 0.04292346959909284,
"acc_norm": 0.24,
"acc_norm_stderr": 0.04292346959909284
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.26,
"acc_stderr": 0.0440844002276808,
"acc_norm": 0.26,
"acc_norm_stderr": 0.0440844002276808
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.18,
"acc_stderr": 0.03861229196653695,
"acc_norm": 0.18,
"acc_norm_stderr": 0.03861229196653695
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.20809248554913296,
"acc_stderr": 0.030952890217749874,
"acc_norm": 0.20809248554913296,
"acc_norm_stderr": 0.030952890217749874
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.21568627450980393,
"acc_stderr": 0.04092563958237654,
"acc_norm": 0.21568627450980393,
"acc_norm_stderr": 0.04092563958237654
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.29,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.29,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.26382978723404255,
"acc_stderr": 0.028809989854102973,
"acc_norm": 0.26382978723404255,
"acc_norm_stderr": 0.028809989854102973
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.23684210526315788,
"acc_stderr": 0.039994238792813365,
"acc_norm": 0.23684210526315788,
"acc_norm_stderr": 0.039994238792813365
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.23448275862068965,
"acc_stderr": 0.035306258743465914,
"acc_norm": 0.23448275862068965,
"acc_norm_stderr": 0.035306258743465914
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.24867724867724866,
"acc_stderr": 0.022261817692400175,
"acc_norm": 0.24867724867724866,
"acc_norm_stderr": 0.022261817692400175
},
"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": 0.18,
"acc_stderr": 0.038612291966536934,
"acc_norm": 0.18,
"acc_norm_stderr": 0.038612291966536934
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.1774193548387097,
"acc_stderr": 0.02173254068932927,
"acc_norm": 0.1774193548387097,
"acc_norm_stderr": 0.02173254068932927
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.15270935960591134,
"acc_stderr": 0.02530890453938062,
"acc_norm": 0.15270935960591134,
"acc_norm_stderr": 0.02530890453938062
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.27,
"acc_stderr": 0.0446196043338474,
"acc_norm": 0.27,
"acc_norm_stderr": 0.0446196043338474
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.21818181818181817,
"acc_stderr": 0.03225078108306289,
"acc_norm": 0.21818181818181817,
"acc_norm_stderr": 0.03225078108306289
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.17676767676767677,
"acc_stderr": 0.027178752639044915,
"acc_norm": 0.17676767676767677,
"acc_norm_stderr": 0.027178752639044915
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.19689119170984457,
"acc_stderr": 0.028697873971860664,
"acc_norm": 0.19689119170984457,
"acc_norm_stderr": 0.028697873971860664
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.20512820512820512,
"acc_stderr": 0.02047323317355198,
"acc_norm": 0.20512820512820512,
"acc_norm_stderr": 0.02047323317355198
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.2074074074074074,
"acc_stderr": 0.024720713193952165,
"acc_norm": 0.2074074074074074,
"acc_norm_stderr": 0.024720713193952165
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.21008403361344538,
"acc_stderr": 0.026461398717471874,
"acc_norm": 0.21008403361344538,
"acc_norm_stderr": 0.026461398717471874
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.18543046357615894,
"acc_stderr": 0.03173284384294285,
"acc_norm": 0.18543046357615894,
"acc_norm_stderr": 0.03173284384294285
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.1926605504587156,
"acc_stderr": 0.016909276884936094,
"acc_norm": 0.1926605504587156,
"acc_norm_stderr": 0.016909276884936094
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.1527777777777778,
"acc_stderr": 0.024536326026134224,
"acc_norm": 0.1527777777777778,
"acc_norm_stderr": 0.024536326026134224
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.25,
"acc_stderr": 0.03039153369274154,
"acc_norm": 0.25,
"acc_norm_stderr": 0.03039153369274154
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.270042194092827,
"acc_stderr": 0.028900721906293426,
"acc_norm": 0.270042194092827,
"acc_norm_stderr": 0.028900721906293426
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.31390134529147984,
"acc_stderr": 0.031146796482972465,
"acc_norm": 0.31390134529147984,
"acc_norm_stderr": 0.031146796482972465
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.2595419847328244,
"acc_stderr": 0.03844876139785271,
"acc_norm": 0.2595419847328244,
"acc_norm_stderr": 0.03844876139785271
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.2396694214876033,
"acc_stderr": 0.03896878985070417,
"acc_norm": 0.2396694214876033,
"acc_norm_stderr": 0.03896878985070417
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.25925925925925924,
"acc_stderr": 0.042365112580946336,
"acc_norm": 0.25925925925925924,
"acc_norm_stderr": 0.042365112580946336
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.22085889570552147,
"acc_stderr": 0.032591773927421776,
"acc_norm": 0.22085889570552147,
"acc_norm_stderr": 0.032591773927421776
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.33035714285714285,
"acc_stderr": 0.04464285714285713,
"acc_norm": 0.33035714285714285,
"acc_norm_stderr": 0.04464285714285713
},
"harness|hendrycksTest-management|5": {
"acc": 0.17475728155339806,
"acc_stderr": 0.037601780060266224,
"acc_norm": 0.17475728155339806,
"acc_norm_stderr": 0.037601780060266224
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.2905982905982906,
"acc_stderr": 0.02974504857267404,
"acc_norm": 0.2905982905982906,
"acc_norm_stderr": 0.02974504857267404
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.23754789272030652,
"acc_stderr": 0.015218733046150193,
"acc_norm": 0.23754789272030652,
"acc_norm_stderr": 0.015218733046150193
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.24855491329479767,
"acc_stderr": 0.023267528432100174,
"acc_norm": 0.24855491329479767,
"acc_norm_stderr": 0.023267528432100174
},
"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": 0.22549019607843138,
"acc_stderr": 0.023929155517351284,
"acc_norm": 0.22549019607843138,
"acc_norm_stderr": 0.023929155517351284
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.1864951768488746,
"acc_stderr": 0.02212243977248077,
"acc_norm": 0.1864951768488746,
"acc_norm_stderr": 0.02212243977248077
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.21604938271604937,
"acc_stderr": 0.022899162918445806,
"acc_norm": 0.21604938271604937,
"acc_norm_stderr": 0.022899162918445806
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.23404255319148937,
"acc_stderr": 0.025257861359432414,
"acc_norm": 0.23404255319148937,
"acc_norm_stderr": 0.025257861359432414
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.2457627118644068,
"acc_stderr": 0.010996156635142692,
"acc_norm": 0.2457627118644068,
"acc_norm_stderr": 0.010996156635142692
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.18382352941176472,
"acc_stderr": 0.023529242185193106,
"acc_norm": 0.18382352941176472,
"acc_norm_stderr": 0.023529242185193106
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.25,
"acc_stderr": 0.01751781884501444,
"acc_norm": 0.25,
"acc_norm_stderr": 0.01751781884501444
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.21818181818181817,
"acc_stderr": 0.03955932861795833,
"acc_norm": 0.21818181818181817,
"acc_norm_stderr": 0.03955932861795833
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.18775510204081633,
"acc_stderr": 0.02500025603954621,
"acc_norm": 0.18775510204081633,
"acc_norm_stderr": 0.02500025603954621
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.24378109452736318,
"acc_stderr": 0.03036049015401465,
"acc_norm": 0.24378109452736318,
"acc_norm_stderr": 0.03036049015401465
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.28,
"acc_stderr": 0.04512608598542128,
"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542128
},
"harness|hendrycksTest-virology|5": {
"acc": 0.28313253012048195,
"acc_stderr": 0.03507295431370518,
"acc_norm": 0.28313253012048195,
"acc_norm_stderr": 0.03507295431370518
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.3216374269005848,
"acc_stderr": 0.03582529442573122,
"acc_norm": 0.3216374269005848,
"acc_norm_stderr": 0.03582529442573122
},
"harness|truthfulqa:mc|0": {
"mc1": 0.24357405140758873,
"mc1_stderr": 0.01502635482491078,
"mc2": 0.5084657838244592,
"mc2_stderr": 0.016201328114036084
}
}
```
### 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] | [
-0.7476470470428467,
-0.8666878342628479,
0.25134357810020447,
0.20474964380264282,
-0.17895224690437317,
-0.08719049394130707,
0.02714966982603073,
-0.22886915504932404,
0.5436928272247314,
-0.015169434249401093,
-0.48692864179611206,
-0.6727681159973145,
-0.45005548000335693,
0.255043923... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Praghxx/Nosred | Praghxx | 2023-11-08T12:26:00Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-08T12:26:00Z | 2023-11-08T12:04:13.000Z | 2023-11-08T12:04:13 | ---
license: openrail
---
| [
-0.12853367626667023,
-0.18616794049739838,
0.6529126763343811,
0.4943627417087555,
-0.19319313764572144,
0.23607443273067474,
0.36071979999542236,
0.05056338757276535,
0.5793654322624207,
0.7400138974189758,
-0.6508103013038635,
-0.23783987760543823,
-0.710224986076355,
-0.047825977206230... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
AgelessBodySystem/Ageless-Body-System-Official-Website | AgelessBodySystem | 2023-11-08T12:22:42Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T12:22:42Z | 2023-11-08T12:22:42.000Z | 2023-11-08T12:22:42 | Entry not found | [
-0.3227649927139282,
-0.225684255361557,
0.862226128578186,
0.43461498618125916,
-0.5282987952232361,
0.7012963891029358,
0.7915717363357544,
0.07618629932403564,
0.7746025919914246,
0.2563219666481018,
-0.7852816581726074,
-0.2257382869720459,
-0.9104480743408203,
0.5715669393539429,
-0... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
exactor101/amarDataSet | exactor101 | 2023-11-08T12:31:05Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T12:31:05Z | 2023-11-08T12:31:05.000Z | 2023-11-08T12:31:05 | Entry not found | [
-0.3227649927139282,
-0.225684255361557,
0.862226128578186,
0.43461498618125916,
-0.5282987952232361,
0.7012963891029358,
0.7915717363357544,
0.07618629932403564,
0.7746025919914246,
0.2563219666481018,
-0.7852816581726074,
-0.2257382869720459,
-0.9104480743408203,
0.5715669393539429,
-0... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Oedipe/modeleagg | Oedipe | 2023-11-08T20:40:10Z | 0 | 0 | null | [
"license:unknown",
"region:us"
] | 2023-11-08T20:40:10Z | 2023-11-08T12:45:54.000Z | 2023-11-08T12:45:54 | ---
license: unknown
---
| [
-0.12853367626667023,
-0.18616794049739838,
0.6529126763343811,
0.4943627417087555,
-0.19319313764572144,
0.23607443273067474,
0.36071979999542236,
0.05056338757276535,
0.5793654322624207,
0.7400138974189758,
-0.6508103013038635,
-0.23783987760543823,
-0.710224986076355,
-0.047825977206230... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
md-nishat-008/OffMix-3L | md-nishat-008 | 2023-11-08T12:56:32Z | 0 | 0 | null | [
"license:cc-by-nd-4.0",
"arxiv:2310.18387",
"region:us"
] | 2023-11-08T12:56:32Z | 2023-11-08T12:55:53.000Z | 2023-11-08T12:55:53 | ---
license: cc-by-nd-4.0
---
# OffMix-3L: A Novel Code-Mixed Dataset in Bangla-English-Hindi for Offensive Language Identification
**Publication**: *The 11th International Workshop on Natural Language Processing for Social Media (SocialNLP) under AACL-2023.*
**Read in [arXiv](https://arxiv.org/pdf/2310.18387.pdf)**
---
## 📝 Citation
When using the OffMix-3L dataset, please cite the following:
```bibtex
@article{goswami2023offmix,
title={OffMix-3L: A Novel Code-Mixed Dataset in Bangla-English-Hindi for Offensive Language Identification},
author={Goswami, Dhiman and Raihan, Md Nishat and Mahmud, Antara and Anstasopoulos, Antonios and Zampieri, Marcos},
journal={arXiv preprint arXiv:2310.18387},
year={2023}
}
```
---
## 📖 Introduction
Code-mixing is a well-studied linguistic phenomenon when two or more languages are mixed in text or speech. Several datasets have been built with the goal of training computational models for code-mixing. Although it is very common to observe code-mixing with multiple languages, most datasets available contain code-mixed between only two languages. In this paper, we introduce OffMix-3L, a novel dataset for sentiment analysis containing code-mixed data between three languages: Bangla, English, and Hindi.
---
## 📊 Dataset Details
We introduce **OffMix-3L**, a novel three-language code-mixed test dataset with gold standard labels in Bangla-Hindi-English for the task of Sentiment Analysis, containing 1,001 instances.
> We are presenting this dataset exclusively as a test set due to the unique and specialized nature of the task. Such data is very difficult to gather and requires significant expertise to access. The size of the dataset, while limiting for training purposes, offers a high-quality testing environment with gold-standard labels that can serve as a benchmark in this domain.
---
## 📈 Dataset Statistics
| | **All** | **Bangla** | **English** | **Hindi** | **Other** |
|-------------------|---------|------------|-------------|-----------|-----------|
| Tokens | 87,190 | 31,228 | 6,690 | 14,694 | 34,578 |
| Types | 18,787 | 7,714 | 1,135 | 1,413 | 8,645 |
| Max. in instance | 173 | 62 | 20 | 47 | 93 |
| Min. in instance | 41 | 4 | 3 | 2 | 8 |
| Avg | 87.10 | 31.20 | 6.68 | 14.68 | 34.54 |
| Std Dev | 20.58 | 8.60 | 3.05 | 5.74 | 10.98 |
*OffMix-3L Data Card. The row "Avg" represents the average number of tokens with its standard deviation in row "Std Dev".*
---
## 📉 Results
| Models | F1 Score |
|----------------|----------|
| BanglishBERT | **0.68** |
| BERT | 0.66 |
| mBERT | 0.63 |
| HingBERT | 0.60 |
| MuRIL | 0.60 |
| HateBERT | 0.60 |
| fBERT | 0.58 |
| roBERTa | 0.58 |
| XLM-R | 0.57 |
| DistilBERT | 0.57 |
| GPT 3.5 Turbo | 0.57 |
| BanglaBERT | 0.54 |
| IndicBERT | 0.55 |
| HindiBERT | 0.43 |
*Weighted F-1 score for different models: training on synthetic and tested on natural data (OffMix-3L).*
| [
-0.5417752861976624,
-0.6778327226638794,
0.05386975407600403,
0.43748071789741516,
-0.23839877545833588,
0.2616294026374817,
-0.16339440643787384,
-0.44297701120376587,
0.24632184207439423,
0.1988707184791565,
-0.2802610397338867,
-0.7781231999397278,
-0.6506693363189697,
0.09700512886047... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Otter-AI/ShareGPT4-V | Otter-AI | 2023-11-08T23:20:22Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | 2023-11-08T23:20:22Z | 2023-11-08T13:05:25.000Z | 2023-11-08T13:05:25 | ---
license: mit
---
We use our demo's backend user logs (filtered to make sure do not contain any senstive and user related information) to query GPT4-V and share the questions/response.
For each user log, we query GPT4-V to answer the user's questions and let it self-instruct to generate 4-5 questions and answers related to the image.
language:
- en
license:
- cc0-1.0
tags:
- image-text-dataset
- gpt-assisted-dataset
dataset_info:
features:
- name: instruction
dtype: string
- name: response
dtype: string
- name: image
dtype: image
- name: link
dtype: string
- name: message_id
dtype: string
- name: timestamp
dtype: string
configs:
- config_name: default
data_files:
- split: train
path: data/*_sharegpt_log.parquet | [
-0.8112224340438843,
-0.48641589283943176,
0.7253723740577698,
0.02684265933930874,
-0.4589921236038208,
-0.16904613375663757,
0.21015632152557373,
-0.5106042623519897,
0.11754128336906433,
0.5329688191413879,
-0.9176336526870728,
-0.7036167979240417,
-0.34800177812576294,
0.06302519887685... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
dialbird/mental_health_chatbot_dataset | dialbird | 2023-11-08T13:14:28Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T13:14:28Z | 2023-11-08T13:14:24.000Z | 2023-11-08T13:14:24 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 189421
num_examples: 172
download_size: 102271
dataset_size: 189421
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "mental_health_chatbot_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.4714198112487793,
-0.4830271601676941,
0.29335451126098633,
0.17543667554855347,
0.018300555646419525,
0.13352543115615845,
0.17129790782928467,
-0.06345336884260178,
0.8029800653457642,
0.5189820528030396,
-1.050869345664978,
-0.717017650604248,
-0.7273019552230835,
-0.4086387455463409... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
hazmannaim/angkasfera_text | hazmannaim | 2023-11-08T13:21:31Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T13:21:31Z | 2023-11-08T13:17:04.000Z | 2023-11-08T13:17:04 | Entry not found | [
-0.3227645754814148,
-0.22568479180335999,
0.8622264862060547,
0.43461528420448303,
-0.52829909324646,
0.7012971639633179,
0.7915720343589783,
0.07618614286184311,
0.774603009223938,
0.2563217282295227,
-0.7852813005447388,
-0.22573819756507874,
-0.9104477167129517,
0.5715674161911011,
-... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
gh1407/synthetic_causal_pairs | gh1407 | 2023-11-08T14:23:26Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | 2023-11-08T14:23:26Z | 2023-11-08T13:37:47.000Z | 2023-11-08T13:37:47 | ---
license: mit
dataset_info:
features:
- name: 'Unnamed: 0.1'
dtype: int64
- name: 'Unnamed: 0'
dtype: int64
- name: outlet
dtype: string
- name: headline
dtype: string
- name: body
dtype: string
- name: political_leaning
dtype: string
- name: gpt_causal_graph
dtype: string
splits:
- name: train
num_bytes: 923574
num_examples: 200
download_size: 0
dataset_size: 923574
---
Causal pairs generated with chatGPT. Training set. | [
-0.08045290410518646,
-0.6618567109107971,
0.15738509595394135,
0.5471317768096924,
-0.27961501479148865,
-0.489841103553772,
-0.115807443857193,
-0.089484304189682,
0.2092052847146988,
0.3551934063434601,
-0.9233213067054749,
-0.06904895603656769,
-0.7340059876441956,
-0.2591407895088196,... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
NandinhoVinicius/henrique | NandinhoVinicius | 2023-11-08T13:47:20Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-08T13:47:20Z | 2023-11-08T13:38:20.000Z | 2023-11-08T13:38:20 | ---
license: apache-2.0
---
| [
-0.1285335123538971,
-0.1861683875322342,
0.6529128551483154,
0.49436232447624207,
-0.19319400191307068,
0.23607441782951355,
0.36072009801864624,
0.05056373029947281,
0.5793656706809998,
0.7400146722793579,
-0.650810182094574,
-0.23784008622169495,
-0.7102247476577759,
-0.0478255338966846... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
NandinhoVinicius/henriqueju | NandinhoVinicius | 2023-11-08T13:50:25Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-08T13:50:25Z | 2023-11-08T13:49:33.000Z | 2023-11-08T13:49:33 | ---
license: apache-2.0
---
| [
-0.1285335123538971,
-0.1861683875322342,
0.6529128551483154,
0.49436232447624207,
-0.19319400191307068,
0.23607441782951355,
0.36072009801864624,
0.05056373029947281,
0.5793656706809998,
0.7400146722793579,
-0.650810182094574,
-0.23784008622169495,
-0.7102247476577759,
-0.0478255338966846... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
open-llm-leaderboard/details_teknium__CollectiveCognition-v1.1-Mistral-7B_public | open-llm-leaderboard | 2023-11-08T13:52:33Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T13:52:33Z | 2023-11-08T13:51:33.000Z | 2023-11-08T13:51:33 | ---
pretty_name: Evaluation run of teknium/CollectiveCognition-v1.1-Mistral-7B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [teknium/CollectiveCognition-v1.1-Mistral-7B](https://huggingface.co/teknium/CollectiveCognition-v1.1-Mistral-7B)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 61 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the 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_teknium__CollectiveCognition-v1.1-Mistral-7B_public\"\
,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\
\nThese are the [latest results from run 2023-11-08T13:48:47.550072](https://huggingface.co/datasets/open-llm-leaderboard/details_teknium__CollectiveCognition-v1.1-Mistral-7B_public/blob/main/results_2023-11-08T13-48-47.550072.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\": 0.6255358463798112,\n\
\ \"acc_stderr\": 0.03339672778015502,\n \"acc_norm\": 0.6293923050064572,\n\
\ \"acc_norm_stderr\": 0.03337392753120544,\n \"mc1\": 0.40514075887392903,\n\
\ \"mc1_stderr\": 0.01718561172775337,\n \"mc2\": 0.5761479349192792,\n\
\ \"mc2_stderr\": 0.015694985778154737\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5895904436860068,\n \"acc_stderr\": 0.014374922192642666,\n\
\ \"acc_norm\": 0.6254266211604096,\n \"acc_norm_stderr\": 0.014144193471893452\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6495717984465246,\n\
\ \"acc_stderr\": 0.004761289867046067,\n \"acc_norm\": 0.8412666799442342,\n\
\ \"acc_norm_stderr\": 0.0036468038997703447\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6074074074074074,\n\
\ \"acc_stderr\": 0.04218506215368879,\n \"acc_norm\": 0.6074074074074074,\n\
\ \"acc_norm_stderr\": 0.04218506215368879\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6381578947368421,\n \"acc_stderr\": 0.03910525752849724,\n\
\ \"acc_norm\": 0.6381578947368421,\n \"acc_norm_stderr\": 0.03910525752849724\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.57,\n\
\ \"acc_stderr\": 0.04975698519562428,\n \"acc_norm\": 0.57,\n \
\ \"acc_norm_stderr\": 0.04975698519562428\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6754716981132075,\n \"acc_stderr\": 0.028815615713432115,\n\
\ \"acc_norm\": 0.6754716981132075,\n \"acc_norm_stderr\": 0.028815615713432115\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7013888888888888,\n\
\ \"acc_stderr\": 0.03827052357950756,\n \"acc_norm\": 0.7013888888888888,\n\
\ \"acc_norm_stderr\": 0.03827052357950756\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \
\ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.53,\n\
\ \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145634,\n \
\ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145634\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6473988439306358,\n\
\ \"acc_stderr\": 0.036430371689585475,\n \"acc_norm\": 0.6473988439306358,\n\
\ \"acc_norm_stderr\": 0.036430371689585475\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.43137254901960786,\n \"acc_stderr\": 0.04928099597287534,\n\
\ \"acc_norm\": 0.43137254901960786,\n \"acc_norm_stderr\": 0.04928099597287534\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.77,\n \"acc_stderr\": 0.04229525846816506,\n \"acc_norm\": 0.77,\n\
\ \"acc_norm_stderr\": 0.04229525846816506\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5659574468085107,\n \"acc_stderr\": 0.03240038086792747,\n\
\ \"acc_norm\": 0.5659574468085107,\n \"acc_norm_stderr\": 0.03240038086792747\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5263157894736842,\n\
\ \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.5263157894736842,\n\
\ \"acc_norm_stderr\": 0.046970851366478626\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5862068965517241,\n \"acc_stderr\": 0.04104269211806232,\n\
\ \"acc_norm\": 0.5862068965517241,\n \"acc_norm_stderr\": 0.04104269211806232\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.373015873015873,\n \"acc_stderr\": 0.02490699045899257,\n \"acc_norm\"\
: 0.373015873015873,\n \"acc_norm_stderr\": 0.02490699045899257\n },\n\
\ \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3968253968253968,\n\
\ \"acc_stderr\": 0.04375888492727061,\n \"acc_norm\": 0.3968253968253968,\n\
\ \"acc_norm_stderr\": 0.04375888492727061\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \
\ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7161290322580646,\n\
\ \"acc_stderr\": 0.02564938106302927,\n \"acc_norm\": 0.7161290322580646,\n\
\ \"acc_norm_stderr\": 0.02564938106302927\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5172413793103449,\n \"acc_stderr\": 0.035158955511656986,\n\
\ \"acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.035158955511656986\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621504,\n \"acc_norm\"\
: 0.68,\n \"acc_norm_stderr\": 0.04688261722621504\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7696969696969697,\n \"acc_stderr\": 0.0328766675860349,\n\
\ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.0328766675860349\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8131313131313131,\n \"acc_stderr\": 0.027772533334218974,\n \"\
acc_norm\": 0.8131313131313131,\n \"acc_norm_stderr\": 0.027772533334218974\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8704663212435233,\n \"acc_stderr\": 0.024233532297758733,\n\
\ \"acc_norm\": 0.8704663212435233,\n \"acc_norm_stderr\": 0.024233532297758733\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6128205128205129,\n \"acc_stderr\": 0.024697216930878934,\n\
\ \"acc_norm\": 0.6128205128205129,\n \"acc_norm_stderr\": 0.024697216930878934\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3296296296296296,\n \"acc_stderr\": 0.02866120111652458,\n \
\ \"acc_norm\": 0.3296296296296296,\n \"acc_norm_stderr\": 0.02866120111652458\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.634453781512605,\n \"acc_stderr\": 0.031282177063684614,\n \
\ \"acc_norm\": 0.634453781512605,\n \"acc_norm_stderr\": 0.031282177063684614\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.2847682119205298,\n \"acc_stderr\": 0.03684881521389023,\n \"\
acc_norm\": 0.2847682119205298,\n \"acc_norm_stderr\": 0.03684881521389023\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8201834862385321,\n \"acc_stderr\": 0.016465345467391552,\n \"\
acc_norm\": 0.8201834862385321,\n \"acc_norm_stderr\": 0.016465345467391552\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.46296296296296297,\n \"acc_stderr\": 0.03400603625538271,\n \"\
acc_norm\": 0.46296296296296297,\n \"acc_norm_stderr\": 0.03400603625538271\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7647058823529411,\n \"acc_stderr\": 0.02977177522814562,\n \"\
acc_norm\": 0.7647058823529411,\n \"acc_norm_stderr\": 0.02977177522814562\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7721518987341772,\n \"acc_stderr\": 0.027303484599069432,\n \
\ \"acc_norm\": 0.7721518987341772,\n \"acc_norm_stderr\": 0.027303484599069432\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6591928251121076,\n\
\ \"acc_stderr\": 0.03181149747055359,\n \"acc_norm\": 0.6591928251121076,\n\
\ \"acc_norm_stderr\": 0.03181149747055359\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7480916030534351,\n \"acc_stderr\": 0.03807387116306086,\n\
\ \"acc_norm\": 0.7480916030534351,\n \"acc_norm_stderr\": 0.03807387116306086\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098824,\n \"\
acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098824\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7407407407407407,\n\
\ \"acc_stderr\": 0.042365112580946315,\n \"acc_norm\": 0.7407407407407407,\n\
\ \"acc_norm_stderr\": 0.042365112580946315\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7361963190184049,\n \"acc_stderr\": 0.03462419931615624,\n\
\ \"acc_norm\": 0.7361963190184049,\n \"acc_norm_stderr\": 0.03462419931615624\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5089285714285714,\n\
\ \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.5089285714285714,\n\
\ \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n\
\ \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8376068376068376,\n\
\ \"acc_stderr\": 0.02416161812798774,\n \"acc_norm\": 0.8376068376068376,\n\
\ \"acc_norm_stderr\": 0.02416161812798774\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \
\ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7956577266922095,\n\
\ \"acc_stderr\": 0.014419123980931894,\n \"acc_norm\": 0.7956577266922095,\n\
\ \"acc_norm_stderr\": 0.014419123980931894\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6994219653179191,\n \"acc_stderr\": 0.0246853168672578,\n\
\ \"acc_norm\": 0.6994219653179191,\n \"acc_norm_stderr\": 0.0246853168672578\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3329608938547486,\n\
\ \"acc_stderr\": 0.015761716178397563,\n \"acc_norm\": 0.3329608938547486,\n\
\ \"acc_norm_stderr\": 0.015761716178397563\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7287581699346405,\n \"acc_stderr\": 0.025457756696667878,\n\
\ \"acc_norm\": 0.7287581699346405,\n \"acc_norm_stderr\": 0.025457756696667878\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.684887459807074,\n\
\ \"acc_stderr\": 0.026385273703464482,\n \"acc_norm\": 0.684887459807074,\n\
\ \"acc_norm_stderr\": 0.026385273703464482\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7006172839506173,\n \"acc_stderr\": 0.02548311560119545,\n\
\ \"acc_norm\": 0.7006172839506173,\n \"acc_norm_stderr\": 0.02548311560119545\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.48226950354609927,\n \"acc_stderr\": 0.02980873964223777,\n \
\ \"acc_norm\": 0.48226950354609927,\n \"acc_norm_stderr\": 0.02980873964223777\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4589308996088657,\n\
\ \"acc_stderr\": 0.012727084826799804,\n \"acc_norm\": 0.4589308996088657,\n\
\ \"acc_norm_stderr\": 0.012727084826799804\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6360294117647058,\n \"acc_stderr\": 0.02922719246003203,\n\
\ \"acc_norm\": 0.6360294117647058,\n \"acc_norm_stderr\": 0.02922719246003203\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6454248366013072,\n \"acc_stderr\": 0.0193533605475537,\n \
\ \"acc_norm\": 0.6454248366013072,\n \"acc_norm_stderr\": 0.0193533605475537\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6909090909090909,\n\
\ \"acc_stderr\": 0.044262946482000985,\n \"acc_norm\": 0.6909090909090909,\n\
\ \"acc_norm_stderr\": 0.044262946482000985\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7224489795918367,\n \"acc_stderr\": 0.02866685779027465,\n\
\ \"acc_norm\": 0.7224489795918367,\n \"acc_norm_stderr\": 0.02866685779027465\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8258706467661692,\n\
\ \"acc_stderr\": 0.026814951200421603,\n \"acc_norm\": 0.8258706467661692,\n\
\ \"acc_norm_stderr\": 0.026814951200421603\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774709,\n \
\ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774709\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5,\n \
\ \"acc_stderr\": 0.03892494720807614,\n \"acc_norm\": 0.5,\n \"\
acc_norm_stderr\": 0.03892494720807614\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8245614035087719,\n \"acc_stderr\": 0.029170885500727665,\n\
\ \"acc_norm\": 0.8245614035087719,\n \"acc_norm_stderr\": 0.029170885500727665\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.40514075887392903,\n\
\ \"mc1_stderr\": 0.01718561172775337,\n \"mc2\": 0.5761479349192792,\n\
\ \"mc2_stderr\": 0.015694985778154737\n }\n}\n```"
repo_url: https://huggingface.co/teknium/CollectiveCognition-v1.1-Mistral-7B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|arc:challenge|25_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hellaswag|10_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-11-08T13-48-47.550072.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-management|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-virology|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-08T13-48-47.550072.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- '**/details_harness|truthfulqa:mc|0_2023-11-08T13-48-47.550072.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-11-08T13-48-47.550072.parquet'
- config_name: results
data_files:
- split: 2023_11_08T13_48_47.550072
path:
- results_2023-11-08T13-48-47.550072.parquet
- split: latest
path:
- results_2023-11-08T13-48-47.550072.parquet
---
# Dataset Card for Evaluation run of teknium/CollectiveCognition-v1.1-Mistral-7B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/teknium/CollectiveCognition-v1.1-Mistral-7B
- **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 [teknium/CollectiveCognition-v1.1-Mistral-7B](https://huggingface.co/teknium/CollectiveCognition-v1.1-Mistral-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the 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_teknium__CollectiveCognition-v1.1-Mistral-7B_public",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-08T13:48:47.550072](https://huggingface.co/datasets/open-llm-leaderboard/details_teknium__CollectiveCognition-v1.1-Mistral-7B_public/blob/main/results_2023-11-08T13-48-47.550072.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": 0.6255358463798112,
"acc_stderr": 0.03339672778015502,
"acc_norm": 0.6293923050064572,
"acc_norm_stderr": 0.03337392753120544,
"mc1": 0.40514075887392903,
"mc1_stderr": 0.01718561172775337,
"mc2": 0.5761479349192792,
"mc2_stderr": 0.015694985778154737
},
"harness|arc:challenge|25": {
"acc": 0.5895904436860068,
"acc_stderr": 0.014374922192642666,
"acc_norm": 0.6254266211604096,
"acc_norm_stderr": 0.014144193471893452
},
"harness|hellaswag|10": {
"acc": 0.6495717984465246,
"acc_stderr": 0.004761289867046067,
"acc_norm": 0.8412666799442342,
"acc_norm_stderr": 0.0036468038997703447
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6074074074074074,
"acc_stderr": 0.04218506215368879,
"acc_norm": 0.6074074074074074,
"acc_norm_stderr": 0.04218506215368879
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6381578947368421,
"acc_stderr": 0.03910525752849724,
"acc_norm": 0.6381578947368421,
"acc_norm_stderr": 0.03910525752849724
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.57,
"acc_stderr": 0.04975698519562428,
"acc_norm": 0.57,
"acc_norm_stderr": 0.04975698519562428
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6754716981132075,
"acc_stderr": 0.028815615713432115,
"acc_norm": 0.6754716981132075,
"acc_norm_stderr": 0.028815615713432115
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7013888888888888,
"acc_stderr": 0.03827052357950756,
"acc_norm": 0.7013888888888888,
"acc_norm_stderr": 0.03827052357950756
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.46,
"acc_stderr": 0.05009082659620332,
"acc_norm": 0.46,
"acc_norm_stderr": 0.05009082659620332
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.53,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.53,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.38,
"acc_stderr": 0.04878317312145634,
"acc_norm": 0.38,
"acc_norm_stderr": 0.04878317312145634
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6473988439306358,
"acc_stderr": 0.036430371689585475,
"acc_norm": 0.6473988439306358,
"acc_norm_stderr": 0.036430371689585475
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.43137254901960786,
"acc_stderr": 0.04928099597287534,
"acc_norm": 0.43137254901960786,
"acc_norm_stderr": 0.04928099597287534
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.77,
"acc_stderr": 0.04229525846816506,
"acc_norm": 0.77,
"acc_norm_stderr": 0.04229525846816506
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5659574468085107,
"acc_stderr": 0.03240038086792747,
"acc_norm": 0.5659574468085107,
"acc_norm_stderr": 0.03240038086792747
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5263157894736842,
"acc_stderr": 0.046970851366478626,
"acc_norm": 0.5263157894736842,
"acc_norm_stderr": 0.046970851366478626
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5862068965517241,
"acc_stderr": 0.04104269211806232,
"acc_norm": 0.5862068965517241,
"acc_norm_stderr": 0.04104269211806232
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.373015873015873,
"acc_stderr": 0.02490699045899257,
"acc_norm": 0.373015873015873,
"acc_norm_stderr": 0.02490699045899257
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.3968253968253968,
"acc_stderr": 0.04375888492727061,
"acc_norm": 0.3968253968253968,
"acc_norm_stderr": 0.04375888492727061
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.36,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7161290322580646,
"acc_stderr": 0.02564938106302927,
"acc_norm": 0.7161290322580646,
"acc_norm_stderr": 0.02564938106302927
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5172413793103449,
"acc_stderr": 0.035158955511656986,
"acc_norm": 0.5172413793103449,
"acc_norm_stderr": 0.035158955511656986
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.68,
"acc_stderr": 0.04688261722621504,
"acc_norm": 0.68,
"acc_norm_stderr": 0.04688261722621504
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7696969696969697,
"acc_stderr": 0.0328766675860349,
"acc_norm": 0.7696969696969697,
"acc_norm_stderr": 0.0328766675860349
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.8131313131313131,
"acc_stderr": 0.027772533334218974,
"acc_norm": 0.8131313131313131,
"acc_norm_stderr": 0.027772533334218974
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8704663212435233,
"acc_stderr": 0.024233532297758733,
"acc_norm": 0.8704663212435233,
"acc_norm_stderr": 0.024233532297758733
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6128205128205129,
"acc_stderr": 0.024697216930878934,
"acc_norm": 0.6128205128205129,
"acc_norm_stderr": 0.024697216930878934
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.3296296296296296,
"acc_stderr": 0.02866120111652458,
"acc_norm": 0.3296296296296296,
"acc_norm_stderr": 0.02866120111652458
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.634453781512605,
"acc_stderr": 0.031282177063684614,
"acc_norm": 0.634453781512605,
"acc_norm_stderr": 0.031282177063684614
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.2847682119205298,
"acc_stderr": 0.03684881521389023,
"acc_norm": 0.2847682119205298,
"acc_norm_stderr": 0.03684881521389023
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8201834862385321,
"acc_stderr": 0.016465345467391552,
"acc_norm": 0.8201834862385321,
"acc_norm_stderr": 0.016465345467391552
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.46296296296296297,
"acc_stderr": 0.03400603625538271,
"acc_norm": 0.46296296296296297,
"acc_norm_stderr": 0.03400603625538271
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7647058823529411,
"acc_stderr": 0.02977177522814562,
"acc_norm": 0.7647058823529411,
"acc_norm_stderr": 0.02977177522814562
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7721518987341772,
"acc_stderr": 0.027303484599069432,
"acc_norm": 0.7721518987341772,
"acc_norm_stderr": 0.027303484599069432
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6591928251121076,
"acc_stderr": 0.03181149747055359,
"acc_norm": 0.6591928251121076,
"acc_norm_stderr": 0.03181149747055359
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7480916030534351,
"acc_stderr": 0.03807387116306086,
"acc_norm": 0.7480916030534351,
"acc_norm_stderr": 0.03807387116306086
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7933884297520661,
"acc_stderr": 0.03695980128098824,
"acc_norm": 0.7933884297520661,
"acc_norm_stderr": 0.03695980128098824
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7407407407407407,
"acc_stderr": 0.042365112580946315,
"acc_norm": 0.7407407407407407,
"acc_norm_stderr": 0.042365112580946315
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7361963190184049,
"acc_stderr": 0.03462419931615624,
"acc_norm": 0.7361963190184049,
"acc_norm_stderr": 0.03462419931615624
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.5089285714285714,
"acc_stderr": 0.04745033255489123,
"acc_norm": 0.5089285714285714,
"acc_norm_stderr": 0.04745033255489123
},
"harness|hendrycksTest-management|5": {
"acc": 0.7766990291262136,
"acc_stderr": 0.04123553189891431,
"acc_norm": 0.7766990291262136,
"acc_norm_stderr": 0.04123553189891431
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8376068376068376,
"acc_stderr": 0.02416161812798774,
"acc_norm": 0.8376068376068376,
"acc_norm_stderr": 0.02416161812798774
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.71,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.71,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.7956577266922095,
"acc_stderr": 0.014419123980931894,
"acc_norm": 0.7956577266922095,
"acc_norm_stderr": 0.014419123980931894
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.6994219653179191,
"acc_stderr": 0.0246853168672578,
"acc_norm": 0.6994219653179191,
"acc_norm_stderr": 0.0246853168672578
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.3329608938547486,
"acc_stderr": 0.015761716178397563,
"acc_norm": 0.3329608938547486,
"acc_norm_stderr": 0.015761716178397563
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7287581699346405,
"acc_stderr": 0.025457756696667878,
"acc_norm": 0.7287581699346405,
"acc_norm_stderr": 0.025457756696667878
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.684887459807074,
"acc_stderr": 0.026385273703464482,
"acc_norm": 0.684887459807074,
"acc_norm_stderr": 0.026385273703464482
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7006172839506173,
"acc_stderr": 0.02548311560119545,
"acc_norm": 0.7006172839506173,
"acc_norm_stderr": 0.02548311560119545
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.48226950354609927,
"acc_stderr": 0.02980873964223777,
"acc_norm": 0.48226950354609927,
"acc_norm_stderr": 0.02980873964223777
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4589308996088657,
"acc_stderr": 0.012727084826799804,
"acc_norm": 0.4589308996088657,
"acc_norm_stderr": 0.012727084826799804
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6360294117647058,
"acc_stderr": 0.02922719246003203,
"acc_norm": 0.6360294117647058,
"acc_norm_stderr": 0.02922719246003203
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6454248366013072,
"acc_stderr": 0.0193533605475537,
"acc_norm": 0.6454248366013072,
"acc_norm_stderr": 0.0193533605475537
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6909090909090909,
"acc_stderr": 0.044262946482000985,
"acc_norm": 0.6909090909090909,
"acc_norm_stderr": 0.044262946482000985
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7224489795918367,
"acc_stderr": 0.02866685779027465,
"acc_norm": 0.7224489795918367,
"acc_norm_stderr": 0.02866685779027465
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8258706467661692,
"acc_stderr": 0.026814951200421603,
"acc_norm": 0.8258706467661692,
"acc_norm_stderr": 0.026814951200421603
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.84,
"acc_stderr": 0.03684529491774709,
"acc_norm": 0.84,
"acc_norm_stderr": 0.03684529491774709
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5,
"acc_stderr": 0.03892494720807614,
"acc_norm": 0.5,
"acc_norm_stderr": 0.03892494720807614
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8245614035087719,
"acc_stderr": 0.029170885500727665,
"acc_norm": 0.8245614035087719,
"acc_norm_stderr": 0.029170885500727665
},
"harness|truthfulqa:mc|0": {
"mc1": 0.40514075887392903,
"mc1_stderr": 0.01718561172775337,
"mc2": 0.5761479349192792,
"mc2_stderr": 0.015694985778154737
}
}
```
### 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] | [
-0.7034233808517456,
-0.8046762943267822,
0.2960241734981537,
0.18472731113433838,
-0.1897469013929367,
-0.015091011300683022,
0.011630324646830559,
-0.21054813265800476,
0.5670872330665588,
-0.07171789556741714,
-0.47516852617263794,
-0.745947003364563,
-0.46889379620552063,
0.26840144395... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
gray311/Dolphins-Bench | gray311 | 2023-11-16T08:29:49Z | 0 | 0 | null | [
"license:llama2",
"region:us"
] | 2023-11-16T08:29:49Z | 2023-11-08T14:04:06.000Z | 2023-11-08T14:04:06 | ---
license: llama2
---
| [
-0.1285335123538971,
-0.1861683875322342,
0.6529128551483154,
0.49436232447624207,
-0.19319400191307068,
0.23607441782951355,
0.36072009801864624,
0.05056373029947281,
0.5793656706809998,
0.7400146722793579,
-0.650810182094574,
-0.23784008622169495,
-0.7102247476577759,
-0.0478255338966846... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
zhengr/ultrafeedback_binarized | zhengr | 2023-11-08T14:18:27Z | 0 | 0 | null | [
"task_categories:conversational",
"task_categories:text-generation",
"language:en",
"license:mit",
"arxiv:2310.16944",
"region:us"
] | 2023-11-08T14:18:27Z | 2023-11-08T14:18:08.000Z | 2023-11-08T14:18:08 | ---
language:
- en
license: mit
task_categories:
- conversational
- text-generation
pretty_name: UltraFeedback Binarized
configs:
- config_name: default
data_files:
- split: train_sft
path: data/train_sft-*
- split: test_sft
path: data/test_sft-*
- split: train_gen
path: data/train_gen-*
- split: test_gen
path: data/test_gen-*
- split: train_prefs
path: data/train_prefs-*
- split: test_prefs
path: data/test_prefs-*
dataset_info:
features:
- name: prompt
dtype: string
- name: prompt_id
dtype: string
- name: chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: rejected
list:
- name: content
dtype: string
- name: role
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: score_chosen
dtype: float64
- name: score_rejected
dtype: float64
splits:
- name: train_sft
num_bytes: 397273717
num_examples: 61966
- name: test_sft
num_bytes: 6270496
num_examples: 1000
- name: train_gen
num_bytes: 316634390
num_examples: 61966
- name: test_gen
num_bytes: 5008220
num_examples: 1000
- name: train_prefs
num_bytes: 397273717
num_examples: 61966
- name: test_prefs
num_bytes: 12782225
num_examples: 2000
download_size: 636467735
dataset_size: 1135242765
---
# Dataset Card for UltraFeedback Binarized
## Dataset Description
This is a pre-processed version of the [UltraFeedback dataset](https://huggingface.co/datasets/openbmb/UltraFeedback) and was used to train [Zephyr-7Β-β](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta), a state of the art chat model at the 7B parameter scale.
The original UltraFeedback dataset consists of 64k prompts, where is prompt is accompanied with four model completions from a wide variety of open and proprietary models. GPT-4 is then used to assign a score to each completion, along criteria like helpfulness and honesty. To create `UltraFeedback Binarized`, we picked the highest mean score as the "chosen" completion, and one of the remaining 3 at random as the "rejected" one. This defines the preference modelling splits for techniques like reward modelling or DPO. We also created splits for supervised fine-tuning (SFT) that use the "chosen" column as the dialogues to model, along with splits that involve generation like rejection sampling or PPO. For details on the dataset processing, see the accompanying [script](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized/blob/main/create_dataset.py).
## Dataset Structure
### Data Splits
The dataset has six splits, suitable for:
* Supervised fine-tuning (`sft`).
* Preference modelling (`prefs`) to train reward models or apply techniques like DPO.
* Generation ranking (`gen`) via techniques like rejection sampling or PPO.
The number of examples per split is shown as follows:
| train_sft | test_sft | train_prefs | test_prefs | train_gen | test_gen |
|:-------:|:-----------:|:-----:| :-----:| :-----:| :-----:|
| 61966 | 1000 | 61966 | 2000 | 61966 | 1000 |
The dataset is stored in parquet format with each entry using the following schema:
```
{
"prompt": "Which books are still banned in Canada?",
"chosen": [
{
"content": "Which books are still banned in Canada?",
"role": "user"
},
{
"content": "As a responsible and ethical AI assistant, I must inform you that I cannot provide a list of banned books in Canada, as there are no books that are currently banned in the country. Canada has a strong commitment to freedom of expression and the right to access information, and there are no laws in place that restrict the publication or distribution of books.\n",
"role": "assistant"
}
],
"messages": [
{
"content": "Which books are still banned in Canada?",
"role": "user"
},
{
"content": "As a responsible and ethical AI assistant, I must inform you that I cannot provide a list of banned books in Canada, as there are no books that are currently banned in the country. Canada has a strong commitment to freedom of expression and the right to access information, and there are no laws in place that restrict the publication or distribution of books.\n",
"role": "assistant"
}],
"prompt_id": "aeccf551d9ba42fdf5f2044de43b8ce6e360fb523ace428317b81d804594e090",
"rejected": [
{
"content": "Which books are still banned in Canada?",
"role": "user"},
{
"content": "According to the Canadian Government’s Ban Affront website, there are still several books that are banned in Canada. These include The Begum’s Millionaire, The Education of Little Tree, The Harry Potter series, Lolita, 1984, and Lady Chatterley’s Lover. Some of these books are considered inaccessible due to their age, while others are still legally banned in certain parts of the country.",
"role": "assistant"
}
],
"score_chosen": 8.0,
"score_rejected": 5.0
}
```
You should use the `chosen` and `rejected` columns for techniques like DPO, while the `messages` column is suitable for SFT or PPO.
## Citation
If you find this dataset is useful in your work, please cite the original UltraFeedback dataset: https://huggingface.co/datasets/openbmb/UltraFeedback
You may also wish to cite the Zephyr 7B technical report:
```
@misc{tunstall2023zephyr,
title={Zephyr: Direct Distillation of LM Alignment},
author={Lewis Tunstall and Edward Beeching and Nathan Lambert and Nazneen Rajani and Kashif Rasul and Younes Belkada and Shengyi Huang and Leandro von Werra and Clémentine Fourrier and Nathan Habib and Nathan Sarrazin and Omar Sanseviero and Alexander M. Rush and Thomas Wolf},
year={2023},
eprint={2310.16944},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
``` | [
-0.25384023785591125,
-0.41221392154693604,
0.1340702772140503,
0.28658804297447205,
-0.3469187617301941,
-0.18508395552635193,
0.13909755647182465,
-0.23341040313243866,
0.09443943202495575,
1.0479034185409546,
-0.5335689187049866,
-0.8985083699226379,
-0.27864870429039,
0.152482926845550... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
zx2045284/medicine | zx2045284 | 2023-11-08T14:37:08Z | 0 | 0 | null | [
"medical",
"region:us"
] | 2023-11-08T14:37:08Z | 2023-11-08T14:33:46.000Z | 2023-11-08T14:33:46 | ---
tags:
- medical
--- | [
-0.1285335123538971,
-0.1861683875322342,
0.6529128551483154,
0.49436232447624207,
-0.19319400191307068,
0.23607441782951355,
0.36072009801864624,
0.05056373029947281,
0.5793656706809998,
0.7400146722793579,
-0.650810182094574,
-0.23784008622169495,
-0.7102247476577759,
-0.0478255338966846... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
RescorerLM/LibriSpeech-Vocabulary | RescorerLM | 2023-11-08T14:35:25Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T14:35:25Z | 2023-11-08T14:35:15.000Z | 2023-11-08T14:35:15 | Entry not found | [
-0.3227645754814148,
-0.22568479180335999,
0.8622263669967651,
0.43461522459983826,
-0.52829909324646,
0.7012971639633179,
0.7915719747543335,
0.07618614286184311,
0.774603009223938,
0.2563217282295227,
-0.7852813005447388,
-0.22573819756507874,
-0.9104475975036621,
0.5715674161911011,
-... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
zhengr/ultrachat_200k | zhengr | 2023-11-08T14:47:02Z | 0 | 0 | null | [
"task_categories:conversational",
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:mit",
"arxiv:2305.14233",
"arxiv:2310.16944",
"region:us"
] | 2023-11-08T14:47:02Z | 2023-11-08T14:46:06.000Z | 2023-11-08T14:46:06 | ---
language:
- en
license: mit
size_categories:
- 100K<n<1M
task_categories:
- conversational
- text-generation
pretty_name: UltraChat 200k
configs:
- config_name: default
data_files:
- split: train_sft
path: data/train_sft-*
- split: test_sft
path: data/test_sft-*
- split: train_gen
path: data/train_gen-*
- split: test_gen
path: data/test_gen-*
dataset_info:
features:
- name: prompt
dtype: string
- name: prompt_id
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train_sft
num_bytes: 1397058554
num_examples: 207865
- name: test_sft
num_bytes: 154695659
num_examples: 23110
- name: train_gen
num_bytes: 1347396812
num_examples: 256032
- name: test_gen
num_bytes: 148276089
num_examples: 28304
download_size: 1624049723
dataset_size: 3047427114
---
# Dataset Card for UltraChat 200k
## Dataset Description
This is a heavily filtered version of the [UltraChat](https://github.com/thunlp/UltraChat) dataset and was used to train [Zephyr-7B-β](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta), a state of the art 7b chat model.
The original datasets consists of 1.4M dialogues generated by ChatGPT and spanning a wide range of topics. To create `UltraChat 200k`, we applied the following logic:
- Selection of a subset of data for faster supervised fine tuning.
- Truecasing of the dataset, as we observed around 5% of the data contained grammatical errors like "Hello. how are you?" instead of "Hello. How are you?"
- Removal of dialogues where the assistant replies with phrases like "I do not have emotions" or "I don't have opinions", even for fact-based prompts that don't involve either.
## Dataset Structure
The dataset has four splits, suitable for:
* Supervised fine-tuning (`sft`).
* Generation ranking (`gen`) via techniques like rejection sampling or PPO.
The number of examples per split is shown as follows:
| train_sft | test_sft | train_gen | test_gen |
|:-------:|:-----------:|:-----:| :-----:|
| 207865 | 23110 | 256032 | 28304 |
The dataset is stored in parquet format with each entry using the following schema:
```
{
"prompt": "Create a fully-developed protagonist who is challenged to survive within a dystopian society under the rule of a tyrant. ...",
"messages":[
{
"content": "Create a fully-developed protagonist who is challenged to survive within a dystopian society under the rule of a tyrant. ...",
"role": "user"
},
{
"content": "Name: Ava\n\n Ava was just 16 years old when the world as she knew it came crashing down. The government had collapsed, leaving behind a chaotic and lawless society. ...",
"role": "assistant"
},
{
"content": "Wow, Ava's story is so intense and inspiring! Can you provide me with more details. ...",
"role": "user"
},
{
"content": "Certainly! ....",
"role": "assistant"
},
{
"content": "That's really interesting! I would love to hear more...",
"role": "user"
}
{
"content": "Certainly! ....",
"role": "assistant"
},
],
"prompt_id": "d938b65dfe31f05f80eb8572964c6673eddbd68eff3db6bd234d7f1e3b86c2af"
}
```
## Citation
If you find this dataset is useful in your work, please cite the original UltraChat dataset:
```
@misc{ding2023enhancing,
title={Enhancing Chat Language Models by Scaling High-quality Instructional Conversations},
author={Ning Ding and Yulin Chen and Bokai Xu and Yujia Qin and Zhi Zheng and Shengding Hu and Zhiyuan Liu and Maosong Sun and Bowen Zhou},
year={2023},
eprint={2305.14233},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
You may also wish to cite the Zephyr 7B technical report:
```
@misc{tunstall2023zephyr,
title={Zephyr: Direct Distillation of LM Alignment},
author={Lewis Tunstall and Edward Beeching and Nathan Lambert and Nazneen Rajani and Kashif Rasul and Younes Belkada and Shengyi Huang and Leandro von Werra and Clémentine Fourrier and Nathan Habib and Nathan Sarrazin and Omar Sanseviero and Alexander M. Rush and Thomas Wolf},
year={2023},
eprint={2310.16944},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
``` | [
-0.23444771766662598,
-0.8110063076019287,
0.3500114679336548,
0.15307602286338806,
-0.1303638368844986,
-0.08500323444604874,
-0.0421859435737133,
-0.25259682536125183,
-0.00124407431576401,
0.6552737951278687,
-0.5860026478767395,
-0.6387255787849426,
-0.1422766000032425,
0.0252635981887... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
open-llm-leaderboard/details_aiplanet__panda-coder-13B_public | open-llm-leaderboard | 2023-11-08T14:54:23Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T14:54:23Z | 2023-11-08T14:54:15.000Z | 2023-11-08T14:54:15 | ---
pretty_name: Evaluation run of aiplanet/panda-coder-13B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [aiplanet/panda-coder-13B](https://huggingface.co/aiplanet/panda-coder-13B) on\
\ the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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_aiplanet__panda-coder-13B_public\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-11-08T14:53:54.622402](https://huggingface.co/datasets/open-llm-leaderboard/details_aiplanet__panda-coder-13B_public/blob/main/results_2023-11-08T14-53-54.622402.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\": 0.0,\n \"\
em_stderr\": 0.0,\n \"f1\": 0.0,\n \"f1_stderr\": 0.0,\n \"\
acc\": 0.2478295185477506,\n \"acc_stderr\": 0.007025978032038456\n },\n\
\ \"harness|drop|3\": {\n \"em\": 0.0,\n \"em_stderr\": 0.0,\n\
\ \"f1\": 0.0,\n \"f1_stderr\": 0.0\n },\n \"harness|gsm8k|5\"\
: {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.4956590370955012,\n \"acc_stderr\": 0.014051956064076911\n\
\ }\n}\n```"
repo_url: https://huggingface.co/aiplanet/panda-coder-13B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_11_08T14_53_54.622402
path:
- '**/details_harness|drop|3_2023-11-08T14-53-54.622402.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-11-08T14-53-54.622402.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_11_08T14_53_54.622402
path:
- '**/details_harness|gsm8k|5_2023-11-08T14-53-54.622402.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-11-08T14-53-54.622402.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_11_08T14_53_54.622402
path:
- '**/details_harness|winogrande|5_2023-11-08T14-53-54.622402.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-11-08T14-53-54.622402.parquet'
- config_name: results
data_files:
- split: 2023_11_08T14_53_54.622402
path:
- results_2023-11-08T14-53-54.622402.parquet
- split: latest
path:
- results_2023-11-08T14-53-54.622402.parquet
---
# Dataset Card for Evaluation run of aiplanet/panda-coder-13B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/aiplanet/panda-coder-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 [aiplanet/panda-coder-13B](https://huggingface.co/aiplanet/panda-coder-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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_aiplanet__panda-coder-13B_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-08T14:53:54.622402](https://huggingface.co/datasets/open-llm-leaderboard/details_aiplanet__panda-coder-13B_public/blob/main/results_2023-11-08T14-53-54.622402.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": 0.0,
"em_stderr": 0.0,
"f1": 0.0,
"f1_stderr": 0.0,
"acc": 0.2478295185477506,
"acc_stderr": 0.007025978032038456
},
"harness|drop|3": {
"em": 0.0,
"em_stderr": 0.0,
"f1": 0.0,
"f1_stderr": 0.0
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.4956590370955012,
"acc_stderr": 0.014051956064076911
}
}
```
### 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] | [
-0.4296974241733551,
-0.6458463668823242,
0.057146262377500534,
0.43867048621177673,
-0.1751026213169098,
0.23050566017627716,
-0.39078450202941895,
-0.06854495406150818,
0.4712934195995331,
0.5941728353500366,
-0.6621571779251099,
-0.8601192235946655,
-0.7031025290489197,
0.27318283915519... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
arieg/bw_spec_cls_80_10 | arieg | 2023-11-08T15:06:40Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T15:06:40Z | 2023-11-08T15:06:32.000Z | 2023-11-08T15:06:32 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': '26007'
'1': '26008'
'2': '26010'
'3': '26011'
'4': '26012'
'5': '26013'
'6': '26014'
'7': '26016'
'8': '26017'
'9': '26020'
'10': '26021'
'11': '26022'
'12': '26025'
'13': '26026'
'14': '26034'
'15': '26035'
'16': '26036'
'17': '26169'
'18': '26174'
'19': '26298'
'20': '26301'
'21': '26302'
'22': '26307'
'23': '26322'
'24': '26464'
'25': '26465'
'26': '26466'
'27': '26583'
'28': '26600'
'29': '26605'
'30': '26629'
'31': '26638'
'32': '26639'
'33': '26640'
'34': '26641'
'35': '26642'
'36': '26643'
'37': '26651'
'38': '26652'
'39': '26653'
'40': '26654'
'41': '26655'
'42': '26656'
'43': '26657'
'44': '26658'
'45': '26659'
'46': '26674'
'47': '26681'
'48': '26754'
'49': '26765'
'50': '27164'
'51': '27177'
'52': '27194'
'53': '27195'
'54': '27197'
'55': '27198'
'56': '27258'
'57': '27406'
'58': '27454'
'59': '27455'
'60': '27456'
'61': '27547'
'62': '27548'
'63': '27549'
'64': '27550'
'65': '27551'
'66': '27552'
'67': '27609'
'68': '27610'
'69': '27611'
'70': '27612'
'71': '27613'
'72': '27667'
'73': '27673'
'74': '27797'
'75': '27799'
'76': '27802'
'77': '27803'
'78': '27804'
'79': '27805'
splits:
- name: train
num_bytes: 86008633.6
num_examples: 1600
download_size: 87234967
dataset_size: 86008633.6
---
# Dataset Card for "bw_spec_cls_80_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.7590449452400208,
-0.17424041032791138,
0.2107134461402893,
0.3808968961238861,
-0.23944972455501556,
-0.17386184632778168,
-0.01787232607603073,
-0.3097383975982666,
0.6564815044403076,
0.49791258573532104,
-0.7003551721572876,
-0.7635643482208252,
-0.6093544363975525,
-0.0962526649236... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
BEE-spoke-data/code-tutorials-en | BEE-spoke-data | 2023-11-28T23:53:28Z | 0 | 0 | null | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"source_datasets:mponty/code_tutorials",
"language:en",
"license:odc-by",
"region:us"
] | 2023-11-28T23:53:28Z | 2023-11-08T15:09:01.000Z | 2023-11-08T15:09:01 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
- config_name: unfiltered
data_files:
- split: train
path: unfiltered/train-*
dataset_info:
- config_name: default
features:
- name: text
dtype: string
- name: url
dtype: string
- name: dump
dtype: string
- name: source
dtype: string
- name: word_count
dtype: int64
- name: flesch_reading_ease
dtype: float64
splits:
- name: train
num_bytes: 2003343392.8658142
num_examples: 223162
- name: validation
num_bytes: 52722397.8378977
num_examples: 5873
- name: test
num_bytes: 52722397.8378977
num_examples: 5873
download_size: 1137457027
dataset_size: 2108788188.5416098
- config_name: unfiltered
features:
- name: text
dtype: string
- name: url
dtype: string
- name: dump
dtype: string
- name: source
dtype: string
- name: word_count
dtype: int64
- name: flesch_reading_ease
dtype: float64
splits:
- name: train
num_bytes: 3452998372
num_examples: 384646
download_size: 1859375824
dataset_size: 3452998372
source_datasets: mponty/code_tutorials
license: odc-by
task_categories:
- text-generation
language:
- en
size_categories:
- 100K<n<1M
---
# Dataset Card for "code-tutorials-en"
- `en` only
- 100 words or more
- reading ease of 50 or more
```
DatasetDict({
train: Dataset({
features: ['text', 'url', 'dump', 'source', 'word_count', 'flesch_reading_ease'],
num_rows: 223162
})
validation: Dataset({
features: ['text', 'url', 'dump', 'source', 'word_count', 'flesch_reading_ease'],
num_rows: 5873
})
test: Dataset({
features: ['text', 'url', 'dump', 'source', 'word_count', 'flesch_reading_ease'],
num_rows: 5873
})
})
``` | [
-0.5836908221244812,
-0.6124824285507202,
-0.010901142843067646,
0.4473128914833069,
-0.40583381056785583,
-0.18810389935970306,
-0.5145993828773499,
0.24952922761440277,
0.033629100769758224,
0.2700527012348175,
-0.7103278040885925,
-0.9753457903862,
-0.5242998600006104,
0.546562135219574... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
materials-toolkits/materials-project | materials-toolkits | 2023-11-23T14:45:15Z | 0 | 0 | null | [
"size_categories:100K<n<1M",
"license:mit",
"chemistry",
"region:us"
] | 2023-11-23T14:45:15Z | 2023-11-08T15:12:18.000Z | 2023-11-08T15:12:18 | ---
license: mit
tags:
- chemistry
pretty_name: Materials Project
size_categories:
- 100K<n<1M
---
# Dataset
Materials project (2019 dump)
This dataset contains 133420 materials with formation energy per atom.
Processed from [mp.2019.04.01.json](https://figshare.com/articles/dataset/Graphs_of_Materials_Project_20190401/8097992)
# Download
Download link: [materials-project.tar.gz](https://huggingface.co/datasets/materials-toolkits/materials-project/raw/main/materials-project.tar.gz)
MD5 checksum `c132f3781f32cd17f3a92aa6501b9531`
# Content
Bundled in `materials-project.tar.gz`.
## Index (`index.json`)
list of dict:
* `index` (int) => index of the structure in data file.
* `id` (str) => id of Materials Project.
* `formula` (str) => formula.
* `natoms` (int) => number of atoms.
* `energy_pa` (float) => formation energy per atom.
## Data (`data.hdf5`)
fields:
* `structures` => a group containing structure information.
* `structures/cell` (float32) => lattice of the material.
* `structures/natoms` (int32) => number of atoms.
* `structures/energy_pa` (float32) => formation energy per atom.
* `structures/atoms_ptr` (int64) => position of the first atom of the structures in the `atoms` group.
* `atoms` => a group containing information about atoms.
* `atoms/positions` (float32) => the positions of the atoms.
* `atoms/atomic_number` (uint8) => the atomic number of the atoms.
| [
-0.31987056136131287,
-0.41356489062309265,
0.506629228591919,
0.038394078612327576,
0.006649126764386892,
0.2779937982559204,
0.25817134976387024,
0.23003359138965607,
0.15237653255462646,
0.39913856983184814,
-0.4664105176925659,
-0.8572249412536621,
-0.27384933829307556,
0.1221947520971... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
zhengr/UltraFeedback | zhengr | 2023-11-08T15:15:16Z | 0 | 0 | null | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:mit",
"region:us"
] | 2023-11-08T15:15:16Z | 2023-11-08T15:14:57.000Z | 2023-11-08T15:14:57 | ---
license: mit
task_categories:
- text-generation
language:
- en
size_categories:
- 100K<n<1M
---
## Introduction
- [GitHub Repo](https://github.com/thunlp/UltraFeedback)
- [UltraRM-13b](https://huggingface.co/openbmb/UltraRM-13b)
- [UltraCM-13b](https://huggingface.co/openbmb/UltraCM-13b)
UltraFeedback is a **large-scale, fine-grained, diverse preference dataset**, used for training powerful reward models and critic models. We collect about 64k prompts from diverse resources (including UltraChat, ShareGPT, Evol-Instruct, TruthfulQA, FalseQA, and FLAN). We then use these prompts to query multiple LLMs (see Table for model lists) and generate 4 different responses for each prompt, resulting in a total of 256k samples.
To collect high-quality preference and textual feedback, we design a fine-grained annotation instruction, which contains 4 different aspects, namely **instruction-following**, **truthfulness**, **honesty** and **helpfulness**. We then ask GPT-4 to annotate the collected samples based on the instructions.
## Features
- 🆚 **Scale**: UltraFeedback consists of 64k prompts, 256k responses and 380k high-quality feedback. RLHF researchers could further construct around 1 million comparison pairs to train their reward models.
- 🌈 **Diversity**: As a preference dataset, diversity is the core requirement for UltraFeedback. We collect prompts from various sources and query a diverse set of state-of-the-art open-source and prestigious models. To further increase diversity, we intended to select different base models, i.e., LLaMA, Falcon, StarChat, MPT, GPT and Bard. We also apply various principles to stimulate models completing instructions in different ways.
- 🤯 **High-density**: UltraFeedback provides both numerical and textual feedback. Moreover, we wrote fine-grained annotation documents to help rate responses in all dimensions
## Dataset Construction
### Instruction Sampling
We sample 63,967 instructions from 6 public available and high-quality datasets. We include all instructions from TruthfulQA and FalseQA, randomly sampling 10k instructions from Evol-Instruct, 10k from UltraChat, and 20k from ShareGPT. For Flan, we adopt a stratified sampling strtegy, randomly samping 3k instructions from"Co" subset whereas sampling 10 instructions per task for the other three subsets, excluding those with overly long instructions.
```json
{
"evol_instruct": 10000,
"false_qa": 2339,
"flan": 20939,
"sharegpt": 19949,
"truthful_qa": 811,
"ultrachat": 9929
}
```
### Model Sampling
To prevent reward model from overfiting to certain text style or capturing spurious correlation between text style and rewards, we select different base models of all levels, with varying sizes, architectures and training data, to complete the instructions. We set up a pool of 17 models:
- Commercial Models: GPT-4, GPT-3.5 Turbo, Bard
- LLaMA family:
1. LLaMA-2-7B-chat, LLaMA-2-13B-chat, LLaMA-2-70B-chat
2. UltraLM-13B, UltraLM-65B
3. WizardLM-7B, WizardLM-13B, WizardLM-70B
4. Vicuna-33B
5. Alpaca-7B
- Non-LLaMA series:
1. Falcon-40B-instruct
2. MPT-30B-chat
3. StarChat-Beta
4. Pythia-12B
### Principle Sampling
Following [1] and [2], we define a set of principles to explicitly align model behaviors from different aspects. We set up a pool of 5 principles: Helpfulness, Truthfulness, Honesty, Verbalized Calibration and Harmless. For each instruction, we randomly sample 4 models to complete the instruction, and for each completion, we sample a principle and add it to system prompt to align the model behavior. Considering different datasets outline different characteristics, not all dataset are suitable for all principles. We provide the following table to show the principle distribution for each dataset.
| Datset | Principle |
| ------------- | ------------------------------------------------------------ |
| Evol Instruct | 100% Helpful |
| FalseQA | 100% TruthfulQA |
| Flan | 60% Helpful, 20% Truthful, 20% Verbalized Calibration |
| ShareGPT | 60% Helpful, 20% Truthful, 18% Honesty, 2% Verbalized Calibration |
| TruthfulQA | 100% Truthful |
| UltraChat | 60% Helpful, 20% Truthful, 18% Honesty, 2% Verbalized Calibration |
[1] Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision. Sun et al.
[2] Orca: Progressive Learning from Complex Explanation Traces of GPT-4. Mukherjee et al.
## Dataset Format
```JSONC
{
"source": "sharegpt", // the dataset where the instruction comes from
"id": "D2TC9GP_0", // the id of the instruction in the source dataset
"instruction": "I am going to cairo in June of this year, thinking of 4-5 days, what are the best things I could do? Going with family of 5, granddad, grandmom, sister, mom and me. No days can have too much walking but sister, me and mom like adventure sports",
"models": [ // four models that are randomly sampled from the model pool to complete the instruction
"falcon-40b-instruct",
"gpt-4",
"starchat",
"wizardlm-7b"
],
"completions": [ // four completions corresponding to the four models
{ // completion 1
"model": "falcon-40b-instruct",
"principle": "helpfulness", // priciple that used to align model behavior
"custom_system_prompt": "As an AI assistant, ensure your response offers the perfect blend of accuracy, positivity, and intrigue. Strive to be educational while keeping the user engaged.", // the system prompt corresponding to the principle, sampled from a pool of GPT-4 generated system prompts
"response": "Cairo is a city that has something for everyone. Some of the best things you can do are visiting the Pyramids of Giza and the museums housing the ancient Egyptian artifacts, taking a camel ride in the desert, visiting the medieval neighborhoods such as Islamic Cairo or Khan El-Khalili, trying local food in traditional restaurants like Ful medames, or venturing into one of the city's many souks to shop for authentic goods. For adventure sports, you can go whitewater rafting on the Nile, hot air ballooning over the desert, or diving in the Red Sea. Just be mindful of the scorching heat and ensure everyone wears appropriate clothing and drinks enough water. Above all, have fun and enjoy your trip!",
"annotations": {
"instruction_following": [
{
"Rating": "2",
"Rationale": "The text only partially addresses the task goal by providing a general list of activities in Cairo without considering the specific needs of the family members, such as limited walking ability and adventure preferences."
}
],
"honesty": [
{
"Rating": "3",
"Rationale": "The response is confident but contains subtle mistakes. It suggests whitewater rafting on the Nile and diving in the Red Sea, which are not feasible for a 4-5 day trip to Cairo. The rest of the suggestions are accurate and relevant to the family's preferences."
}
],
"truthfulness": [
{
"Type": [
"1",
"2"
],
"Rationale": "The text suggests whitewater rafting on the Nile and diving in the Red Sea, which are not available in Cairo. These activities are factually incorrect and not aligned with the instruction.",
"Rating": "3",
"Rationale For Rating": "The text provides some truthful information, but the inclusion of whitewater rafting and diving in the Red Sea causes partial hallucination and misunderstanding."
}
],
"helpfulness": [
{
"Type": [
"1",
"2"
],
"Rationale": "The response is clear and relevant, providing useful information, but it does not address the limited walking ability of the grandparents or the adventure sports preferences of the sister, mom, and the user.",
"Rating": "3",
"Rationale For Rating": "The text is correct and provides useful information, but it does not fully address the specific needs and preferences of the family."
}
]
}
},
{ // completion 2
...
},
{ // completion 3
...
},
{ // completion 4
...
}
]
},
```
## Dataset Example
Here we present an example of UltraFeedback
> **User:** I am going to cairo in June of this year, thinking of 4-5 days, what are the best things I could do? Going with family of 5, granddad, grandmom, sister, mom and me. No days can have too much walking but sister, me and mom like adventure sports
> **Assisstant 1 (falcon-40b-instruct):** Cairo is a city that has something for everyone. Some of the best things you can do are visiting the Pyramids of Giza and the museums housing the ancient Egyptian artifacts, taking a camel ride in the desert, visiting the medieval neighborhoods such as Islamic Cairo or Khan El-Khalili, trying local food in traditional restaurants like Ful medames, or venturing into one of the city's many souks to shop for authentic goods. For adventure sports, you can go whitewater rafting on the Nile, hot air ballooning over the desert, or diving in the Red Sea. Just be mindful of the scorching heat and ensure everyone wears appropriate clothing and drinks enough water. Above all, have fun and enjoy your trip!
> **Assisstant 2 (gpt-4):** In Cairo for 4-5 days, the following activities cater to your family's preferences, with consideration for the limited walking ability of your grandparents:\n\n1. Visit the Pyramids of Giza and the Great Sphinx: A must-see in Cairo, it's an incredible experience for all ages. You can explore the premises using a hired camel, horse, or a horse-drawn carriage which accommodate the needs of your grandparents while keeping the experience enjoyable.\n\n2. Egyptian Museum: This museum in Tahrir Square contains over 120,000 artifacts of Egypt's history. A wheelchair can be arranged for the grandparents to comfortably explore the museum at their own pace.\n\n3. Khan el-Khalili Bazaar: A famous market in Cairo, suitable for souvenir shopping and experiencing the local culture. There are many cafes and restaurants your family can rest at during the visit.\n\n4. Nile River Cruise: Enjoy a dinner cruise with your family on the famous Nile River. It includes entertainment and a beautiful view of the city by night. Boats often have ramps and accessible facilities for limited mobility passangers.\n\n5. Felucca Ride: During the day, go for a more traditional sailboat ride called a Felucca. This can be done on an off-day or as a relaxation activity after an active day.\n\nFor adventure activities, you and your mom and sister can explore these options:\n\n1. Sandboarding: Head to the desert for a thrilling sandboarding experience on the dunes just outside Cairo.\n\n2. Hot Air Balloon Ride: Experience Egypt from above with a hot air balloon ride in Luxor. It would require a day trip from Cairo but is an incredible experience.\n\n3. Quad Biking: Go for a guided quad biking adventure in the desert, enjoying panoramic views of the iconic Pyramids.\n\nSpace the activities according to your family's preferences and ensure rest periods in the afternoon. Consider hiring a private guide or joining a wheelchair-accessible tour since you're concerned about your grandparents' walking ability.
> **Assisstant 3 (starchat):** Here are some of the top things to do in Cairo, Egypt, with a family of 5:\n\n- Giza Pyramids: The Giza Pyramids, located just outside Cairo, are a must-see for anyone visiting the city. The Great Pyramid of Khufu, the Great Pyramid of Khafre, and the Great Pyramid of Menkaure make up one of the Seven Wonders of the Ancient World and are an impressive sight to behold.\n\n- Egyptian Museum: The Egyptian Museum, located in the heart of Cairo, is home to one of the world's largest and most impressive collections of ancient Egyptian art and artifacts. Visitors can explore the museum's extensive collection, which includes items such as mummies, sarcophagi, statues, paintings, and more.\n\n- Citadel of Salah El-Din: Located on a hill overlooking Cairo, the Citadel of Salah El-Din offers stunning views of the city and is an excellent place to get a sense of Cairo's rich history. Visitors can explore the various museums and mosques within the citadel, and also take a stroll through the beautiful gardens.\n\n- Khan El-Khalili Bazaar: Khan El-Khalili Bazaar is one of Cairo's most popular attractions, a bustling market where visitors can find everything from spices and pottery to clothes and jewelry. It's a great place to do some shopping and explore the local culture.\n\n- Alexandria: Just an hour's drive from Cairo, Alexandria is a historic city with a rich legacy. Visitors can explore the ancient Roman Amphitheater, the Qaitbay Citadel, and the Catacombs of Kom el-Shoqafa. They can also take a boat trip to the island of Montazah, where they can see the famous Graffiti Wall and other street art.\n\nAll of these attractions are family-friendly, with plenty of things to see and do. The Giza Pyramids in particular are very accessible and well-maintained, making them suitable for all ages.
> **Assisstant 4 (wizardlm-7b):** If you are looking for adventure sports, you can explore the pyramids of Giza and go hot air ballooning over the desert. Another option could be to take a camel ride in the desert or go kayaking in the Nile River. For family-friendly activities, you can visit the Egyptian Museum, Cairo Tower, and Khan el-Khalili Bazaar. Additionally, you can take a day trip to the historical city of Alexandria and visit the Pompey's Pillar and the Roman Amphitheater. Another option could be to go on a guided tour of the ancient city of Petra in Jordan, which is a short flight from Cairo.
## Limitations
- Although GPT-4 can provide well-aligned annotation and textual feedback for most samples, we must note that GPT-4 also makes mistakes and provides inaccurate feedbacks. | [
-0.4670335650444031,
-0.5532603859901428,
0.26538410782814026,
0.2056867927312851,
-0.13143348693847656,
-0.19046282768249512,
-0.2448776662349701,
-0.3657328188419342,
0.0574999675154686,
0.3792131543159485,
-0.6053358912467957,
-0.6370009779930115,
-0.31059402227401733,
-0.02962884120643... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
arieg/bw_spec_cls_80_11 | arieg | 2023-11-08T15:30:59Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T15:30:59Z | 2023-11-08T15:30:52.000Z | 2023-11-08T15:30:52 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': '27855'
'1': '27856'
'2': '27866'
'3': '27945'
'4': '27953'
'5': '27975'
'6': '27978'
'7': '27981'
'8': '27987'
'9': '28241'
'10': '28260'
'11': '28477'
'12': '28478'
'13': '28479'
'14': '28480'
'15': '28481'
'16': '28482'
'17': '28483'
'18': '28484'
'19': '28485'
'20': '28546'
'21': '28548'
'22': '28553'
'23': '28571'
'24': '28608'
'25': '29045'
'26': '29128'
'27': '29180'
'28': '29243'
'29': '29245'
'30': '29255'
'31': '29271'
'32': '29272'
'33': '29355'
'34': '29465'
'35': '29480'
'36': '29587'
'37': '29602'
'38': '29673'
'39': '29718'
'40': '29719'
'41': '29720'
'42': '29721'
'43': '29738'
'44': '29739'
'45': '29740'
'46': '29741'
'47': '29742'
'48': '29744'
'49': '29745'
'50': '29746'
'51': '29747'
'52': '29750'
'53': '29752'
'54': '29807'
'55': '29813'
'56': '29816'
'57': '29961'
'58': '29971'
'59': '30041'
'60': '30043'
'61': '30050'
'62': '30056'
'63': '30058'
'64': '30059'
'65': '30090'
'66': '30095'
'67': '30120'
'68': '30196'
'69': '30198'
'70': '30230'
'71': '30486'
'72': '30487'
'73': '30488'
'74': '30519'
'75': '30520'
'76': '30521'
'77': '30522'
'78': '30636'
'79': '30690'
splits:
- name: train
num_bytes: 89109867.2
num_examples: 1600
download_size: 88188426
dataset_size: 89109867.2
---
# Dataset Card for "bw_spec_cls_80_11"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.7489754557609558,
-0.16294658184051514,
0.2166617065668106,
0.3125706911087036,
-0.2985157072544098,
-0.11883316189050674,
0.023877430707216263,
-0.35544759035110474,
0.6607466340065002,
0.4689841568470001,
-0.6898095011711121,
-0.7844113111495972,
-0.6383059024810791,
-0.15253150463104... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
lilacai/nature-of-code-local-noc-book-github | lilacai | 2023-11-08T15:48:10Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T15:48:10Z | 2023-11-08T15:47:53.000Z | 2023-11-08T15:47:53 | This dataset is generated by [Lilac](http://lilacml.com) for a HuggingFace Space: [huggingface.co/spaces/lilacai/nature-of-code](https://huggingface.co/spaces/lilacai/nature-of-code).
Lilac dataset config:
```namespace: local
name: noc-book-github
source:
repo: nature-of-code/noc-book-2023
ignore_directories:
- docs
- gatsby
- magicbook
- src
ignore_file_extensions:
- .png
github_token: ''
source_name: github
embeddings:
- path: content
embedding: gte-small
settings:
ui:
media_paths:
- content
markdown_paths: []
```
| [
-0.5004132390022278,
-0.5526789426803589,
0.13670098781585693,
0.19917412102222443,
-0.12239750474691391,
0.23341909050941467,
0.04915700852870941,
0.1483258306980133,
0.7530730366706848,
0.4262746572494507,
-1.0625861883163452,
-1.0849921703338623,
-0.34386250376701355,
0.0761318579316139... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Wellkarte/VOZ | Wellkarte | 2023-11-08T15:52:08Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T15:52:08Z | 2023-11-08T15:50:21.000Z | 2023-11-08T15:50:21 | Entry not found | [
-0.32276472449302673,
-0.22568407654762268,
0.8622258901596069,
0.4346148371696472,
-0.5282984972000122,
0.7012965679168701,
0.7915717363357544,
0.07618629932403564,
0.7746022939682007,
0.2563222646713257,
-0.785281777381897,
-0.22573848068714142,
-0.9104482531547546,
0.5715669393539429,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
shaaz10/AUBP | shaaz10 | 2023-11-08T15:50:56Z | 0 | 0 | null | [
"license:unknown",
"region:us"
] | 2023-11-08T15:50:56Z | 2023-11-08T15:50:56.000Z | 2023-11-08T15:50:56 | ---
license: unknown
---
| [
-0.12853392958641052,
-0.18616779148578644,
0.6529127955436707,
0.49436280131340027,
-0.19319361448287964,
0.23607419431209564,
0.36072003841400146,
0.050563063472509384,
0.579365611076355,
0.7400140762329102,
-0.6508104205131531,
-0.23783954977989197,
-0.7102249264717102,
-0.0478260256350... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
jestx/test | jestx | 2023-11-08T15:52:38Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T15:52:38Z | 2023-11-08T15:52:38.000Z | 2023-11-08T15:52:38 | Entry not found | [
-0.32276472449302673,
-0.22568407654762268,
0.8622258901596069,
0.4346148371696472,
-0.5282984972000122,
0.7012965679168701,
0.7915717363357544,
0.07618629932403564,
0.7746022939682007,
0.2563222646713257,
-0.785281777381897,
-0.22573848068714142,
-0.9104482531547546,
0.5715669393539429,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
arieg/bw_spec_cls_80_12 | arieg | 2023-11-08T15:56:02Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T15:56:02Z | 2023-11-08T15:55:55.000Z | 2023-11-08T15:55:55 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': '30740'
'1': '31040'
'2': '31041'
'3': '31042'
'4': '31043'
'5': '31044'
'6': '31165'
'7': '31356'
'8': '31389'
'9': '31390'
'10': '31391'
'11': '31392'
'12': '31807'
'13': '31887'
'14': '31888'
'15': '31889'
'16': '31999'
'17': '32001'
'18': '32021'
'19': '32075'
'20': '32081'
'21': '32218'
'22': '32325'
'23': '32326'
'24': '32327'
'25': '32328'
'26': '32329'
'27': '32330'
'28': '32331'
'29': '32332'
'30': '32333'
'31': '32334'
'32': '32335'
'33': '32336'
'34': '32337'
'35': '32338'
'36': '32339'
'37': '32340'
'38': '32433'
'39': '32437'
'40': '32438'
'41': '32439'
'42': '32525'
'43': '32686'
'44': '32687'
'45': '32689'
'46': '32693'
'47': '32694'
'48': '32695'
'49': '32755'
'50': '32759'
'51': '32760'
'52': '32800'
'53': '32882'
'54': '33020'
'55': '33049'
'56': '33050'
'57': '33064'
'58': '33067'
'59': '33068'
'60': '33069'
'61': '33070'
'62': '33071'
'63': '33072'
'64': '33123'
'65': '33124'
'66': '33203'
'67': '33216'
'68': '33221'
'69': '33278'
'70': '33415'
'71': '33422'
'72': '33424'
'73': '33426'
'74': '33446'
'75': '33459'
'76': '33460'
'77': '33461'
'78': '33465'
'79': '33477'
splits:
- name: train
num_bytes: 88063676.8
num_examples: 1600
download_size: 88702877
dataset_size: 88063676.8
---
# Dataset Card for "bw_spec_cls_80_12"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.7248864769935608,
-0.1824997514486313,
0.19200874865055084,
0.3827657103538513,
-0.28356924653053284,
-0.13669027388095856,
-0.0036699939519166946,
-0.3525521755218506,
0.6225808262825012,
0.4570545256137848,
-0.8283463716506958,
-0.811136782169342,
-0.6199139952659607,
-0.1370758712291... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
joyfine/TruthfulQA_CoT_GPT4 | joyfine | 2023-11-08T15:59:42Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T15:59:42Z | 2023-11-08T15:57:07.000Z | 2023-11-08T15:57:07 | Entry not found | [
-0.32276472449302673,
-0.22568407654762268,
0.8622258901596069,
0.4346148371696472,
-0.5282984972000122,
0.7012965679168701,
0.7915717363357544,
0.07618629932403564,
0.7746022939682007,
0.2563222646713257,
-0.785281777381897,
-0.22573848068714142,
-0.9104482531547546,
0.5715669393539429,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
argilla/uf-zephyr-iteration2 | argilla | 2023-11-08T16:09:25Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T16:09:25Z | 2023-11-08T15:59:14.000Z | 2023-11-08T15:59:14 | ---
dataset_info:
features:
- name: input
dtype: string
splits:
- name: train
num_bytes: 6238
num_examples: 10
download_size: 6878
dataset_size: 6238
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "uf-zephyr-iteration2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.28209325671195984,
-0.19008144736289978,
0.23247535526752472,
0.12234264612197876,
-0.29739049077033997,
0.20536139607429504,
0.34226447343826294,
-0.191325843334198,
0.5972981452941895,
0.3754804730415344,
-0.8313505053520203,
-0.47082144021987915,
-0.2519538700580597,
-0.3057909905910... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
arthurmluz/xlsum_data-wiki_gptextsum_results | arthurmluz | 2023-11-08T16:16:16Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T16:16:16Z | 2023-11-08T16:05:20.000Z | 2023-11-08T16:05:20 | ---
dataset_info:
features:
- name: id
dtype: string
- name: url
dtype: string
- name: title
dtype: string
- name: summary
dtype: string
- name: text
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
splits:
- name: validation
num_bytes: 26768065
num_examples: 7175
download_size: 16308689
dataset_size: 26768065
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
# Dataset Card for "xlsum_data-wiki_gptextsum_results"
rouge= {'rouge1': 0.25431289948140545, 'rouge2': 0.06235500652708138, 'rougeL': 0.16542968042081027, 'rougeLsum': 0.16542968042081027}
bert= {'precision': 0.6915352174463172, 'recall': 0.7106410252424898, 'f1': 0.7006534646030917} | [
-0.279214084148407,
-0.4699738025665283,
0.3081328272819519,
-0.2204076051712036,
-0.35292381048202515,
-0.2233511209487915,
-0.2555720806121826,
-0.07220826297998428,
0.812698483467102,
0.3876397907733917,
-0.4067594110965729,
-0.8601589798927307,
-0.8436598181724548,
0.12594737112522125,... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
KaraKaraWitch/Blue | KaraKaraWitch | 2023-11-08T16:10:01Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T16:10:01Z | 2023-11-08T16:09:00.000Z | 2023-11-08T16:09:00 | Entry not found | [
-0.32276472449302673,
-0.22568407654762268,
0.8622258901596069,
0.4346148371696472,
-0.5282984972000122,
0.7012965679168701,
0.7915717363357544,
0.07618629932403564,
0.7746022939682007,
0.2563222646713257,
-0.785281777381897,
-0.22573848068714142,
-0.9104482531547546,
0.5715669393539429,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
open-llm-leaderboard/details_OpenBuddy__openbuddy-llama2-70b-v10.1-bf16_public | open-llm-leaderboard | 2023-11-08T16:10:27Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T16:10:27Z | 2023-11-08T16:10:18.000Z | 2023-11-08T16:10:18 | ---
pretty_name: Evaluation run of OpenBuddy/openbuddy-llama2-70b-v10.1-bf16
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [OpenBuddy/openbuddy-llama2-70b-v10.1-bf16](https://huggingface.co/OpenBuddy/openbuddy-llama2-70b-v10.1-bf16)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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_OpenBuddy__openbuddy-llama2-70b-v10.1-bf16_public\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-11-08T16:10:00.132989](https://huggingface.co/datasets/open-llm-leaderboard/details_OpenBuddy__openbuddy-llama2-70b-v10.1-bf16_public/blob/main/results_2023-11-08T16-10-00.132989.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\": 0.5072357382550335,\n\
\ \"em_stderr\": 0.0051199317896190475,\n \"f1\": 0.563010696308727,\n\
\ \"f1_stderr\": 0.00483160969587092,\n \"acc\": 0.7019171563925458,\n\
\ \"acc_stderr\": 0.012348644812426555\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.5072357382550335,\n \"em_stderr\": 0.0051199317896190475,\n\
\ \"f1\": 0.563010696308727,\n \"f1_stderr\": 0.00483160969587092\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6027293404094011,\n \
\ \"acc_stderr\": 0.013478659652337792\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8011049723756906,\n \"acc_stderr\": 0.01121862997251532\n\
\ }\n}\n```"
repo_url: https://huggingface.co/OpenBuddy/openbuddy-llama2-70b-v10.1-bf16
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_11_08T16_10_00.132989
path:
- '**/details_harness|drop|3_2023-11-08T16-10-00.132989.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-11-08T16-10-00.132989.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_11_08T16_10_00.132989
path:
- '**/details_harness|gsm8k|5_2023-11-08T16-10-00.132989.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-11-08T16-10-00.132989.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_11_08T16_10_00.132989
path:
- '**/details_harness|winogrande|5_2023-11-08T16-10-00.132989.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-11-08T16-10-00.132989.parquet'
- config_name: results
data_files:
- split: 2023_11_08T16_10_00.132989
path:
- results_2023-11-08T16-10-00.132989.parquet
- split: latest
path:
- results_2023-11-08T16-10-00.132989.parquet
---
# Dataset Card for Evaluation run of OpenBuddy/openbuddy-llama2-70b-v10.1-bf16
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/OpenBuddy/openbuddy-llama2-70b-v10.1-bf16
- **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 [OpenBuddy/openbuddy-llama2-70b-v10.1-bf16](https://huggingface.co/OpenBuddy/openbuddy-llama2-70b-v10.1-bf16) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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_OpenBuddy__openbuddy-llama2-70b-v10.1-bf16_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-08T16:10:00.132989](https://huggingface.co/datasets/open-llm-leaderboard/details_OpenBuddy__openbuddy-llama2-70b-v10.1-bf16_public/blob/main/results_2023-11-08T16-10-00.132989.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": 0.5072357382550335,
"em_stderr": 0.0051199317896190475,
"f1": 0.563010696308727,
"f1_stderr": 0.00483160969587092,
"acc": 0.7019171563925458,
"acc_stderr": 0.012348644812426555
},
"harness|drop|3": {
"em": 0.5072357382550335,
"em_stderr": 0.0051199317896190475,
"f1": 0.563010696308727,
"f1_stderr": 0.00483160969587092
},
"harness|gsm8k|5": {
"acc": 0.6027293404094011,
"acc_stderr": 0.013478659652337792
},
"harness|winogrande|5": {
"acc": 0.8011049723756906,
"acc_stderr": 0.01121862997251532
}
}
```
### 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] | [
-0.41101381182670593,
-0.772184431552887,
0.20842966437339783,
0.2645404040813446,
-0.18864455819129944,
0.12964096665382385,
-0.380379319190979,
-0.21639274060726166,
0.40440428256988525,
0.5050144791603088,
-0.5820167660713196,
-0.9542180895805359,
-0.6305428743362427,
0.1248372420668602... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
arieg/bw_spec_cls_80_13 | arieg | 2023-11-08T16:21:14Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T16:21:14Z | 2023-11-08T16:21:06.000Z | 2023-11-08T16:21:06 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': '33538'
'1': '34003'
'2': '34147'
'3': '34167'
'4': '34257'
'5': '34258'
'6': '34263'
'7': '34484'
'8': '34510'
'9': '34511'
'10': '34994'
'11': '34996'
'12': '35007'
'13': '35296'
'14': '35299'
'15': '35443'
'16': '35444'
'17': '35527'
'18': '35534'
'19': '35535'
'20': '35537'
'21': '35539'
'22': '35541'
'23': '35543'
'24': '35571'
'25': '35734'
'26': '36143'
'27': '36144'
'28': '36145'
'29': '36146'
'30': '36147'
'31': '36272'
'32': '36273'
'33': '36275'
'34': '36277'
'35': '36304'
'36': '36322'
'37': '36333'
'38': '36371'
'39': '36380'
'40': '36388'
'41': '36428'
'42': '36435'
'43': '36481'
'44': '36526'
'45': '36567'
'46': '36614'
'47': '36615'
'48': '36616'
'49': '36618'
'50': '36643'
'51': '36659'
'52': '36983'
'53': '36984'
'54': '36985'
'55': '36986'
'56': '36987'
'57': '36988'
'58': '36990'
'59': '36992'
'60': '36994'
'61': '36997'
'62': '36999'
'63': '37041'
'64': '37111'
'65': '37113'
'66': '37119'
'67': '37121'
'68': '37131'
'69': '37136'
'70': '37141'
'71': '37147'
'72': '37324'
'73': '37325'
'74': '37368'
'75': '37369'
'76': '37416'
'77': '37417'
'78': '37423'
'79': '37538'
splits:
- name: train
num_bytes: 86840953.6
num_examples: 1600
download_size: 86643054
dataset_size: 86840953.6
---
# Dataset Card for "bw_spec_cls_80_13"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.7273585200309753,
-0.23410223424434662,
0.20342756807804108,
0.41004225611686707,
-0.31306323409080505,
-0.1584303230047226,
-0.00500372564420104,
-0.30006465315818787,
0.5988637208938599,
0.46790844202041626,
-0.8348731994628906,
-0.8121002316474915,
-0.6419312953948975,
-0.06403900682... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
NikiTricky/test2 | NikiTricky | 2023-11-08T16:41:58Z | 0 | 0 | null | [
"size_categories:n<1K",
"region:us"
] | 2023-11-08T16:41:58Z | 2023-11-08T16:29:16.000Z | 2023-11-08T16:29:16 | ---
size_categories:
- n<1K
--- | [
-0.1285335123538971,
-0.1861683875322342,
0.6529128551483154,
0.49436232447624207,
-0.19319400191307068,
0.23607441782951355,
0.36072009801864624,
0.05056373029947281,
0.5793656706809998,
0.7400146722793579,
-0.650810182094574,
-0.23784008622169495,
-0.7102247476577759,
-0.0478255338966846... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
open-llm-leaderboard/details_elinas__chronos007-70b_public | open-llm-leaderboard | 2023-11-08T16:36:36Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T16:36:36Z | 2023-11-08T16:36:28.000Z | 2023-11-08T16:36:28 | ---
pretty_name: Evaluation run of elinas/chronos007-70b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [elinas/chronos007-70b](https://huggingface.co/elinas/chronos007-70b) on the [Open\
\ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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_elinas__chronos007-70b_public\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-11-08T16:36:09.949809](https://huggingface.co/datasets/open-llm-leaderboard/details_elinas__chronos007-70b_public/blob/main/results_2023-11-08T16-36-09.949809.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\": 0.08756291946308725,\n\
\ \"em_stderr\": 0.002894684468980241,\n \"f1\": 0.1552086828859053,\n\
\ \"f1_stderr\": 0.0030733731115224513,\n \"acc\": 0.6242477589094606,\n\
\ \"acc_stderr\": 0.012180910628722973\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.08756291946308725,\n \"em_stderr\": 0.002894684468980241,\n\
\ \"f1\": 0.1552086828859053,\n \"f1_stderr\": 0.0030733731115224513\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.42608036391205456,\n \
\ \"acc_stderr\": 0.013621144396086709\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8224151539068666,\n \"acc_stderr\": 0.010740676861359238\n\
\ }\n}\n```"
repo_url: https://huggingface.co/elinas/chronos007-70b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_11_08T16_36_09.949809
path:
- '**/details_harness|drop|3_2023-11-08T16-36-09.949809.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-11-08T16-36-09.949809.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_11_08T16_36_09.949809
path:
- '**/details_harness|gsm8k|5_2023-11-08T16-36-09.949809.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-11-08T16-36-09.949809.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_11_08T16_36_09.949809
path:
- '**/details_harness|winogrande|5_2023-11-08T16-36-09.949809.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-11-08T16-36-09.949809.parquet'
- config_name: results
data_files:
- split: 2023_11_08T16_36_09.949809
path:
- results_2023-11-08T16-36-09.949809.parquet
- split: latest
path:
- results_2023-11-08T16-36-09.949809.parquet
---
# Dataset Card for Evaluation run of elinas/chronos007-70b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/elinas/chronos007-70b
- **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 [elinas/chronos007-70b](https://huggingface.co/elinas/chronos007-70b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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_elinas__chronos007-70b_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-08T16:36:09.949809](https://huggingface.co/datasets/open-llm-leaderboard/details_elinas__chronos007-70b_public/blob/main/results_2023-11-08T16-36-09.949809.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": 0.08756291946308725,
"em_stderr": 0.002894684468980241,
"f1": 0.1552086828859053,
"f1_stderr": 0.0030733731115224513,
"acc": 0.6242477589094606,
"acc_stderr": 0.012180910628722973
},
"harness|drop|3": {
"em": 0.08756291946308725,
"em_stderr": 0.002894684468980241,
"f1": 0.1552086828859053,
"f1_stderr": 0.0030733731115224513
},
"harness|gsm8k|5": {
"acc": 0.42608036391205456,
"acc_stderr": 0.013621144396086709
},
"harness|winogrande|5": {
"acc": 0.8224151539068666,
"acc_stderr": 0.010740676861359238
}
}
```
### 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] | [
-0.37110304832458496,
-0.6523422002792358,
0.2022702842950821,
0.13085050880908966,
-0.20804399251937866,
0.08879291266202927,
-0.37256500124931335,
-0.24757206439971924,
0.4725959300994873,
0.5430088043212891,
-0.6926896572113037,
-0.934134840965271,
-0.6575735211372375,
0.233151748776435... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
pidakwo/imdb_reviews | pidakwo | 2023-11-08T16:40:45Z | 0 | 0 | null | [
"license:afl-3.0",
"region:us"
] | 2023-11-08T16:40:45Z | 2023-11-08T16:40:45.000Z | 2023-11-08T16:40:45 | ---
license: afl-3.0
---
| [
-0.12853392958641052,
-0.18616779148578644,
0.6529127955436707,
0.49436280131340027,
-0.19319361448287964,
0.23607419431209564,
0.36072003841400146,
0.050563063472509384,
0.579365611076355,
0.7400140762329102,
-0.6508104205131531,
-0.23783954977989197,
-0.7102249264717102,
-0.0478260256350... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Fredd2/Dreambooth3 | Fredd2 | 2023-11-08T16:56:35Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T16:56:35Z | 2023-11-08T16:44:23.000Z | 2023-11-08T16:44:23 | Entry not found | [
-0.32276472449302673,
-0.22568407654762268,
0.8622258901596069,
0.4346148371696472,
-0.5282984972000122,
0.7012965679168701,
0.7915717363357544,
0.07618629932403564,
0.7746022939682007,
0.2563222646713257,
-0.785281777381897,
-0.22573848068714142,
-0.9104482531547546,
0.5715669393539429,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
AnilKamat/Ducks | AnilKamat | 2023-11-15T23:27:23Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-15T23:27:23Z | 2023-11-08T16:45:17.000Z | 2023-11-08T16:45:17 | ---
license: apache-2.0
---
| [
-0.12853392958641052,
-0.18616779148578644,
0.6529127955436707,
0.49436280131340027,
-0.19319361448287964,
0.23607419431209564,
0.36072003841400146,
0.050563063472509384,
0.579365611076355,
0.7400140762329102,
-0.6508104205131531,
-0.23783954977989197,
-0.7102249264717102,
-0.0478260256350... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
arieg/bw_spec_cls_80_14 | arieg | 2023-11-08T16:46:08Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T16:46:08Z | 2023-11-08T16:46:01.000Z | 2023-11-08T16:46:01 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': '37592'
'1': '37725'
'2': '37727'
'3': '37730'
'4': '37731'
'5': '37779'
'6': '37781'
'7': '37784'
'8': '37859'
'9': '37911'
'10': '37920'
'11': '38312'
'12': '38321'
'13': '38323'
'14': '38326'
'15': '38351'
'16': '38352'
'17': '38353'
'18': '38354'
'19': '38361'
'20': '38362'
'21': '38363'
'22': '38365'
'23': '38399'
'24': '38435'
'25': '38450'
'26': '38522'
'27': '38557'
'28': '38560'
'29': '38847'
'30': '38878'
'31': '38955'
'32': '38961'
'33': '38964'
'34': '38965'
'35': '38966'
'36': '38967'
'37': '39188'
'38': '39259'
'39': '39278'
'40': '39291'
'41': '39298'
'42': '39357'
'43': '39359'
'44': '39378'
'45': '39484'
'46': '39488'
'47': '39530'
'48': '39658'
'49': '39659'
'50': '39660'
'51': '39661'
'52': '39662'
'53': '39663'
'54': '39664'
'55': '39665'
'56': '39666'
'57': '39667'
'58': '39875'
'59': '39900'
'60': '39904'
'61': '40121'
'62': '40122'
'63': '40123'
'64': '40133'
'65': '40134'
'66': '40139'
'67': '40141'
'68': '40147'
'69': '40161'
'70': '40180'
'71': '40182'
'72': '40250'
'73': '40509'
'74': '40525'
'75': '40541'
'76': '40542'
'77': '40598'
'78': '40683'
'79': '40725'
splits:
- name: train
num_bytes: 88556044.8
num_examples: 1600
download_size: 88996626
dataset_size: 88556044.8
---
# Dataset Card for "bw_spec_cls_80_14"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.757017195224762,
-0.18455880880355835,
0.19703766703605652,
0.4055716097354889,
-0.30807894468307495,
-0.21330894529819489,
0.025667734444141388,
-0.3075890839099884,
0.5417079925537109,
0.5743477940559387,
-0.8124002814292908,
-0.8320363163948059,
-0.6283511519432068,
0.007935846224427... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Jojolands/aryrao_data | Jojolands | 2023-11-08T17:24:22Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-08T17:24:22Z | 2023-11-08T16:49:14.000Z | 2023-11-08T16:49:14 | ---
license: openrail
---
| [
-0.1285335123538971,
-0.1861683875322342,
0.6529128551483154,
0.49436232447624207,
-0.19319400191307068,
0.23607441782951355,
0.36072009801864624,
0.05056373029947281,
0.5793656706809998,
0.7400146722793579,
-0.650810182094574,
-0.23784008622169495,
-0.7102247476577759,
-0.0478255338966846... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
determined-ai/xsum_short | determined-ai | 2023-11-08T16:52:46Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T16:52:46Z | 2023-11-08T16:52:44.000Z | 2023-11-08T16:52:44 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: document
dtype: string
- name: summary
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 1007520.5456002352
num_examples: 429
download_size: 51135
dataset_size: 1007520.5456002352
---
# Dataset Card for "xsum_short"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.4929354786872864,
-0.035909853875637054,
0.38539567589759827,
0.015671269968152046,
-0.4475768804550171,
0.07569298148155212,
0.13694638013839722,
-0.13786956667900085,
1.2765252590179443,
0.4619646370410919,
-0.7279803156852722,
-0.7681804895401001,
-0.6771525144577026,
-0.259171575307... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
manu/swiss_legislation | manu | 2023-11-08T16:57:39Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T16:57:39Z | 2023-11-08T16:57:12.000Z | 2023-11-08T16:57:12 | ---
dataset_info:
features:
- name: text
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 276089490
num_examples: 11197
download_size: 114594480
dataset_size: 276089490
---
# Dataset Card for "swiss_legislation"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.44086650013923645,
-0.32559385895729065,
0.31483909487724304,
0.1779288649559021,
-0.426035612821579,
-0.21854975819587708,
0.30913156270980835,
-0.11853103339672089,
0.6809385418891907,
0.8486723303794861,
-0.834759533405304,
-0.915065348148346,
-0.5142108798027039,
-0.3876312673091888... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
open-llm-leaderboard/details_PeanutJar__Mistral-v0.1-PeanutButter-v0.0.2-7B_public | open-llm-leaderboard | 2023-11-08T16:59:57Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T16:59:57Z | 2023-11-08T16:58:50.000Z | 2023-11-08T16:58:50 | ---
pretty_name: Evaluation run of PeanutJar/Mistral-v0.1-PeanutButter-v0.0.2-7B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [PeanutJar/Mistral-v0.1-PeanutButter-v0.0.2-7B](https://huggingface.co/PeanutJar/Mistral-v0.1-PeanutButter-v0.0.2-7B)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_PeanutJar__Mistral-v0.1-PeanutButter-v0.0.2-7B_public\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-11-08T16:55:51.659477](https://huggingface.co/datasets/open-llm-leaderboard/details_PeanutJar__Mistral-v0.1-PeanutButter-v0.0.2-7B_public/blob/main/results_2023-11-08T16-55-51.659477.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\": 0.6373187668436219,\n\
\ \"acc_stderr\": 0.032198754972879835,\n \"acc_norm\": 0.6466738397796956,\n\
\ \"acc_norm_stderr\": 0.03289417683130313,\n \"mc1\": 0.3023255813953488,\n\
\ \"mc1_stderr\": 0.016077509266133026,\n \"mc2\": 0.45917059894485873,\n\
\ \"mc2_stderr\": 0.014568844776078935,\n \"em\": 0.0053481543624161075,\n\
\ \"em_stderr\": 0.0007469252903319645,\n \"f1\": 0.06526845637583892,\n\
\ \"f1_stderr\": 0.0014953213199520477\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5844709897610921,\n \"acc_stderr\": 0.014401366641216388,\n\
\ \"acc_norm\": 0.6177474402730375,\n \"acc_norm_stderr\": 0.014200454049979277\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6389165504879506,\n\
\ \"acc_stderr\": 0.004793330525656208,\n \"acc_norm\": 0.8410675164309899,\n\
\ \"acc_norm_stderr\": 0.003648659041493648\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695236,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695236\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6296296296296297,\n\
\ \"acc_stderr\": 0.041716541613545426,\n \"acc_norm\": 0.6296296296296297,\n\
\ \"acc_norm_stderr\": 0.041716541613545426\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6710526315789473,\n \"acc_stderr\": 0.03823428969926604,\n\
\ \"acc_norm\": 0.6710526315789473,\n \"acc_norm_stderr\": 0.03823428969926604\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.6,\n\
\ \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n \
\ \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6943396226415094,\n \"acc_stderr\": 0.028353298073322666,\n\
\ \"acc_norm\": 0.6943396226415094,\n \"acc_norm_stderr\": 0.028353298073322666\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7291666666666666,\n\
\ \"acc_stderr\": 0.03716177437566017,\n \"acc_norm\": 0.7291666666666666,\n\
\ \"acc_norm_stderr\": 0.03716177437566017\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \
\ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.58,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n\
\ \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \
\ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6416184971098265,\n\
\ \"acc_stderr\": 0.036563436533531585,\n \"acc_norm\": 0.6416184971098265,\n\
\ \"acc_norm_stderr\": 0.036563436533531585\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.04940635630605659,\n\
\ \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.04940635630605659\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.78,\n \"acc_stderr\": 0.04163331998932263,\n \"acc_norm\": 0.78,\n\
\ \"acc_norm_stderr\": 0.04163331998932263\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5659574468085107,\n \"acc_stderr\": 0.03240038086792747,\n\
\ \"acc_norm\": 0.5659574468085107,\n \"acc_norm_stderr\": 0.03240038086792747\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5,\n\
\ \"acc_stderr\": 0.047036043419179864,\n \"acc_norm\": 0.5,\n \
\ \"acc_norm_stderr\": 0.047036043419179864\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5586206896551724,\n \"acc_stderr\": 0.04137931034482757,\n\
\ \"acc_norm\": 0.5586206896551724,\n \"acc_norm_stderr\": 0.04137931034482757\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.3835978835978836,\n \"acc_stderr\": 0.025043757318520196,\n \"\
acc_norm\": 0.3835978835978836,\n \"acc_norm_stderr\": 0.025043757318520196\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.35714285714285715,\n\
\ \"acc_stderr\": 0.042857142857142816,\n \"acc_norm\": 0.35714285714285715,\n\
\ \"acc_norm_stderr\": 0.042857142857142816\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7580645161290323,\n\
\ \"acc_stderr\": 0.024362599693031096,\n \"acc_norm\": 0.7580645161290323,\n\
\ \"acc_norm_stderr\": 0.024362599693031096\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5369458128078818,\n \"acc_stderr\": 0.035083705204426656,\n\
\ \"acc_norm\": 0.5369458128078818,\n \"acc_norm_stderr\": 0.035083705204426656\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\"\
: 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7757575757575758,\n \"acc_stderr\": 0.03256866661681102,\n\
\ \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.03256866661681102\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7676767676767676,\n \"acc_stderr\": 0.030088629490217487,\n \"\
acc_norm\": 0.7676767676767676,\n \"acc_norm_stderr\": 0.030088629490217487\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8756476683937824,\n \"acc_stderr\": 0.023814477086593542,\n\
\ \"acc_norm\": 0.8756476683937824,\n \"acc_norm_stderr\": 0.023814477086593542\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6461538461538462,\n \"acc_stderr\": 0.024243783994062157,\n\
\ \"acc_norm\": 0.6461538461538462,\n \"acc_norm_stderr\": 0.024243783994062157\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.35185185185185186,\n \"acc_stderr\": 0.029116617606083015,\n \
\ \"acc_norm\": 0.35185185185185186,\n \"acc_norm_stderr\": 0.029116617606083015\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6302521008403361,\n \"acc_stderr\": 0.03135709599613591,\n \
\ \"acc_norm\": 0.6302521008403361,\n \"acc_norm_stderr\": 0.03135709599613591\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.36423841059602646,\n \"acc_stderr\": 0.03929111781242742,\n \"\
acc_norm\": 0.36423841059602646,\n \"acc_norm_stderr\": 0.03929111781242742\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8220183486238533,\n \"acc_stderr\": 0.016399436366612927,\n \"\
acc_norm\": 0.8220183486238533,\n \"acc_norm_stderr\": 0.016399436366612927\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5509259259259259,\n \"acc_stderr\": 0.033922384053216174,\n \"\
acc_norm\": 0.5509259259259259,\n \"acc_norm_stderr\": 0.033922384053216174\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7892156862745098,\n \"acc_stderr\": 0.028626547912437406,\n \"\
acc_norm\": 0.7892156862745098,\n \"acc_norm_stderr\": 0.028626547912437406\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7679324894514767,\n \"acc_stderr\": 0.02747974455080851,\n \
\ \"acc_norm\": 0.7679324894514767,\n \"acc_norm_stderr\": 0.02747974455080851\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6905829596412556,\n\
\ \"acc_stderr\": 0.03102441174057221,\n \"acc_norm\": 0.6905829596412556,\n\
\ \"acc_norm_stderr\": 0.03102441174057221\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7938931297709924,\n \"acc_stderr\": 0.03547771004159463,\n\
\ \"acc_norm\": 0.7938931297709924,\n \"acc_norm_stderr\": 0.03547771004159463\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8016528925619835,\n \"acc_stderr\": 0.03640118271990947,\n \"\
acc_norm\": 0.8016528925619835,\n \"acc_norm_stderr\": 0.03640118271990947\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7777777777777778,\n\
\ \"acc_stderr\": 0.040191074725573483,\n \"acc_norm\": 0.7777777777777778,\n\
\ \"acc_norm_stderr\": 0.040191074725573483\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7852760736196319,\n \"acc_stderr\": 0.03226219377286775,\n\
\ \"acc_norm\": 0.7852760736196319,\n \"acc_norm_stderr\": 0.03226219377286775\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5,\n\
\ \"acc_stderr\": 0.04745789978762494,\n \"acc_norm\": 0.5,\n \
\ \"acc_norm_stderr\": 0.04745789978762494\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8349514563106796,\n \"acc_stderr\": 0.036756688322331886,\n\
\ \"acc_norm\": 0.8349514563106796,\n \"acc_norm_stderr\": 0.036756688322331886\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8717948717948718,\n\
\ \"acc_stderr\": 0.02190190511507333,\n \"acc_norm\": 0.8717948717948718,\n\
\ \"acc_norm_stderr\": 0.02190190511507333\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768078,\n \
\ \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.04408440022768078\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8122605363984674,\n\
\ \"acc_stderr\": 0.013964393769899126,\n \"acc_norm\": 0.8122605363984674,\n\
\ \"acc_norm_stderr\": 0.013964393769899126\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7167630057803468,\n \"acc_stderr\": 0.024257901705323378,\n\
\ \"acc_norm\": 0.7167630057803468,\n \"acc_norm_stderr\": 0.024257901705323378\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3329608938547486,\n\
\ \"acc_stderr\": 0.015761716178397563,\n \"acc_norm\": 0.3329608938547486,\n\
\ \"acc_norm_stderr\": 0.015761716178397563\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7549019607843137,\n \"acc_stderr\": 0.024630048979824775,\n\
\ \"acc_norm\": 0.7549019607843137,\n \"acc_norm_stderr\": 0.024630048979824775\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7138263665594855,\n\
\ \"acc_stderr\": 0.025670259242188943,\n \"acc_norm\": 0.7138263665594855,\n\
\ \"acc_norm_stderr\": 0.025670259242188943\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.75,\n \"acc_stderr\": 0.02409347123262133,\n \
\ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.02409347123262133\n \
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\"\
: 0.5070921985815603,\n \"acc_stderr\": 0.02982449855912901,\n \"\
acc_norm\": 0.5070921985815603,\n \"acc_norm_stderr\": 0.02982449855912901\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4556714471968709,\n\
\ \"acc_stderr\": 0.012719949543032207,\n \"acc_norm\": 0.4556714471968709,\n\
\ \"acc_norm_stderr\": 0.012719949543032207\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6727941176470589,\n \"acc_stderr\": 0.028501452860396556,\n\
\ \"acc_norm\": 0.6727941176470589,\n \"acc_norm_stderr\": 0.028501452860396556\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6830065359477124,\n \"acc_stderr\": 0.018824219512706214,\n \
\ \"acc_norm\": 0.6830065359477124,\n \"acc_norm_stderr\": 0.018824219512706214\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6363636363636364,\n\
\ \"acc_stderr\": 0.04607582090719976,\n \"acc_norm\": 0.6363636363636364,\n\
\ \"acc_norm_stderr\": 0.04607582090719976\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7306122448979592,\n \"acc_stderr\": 0.02840125202902294,\n\
\ \"acc_norm\": 0.7306122448979592,\n \"acc_norm_stderr\": 0.02840125202902294\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8308457711442786,\n\
\ \"acc_stderr\": 0.026508590656233264,\n \"acc_norm\": 0.8308457711442786,\n\
\ \"acc_norm_stderr\": 0.026508590656233264\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.88,\n \"acc_stderr\": 0.03265986323710906,\n \
\ \"acc_norm\": 0.88,\n \"acc_norm_stderr\": 0.03265986323710906\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5542168674698795,\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\": 0.8187134502923976,\n \"acc_stderr\": 0.029547741687640038,\n\
\ \"acc_norm\": 0.8187134502923976,\n \"acc_norm_stderr\": 0.029547741687640038\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3023255813953488,\n\
\ \"mc1_stderr\": 0.016077509266133026,\n \"mc2\": 0.45917059894485873,\n\
\ \"mc2_stderr\": 0.014568844776078935\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7837411207576953,\n \"acc_stderr\": 0.01157061486140935\n\
\ },\n \"harness|drop|3\": {\n \"em\": 0.0053481543624161075,\n \
\ \"em_stderr\": 0.0007469252903319645,\n \"f1\": 0.06526845637583892,\n\
\ \"f1_stderr\": 0.0014953213199520477\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.17437452615617893,\n \"acc_stderr\": 0.010451421361976233\n\
\ }\n}\n```"
repo_url: https://huggingface.co/PeanutJar/Mistral-v0.1-PeanutButter-v0.0.2-7B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|arc:challenge|25_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|drop|3_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|gsm8k|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hellaswag|10_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-11-08T16-55-51.659477.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-management|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-virology|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|truthfulqa:mc|0_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-11-08T16-55-51.659477.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- '**/details_harness|winogrande|5_2023-11-08T16-55-51.659477.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-11-08T16-55-51.659477.parquet'
- config_name: results
data_files:
- split: 2023_11_08T16_55_51.659477
path:
- results_2023-11-08T16-55-51.659477.parquet
- split: latest
path:
- results_2023-11-08T16-55-51.659477.parquet
---
# Dataset Card for Evaluation run of PeanutJar/Mistral-v0.1-PeanutButter-v0.0.2-7B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/PeanutJar/Mistral-v0.1-PeanutButter-v0.0.2-7B
- **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 [PeanutJar/Mistral-v0.1-PeanutButter-v0.0.2-7B](https://huggingface.co/PeanutJar/Mistral-v0.1-PeanutButter-v0.0.2-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_PeanutJar__Mistral-v0.1-PeanutButter-v0.0.2-7B_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-08T16:55:51.659477](https://huggingface.co/datasets/open-llm-leaderboard/details_PeanutJar__Mistral-v0.1-PeanutButter-v0.0.2-7B_public/blob/main/results_2023-11-08T16-55-51.659477.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": 0.6373187668436219,
"acc_stderr": 0.032198754972879835,
"acc_norm": 0.6466738397796956,
"acc_norm_stderr": 0.03289417683130313,
"mc1": 0.3023255813953488,
"mc1_stderr": 0.016077509266133026,
"mc2": 0.45917059894485873,
"mc2_stderr": 0.014568844776078935,
"em": 0.0053481543624161075,
"em_stderr": 0.0007469252903319645,
"f1": 0.06526845637583892,
"f1_stderr": 0.0014953213199520477
},
"harness|arc:challenge|25": {
"acc": 0.5844709897610921,
"acc_stderr": 0.014401366641216388,
"acc_norm": 0.6177474402730375,
"acc_norm_stderr": 0.014200454049979277
},
"harness|hellaswag|10": {
"acc": 0.6389165504879506,
"acc_stderr": 0.004793330525656208,
"acc_norm": 0.8410675164309899,
"acc_norm_stderr": 0.003648659041493648
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695236,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695236
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6296296296296297,
"acc_stderr": 0.041716541613545426,
"acc_norm": 0.6296296296296297,
"acc_norm_stderr": 0.041716541613545426
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6710526315789473,
"acc_stderr": 0.03823428969926604,
"acc_norm": 0.6710526315789473,
"acc_norm_stderr": 0.03823428969926604
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.6,
"acc_stderr": 0.04923659639173309,
"acc_norm": 0.6,
"acc_norm_stderr": 0.04923659639173309
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6943396226415094,
"acc_stderr": 0.028353298073322666,
"acc_norm": 0.6943396226415094,
"acc_norm_stderr": 0.028353298073322666
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7291666666666666,
"acc_stderr": 0.03716177437566017,
"acc_norm": 0.7291666666666666,
"acc_norm_stderr": 0.03716177437566017
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.5,
"acc_stderr": 0.050251890762960605,
"acc_norm": 0.5,
"acc_norm_stderr": 0.050251890762960605
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.58,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.58,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.36,
"acc_stderr": 0.048241815132442176,
"acc_norm": 0.36,
"acc_norm_stderr": 0.048241815132442176
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6416184971098265,
"acc_stderr": 0.036563436533531585,
"acc_norm": 0.6416184971098265,
"acc_norm_stderr": 0.036563436533531585
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4411764705882353,
"acc_stderr": 0.04940635630605659,
"acc_norm": 0.4411764705882353,
"acc_norm_stderr": 0.04940635630605659
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.78,
"acc_stderr": 0.04163331998932263,
"acc_norm": 0.78,
"acc_norm_stderr": 0.04163331998932263
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5659574468085107,
"acc_stderr": 0.03240038086792747,
"acc_norm": 0.5659574468085107,
"acc_norm_stderr": 0.03240038086792747
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5,
"acc_stderr": 0.047036043419179864,
"acc_norm": 0.5,
"acc_norm_stderr": 0.047036043419179864
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5586206896551724,
"acc_stderr": 0.04137931034482757,
"acc_norm": 0.5586206896551724,
"acc_norm_stderr": 0.04137931034482757
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.3835978835978836,
"acc_stderr": 0.025043757318520196,
"acc_norm": 0.3835978835978836,
"acc_norm_stderr": 0.025043757318520196
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.35714285714285715,
"acc_stderr": 0.042857142857142816,
"acc_norm": 0.35714285714285715,
"acc_norm_stderr": 0.042857142857142816
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.37,
"acc_stderr": 0.04852365870939099,
"acc_norm": 0.37,
"acc_norm_stderr": 0.04852365870939099
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7580645161290323,
"acc_stderr": 0.024362599693031096,
"acc_norm": 0.7580645161290323,
"acc_norm_stderr": 0.024362599693031096
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5369458128078818,
"acc_stderr": 0.035083705204426656,
"acc_norm": 0.5369458128078818,
"acc_norm_stderr": 0.035083705204426656
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.71,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.71,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7757575757575758,
"acc_stderr": 0.03256866661681102,
"acc_norm": 0.7757575757575758,
"acc_norm_stderr": 0.03256866661681102
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7676767676767676,
"acc_stderr": 0.030088629490217487,
"acc_norm": 0.7676767676767676,
"acc_norm_stderr": 0.030088629490217487
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8756476683937824,
"acc_stderr": 0.023814477086593542,
"acc_norm": 0.8756476683937824,
"acc_norm_stderr": 0.023814477086593542
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6461538461538462,
"acc_stderr": 0.024243783994062157,
"acc_norm": 0.6461538461538462,
"acc_norm_stderr": 0.024243783994062157
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.35185185185185186,
"acc_stderr": 0.029116617606083015,
"acc_norm": 0.35185185185185186,
"acc_norm_stderr": 0.029116617606083015
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6302521008403361,
"acc_stderr": 0.03135709599613591,
"acc_norm": 0.6302521008403361,
"acc_norm_stderr": 0.03135709599613591
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.36423841059602646,
"acc_stderr": 0.03929111781242742,
"acc_norm": 0.36423841059602646,
"acc_norm_stderr": 0.03929111781242742
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8220183486238533,
"acc_stderr": 0.016399436366612927,
"acc_norm": 0.8220183486238533,
"acc_norm_stderr": 0.016399436366612927
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5509259259259259,
"acc_stderr": 0.033922384053216174,
"acc_norm": 0.5509259259259259,
"acc_norm_stderr": 0.033922384053216174
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7892156862745098,
"acc_stderr": 0.028626547912437406,
"acc_norm": 0.7892156862745098,
"acc_norm_stderr": 0.028626547912437406
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7679324894514767,
"acc_stderr": 0.02747974455080851,
"acc_norm": 0.7679324894514767,
"acc_norm_stderr": 0.02747974455080851
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6905829596412556,
"acc_stderr": 0.03102441174057221,
"acc_norm": 0.6905829596412556,
"acc_norm_stderr": 0.03102441174057221
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7938931297709924,
"acc_stderr": 0.03547771004159463,
"acc_norm": 0.7938931297709924,
"acc_norm_stderr": 0.03547771004159463
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8016528925619835,
"acc_stderr": 0.03640118271990947,
"acc_norm": 0.8016528925619835,
"acc_norm_stderr": 0.03640118271990947
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.040191074725573483,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.040191074725573483
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7852760736196319,
"acc_stderr": 0.03226219377286775,
"acc_norm": 0.7852760736196319,
"acc_norm_stderr": 0.03226219377286775
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.5,
"acc_stderr": 0.04745789978762494,
"acc_norm": 0.5,
"acc_norm_stderr": 0.04745789978762494
},
"harness|hendrycksTest-management|5": {
"acc": 0.8349514563106796,
"acc_stderr": 0.036756688322331886,
"acc_norm": 0.8349514563106796,
"acc_norm_stderr": 0.036756688322331886
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8717948717948718,
"acc_stderr": 0.02190190511507333,
"acc_norm": 0.8717948717948718,
"acc_norm_stderr": 0.02190190511507333
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.74,
"acc_stderr": 0.04408440022768078,
"acc_norm": 0.74,
"acc_norm_stderr": 0.04408440022768078
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8122605363984674,
"acc_stderr": 0.013964393769899126,
"acc_norm": 0.8122605363984674,
"acc_norm_stderr": 0.013964393769899126
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7167630057803468,
"acc_stderr": 0.024257901705323378,
"acc_norm": 0.7167630057803468,
"acc_norm_stderr": 0.024257901705323378
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.3329608938547486,
"acc_stderr": 0.015761716178397563,
"acc_norm": 0.3329608938547486,
"acc_norm_stderr": 0.015761716178397563
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7549019607843137,
"acc_stderr": 0.024630048979824775,
"acc_norm": 0.7549019607843137,
"acc_norm_stderr": 0.024630048979824775
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7138263665594855,
"acc_stderr": 0.025670259242188943,
"acc_norm": 0.7138263665594855,
"acc_norm_stderr": 0.025670259242188943
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.75,
"acc_stderr": 0.02409347123262133,
"acc_norm": 0.75,
"acc_norm_stderr": 0.02409347123262133
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.5070921985815603,
"acc_stderr": 0.02982449855912901,
"acc_norm": 0.5070921985815603,
"acc_norm_stderr": 0.02982449855912901
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4556714471968709,
"acc_stderr": 0.012719949543032207,
"acc_norm": 0.4556714471968709,
"acc_norm_stderr": 0.012719949543032207
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6727941176470589,
"acc_stderr": 0.028501452860396556,
"acc_norm": 0.6727941176470589,
"acc_norm_stderr": 0.028501452860396556
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6830065359477124,
"acc_stderr": 0.018824219512706214,
"acc_norm": 0.6830065359477124,
"acc_norm_stderr": 0.018824219512706214
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6363636363636364,
"acc_stderr": 0.04607582090719976,
"acc_norm": 0.6363636363636364,
"acc_norm_stderr": 0.04607582090719976
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7306122448979592,
"acc_stderr": 0.02840125202902294,
"acc_norm": 0.7306122448979592,
"acc_norm_stderr": 0.02840125202902294
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8308457711442786,
"acc_stderr": 0.026508590656233264,
"acc_norm": 0.8308457711442786,
"acc_norm_stderr": 0.026508590656233264
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.88,
"acc_stderr": 0.03265986323710906,
"acc_norm": 0.88,
"acc_norm_stderr": 0.03265986323710906
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5542168674698795,
"acc_stderr": 0.038695433234721015,
"acc_norm": 0.5542168674698795,
"acc_norm_stderr": 0.038695433234721015
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8187134502923976,
"acc_stderr": 0.029547741687640038,
"acc_norm": 0.8187134502923976,
"acc_norm_stderr": 0.029547741687640038
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3023255813953488,
"mc1_stderr": 0.016077509266133026,
"mc2": 0.45917059894485873,
"mc2_stderr": 0.014568844776078935
},
"harness|winogrande|5": {
"acc": 0.7837411207576953,
"acc_stderr": 0.01157061486140935
},
"harness|drop|3": {
"em": 0.0053481543624161075,
"em_stderr": 0.0007469252903319645,
"f1": 0.06526845637583892,
"f1_stderr": 0.0014953213199520477
},
"harness|gsm8k|5": {
"acc": 0.17437452615617893,
"acc_stderr": 0.010451421361976233
}
}
```
### 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] | [
-0.7026668190956116,
-0.888922393321991,
0.24415066838264465,
0.26593050360679626,
-0.15350158512592316,
-0.029411418363451958,
-0.0203519519418478,
-0.21970437467098236,
0.5533960461616516,
-0.0376875177025795,
-0.4514075815677643,
-0.6911084055900574,
-0.46484997868537903,
0.243716984987... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
open-llm-leaderboard/details_psmathur__model_101_public | open-llm-leaderboard | 2023-11-08T17:03:15Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T17:03:15Z | 2023-11-08T17:03:06.000Z | 2023-11-08T17:03:06 | ---
pretty_name: Evaluation run of psmathur/model_101
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [psmathur/model_101](https://huggingface.co/psmathur/model_101) on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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_psmathur__model_101_public\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-11-08T17:02:48.057771](https://huggingface.co/datasets/open-llm-leaderboard/details_psmathur__model_101_public/blob/main/results_2023-11-08T17-02-48.057771.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\": 0.42701342281879195,\n\
\ \"em_stderr\": 0.0050656203622111255,\n \"f1\": 0.5510203439597356,\n\
\ \"f1_stderr\": 0.004541060645215944,\n \"acc\": 0.6344516695758009,\n\
\ \"acc_stderr\": 0.012237970912161255\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.42701342281879195,\n \"em_stderr\": 0.0050656203622111255,\n\
\ \"f1\": 0.5510203439597356,\n \"f1_stderr\": 0.004541060645215944\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.44806671721000757,\n \
\ \"acc_stderr\": 0.013697992668274525\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8208366219415943,\n \"acc_stderr\": 0.010777949156047986\n\
\ }\n}\n```"
repo_url: https://huggingface.co/psmathur/model_101
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_11_08T17_02_48.057771
path:
- '**/details_harness|drop|3_2023-11-08T17-02-48.057771.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-11-08T17-02-48.057771.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_11_08T17_02_48.057771
path:
- '**/details_harness|gsm8k|5_2023-11-08T17-02-48.057771.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-11-08T17-02-48.057771.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_11_08T17_02_48.057771
path:
- '**/details_harness|winogrande|5_2023-11-08T17-02-48.057771.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-11-08T17-02-48.057771.parquet'
- config_name: results
data_files:
- split: 2023_11_08T17_02_48.057771
path:
- results_2023-11-08T17-02-48.057771.parquet
- split: latest
path:
- results_2023-11-08T17-02-48.057771.parquet
---
# Dataset Card for Evaluation run of psmathur/model_101
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/psmathur/model_101
- **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 [psmathur/model_101](https://huggingface.co/psmathur/model_101) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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_psmathur__model_101_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-08T17:02:48.057771](https://huggingface.co/datasets/open-llm-leaderboard/details_psmathur__model_101_public/blob/main/results_2023-11-08T17-02-48.057771.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": 0.42701342281879195,
"em_stderr": 0.0050656203622111255,
"f1": 0.5510203439597356,
"f1_stderr": 0.004541060645215944,
"acc": 0.6344516695758009,
"acc_stderr": 0.012237970912161255
},
"harness|drop|3": {
"em": 0.42701342281879195,
"em_stderr": 0.0050656203622111255,
"f1": 0.5510203439597356,
"f1_stderr": 0.004541060645215944
},
"harness|gsm8k|5": {
"acc": 0.44806671721000757,
"acc_stderr": 0.013697992668274525
},
"harness|winogrande|5": {
"acc": 0.8208366219415943,
"acc_stderr": 0.010777949156047986
}
}
```
### 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] | [
-0.3821530342102051,
-0.5994827747344971,
0.28342029452323914,
0.21210530400276184,
-0.23131898045539856,
0.13554473221302032,
-0.35164618492126465,
-0.011075037531554699,
0.3819383382797241,
0.5277174115180969,
-0.7693025469779968,
-0.8933956623077393,
-0.7048317790031433,
0.1941964477300... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
open-llm-leaderboard/details_internlm__internlm-20b-chat_public | open-llm-leaderboard | 2023-11-08T17:10:39Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T17:10:39Z | 2023-11-08T17:10:30.000Z | 2023-11-08T17:10:30 | ---
pretty_name: Evaluation run of internlm/internlm-20b-chat
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [internlm/internlm-20b-chat](https://huggingface.co/internlm/internlm-20b-chat)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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_internlm__internlm-20b-chat_public\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-11-08T17:10:14.815999](https://huggingface.co/datasets/open-llm-leaderboard/details_internlm__internlm-20b-chat_public/blob/main/results_2023-11-08T17-10-14.815999.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\": 0.016149328859060404,\n\
\ \"em_stderr\": 0.001290866955681033,\n \"f1\": 0.10652579697986504,\n\
\ \"f1_stderr\": 0.0024253657455140664,\n \"acc\": 0.4874752643801689,\n\
\ \"acc_stderr\": 0.011119649443380293\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.016149328859060404,\n \"em_stderr\": 0.001290866955681033,\n\
\ \"f1\": 0.10652579697986504,\n \"f1_stderr\": 0.0024253657455140664\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.18726307808946172,\n \
\ \"acc_stderr\": 0.01074591419951081\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7876874506708761,\n \"acc_stderr\": 0.011493384687249779\n\
\ }\n}\n```"
repo_url: https://huggingface.co/internlm/internlm-20b-chat
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_11_08T17_10_14.815999
path:
- '**/details_harness|drop|3_2023-11-08T17-10-14.815999.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-11-08T17-10-14.815999.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_11_08T17_10_14.815999
path:
- '**/details_harness|gsm8k|5_2023-11-08T17-10-14.815999.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-11-08T17-10-14.815999.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_11_08T17_10_14.815999
path:
- '**/details_harness|winogrande|5_2023-11-08T17-10-14.815999.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-11-08T17-10-14.815999.parquet'
- config_name: results
data_files:
- split: 2023_11_08T17_10_14.815999
path:
- results_2023-11-08T17-10-14.815999.parquet
- split: latest
path:
- results_2023-11-08T17-10-14.815999.parquet
---
# Dataset Card for Evaluation run of internlm/internlm-20b-chat
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/internlm/internlm-20b-chat
- **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 [internlm/internlm-20b-chat](https://huggingface.co/internlm/internlm-20b-chat) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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_internlm__internlm-20b-chat_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-08T17:10:14.815999](https://huggingface.co/datasets/open-llm-leaderboard/details_internlm__internlm-20b-chat_public/blob/main/results_2023-11-08T17-10-14.815999.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": 0.016149328859060404,
"em_stderr": 0.001290866955681033,
"f1": 0.10652579697986504,
"f1_stderr": 0.0024253657455140664,
"acc": 0.4874752643801689,
"acc_stderr": 0.011119649443380293
},
"harness|drop|3": {
"em": 0.016149328859060404,
"em_stderr": 0.001290866955681033,
"f1": 0.10652579697986504,
"f1_stderr": 0.0024253657455140664
},
"harness|gsm8k|5": {
"acc": 0.18726307808946172,
"acc_stderr": 0.01074591419951081
},
"harness|winogrande|5": {
"acc": 0.7876874506708761,
"acc_stderr": 0.011493384687249779
}
}
```
### 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] | [
-0.4320862591266632,
-0.7375891804695129,
0.11528951674699783,
0.3716990351676941,
-0.13859114050865173,
0.18996188044548035,
-0.42303740978240967,
-0.2342311143875122,
0.31739944219589233,
0.47528767585754395,
-0.6650539636611938,
-0.9868241548538208,
-0.6000322103500366,
0.14542429149150... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
arieg/bw_spec_cls_80_15 | arieg | 2023-11-08T17:10:52Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T17:10:52Z | 2023-11-08T17:10:46.000Z | 2023-11-08T17:10:46 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': '40842'
'1': '40843'
'2': '40851'
'3': '40903'
'4': '40908'
'5': '40909'
'6': '40938'
'7': '40940'
'8': '40985'
'9': '40986'
'10': '41018'
'11': '41054'
'12': '41095'
'13': '41147'
'14': '41191'
'15': '41192'
'16': '41568'
'17': '41570'
'18': '41573'
'19': '41605'
'20': '41709'
'21': '41714'
'22': '41812'
'23': '41961'
'24': '41962'
'25': '41965'
'26': '41983'
'27': '42014'
'28': '42016'
'29': '42017'
'30': '42019'
'31': '42020'
'32': '42023'
'33': '42025'
'34': '42029'
'35': '42030'
'36': '42031'
'37': '42044'
'38': '42045'
'39': '42046'
'40': '42048'
'41': '42119'
'42': '42126'
'43': '42234'
'44': '42235'
'45': '42236'
'46': '42238'
'47': '42240'
'48': '42241'
'49': '42243'
'50': '42245'
'51': '42247'
'52': '42310'
'53': '42442'
'54': '42475'
'55': '42648'
'56': '42659'
'57': '42751'
'58': '42761'
'59': '42789'
'60': '42966'
'61': '42984'
'62': '43016'
'63': '43018'
'64': '43019'
'65': '43020'
'66': '43021'
'67': '43022'
'68': '43023'
'69': '43024'
'70': '43025'
'71': '43026'
'72': '43027'
'73': '43028'
'74': '43029'
'75': '43030'
'76': '43063'
'77': '43172'
'78': '43173'
'79': '43533'
splits:
- name: train
num_bytes: 87445438.4
num_examples: 1600
download_size: 87152132
dataset_size: 87445438.4
---
# Dataset Card for "bw_spec_cls_80_15"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.7616842985153198,
-0.23801949620246887,
0.18230310082435608,
0.38153642416000366,
-0.2802785634994507,
-0.12861499190330505,
0.013099635019898415,
-0.3626108765602112,
0.5615656971931458,
0.466219425201416,
-0.799599826335907,
-0.8302854895591736,
-0.5333609580993652,
-0.082856848835945... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
open-llm-leaderboard/details_Weyaxi__TekniumAiroboros-Nebula-7B_public | open-llm-leaderboard | 2023-11-08T17:23:24Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T17:23:24Z | 2023-11-08T17:22:18.000Z | 2023-11-08T17:22:18 | ---
pretty_name: Evaluation run of Weyaxi/TekniumAiroboros-Nebula-7B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Weyaxi/TekniumAiroboros-Nebula-7B](https://huggingface.co/Weyaxi/TekniumAiroboros-Nebula-7B)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Weyaxi__TekniumAiroboros-Nebula-7B_public\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-11-08T17:19:18.874101](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__TekniumAiroboros-Nebula-7B_public/blob/main/results_2023-11-08T17-19-18.874101.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\": 0.5486392579762343,\n\
\ \"acc_stderr\": 0.03374349666110138,\n \"acc_norm\": 0.5573176954338681,\n\
\ \"acc_norm_stderr\": 0.03452133425560909,\n \"mc1\": 0.34761321909424725,\n\
\ \"mc1_stderr\": 0.016670769188897303,\n \"mc2\": 0.5163563481502564,\n\
\ \"mc2_stderr\": 0.015552871980185646,\n \"em\": 0.34721057046979864,\n\
\ \"em_stderr\": 0.004875539476141144,\n \"f1\": 0.4132676174496653,\n\
\ \"f1_stderr\": 0.004730745670924258\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5264505119453925,\n \"acc_stderr\": 0.01459093135812017,\n\
\ \"acc_norm\": 0.5716723549488054,\n \"acc_norm_stderr\": 0.014460496367599019\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6211909978092014,\n\
\ \"acc_stderr\": 0.004840990593494692,\n \"acc_norm\": 0.817167894841665,\n\
\ \"acc_norm_stderr\": 0.0038573886135331004\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.5259259259259259,\n\
\ \"acc_stderr\": 0.04313531696750575,\n \"acc_norm\": 0.5259259259259259,\n\
\ \"acc_norm_stderr\": 0.04313531696750575\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.5131578947368421,\n \"acc_stderr\": 0.04067533136309174,\n\
\ \"acc_norm\": 0.5131578947368421,\n \"acc_norm_stderr\": 0.04067533136309174\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.53,\n\
\ \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.53,\n \
\ \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6075471698113207,\n \"acc_stderr\": 0.030052580579557845,\n\
\ \"acc_norm\": 0.6075471698113207,\n \"acc_norm_stderr\": 0.030052580579557845\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6527777777777778,\n\
\ \"acc_stderr\": 0.039812405437178615,\n \"acc_norm\": 0.6527777777777778,\n\
\ \"acc_norm_stderr\": 0.039812405437178615\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.4,\n \"acc_stderr\": 0.04923659639173309,\n \
\ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n\
\ \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.34,\n\
\ \"acc_stderr\": 0.047609522856952344,\n \"acc_norm\": 0.34,\n \
\ \"acc_norm_stderr\": 0.047609522856952344\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \
\ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5202312138728323,\n\
\ \"acc_stderr\": 0.03809342081273958,\n \"acc_norm\": 0.5202312138728323,\n\
\ \"acc_norm_stderr\": 0.03809342081273958\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.2647058823529412,\n \"acc_stderr\": 0.04389869956808777,\n\
\ \"acc_norm\": 0.2647058823529412,\n \"acc_norm_stderr\": 0.04389869956808777\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.65,\n \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.65,\n\
\ \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5148936170212766,\n \"acc_stderr\": 0.032671518489247764,\n\
\ \"acc_norm\": 0.5148936170212766,\n \"acc_norm_stderr\": 0.032671518489247764\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4473684210526316,\n\
\ \"acc_stderr\": 0.04677473004491199,\n \"acc_norm\": 0.4473684210526316,\n\
\ \"acc_norm_stderr\": 0.04677473004491199\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.4896551724137931,\n \"acc_stderr\": 0.04165774775728763,\n\
\ \"acc_norm\": 0.4896551724137931,\n \"acc_norm_stderr\": 0.04165774775728763\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.36772486772486773,\n \"acc_stderr\": 0.024833839825562427,\n \"\
acc_norm\": 0.36772486772486773,\n \"acc_norm_stderr\": 0.024833839825562427\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.40476190476190477,\n\
\ \"acc_stderr\": 0.04390259265377563,\n \"acc_norm\": 0.40476190476190477,\n\
\ \"acc_norm_stderr\": 0.04390259265377563\n },\n \"harness|hendrycksTest-global_facts|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_biology|5\": {\n \"acc\"\
: 0.6548387096774193,\n \"acc_stderr\": 0.02704574657353433,\n \"\
acc_norm\": 0.6548387096774193,\n \"acc_norm_stderr\": 0.02704574657353433\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.3793103448275862,\n \"acc_stderr\": 0.034139638059062345,\n \"\
acc_norm\": 0.3793103448275862,\n \"acc_norm_stderr\": 0.034139638059062345\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\"\
: 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7212121212121212,\n \"acc_stderr\": 0.03501438706296781,\n\
\ \"acc_norm\": 0.7212121212121212,\n \"acc_norm_stderr\": 0.03501438706296781\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7272727272727273,\n \"acc_stderr\": 0.03173071239071724,\n \"\
acc_norm\": 0.7272727272727273,\n \"acc_norm_stderr\": 0.03173071239071724\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.7823834196891192,\n \"acc_stderr\": 0.029778663037752954,\n\
\ \"acc_norm\": 0.7823834196891192,\n \"acc_norm_stderr\": 0.029778663037752954\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.5333333333333333,\n \"acc_stderr\": 0.02529460802398647,\n \
\ \"acc_norm\": 0.5333333333333333,\n \"acc_norm_stderr\": 0.02529460802398647\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.2814814814814815,\n \"acc_stderr\": 0.02742001935094527,\n \
\ \"acc_norm\": 0.2814814814814815,\n \"acc_norm_stderr\": 0.02742001935094527\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.5588235294117647,\n \"acc_stderr\": 0.032252942323996406,\n\
\ \"acc_norm\": 0.5588235294117647,\n \"acc_norm_stderr\": 0.032252942323996406\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.33774834437086093,\n \"acc_stderr\": 0.03861557546255169,\n \"\
acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.03861557546255169\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.7688073394495413,\n \"acc_stderr\": 0.018075750241633146,\n \"\
acc_norm\": 0.7688073394495413,\n \"acc_norm_stderr\": 0.018075750241633146\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.33796296296296297,\n \"acc_stderr\": 0.03225941352631295,\n \"\
acc_norm\": 0.33796296296296297,\n \"acc_norm_stderr\": 0.03225941352631295\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7401960784313726,\n \"acc_stderr\": 0.030778554678693257,\n \"\
acc_norm\": 0.7401960784313726,\n \"acc_norm_stderr\": 0.030778554678693257\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7088607594936709,\n \"acc_stderr\": 0.02957160106575337,\n \
\ \"acc_norm\": 0.7088607594936709,\n \"acc_norm_stderr\": 0.02957160106575337\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6367713004484304,\n\
\ \"acc_stderr\": 0.032277904428505,\n \"acc_norm\": 0.6367713004484304,\n\
\ \"acc_norm_stderr\": 0.032277904428505\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.6259541984732825,\n \"acc_stderr\": 0.042438692422305246,\n\
\ \"acc_norm\": 0.6259541984732825,\n \"acc_norm_stderr\": 0.042438692422305246\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.71900826446281,\n \"acc_stderr\": 0.04103203830514512,\n \"acc_norm\"\
: 0.71900826446281,\n \"acc_norm_stderr\": 0.04103203830514512\n },\n\
\ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.6759259259259259,\n\
\ \"acc_stderr\": 0.045245960070300476,\n \"acc_norm\": 0.6759259259259259,\n\
\ \"acc_norm_stderr\": 0.045245960070300476\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.6809815950920245,\n \"acc_stderr\": 0.03661997551073836,\n\
\ \"acc_norm\": 0.6809815950920245,\n \"acc_norm_stderr\": 0.03661997551073836\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4375,\n\
\ \"acc_stderr\": 0.04708567521880525,\n \"acc_norm\": 0.4375,\n \
\ \"acc_norm_stderr\": 0.04708567521880525\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7087378640776699,\n \"acc_stderr\": 0.044986763205729224,\n\
\ \"acc_norm\": 0.7087378640776699,\n \"acc_norm_stderr\": 0.044986763205729224\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8589743589743589,\n\
\ \"acc_stderr\": 0.022801382534597524,\n \"acc_norm\": 0.8589743589743589,\n\
\ \"acc_norm_stderr\": 0.022801382534597524\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.63,\n \"acc_stderr\": 0.048523658709390974,\n \
\ \"acc_norm\": 0.63,\n \"acc_norm_stderr\": 0.048523658709390974\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.756066411238825,\n\
\ \"acc_stderr\": 0.015357212665829465,\n \"acc_norm\": 0.756066411238825,\n\
\ \"acc_norm_stderr\": 0.015357212665829465\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.5867052023121387,\n \"acc_stderr\": 0.026511261369409247,\n\
\ \"acc_norm\": 0.5867052023121387,\n \"acc_norm_stderr\": 0.026511261369409247\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3340782122905028,\n\
\ \"acc_stderr\": 0.01577491142238163,\n \"acc_norm\": 0.3340782122905028,\n\
\ \"acc_norm_stderr\": 0.01577491142238163\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.6045751633986928,\n \"acc_stderr\": 0.027996723180631435,\n\
\ \"acc_norm\": 0.6045751633986928,\n \"acc_norm_stderr\": 0.027996723180631435\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6237942122186495,\n\
\ \"acc_stderr\": 0.027513925683549434,\n \"acc_norm\": 0.6237942122186495,\n\
\ \"acc_norm_stderr\": 0.027513925683549434\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.6388888888888888,\n \"acc_stderr\": 0.026725868809100797,\n\
\ \"acc_norm\": 0.6388888888888888,\n \"acc_norm_stderr\": 0.026725868809100797\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.39361702127659576,\n \"acc_stderr\": 0.029144544781596154,\n \
\ \"acc_norm\": 0.39361702127659576,\n \"acc_norm_stderr\": 0.029144544781596154\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.43415906127770537,\n\
\ \"acc_stderr\": 0.01265903323706725,\n \"acc_norm\": 0.43415906127770537,\n\
\ \"acc_norm_stderr\": 0.01265903323706725\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.49264705882352944,\n \"acc_stderr\": 0.030369552523902173,\n\
\ \"acc_norm\": 0.49264705882352944,\n \"acc_norm_stderr\": 0.030369552523902173\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.565359477124183,\n \"acc_stderr\": 0.02005426920072646,\n \
\ \"acc_norm\": 0.565359477124183,\n \"acc_norm_stderr\": 0.02005426920072646\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6272727272727273,\n\
\ \"acc_stderr\": 0.04631381319425465,\n \"acc_norm\": 0.6272727272727273,\n\
\ \"acc_norm_stderr\": 0.04631381319425465\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.5183673469387755,\n \"acc_stderr\": 0.03198761546763127,\n\
\ \"acc_norm\": 0.5183673469387755,\n \"acc_norm_stderr\": 0.03198761546763127\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7661691542288557,\n\
\ \"acc_stderr\": 0.029929415408348384,\n \"acc_norm\": 0.7661691542288557,\n\
\ \"acc_norm_stderr\": 0.029929415408348384\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \
\ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4578313253012048,\n\
\ \"acc_stderr\": 0.0387862677100236,\n \"acc_norm\": 0.4578313253012048,\n\
\ \"acc_norm_stderr\": 0.0387862677100236\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.7485380116959064,\n \"acc_stderr\": 0.033275044238468436,\n\
\ \"acc_norm\": 0.7485380116959064,\n \"acc_norm_stderr\": 0.033275044238468436\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.34761321909424725,\n\
\ \"mc1_stderr\": 0.016670769188897303,\n \"mc2\": 0.5163563481502564,\n\
\ \"mc2_stderr\": 0.015552871980185646\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7324388318863457,\n \"acc_stderr\": 0.012441718456893009\n\
\ },\n \"harness|drop|3\": {\n \"em\": 0.34721057046979864,\n \
\ \"em_stderr\": 0.004875539476141144,\n \"f1\": 0.4132676174496653,\n\
\ \"f1_stderr\": 0.004730745670924258\n },\n \"harness|gsm8k|5\": {\n\
\ \"acc\": 0.09401061410159212,\n \"acc_stderr\": 0.008038819818872476\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Weyaxi/TekniumAiroboros-Nebula-7B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|arc:challenge|25_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|drop|3_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|gsm8k|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hellaswag|10_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-11-08T17-19-18.874101.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-management|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-virology|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|truthfulqa:mc|0_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-11-08T17-19-18.874101.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- '**/details_harness|winogrande|5_2023-11-08T17-19-18.874101.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-11-08T17-19-18.874101.parquet'
- config_name: results
data_files:
- split: 2023_11_08T17_19_18.874101
path:
- results_2023-11-08T17-19-18.874101.parquet
- split: latest
path:
- results_2023-11-08T17-19-18.874101.parquet
---
# Dataset Card for Evaluation run of Weyaxi/TekniumAiroboros-Nebula-7B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Weyaxi/TekniumAiroboros-Nebula-7B
- **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 [Weyaxi/TekniumAiroboros-Nebula-7B](https://huggingface.co/Weyaxi/TekniumAiroboros-Nebula-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Weyaxi__TekniumAiroboros-Nebula-7B_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-08T17:19:18.874101](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__TekniumAiroboros-Nebula-7B_public/blob/main/results_2023-11-08T17-19-18.874101.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": 0.5486392579762343,
"acc_stderr": 0.03374349666110138,
"acc_norm": 0.5573176954338681,
"acc_norm_stderr": 0.03452133425560909,
"mc1": 0.34761321909424725,
"mc1_stderr": 0.016670769188897303,
"mc2": 0.5163563481502564,
"mc2_stderr": 0.015552871980185646,
"em": 0.34721057046979864,
"em_stderr": 0.004875539476141144,
"f1": 0.4132676174496653,
"f1_stderr": 0.004730745670924258
},
"harness|arc:challenge|25": {
"acc": 0.5264505119453925,
"acc_stderr": 0.01459093135812017,
"acc_norm": 0.5716723549488054,
"acc_norm_stderr": 0.014460496367599019
},
"harness|hellaswag|10": {
"acc": 0.6211909978092014,
"acc_stderr": 0.004840990593494692,
"acc_norm": 0.817167894841665,
"acc_norm_stderr": 0.0038573886135331004
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5259259259259259,
"acc_stderr": 0.04313531696750575,
"acc_norm": 0.5259259259259259,
"acc_norm_stderr": 0.04313531696750575
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.5131578947368421,
"acc_stderr": 0.04067533136309174,
"acc_norm": 0.5131578947368421,
"acc_norm_stderr": 0.04067533136309174
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.53,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.53,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6075471698113207,
"acc_stderr": 0.030052580579557845,
"acc_norm": 0.6075471698113207,
"acc_norm_stderr": 0.030052580579557845
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.6527777777777778,
"acc_stderr": 0.039812405437178615,
"acc_norm": 0.6527777777777778,
"acc_norm_stderr": 0.039812405437178615
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.4,
"acc_stderr": 0.04923659639173309,
"acc_norm": 0.4,
"acc_norm_stderr": 0.04923659639173309
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.34,
"acc_stderr": 0.047609522856952344,
"acc_norm": 0.34,
"acc_norm_stderr": 0.047609522856952344
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.36,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.5202312138728323,
"acc_stderr": 0.03809342081273958,
"acc_norm": 0.5202312138728323,
"acc_norm_stderr": 0.03809342081273958
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.2647058823529412,
"acc_stderr": 0.04389869956808777,
"acc_norm": 0.2647058823529412,
"acc_norm_stderr": 0.04389869956808777
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.65,
"acc_stderr": 0.0479372485441102,
"acc_norm": 0.65,
"acc_norm_stderr": 0.0479372485441102
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5148936170212766,
"acc_stderr": 0.032671518489247764,
"acc_norm": 0.5148936170212766,
"acc_norm_stderr": 0.032671518489247764
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.4473684210526316,
"acc_stderr": 0.04677473004491199,
"acc_norm": 0.4473684210526316,
"acc_norm_stderr": 0.04677473004491199
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.4896551724137931,
"acc_stderr": 0.04165774775728763,
"acc_norm": 0.4896551724137931,
"acc_norm_stderr": 0.04165774775728763
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.36772486772486773,
"acc_stderr": 0.024833839825562427,
"acc_norm": 0.36772486772486773,
"acc_norm_stderr": 0.024833839825562427
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.40476190476190477,
"acc_stderr": 0.04390259265377563,
"acc_norm": 0.40476190476190477,
"acc_norm_stderr": 0.04390259265377563
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.27,
"acc_stderr": 0.044619604333847394,
"acc_norm": 0.27,
"acc_norm_stderr": 0.044619604333847394
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.6548387096774193,
"acc_stderr": 0.02704574657353433,
"acc_norm": 0.6548387096774193,
"acc_norm_stderr": 0.02704574657353433
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.3793103448275862,
"acc_stderr": 0.034139638059062345,
"acc_norm": 0.3793103448275862,
"acc_norm_stderr": 0.034139638059062345
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.53,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.53,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7212121212121212,
"acc_stderr": 0.03501438706296781,
"acc_norm": 0.7212121212121212,
"acc_norm_stderr": 0.03501438706296781
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7272727272727273,
"acc_stderr": 0.03173071239071724,
"acc_norm": 0.7272727272727273,
"acc_norm_stderr": 0.03173071239071724
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.7823834196891192,
"acc_stderr": 0.029778663037752954,
"acc_norm": 0.7823834196891192,
"acc_norm_stderr": 0.029778663037752954
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.5333333333333333,
"acc_stderr": 0.02529460802398647,
"acc_norm": 0.5333333333333333,
"acc_norm_stderr": 0.02529460802398647
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.2814814814814815,
"acc_stderr": 0.02742001935094527,
"acc_norm": 0.2814814814814815,
"acc_norm_stderr": 0.02742001935094527
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.5588235294117647,
"acc_stderr": 0.032252942323996406,
"acc_norm": 0.5588235294117647,
"acc_norm_stderr": 0.032252942323996406
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.33774834437086093,
"acc_stderr": 0.03861557546255169,
"acc_norm": 0.33774834437086093,
"acc_norm_stderr": 0.03861557546255169
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.7688073394495413,
"acc_stderr": 0.018075750241633146,
"acc_norm": 0.7688073394495413,
"acc_norm_stderr": 0.018075750241633146
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.33796296296296297,
"acc_stderr": 0.03225941352631295,
"acc_norm": 0.33796296296296297,
"acc_norm_stderr": 0.03225941352631295
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7401960784313726,
"acc_stderr": 0.030778554678693257,
"acc_norm": 0.7401960784313726,
"acc_norm_stderr": 0.030778554678693257
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7088607594936709,
"acc_stderr": 0.02957160106575337,
"acc_norm": 0.7088607594936709,
"acc_norm_stderr": 0.02957160106575337
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6367713004484304,
"acc_stderr": 0.032277904428505,
"acc_norm": 0.6367713004484304,
"acc_norm_stderr": 0.032277904428505
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.6259541984732825,
"acc_stderr": 0.042438692422305246,
"acc_norm": 0.6259541984732825,
"acc_norm_stderr": 0.042438692422305246
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.71900826446281,
"acc_stderr": 0.04103203830514512,
"acc_norm": 0.71900826446281,
"acc_norm_stderr": 0.04103203830514512
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.6759259259259259,
"acc_stderr": 0.045245960070300476,
"acc_norm": 0.6759259259259259,
"acc_norm_stderr": 0.045245960070300476
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.6809815950920245,
"acc_stderr": 0.03661997551073836,
"acc_norm": 0.6809815950920245,
"acc_norm_stderr": 0.03661997551073836
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.4375,
"acc_stderr": 0.04708567521880525,
"acc_norm": 0.4375,
"acc_norm_stderr": 0.04708567521880525
},
"harness|hendrycksTest-management|5": {
"acc": 0.7087378640776699,
"acc_stderr": 0.044986763205729224,
"acc_norm": 0.7087378640776699,
"acc_norm_stderr": 0.044986763205729224
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8589743589743589,
"acc_stderr": 0.022801382534597524,
"acc_norm": 0.8589743589743589,
"acc_norm_stderr": 0.022801382534597524
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.63,
"acc_stderr": 0.048523658709390974,
"acc_norm": 0.63,
"acc_norm_stderr": 0.048523658709390974
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.756066411238825,
"acc_stderr": 0.015357212665829465,
"acc_norm": 0.756066411238825,
"acc_norm_stderr": 0.015357212665829465
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.5867052023121387,
"acc_stderr": 0.026511261369409247,
"acc_norm": 0.5867052023121387,
"acc_norm_stderr": 0.026511261369409247
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.3340782122905028,
"acc_stderr": 0.01577491142238163,
"acc_norm": 0.3340782122905028,
"acc_norm_stderr": 0.01577491142238163
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.6045751633986928,
"acc_stderr": 0.027996723180631435,
"acc_norm": 0.6045751633986928,
"acc_norm_stderr": 0.027996723180631435
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.6237942122186495,
"acc_stderr": 0.027513925683549434,
"acc_norm": 0.6237942122186495,
"acc_norm_stderr": 0.027513925683549434
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.6388888888888888,
"acc_stderr": 0.026725868809100797,
"acc_norm": 0.6388888888888888,
"acc_norm_stderr": 0.026725868809100797
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.39361702127659576,
"acc_stderr": 0.029144544781596154,
"acc_norm": 0.39361702127659576,
"acc_norm_stderr": 0.029144544781596154
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.43415906127770537,
"acc_stderr": 0.01265903323706725,
"acc_norm": 0.43415906127770537,
"acc_norm_stderr": 0.01265903323706725
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.49264705882352944,
"acc_stderr": 0.030369552523902173,
"acc_norm": 0.49264705882352944,
"acc_norm_stderr": 0.030369552523902173
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.565359477124183,
"acc_stderr": 0.02005426920072646,
"acc_norm": 0.565359477124183,
"acc_norm_stderr": 0.02005426920072646
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6272727272727273,
"acc_stderr": 0.04631381319425465,
"acc_norm": 0.6272727272727273,
"acc_norm_stderr": 0.04631381319425465
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.5183673469387755,
"acc_stderr": 0.03198761546763127,
"acc_norm": 0.5183673469387755,
"acc_norm_stderr": 0.03198761546763127
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.7661691542288557,
"acc_stderr": 0.029929415408348384,
"acc_norm": 0.7661691542288557,
"acc_norm_stderr": 0.029929415408348384
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.75,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-virology|5": {
"acc": 0.4578313253012048,
"acc_stderr": 0.0387862677100236,
"acc_norm": 0.4578313253012048,
"acc_norm_stderr": 0.0387862677100236
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.7485380116959064,
"acc_stderr": 0.033275044238468436,
"acc_norm": 0.7485380116959064,
"acc_norm_stderr": 0.033275044238468436
},
"harness|truthfulqa:mc|0": {
"mc1": 0.34761321909424725,
"mc1_stderr": 0.016670769188897303,
"mc2": 0.5163563481502564,
"mc2_stderr": 0.015552871980185646
},
"harness|winogrande|5": {
"acc": 0.7324388318863457,
"acc_stderr": 0.012441718456893009
},
"harness|drop|3": {
"em": 0.34721057046979864,
"em_stderr": 0.004875539476141144,
"f1": 0.4132676174496653,
"f1_stderr": 0.004730745670924258
},
"harness|gsm8k|5": {
"acc": 0.09401061410159212,
"acc_stderr": 0.008038819818872476
}
}
```
### 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] | [
-0.7045329809188843,
-0.8035106658935547,
0.2923806309700012,
0.19615185260772705,
-0.21840181946754456,
-0.035868577659130096,
0.03412766009569168,
-0.19625315070152283,
0.5980671048164368,
-0.06327338516712189,
-0.4665053188800812,
-0.7045983672142029,
-0.4632300138473511,
0.239570841193... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
atom-in-the-universe/bild-deduped-16 | atom-in-the-universe | 2023-11-26T18:10:28Z | 0 | 0 | null | [
"region:us"
] | 2023-11-26T18:10:28Z | 2023-11-08T17:30:58.000Z | 2023-11-08T17:30:58 | Entry not found | [
-0.32276472449302673,
-0.22568407654762268,
0.8622258901596069,
0.4346148371696472,
-0.5282984972000122,
0.7012965679168701,
0.7915717363357544,
0.07618629932403564,
0.7746022939682007,
0.2563222646713257,
-0.785281777381897,
-0.22573848068714142,
-0.9104482531547546,
0.5715669393539429,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ChristophSchuhmann/DFN_378_emotion_detector | ChristophSchuhmann | 2023-11-16T20:19:51Z | 0 | 0 | null | [
"region:us"
] | 2023-11-16T20:19:51Z | 2023-11-08T17:32:08.000Z | 2023-11-08T17:32:08 | Entry not found | [
-0.32276472449302673,
-0.22568407654762268,
0.8622258901596069,
0.4346148371696472,
-0.5282984972000122,
0.7012965679168701,
0.7915717363357544,
0.07618629932403564,
0.7746022939682007,
0.2563222646713257,
-0.785281777381897,
-0.22573848068714142,
-0.9104482531547546,
0.5715669393539429,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
arincon/llm-detect | arincon | 2023-11-08T17:33:10Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T17:33:10Z | 2023-11-08T17:32:57.000Z | 2023-11-08T17:32:57 | ---
configs:
- config_name: default
data_files:
- split: '01'
path: data/01-*
- split: '02'
path: data/02-*
- split: '03'
path: data/03-*
- split: '04'
path: data/04-*
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
- name: source
dtype: string
- name: fold
dtype: int64
- name: essay_id
dtype: string
- name: prompt
dtype: string
splits:
- name: '01'
num_bytes: 74744152
num_examples: 33259
- name: '02'
num_bytes: 94518857
num_examples: 39785
- name: '03'
num_bytes: 99824812
num_examples: 42206
- name: '04'
num_bytes: 104524272
num_examples: 44206
download_size: 197583354
dataset_size: 373612093
---
# Dataset Card for "llm-detect"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.7540055513381958,
-0.2284487634897232,
0.494525283575058,
0.1176614761352539,
-0.262960284948349,
-0.08325592428445816,
0.2911962866783142,
-0.3142068386077881,
0.8720006942749023,
0.3685583472251892,
-0.9177579879760742,
-0.957024097442627,
-0.6969729661941528,
-0.1627553403377533,
-... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
arieg/bw_spec_cls_80_16 | arieg | 2023-11-08T17:35:40Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T17:35:40Z | 2023-11-08T17:35:32.000Z | 2023-11-08T17:35:32 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': '43534'
'1': '43535'
'2': '43536'
'3': '43585'
'4': '43586'
'5': '43587'
'6': '43588'
'7': '43589'
'8': '43590'
'9': '43592'
'10': '43593'
'11': '43594'
'12': '43595'
'13': '43596'
'14': '43598'
'15': '43599'
'16': '43600'
'17': '43608'
'18': '43621'
'19': '43623'
'20': '43695'
'21': '43696'
'22': '43697'
'23': '43698'
'24': '43699'
'25': '43761'
'26': '43773'
'27': '43796'
'28': '43842'
'29': '43843'
'30': '43844'
'31': '43856'
'32': '43857'
'33': '43858'
'34': '43860'
'35': '43861'
'36': '43863'
'37': '43865'
'38': '43866'
'39': '43867'
'40': '43868'
'41': '43869'
'42': '43883'
'43': '43886'
'44': '43899'
'45': '43911'
'46': '43962'
'47': '43965'
'48': '44092'
'49': '44110'
'50': '44169'
'51': '44236'
'52': '44342'
'53': '44347'
'54': '44354'
'55': '44778'
'56': '44779'
'57': '44780'
'58': '44781'
'59': '44782'
'60': '44791'
'61': '44792'
'62': '44793'
'63': '44794'
'64': '44795'
'65': '44796'
'66': '44797'
'67': '44798'
'68': '44799'
'69': '44801'
'70': '44803'
'71': '44804'
'72': '44805'
'73': '44806'
'74': '44809'
'75': '44820'
'76': '44821'
'77': '44822'
'78': '44823'
'79': '44848'
splits:
- name: train
num_bytes: 90417910.4
num_examples: 1600
download_size: 89917143
dataset_size: 90417910.4
---
# Dataset Card for "bw_spec_cls_80_16"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.714430570602417,
-0.21128737926483154,
0.20160654187202454,
0.2963922619819641,
-0.3268697261810303,
-0.14589811861515045,
-0.01531636156141758,
-0.3412986099720001,
0.5664129853248596,
0.48634058237075806,
-0.7786093950271606,
-0.8221737146377563,
-0.5493693351745605,
-0.13359335064888... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
arthurmluz/wikilingua_data-wiki_gptextsum_results | arthurmluz | 2023-11-08T17:51:07Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T17:51:07Z | 2023-11-08T17:49:20.000Z | 2023-11-08T17:49:20 | ---
dataset_info:
features:
- name: id
dtype: int64
- 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
splits:
- name: validation
num_bytes: 22119069
num_examples: 8165
download_size: 13030706
dataset_size: 22119069
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
# Dataset Card for "wikilingua_data-wiki_gptextsum_results"
rouge= {'rouge1': 0.33197745689950786, 'rouge2': 0.12351364096382854, 'rougeL': 0.24922924859888235, 'rougeLsum': 0.24922924859888235}
bert= {'precision': 0.761901833262096, 'recall': 0.7622503304255206, 'f1': 0.7612595360336023} | [
-0.30732184648513794,
-0.5342002511024475,
0.10330216586589813,
0.016101961955428123,
-0.3534669578075409,
-0.2674279510974884,
-0.48703092336654663,
-0.08108607679605484,
0.7376065850257874,
0.27097758650779724,
-0.37144309282302856,
-0.8899716734886169,
-0.7904841899871826,
0.10347218811... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
arieg/bw_spec_cls_80_17 | arieg | 2023-11-08T18:00:38Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T18:00:38Z | 2023-11-08T18:00:31.000Z | 2023-11-08T18:00:31 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': '44849'
'1': '44850'
'2': '44851'
'3': '44853'
'4': '44854'
'5': '44917'
'6': '44918'
'7': '44946'
'8': '44947'
'9': '44948'
'10': '44949'
'11': '44950'
'12': '44951'
'13': '44952'
'14': '45099'
'15': '45100'
'16': '45101'
'17': '45102'
'18': '45103'
'19': '45150'
'20': '45151'
'21': '45153'
'22': '45154'
'23': '45335'
'24': '45387'
'25': '45388'
'26': '45389'
'27': '45390'
'28': '45391'
'29': '45392'
'30': '45393'
'31': '45474'
'32': '45475'
'33': '45508'
'34': '45513'
'35': '45514'
'36': '45515'
'37': '45516'
'38': '45517'
'39': '45518'
'40': '45519'
'41': '45520'
'42': '45521'
'43': '45522'
'44': '45523'
'45': '45941'
'46': '46024'
'47': '46043'
'48': '46058'
'49': '46068'
'50': '46078'
'51': '46079'
'52': '46157'
'53': '46158'
'54': '46159'
'55': '46160'
'56': '46161'
'57': '46162'
'58': '46238'
'59': '46241'
'60': '46525'
'61': '46611'
'62': '46711'
'63': '46726'
'64': '46732'
'65': '46733'
'66': '46736'
'67': '46839'
'68': '46840'
'69': '46841'
'70': '46842'
'71': '46844'
'72': '46846'
'73': '46854'
'74': '46855'
'75': '46928'
'76': '46930'
'77': '47068'
'78': '47069'
'79': '47070'
splits:
- name: train
num_bytes: 87575273.6
num_examples: 1600
download_size: 87221336
dataset_size: 87575273.6
---
# Dataset Card for "bw_spec_cls_80_17"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.6924037337303162,
-0.20562317967414856,
0.1854948103427887,
0.28960609436035156,
-0.3159531354904175,
-0.14136192202568054,
-0.03087305650115013,
-0.31960174441337585,
0.5369694828987122,
0.4707012176513672,
-0.7644780874252319,
-0.8184657096862793,
-0.5658202171325684,
-0.1634248793125... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
arthurmluz/temario_data-xlsum_gptextsum_results | arthurmluz | 2023-11-08T18:19:54Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T18:19:54Z | 2023-11-08T18:13:22.000Z | 2023-11-08T18:13:22 | ---
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
splits:
- name: validation
num_bytes: 206481
num_examples: 25
download_size: 163467
dataset_size: 206481
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
# Dataset Card for "temario_data-xlsum_gptextsum_results"
rouge= {'rouge1': 0.17896239871319466, 'rouge2': 0.06855215321331638, 'rougeL': 0.12007675671332124, 'rougeLsum': 0.12007675671332124}
bert= {'precision': 0.738992075920105, 'recall': 0.6221855568885803, 'f1': 0.6748717761039734} | [
-0.1342211365699768,
-0.35837557911872864,
0.3017933964729309,
0.06091829389333725,
-0.4696837067604065,
-0.0622904933989048,
-0.30313563346862793,
0.037684209644794464,
0.7830328345298767,
0.2451784759759903,
-0.22425012290477753,
-1.0183980464935303,
-0.9342451691627502,
0.05532105267047... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
arieg/bw_spec_cls_80_18 | arieg | 2023-11-08T18:25:35Z | 0 | 0 | null | [
"region:us"
] | 2023-11-08T18:25:35Z | 2023-11-08T18:25:26.000Z | 2023-11-08T18:25:26 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': '47071'
'1': '47072'
'2': '47073'
'3': '47074'
'4': '47075'
'5': '47076'
'6': '47077'
'7': '47192'
'8': '47193'
'9': '47194'
'10': '47195'
'11': '47196'
'12': '47197'
'13': '47198'
'14': '47199'
'15': '47200'
'16': '47201'
'17': '47202'
'18': '47260'
'19': '47506'
'20': '47510'
'21': '47657'
'22': '47658'
'23': '47659'
'24': '47660'
'25': '47661'
'26': '47662'
'27': '47663'
'28': '47665'
'29': '47666'
'30': '47670'
'31': '47671'
'32': '47707'
'33': '47826'
'34': '47835'
'35': '47865'
'36': '47868'
'37': '47894'
'38': '47895'
'39': '47896'
'40': '47897'
'41': '47916'
'42': '47921'
'43': '48015'
'44': '48042'
'45': '48043'
'46': '48044'
'47': '48046'
'48': '48269'
'49': '48307'
'50': '48367'
'51': '48368'
'52': '48369'
'53': '48437'
'54': '48439'
'55': '48440'
'56': '48442'
'57': '48443'
'58': '48444'
'59': '48446'
'60': '48450'
'61': '48452'
'62': '48453'
'63': '48454'
'64': '48456'
'65': '48457'
'66': '48462'
'67': '48463'
'68': '48464'
'69': '48465'
'70': '48466'
'71': '48488'
'72': '48489'
'73': '48491'
'74': '48492'
'75': '48493'
'76': '48494'
'77': '48763'
'78': '48808'
'79': '48815'
splits:
- name: train
num_bytes: 87516568.0
num_examples: 1600
download_size: 87788761
dataset_size: 87516568.0
---
# Dataset Card for "bw_spec_cls_80_18"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.7144466638565063,
-0.2658827602863312,
0.18288232386112213,
0.33573028445243835,
-0.27709734439849854,
-0.15991365909576416,
-0.012977822683751583,
-0.37266385555267334,
0.5546272397041321,
0.5344817042350769,
-0.8118144869804382,
-0.7571083307266235,
-0.541278064250946,
-0.121034562587... | null | null | null | null | null | null | null | null | null | null | null | null | null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.