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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
rvv-karma/English-Hinglish | rvv-karma | 2023-11-25T10:14:40Z | 0 | 0 | null | [
"task_categories:translation",
"task_categories:text-generation",
"multilinguality:multilingual",
"multilinguality:translation",
"size_categories:10K<n<100K",
"language:en",
"language:hi",
"license:apache-2.0",
"region:us"
] | 2023-11-25T10:14:40Z | 2023-11-25T09:13:41.000Z | 2023-11-25T09:13:41 | ---
dataset_info:
features:
- name: en
dtype: string
- name: hi_en
dtype: string
splits:
- name: train
num_bytes: 12698467
num_examples: 132371
- name: test
num_bytes: 5431064
num_examples: 56731
download_size: 11695921
dataset_size: 18129531
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
multilinguality:
- multilingual
- translation
license: apache-2.0
task_categories:
- translation
- text-generation
language:
- en
- hi
pretty_name: English Hinglish
size_categories:
- 10K<n<100K
---
# English Hinglish
English to Hinglish Dataset processed from [findnitai/english-to-hinglish](https://huggingface.co/datasets/findnitai/english-to-hinglish).
Sources:
1. Hinglish TOP Dataset
2. CMU English Dog
3. HinGE
4. PHINC | [
-0.3609142601490021,
-0.42747917771339417,
0.041499312967061996,
0.7061949968338013,
0.12368915230035782,
-0.1930115669965744,
-0.3931337296962738,
-0.5352955460548401,
0.8049618005752563,
0.8015357851982117,
-0.7104260325431824,
-0.32930195331573486,
-0.5876928567886353,
0.273834049701690... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
BangumiBase/yourlieinapril | BangumiBase | 2023-11-25T11:18:34Z | 0 | 0 | null | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | 2023-11-25T11:18:34Z | 2023-11-25T09:23:04.000Z | 2023-11-25T09:23:04 | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Your Lie In April
This is the image base of bangumi Your Lie in April, we detected 26 characters, 2374 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 609 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 135 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 82 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 45 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 64 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 25 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 89 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 32 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 108 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 118 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 15 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 30 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 86 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 28 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 38 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 27 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 75 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 86 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 83 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 112 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 60 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 13 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 7 | [Download](22/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 23 | 6 | [Download](23/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 24 | 7 | [Download](24/dataset.zip) |  |  |  |  |  |  |  | N/A |
| noise | 394 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
| [
-0.6600795388221741,
-0.14215226471424103,
0.19783027470111847,
0.18550460040569305,
-0.2916400134563446,
-0.13320933282375336,
-0.011002186685800552,
-0.37320655584335327,
0.6712520122528076,
0.5844497084617615,
-0.9204185605049133,
-0.908149778842926,
-0.6888243556022644,
0.5292131304740... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
BangumiBase/natsumesbookoffriends | BangumiBase | 2023-11-25T13:44:22Z | 0 | 0 | null | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | 2023-11-25T13:44:22Z | 2023-11-25T09:23:26.000Z | 2023-11-25T09:23:26 | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Natsume's Book Of Friends
This is the image base of bangumi Natsume's Book of Friends, we detected 60 characters, 6311 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 2720 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 274 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 199 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 233 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 102 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 52 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 89 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 110 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 373 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 74 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 58 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 48 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 150 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 39 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 31 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 89 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 37 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 82 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 87 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 163 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 123 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 43 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 84 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 33 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 16 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 18 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 33 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 23 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 20 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 21 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 34 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 26 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 20 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 22 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 20 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 10 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 27 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 9 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 16 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 104 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 22 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 61 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 11 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 26 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 42 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 8 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 9 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 21 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 8 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 17 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 17 | [Download](50/dataset.zip) |  |  |  |  |  |  |  |  |
| 51 | 10 | [Download](51/dataset.zip) |  |  |  |  |  |  |  |  |
| 52 | 28 | [Download](52/dataset.zip) |  |  |  |  |  |  |  |  |
| 53 | 15 | [Download](53/dataset.zip) |  |  |  |  |  |  |  |  |
| 54 | 102 | [Download](54/dataset.zip) |  |  |  |  |  |  |  |  |
| 55 | 19 | [Download](55/dataset.zip) |  |  |  |  |  |  |  |  |
| 56 | 15 | [Download](56/dataset.zip) |  |  |  |  |  |  |  |  |
| 57 | 8 | [Download](57/dataset.zip) |  |  |  |  |  |  |  |  |
| 58 | 9 | [Download](58/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 151 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
| [
-0.701842725276947,
-0.1449066549539566,
0.13157667219638824,
0.21484118700027466,
-0.25484445691108704,
-0.08149456977844238,
-0.015438133850693703,
-0.38771647214889526,
0.6461436152458191,
0.5342751145362854,
-0.9467491507530212,
-0.8407108783721924,
-0.6566429138183594,
0.5923043489456... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
manojpatil/123 | manojpatil | 2023-11-25T09:59:34Z | 0 | 0 | null | [
"region:us"
] | 2023-11-25T09:59:34Z | 2023-11-25T09:48:09.000Z | 2023-11-25T09:48:09 | ---
dataset_info:
features:
- name: r
dtype: int64
- name: theta
dtype: string
splits:
- name: train
num_bytes: 173
num_examples: 7
download_size: 1415
dataset_size: 173
---
# Dataset Card for "123"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.60223788022995,
-0.23469436168670654,
0.237876757979393,
0.2819271683692932,
-0.4610569477081299,
-0.03715657442808151,
0.41585013270378113,
-0.09026788175106049,
0.8339112401008606,
0.4609663188457489,
-0.8703917264938354,
-0.8312505483627319,
-0.6242144107818604,
0.049765948206186295,... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
BangumiBase/danshikoukouseinonichijou | BangumiBase | 2023-11-25T11:12:24Z | 0 | 0 | null | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | 2023-11-25T11:12:24Z | 2023-11-25T10:04:03.000Z | 2023-11-25T10:04:03 | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Danshi Koukousei No Nichijou
This is the image base of bangumi Danshi Koukousei no Nichijou, we detected 25 characters, 1831 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 320 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 127 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 364 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 29 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 75 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 106 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 20 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 54 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 61 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 69 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 21 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 21 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 54 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 9 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 46 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 229 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 29 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 36 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 56 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 7 | [Download](19/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 20 | 12 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 28 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 7 | [Download](22/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 23 | 7 | [Download](23/dataset.zip) |  |  |  |  |  |  |  | N/A |
| noise | 44 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
| [
-0.7016070485115051,
-0.13375215232372284,
0.1451651155948639,
0.20988577604293823,
-0.29786473512649536,
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0.6507248282432556,
0.5362335443496704,
-0.9385331869125366,
-0.8741868138313293,
-0.6752793788909912,
0.5566938519477... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
sanjay69/kannada-news | sanjay69 | 2023-11-25T10:18:28Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | 2023-11-25T10:18:28Z | 2023-11-25T10:14:35.000Z | 2023-11-25T10:14:35 | ---
license: mit
---
| [
-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 | |
sonup/vehicles | sonup | 2023-11-27T12:32:41Z | 0 | 0 | null | [
"license:cc-by-4.0",
"region:us"
] | 2023-11-27T12:32:41Z | 2023-11-25T10:16:52.000Z | 2023-11-25T10:16:52 | ---
license: cc-by-4.0
dataset_info:
features:
- name: filename
dtype: string
- name: width
dtype: int64
- name: height
dtype: int64
- name: class
dtype: int64
- name: xmin
dtype: int64
- name: ymin
dtype: int64
- name: xmax
dtype: int64
- name: ymax
dtype: int64
splits:
- name: train
num_bytes: 38668
num_examples: 397
download_size: 13698
dataset_size: 38668
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
| [
-0.1285335123538971,
-0.1861683875322342,
0.6529128551483154,
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-0.0478255338966846... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
grubnev/Segmentation_Tigers | grubnev | 2023-11-25T10:33:24Z | 0 | 0 | null | [
"region:us"
] | 2023-11-25T10:33:24Z | 2023-11-25T10:33:24.000Z | 2023-11-25T10:33:24 | Entry not found | [
-0.3227645754814148,
-0.22568479180335999,
0.8622263669967651,
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-0.9104475975036621,
0.5715674161911011,
-... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
bot-yaya/undl_zh2en_aligned | bot-yaya | 2023-11-25T11:39:04Z | 0 | 0 | null | [
"region:us"
] | 2023-11-25T11:39:04Z | 2023-11-25T10:38:20.000Z | 2023-11-25T10:38:20 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: record
dtype: string
- name: clean_para_index_set_pair
dtype: string
- name: src
dtype: string
- name: dst
dtype: string
- name: src_text
dtype: string
- name: dst_text
dtype: string
- name: src_rate
dtype: float64
- name: dst_rate
dtype: float64
splits:
- name: train
num_bytes: 8884444751
num_examples: 15331650
download_size: 2443622169
dataset_size: 8884444751
---
# Dataset Card for "undl_zh2en_aligned"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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bot-yaya/rework_undl_text | bot-yaya | 2023-11-25T16:29:01Z | 0 | 0 | null | [
"region:us"
] | 2023-11-25T16:29:01Z | 2023-11-25T10:39:24.000Z | 2023-11-25T10:39:24 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: ar
dtype: string
- name: zh
dtype: string
- name: en
dtype: string
- name: fr
dtype: string
- name: ru
dtype: string
- name: es
dtype: string
- name: de
dtype: string
- name: record
dtype: string
splits:
- name: train
num_bytes: 48622457871
num_examples: 165840
download_size: 3906189450
dataset_size: 48622457871
---
# Dataset Card for "rework_undl_text"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.28200769424438477,
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-0.02843169867992... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
BangumiBase/nana | BangumiBase | 2023-11-25T13:26:05Z | 0 | 0 | null | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | 2023-11-25T13:26:05Z | 2023-11-25T10:45:08.000Z | 2023-11-25T10:45:08 | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Nana
This is the image base of bangumi NANA, we detected 38 characters, 4462 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 102 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 885 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 60 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 72 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 33 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 19 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 36 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 979 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 105 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 390 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 25 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 60 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 143 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 122 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 76 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 25 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 20 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 50 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 416 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 18 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 83 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 31 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 16 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 29 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 58 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 52 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 39 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 40 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 189 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 38 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 34 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 35 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 60 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 7 | [Download](33/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 34 | 18 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 13 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 6 | [Download](36/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| noise | 78 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
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Petto/lie-detection-dataset | Petto | 2023-11-25T11:11:21Z | 0 | 0 | null | [
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breno30/AlesandroGM | breno30 | 2023-11-28T15:01:00Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-28T15:01:00Z | 2023-11-25T11:22:09.000Z | 2023-11-25T11:22:09 | ---
license: openrail
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qbourbon/convnext-main | qbourbon | 2023-11-25T11:33:30Z | 0 | 0 | null | [
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Xiaoyao-Xiaoshui/Booniebears-ZhaoLin-Dataset | Xiaoyao-Xiaoshui | 2023-11-25T11:45:11Z | 0 | 0 | null | [
"license:gpl-3.0",
"region:us"
] | 2023-11-25T11:45:11Z | 2023-11-25T11:45:11.000Z | 2023-11-25T11:45:11 | ---
license: gpl-3.0
---
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MyRebRIc/ricksanchez | MyRebRIc | 2023-11-25T12:18:45Z | 0 | 0 | null | [
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edsongomes0215/lula | edsongomes0215 | 2023-11-25T12:05:24Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-25T12:05:24Z | 2023-11-25T12:03:42.000Z | 2023-11-25T12:03:42 | ---
license: openrail
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pipyp/vyrocomp | pipyp | 2023-11-25T12:30:57Z | 0 | 0 | null | [
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qbourbon/pb_trainset | qbourbon | 2023-11-25T12:33:33Z | 0 | 0 | null | [
"region:us"
] | 2023-11-25T12:33:33Z | 2023-11-25T12:33:20.000Z | 2023-11-25T12:33:20 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': 000_airplane
'1': 001_alarm_clock
'2': 002_angel
'3': 003_ant
'4': 004_apple
'5': 005_arm
'6': 006_armchair
'7': 007_ashtray
'8': 008_axe
'9': 009_backpack
'10': 010_banana
'11': 011_barn
'12': 012_baseball_bat
'13': 013_basket
'14': 014_bathtub
'15': 015_bear_(animal)
'16': 016_bed
'17': 017_bee
'18': 018_beer-mug
'19': 019_bell
'20': 020_bench
'21': 021_bicycle
'22': 022_binoculars
'23': 023_blimp
'24': 024_book
'25': 025_bookshelf
'26': 026_boomerang
'27': 027_bottle_opener
'28': 028_bowl
'29': 029_brain
'30': 030_bread
'31': 031_bridge
'32': 032_bulldozer
'33': 033_bus
'34': 034_bush
'35': 035_butterfly
'36': 036_cabinet
'37': 037_cactus
'38': 038_cake
'39': 039_calculator
'40': 040_camel
'41': 041_camera
'42': 042_candle
'43': 043_cannon
'44': 044_canoe
'45': 045_car_(sedan)
'46': 046_carrot
'47': 047_castle
'48': 048_cat
'49': 049_cell_phone
'50': 050_chair
'51': 051_chandelier
'52': 052_church
'53': 053_cigarette
'54': 054_cloud
'55': 055_comb
'56': 056_computer_monitor
'57': 057_computer-mouse
'58': 058_couch
'59': 059_cow
'60': 060_crab
'61': 061_crane_(machine)
'62': 062_crocodile
'63': 063_crown
'64': 064_cup
'65': 065_diamond
'66': 066_dog
'67': 067_dolphin
'68': 068_donut
'69': 069_door
'70': 070_door_handle
'71': 071_dragon
'72': 072_duck
'73': 073_ear
'74': 074_elephant
'75': 075_envelope
'76': 076_eye
'77': 077_eyeglasses
'78': 078_face
'79': 079_fan
'80': 080_feather
'81': 081_fire_hydrant
'82': 082_fish
'83': 083_flashlight
'84': 084_floor_lamp
'85': 085_flower_with_stem
'86': 086_flying_bird
'87': 087_flying_saucer
'88': 088_foot
'89': 089_fork
'90': 090_frog
'91': 091_frying-pan
'92': 092_giraffe
'93': 093_grapes
'94': 094_grenade
'95': 095_guitar
'96': 096_hamburger
'97': 097_hammer
'98': 098_hand
'99': 099_harp
'100': 100_hat
'101': 101_head
'102': 102_head-phones
'103': 103_hedgehog
'104': 104_helicopter
'105': 105_helmet
'106': 106_horse
'107': 107_hot_air_balloon
'108': 108_hot-dog
'109': 109_hourglass
'110': 110_house
'111': 111_human-skeleton
'112': 112_ice-cream-cone
'113': 113_ipod
'114': 114_kangaroo
'115': 115_key
'116': 116_keyboard
'117': 117_knife
'118': 118_ladder
'119': 119_laptop
'120': 120_leaf
'121': 121_lightbulb
'122': 122_lighter
'123': 123_lion
'124': 124_lobster
'125': 125_loudspeaker
'126': 126_mailbox
'127': 127_megaphone
'128': 128_mermaid
'129': 129_microphone
'130': 130_microscope
'131': 131_monkey
'132': 132_moon
'133': 133_mosquito
'134': 134_motorbike
'135': 135_mouse_(animal)
'136': 136_mouth
'137': 137_mug
'138': 138_mushroom
'139': 139_nose
'140': 140_octopus
'141': 141_owl
'142': 142_palm_tree
'143': 143_panda
'144': 144_paper_clip
'145': 145_parachute
'146': 146_parking_meter
'147': 147_parrot
'148': 148_pear
'149': 149_pen
'150': 150_penguin
'151': 151_person_sitting
'152': 152_person_walking
'153': 153_piano
'154': 154_pickup_truck
'155': 155_pig
'156': 156_pigeon
'157': 157_pineapple
'158': 158_pipe_(for_smoking)
'159': 159_pizza
'160': 160_potted_plant
'161': 161_power_outlet
'162': 162_present
'163': 163_pretzel
'164': 164_pumpkin
'165': 165_purse
'166': 166_rabbit
'167': 167_race_car
'168': 168_radio
'169': 169_rainbow
'170': 170_revolver
'171': 171_rifle
'172': 172_rollerblades
'173': 173_rooster
'174': 174_sailboat
'175': 175_santa_claus
'176': 176_satellite
'177': 177_satellite_dish
'178': 178_saxophone
'179': 179_scissors
'180': 180_scorpion
'181': 181_screwdriver
'182': 182_sea_turtle
'183': 183_seagull
'184': 184_shark
'185': 185_sheep
'186': 186_ship
'187': 187_shoe
'188': 188_shovel
'189': 189_skateboard
'190': 190_skull
'191': 191_skyscraper
'192': 192_snail
'193': 193_snake
'194': 194_snowboard
'195': 195_snowman
'196': 196_socks
'197': 197_space_shuttle
'198': 198_speed-boat
'199': 199_spider
'200': 200_sponge_bob
'201': 201_spoon
'202': 202_squirrel
'203': 203_standing_bird
'204': 204_stapler
'205': 205_strawberry
'206': 206_streetlight
'207': 207_submarine
'208': 208_suitcase
'209': 209_sun
'210': 210_suv
'211': 211_swan
'212': 212_sword
'213': 213_syringe
'214': 214_t-shirt
'215': 215_table
'216': 216_tablelamp
'217': 217_teacup
'218': 218_teapot
'219': 219_teddy-bear
'220': 220_telephone
'221': 221_tennis-racket
'222': 222_tent
'223': 223_tiger
'224': 224_tire
'225': 225_toilet
'226': 226_tomato
'227': 227_tooth
'228': 228_toothbrush
'229': 229_tractor
'230': 230_traffic_light
'231': 231_train
'232': 232_tree
'233': 233_trombone
'234': 234_trousers
'235': 235_truck
'236': 236_trumpet
'237': 237_tv
'238': 238_umbrella
'239': 239_van
'240': 240_vase
'241': 241_violin
'242': 242_walkie_talkie
'243': 243_wheel
'244': 244_wheelbarrow
'245': 245_windmill
'246': 246_wine-bottle
'247': 247_wineglass
'248': 248_wrist-watch
'249': 249_zebra
'250': mistery_category
splits:
- name: train
num_bytes: 48304697.956562325
num_examples: 1728
download_size: 50150712
dataset_size: 48304697.956562325
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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qbourbon/pb_valset | qbourbon | 2023-11-25T12:33:37Z | 0 | 0 | null | [
"region:us"
] | 2023-11-25T12:33:37Z | 2023-11-25T12:33:33.000Z | 2023-11-25T12:33:33 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': 000_airplane
'1': 001_alarm_clock
'2': 002_angel
'3': 003_ant
'4': 004_apple
'5': 005_arm
'6': 006_armchair
'7': 007_ashtray
'8': 008_axe
'9': 009_backpack
'10': 010_banana
'11': 011_barn
'12': 012_baseball_bat
'13': 013_basket
'14': 014_bathtub
'15': 015_bear_(animal)
'16': 016_bed
'17': 017_bee
'18': 018_beer-mug
'19': 019_bell
'20': 020_bench
'21': 021_bicycle
'22': 022_binoculars
'23': 023_blimp
'24': 024_book
'25': 025_bookshelf
'26': 026_boomerang
'27': 027_bottle_opener
'28': 028_bowl
'29': 029_brain
'30': 030_bread
'31': 031_bridge
'32': 032_bulldozer
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---
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qbourbon/pb_trainset-1 | qbourbon | 2023-11-25T12:38:11Z | 0 | 0 | null | [
"region:us"
] | 2023-11-25T12:38:11Z | 2023-11-25T12:37:50.000Z | 2023-11-25T12:37:50 | ---
dataset_info:
features:
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dtype: image
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configs:
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---
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qbourbon/pb_valset-1 | qbourbon | 2023-11-25T12:38:19Z | 0 | 0 | null | [
"region:us"
] | 2023-11-25T12:38:19Z | 2023-11-25T12:38:12.000Z | 2023-11-25T12:38:12 | ---
dataset_info:
features:
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qbourbon/pb_valset-2 | qbourbon | 2023-11-25T13:24:38Z | 0 | 0 | null | [
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casualdatauser/neet-dataset-mini | casualdatauser | 2023-11-25T13:34:52Z | 0 | 0 | null | [
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license: mit
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Gabriel1322/jclindao | Gabriel1322 | 2023-11-25T13:34:40Z | 0 | 0 | null | [
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] | 2023-11-25T13:44:43Z | 2023-11-25T13:44:43.000Z | 2023-11-25T13:44:43 | ---
license: apache-2.0
---
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sulenur/turkishReviews-ds-small | sulenur | 2023-11-25T13:54:39Z | 0 | 0 | null | [
"region:us"
] | 2023-11-25T13:54:39Z | 2023-11-25T13:54:35.000Z | 2023-11-25T13:54:35 | ---
dataset_info:
features:
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dtype: string
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dtype: int64
splits:
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num_bytes: 1253074.2290889719
num_examples: 3378
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num_examples: 376
download_size: 901581
dataset_size: 1392552.0
configs:
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data_files:
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path: data/train-*
- split: validation
path: data/validation-*
---
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ErhaChen/pixel_game_icon | ErhaChen | 2023-11-25T13:57:04Z | 0 | 0 | null | [
"task_categories:text-to-image",
"license:apache-2.0",
"style",
"pixel",
"icon",
"region:us"
] | 2023-11-25T13:57:04Z | 2023-11-25T13:55:54.000Z | 2023-11-25T13:55:54 | ---
license: apache-2.0
task_categories:
- text-to-image
tags:
- style
- pixel
- icon
--- | [
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DANDANKOKORO/MeuDataset | DANDANKOKORO | 2023-11-25T14:04:25Z | 0 | 0 | null | [
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Partha117/apache_bug_reports | Partha117 | 2023-11-25T19:08:08Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-25T19:08:08Z | 2023-11-25T14:34:01.000Z | 2023-11-25T14:34:01 | ---
license: apache-2.0
dataset_info:
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splits:
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num_bytes: 28828266
num_examples: 22747
download_size: 10160604
dataset_size: 28828266
configs:
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data_files:
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---
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todi1/pasmr1 | todi1 | 2023-11-25T14:46:59Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-25T14:46:59Z | 2023-11-25T14:38:37.000Z | 2023-11-25T14:38:37 | ---
license: openrail
---
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zsy12345/common_google_voice_pa | zsy12345 | 2023-11-25T14:46:10Z | 0 | 0 | null | [
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] | 2023-11-25T14:46:10Z | 2023-11-25T14:46:10.000Z | 2023-11-25T14:46:10 | Entry not found | [
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mangaphd/HausaLexicons | mangaphd | 2023-11-25T15:00:35Z | 0 | 0 | null | [
"license:ecl-2.0",
"doi:10.57967/hf/1390",
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] | 2023-11-25T15:00:35Z | 2023-11-25T14:59:49.000Z | 2023-11-25T14:59:49 | ---
license: ecl-2.0
---
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open-llm-leaderboard/details_NurtureAI__Orca-2-13B-16k_public | open-llm-leaderboard | 2023-11-25T15:00:43Z | 0 | 0 | null | [
"region:us"
] | 2023-11-25T15:00:43Z | 2023-11-25T14:59:55.000Z | 2023-11-25T14:59:55 | ---
pretty_name: Evaluation run of NurtureAI/Orca-2-13B-16k
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [NurtureAI/Orca-2-13B-16k](https://huggingface.co/NurtureAI/Orca-2-13B-16k) 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_NurtureAI__Orca-2-13B-16k_public\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-11-25T14:56:50.761859](https://huggingface.co/datasets/open-llm-leaderboard/details_NurtureAI__Orca-2-13B-16k_public/blob/main/results_2023-11-25T14-56-50.761859.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\": 0.4096720745858261,\n\
\ \"acc_stderr\": 0.034203032603114795,\n \"acc_norm\": 0.41715801816297365,\n\
\ \"acc_norm_stderr\": 0.03505952667633131,\n \"mc1\": 0.29253365973072215,\n\
\ \"mc1_stderr\": 0.015925597445286165,\n \"mc2\": 0.45298090995110557,\n\
\ \"mc2_stderr\": 0.015831655887070334,\n \"em\": 0.2791526845637584,\n\
\ \"em_stderr\": 0.004593906993460012,\n \"f1\": 0.3252799916107391,\n\
\ \"f1_stderr\": 0.004576434040922838\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.48464163822525597,\n \"acc_stderr\": 0.014604496129394911,\n\
\ \"acc_norm\": 0.5366894197952219,\n \"acc_norm_stderr\": 0.01457200052775699\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5056761601274646,\n\
\ \"acc_stderr\": 0.004989459871609183,\n \"acc_norm\": 0.6947819159529974,\n\
\ \"acc_norm_stderr\": 0.004595586027583791\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.047609522856952365,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.047609522856952365\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.37037037037037035,\n\
\ \"acc_stderr\": 0.04171654161354543,\n \"acc_norm\": 0.37037037037037035,\n\
\ \"acc_norm_stderr\": 0.04171654161354543\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.4868421052631579,\n \"acc_stderr\": 0.04067533136309174,\n\
\ \"acc_norm\": 0.4868421052631579,\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\": 0.4528301886792453,\n \"acc_stderr\": 0.03063562795796182,\n\
\ \"acc_norm\": 0.4528301886792453,\n \"acc_norm_stderr\": 0.03063562795796182\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.4097222222222222,\n\
\ \"acc_stderr\": 0.04112490974670787,\n \"acc_norm\": 0.4097222222222222,\n\
\ \"acc_norm_stderr\": 0.04112490974670787\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542127,\n \
\ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542127\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.31,\n \"acc_stderr\": 0.04648231987117317,\n \"acc_norm\": 0.31,\n\
\ \"acc_norm_stderr\": 0.04648231987117317\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.26,\n \"acc_stderr\": 0.044084400227680794,\n \
\ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.044084400227680794\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.3815028901734104,\n\
\ \"acc_stderr\": 0.037038511930995215,\n \"acc_norm\": 0.3815028901734104,\n\
\ \"acc_norm_stderr\": 0.037038511930995215\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.28431372549019607,\n \"acc_stderr\": 0.04488482852329017,\n\
\ \"acc_norm\": 0.28431372549019607,\n \"acc_norm_stderr\": 0.04488482852329017\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
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\ \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.34893617021276596,\n \"acc_stderr\": 0.03115852213135778,\n\
\ \"acc_norm\": 0.34893617021276596,\n \"acc_norm_stderr\": 0.03115852213135778\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.30701754385964913,\n\
\ \"acc_stderr\": 0.0433913832257986,\n \"acc_norm\": 0.30701754385964913,\n\
\ \"acc_norm_stderr\": 0.0433913832257986\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.3931034482758621,\n \"acc_stderr\": 0.040703290137070705,\n\
\ \"acc_norm\": 0.3931034482758621,\n \"acc_norm_stderr\": 0.040703290137070705\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.2830687830687831,\n \"acc_stderr\": 0.023201392938194974,\n \"\
acc_norm\": 0.2830687830687831,\n \"acc_norm_stderr\": 0.023201392938194974\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.21428571428571427,\n\
\ \"acc_stderr\": 0.03670066451047181,\n \"acc_norm\": 0.21428571428571427,\n\
\ \"acc_norm_stderr\": 0.03670066451047181\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.41935483870967744,\n \"acc_stderr\": 0.028071588901091845,\n \"\
acc_norm\": 0.41935483870967744,\n \"acc_norm_stderr\": 0.028071588901091845\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.270935960591133,\n \"acc_stderr\": 0.031270907132976984,\n \"\
acc_norm\": 0.270935960591133,\n \"acc_norm_stderr\": 0.031270907132976984\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|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-high_school_european_history|5\"\
: {\n \"acc\": 0.593939393939394,\n \"acc_stderr\": 0.03834816355401181,\n\
\ \"acc_norm\": 0.593939393939394,\n \"acc_norm_stderr\": 0.03834816355401181\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.48484848484848486,\n \"acc_stderr\": 0.0356071651653106,\n \"\
acc_norm\": 0.48484848484848486,\n \"acc_norm_stderr\": 0.0356071651653106\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.538860103626943,\n \"acc_stderr\": 0.035975244117345775,\n\
\ \"acc_norm\": 0.538860103626943,\n \"acc_norm_stderr\": 0.035975244117345775\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.3153846153846154,\n \"acc_stderr\": 0.02355964698318994,\n \
\ \"acc_norm\": 0.3153846153846154,\n \"acc_norm_stderr\": 0.02355964698318994\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.2222222222222222,\n \"acc_stderr\": 0.025348097468097856,\n \
\ \"acc_norm\": 0.2222222222222222,\n \"acc_norm_stderr\": 0.025348097468097856\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.37815126050420167,\n \"acc_stderr\": 0.03149930577784906,\n\
\ \"acc_norm\": 0.37815126050420167,\n \"acc_norm_stderr\": 0.03149930577784906\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.271523178807947,\n \"acc_stderr\": 0.03631329803969653,\n \"acc_norm\"\
: 0.271523178807947,\n \"acc_norm_stderr\": 0.03631329803969653\n },\n\
\ \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.5082568807339449,\n\
\ \"acc_stderr\": 0.021434399918214338,\n \"acc_norm\": 0.5082568807339449,\n\
\ \"acc_norm_stderr\": 0.021434399918214338\n },\n \"harness|hendrycksTest-high_school_statistics|5\"\
: {\n \"acc\": 0.26851851851851855,\n \"acc_stderr\": 0.030225226160012383,\n\
\ \"acc_norm\": 0.26851851851851855,\n \"acc_norm_stderr\": 0.030225226160012383\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.5588235294117647,\n \"acc_stderr\": 0.034849415144292316,\n \"\
acc_norm\": 0.5588235294117647,\n \"acc_norm_stderr\": 0.034849415144292316\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.6329113924050633,\n \"acc_stderr\": 0.031376240725616185,\n \
\ \"acc_norm\": 0.6329113924050633,\n \"acc_norm_stderr\": 0.031376240725616185\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.47085201793721976,\n\
\ \"acc_stderr\": 0.03350073248773403,\n \"acc_norm\": 0.47085201793721976,\n\
\ \"acc_norm_stderr\": 0.03350073248773403\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.4580152671755725,\n \"acc_stderr\": 0.04369802690578757,\n\
\ \"acc_norm\": 0.4580152671755725,\n \"acc_norm_stderr\": 0.04369802690578757\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
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acc_norm\": 0.5619834710743802,\n \"acc_norm_stderr\": 0.04529146804435792\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.4722222222222222,\n\
\ \"acc_stderr\": 0.04826217294139892,\n \"acc_norm\": 0.4722222222222222,\n\
\ \"acc_norm_stderr\": 0.04826217294139892\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.36809815950920244,\n \"acc_stderr\": 0.03789213935838396,\n\
\ \"acc_norm\": 0.36809815950920244,\n \"acc_norm_stderr\": 0.03789213935838396\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.33035714285714285,\n\
\ \"acc_stderr\": 0.04464285714285715,\n \"acc_norm\": 0.33035714285714285,\n\
\ \"acc_norm_stderr\": 0.04464285714285715\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.4174757281553398,\n \"acc_stderr\": 0.04882840548212238,\n\
\ \"acc_norm\": 0.4174757281553398,\n \"acc_norm_stderr\": 0.04882840548212238\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.6282051282051282,\n\
\ \"acc_stderr\": 0.031660988918880785,\n \"acc_norm\": 0.6282051282051282,\n\
\ \"acc_norm_stderr\": 0.031660988918880785\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\
: 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-miscellaneous|5\"\
: {\n \"acc\": 0.4878671775223499,\n \"acc_stderr\": 0.01787469866749134,\n\
\ \"acc_norm\": 0.4878671775223499,\n \"acc_norm_stderr\": 0.01787469866749134\n\
\ },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.4653179190751445,\n\
\ \"acc_stderr\": 0.026854257928258893,\n \"acc_norm\": 0.4653179190751445,\n\
\ \"acc_norm_stderr\": 0.026854257928258893\n },\n \"harness|hendrycksTest-moral_scenarios|5\"\
: {\n \"acc\": 0.30502793296089387,\n \"acc_stderr\": 0.015398723510916715,\n\
\ \"acc_norm\": 0.30502793296089387,\n \"acc_norm_stderr\": 0.015398723510916715\n\
\ },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.3954248366013072,\n\
\ \"acc_stderr\": 0.027996723180631455,\n \"acc_norm\": 0.3954248366013072,\n\
\ \"acc_norm_stderr\": 0.027996723180631455\n },\n \"harness|hendrycksTest-philosophy|5\"\
: {\n \"acc\": 0.40514469453376206,\n \"acc_stderr\": 0.02788238379132595,\n\
\ \"acc_norm\": 0.40514469453376206,\n \"acc_norm_stderr\": 0.02788238379132595\n\
\ },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.4444444444444444,\n\
\ \"acc_stderr\": 0.027648477877413327,\n \"acc_norm\": 0.4444444444444444,\n\
\ \"acc_norm_stderr\": 0.027648477877413327\n },\n \"harness|hendrycksTest-professional_accounting|5\"\
: {\n \"acc\": 0.3120567375886525,\n \"acc_stderr\": 0.02764012054516993,\n\
\ \"acc_norm\": 0.3120567375886525,\n \"acc_norm_stderr\": 0.02764012054516993\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.3455019556714472,\n\
\ \"acc_stderr\": 0.012145303004087206,\n \"acc_norm\": 0.3455019556714472,\n\
\ \"acc_norm_stderr\": 0.012145303004087206\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.3713235294117647,\n \"acc_stderr\": 0.02934980313976587,\n\
\ \"acc_norm\": 0.3713235294117647,\n \"acc_norm_stderr\": 0.02934980313976587\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.41830065359477125,\n \"acc_stderr\": 0.01995597514583554,\n \
\ \"acc_norm\": 0.41830065359477125,\n \"acc_norm_stderr\": 0.01995597514583554\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.4727272727272727,\n\
\ \"acc_stderr\": 0.04782001791380063,\n \"acc_norm\": 0.4727272727272727,\n\
\ \"acc_norm_stderr\": 0.04782001791380063\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.5591836734693878,\n \"acc_stderr\": 0.03178419114175363,\n\
\ \"acc_norm\": 0.5591836734693878,\n \"acc_norm_stderr\": 0.03178419114175363\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.527363184079602,\n\
\ \"acc_stderr\": 0.035302355173346824,\n \"acc_norm\": 0.527363184079602,\n\
\ \"acc_norm_stderr\": 0.035302355173346824\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.65,\n \"acc_stderr\": 0.047937248544110196,\n \
\ \"acc_norm\": 0.65,\n \"acc_norm_stderr\": 0.047937248544110196\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.40963855421686746,\n\
\ \"acc_stderr\": 0.03828401115079022,\n \"acc_norm\": 0.40963855421686746,\n\
\ \"acc_norm_stderr\": 0.03828401115079022\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.43859649122807015,\n \"acc_stderr\": 0.038057975055904594,\n\
\ \"acc_norm\": 0.43859649122807015,\n \"acc_norm_stderr\": 0.038057975055904594\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.29253365973072215,\n\
\ \"mc1_stderr\": 0.015925597445286165,\n \"mc2\": 0.45298090995110557,\n\
\ \"mc2_stderr\": 0.015831655887070334\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6006314127861089,\n \"acc_stderr\": 0.013764933546717614\n\
\ },\n \"harness|drop|3\": {\n \"em\": 0.2791526845637584,\n \
\ \"em_stderr\": 0.004593906993460012,\n \"f1\": 0.3252799916107391,\n \
\ \"f1_stderr\": 0.004576434040922838\n },\n \"harness|gsm8k|5\": {\n\
\ \"acc\": 0.01819560272934041,\n \"acc_stderr\": 0.0036816118940738727\n\
\ }\n}\n```"
repo_url: https://huggingface.co/NurtureAI/Orca-2-13B-16k
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_25T14_56_50.761859
path:
- '**/details_harness|arc:challenge|25_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|drop|3_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|gsm8k|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hellaswag|10_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-11-25T14-56-50.761859.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-management|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-virology|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|truthfulqa:mc|0_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-11-25T14-56-50.761859.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- '**/details_harness|winogrande|5_2023-11-25T14-56-50.761859.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-11-25T14-56-50.761859.parquet'
- config_name: results
data_files:
- split: 2023_11_25T14_56_50.761859
path:
- results_2023-11-25T14-56-50.761859.parquet
- split: latest
path:
- results_2023-11-25T14-56-50.761859.parquet
---
# Dataset Card for Evaluation run of NurtureAI/Orca-2-13B-16k
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/NurtureAI/Orca-2-13B-16k
- **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 [NurtureAI/Orca-2-13B-16k](https://huggingface.co/NurtureAI/Orca-2-13B-16k) 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_NurtureAI__Orca-2-13B-16k_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-25T14:56:50.761859](https://huggingface.co/datasets/open-llm-leaderboard/details_NurtureAI__Orca-2-13B-16k_public/blob/main/results_2023-11-25T14-56-50.761859.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
{
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"acc_norm": 0.41715801816297365,
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"mc1": 0.29253365973072215,
"mc1_stderr": 0.015925597445286165,
"mc2": 0.45298090995110557,
"mc2_stderr": 0.015831655887070334,
"em": 0.2791526845637584,
"em_stderr": 0.004593906993460012,
"f1": 0.3252799916107391,
"f1_stderr": 0.004576434040922838
},
"harness|arc:challenge|25": {
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"acc_norm": 0.5366894197952219,
"acc_norm_stderr": 0.01457200052775699
},
"harness|hellaswag|10": {
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"acc_stderr": 0.004989459871609183,
"acc_norm": 0.6947819159529974,
"acc_norm_stderr": 0.004595586027583791
},
"harness|hendrycksTest-abstract_algebra|5": {
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"acc_norm": 0.34,
"acc_norm_stderr": 0.047609522856952365
},
"harness|hendrycksTest-anatomy|5": {
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},
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},
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},
"harness|hendrycksTest-clinical_knowledge|5": {
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},
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},
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},
"harness|hendrycksTest-college_computer_science|5": {
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"harness|hendrycksTest-us_foreign_policy|5": {
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"harness|hendrycksTest-world_religions|5": {
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"harness|winogrande|5": {
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"harness|drop|3": {
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"harness|gsm8k|5": {
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}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | [
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license: openrail
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task_categories:
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language:
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pretty_name: Summator 3000
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license: mit
---
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mispeech/speechocean762 | mispeech | 2023-11-25T16:09:00Z | 0 | 0 | null | [
"task_categories:automatic-speech-recognition",
"size_categories:1K<n<10K",
"language:en",
"license:apache-2.0",
"pronunciation-scoring",
"region:us"
] | 2023-11-25T16:09:00Z | 2023-11-25T15:50:48.000Z | 2023-11-25T15:50:48 | ---
license: apache-2.0
task_categories:
- automatic-speech-recognition
language:
- en
tags:
- pronunciation-scoring
pretty_name: speechocean762
size_categories:
- 1K<n<10K
---
# speechocean762: A non-native English corpus for pronunciation scoring task
## Introduction
Pronunciation scoring is a crucial technology in computer-assisted language learning (CALL) systems. The pronunciation quality scores might be given at phoneme-level, word-level, and sentence-level for a typical pronunciation scoring task.
This corpus aims to provide a free public dataset for the pronunciation scoring task.
Key features:
* It is available for free download for both commercial and non-commercial purposes.
* The speaker variety encompasses young children and adults.
* The manual annotations are in multiple aspects at sentence-level, word-level and phoneme-level.
This corpus consists of 5000 English sentences. All the speakers are non-native, and their mother tongue is Mandarin. Half of the speakers are Children, and the others are adults. The information of age and gender are provided.
Five experts made the scores. To avoid subjective bias, each expert scores independently under the same metric.
## The scoring metric
The experts score at three levels: phoneme-level, word-level, and sentence-level.
### Phoneme level
Score the pronunciation goodness of each phoneme within the words.
Score range: 0-2
* 2: pronunciation is correct
* 1: pronunciation is right but has a heavy accent
* 0: pronunciation is incorrect or missed
### Word level
Score the accuracy and stress of each word's pronunciation.
#### Accuracy
Score range: 0 - 10
* 10: The pronunciation of the word is perfect
* 7-9: Most phones in this word are pronounced correctly but have accents
* 4-6: Less than 30% of phones in this word are wrongly pronounced
* 2-3: More than 30% of phones in this word are wrongly pronounced. In another case, the word is mispronounced as some other word. For example, the student mispronounced the word "bag" as "bike"
* 1: The pronunciation is hard to distinguish
* 0: no voice
#### Stress
Score range: {5, 10}
* 10: The stress is correct, or this is a mono-syllable word
* 5: The stress is wrong
### Sentence level
Score the accuracy, fluency, completeness and prosodic at the sentence level.
#### Accuracy
Score range: 0 - 10
* 9-10: The overall pronunciation of the sentence is excellent, with accurate phonology and no obvious pronunciation mistakes
* 7-8: The overall pronunciation of the sentence is good, with a few pronunciation mistakes
* 5-6: The overall pronunciation of the sentence is understandable, with many pronunciation mistakes and accent, but it does not affect the understanding of basic meanings
* 3-4: Poor, clumsy and rigid pronunciation of the sentence as a whole, with serious pronunciation mistakes
* 0-2: Extremely poor pronunciation and only one or two words are recognizable
#### Completeness
Score range: 0.0 - 1.0
The percentage of the words with good pronunciation.
#### Fluency
Score range: 0 - 10
* 8-10: Fluent without noticeable pauses or stammering
* 6-7: Fluent in general, with a few pauses, repetition, and stammering
* 4-5: the speech is a little influent, with many pauses, repetition, and stammering
* 0-3: intermittent, very influent speech, with lots of pauses, repetition, and stammering
#### Prosodic
Score range: 0 - 10
* 9-10: Correct intonation at a stable speaking speed, speak with cadence, and can speak like a native
* 7-8: Nearly correct intonation at a stable speaking speed, nearly smooth and coherent, but with little stammering and few pauses
* 5-6: Unstable speech speed, many stammering and pauses with a poor sense of rhythm
* 3-4: Unstable speech speed, speak too fast or too slow, without the sense of rhythm
* 0-2: Poor intonation and lots of stammering and pauses, unable to read a complete sentence
## Data structure
The following tree shows the file structure of this corpus:
```
├── scores.json
├── scores-detail.json
├── train
│ ├── spk2age
│ ├── spk2gender
│ ├── spk2utt
│ ├── text
│ ├── utt2spk
│ └── wav.scp
├── test
│ ├── spk2age
│ ├── spk2gender
│ ├── spk2utt
│ ├── text
│ ├── utt2spk
│ └── wav.scp
└── WAVE
├── SPEAKER0001
│ ├── 000010011.WAV
│ ├── 000010035.WAV
│ ├── ...
│ └── 000010173.WAV
├── SPEAKER0003
│ ├── 000030012.WAV
│ ├── 000030024.WAV
│ ├── ...
│ └── 000030175.WAV
└── SPEAKER0005
├── 000050003.WAV
├── 000050010.WAV
├── ...
└── 000050175.WAV
```
There are two datasets: `train` and `test`, and both are in Kaldi's data directory style.
The scores are stored in `scores.json`. Here is an example:
```
{
"000010011": { # utt-id
"text": "WE CALL IT BEAR", # transcript text
"accuracy": 8, # sentence-level accuracy score
"completeness": 10.0, # sentence-level completeness score
"fluency": 9, # sentence-level fluency score
"prosodic": 9, # sentence-level prosodic score
"total": 8, # sentence-level total score
"words": [
{
"accuracy": 10, # word-level accuracy score
"stress": 10, # word-level stress score
"total": 10, # word-level total score
"text": "WE", # the word text
"phones": "W IY0", # phones of the word
"phones-accuracy": [2.0, 2.0] # phoneme-level accuracy score
},
{
"accuracy": 10,
"stress": 10,
"total": 10,
"text": "CALL",
"phones": "K AO0 L",
"phones-accuracy": [2.0, 1.8, 1.8]
},
{
"accuracy": 10,
"stress": 10,
"total": 10,
"text": "IT",
"phones": "IH0 T",
"phones-accuracy": [2.0, 2.0]
},
{
"accuracy": 6,
"stress": 10,
"total": 6,
"text": "BEAR",
"phones": "B EH0 R",
"phones-accuracy": [2.0, 1.0, 1.0]
}
]
},
...
}
```
For the phones with an accuracy score lower than 0.5, an extra "mispronunciations" block indicates which phoneme the current phone was actually pronounced.
An example:
```
{
"text": "LISA",
"accuracy": 5,
"phones": ["L", "IY1", "S", "AH0"],
"phones-accuracy": [0.4, 2, 2, 1.2],
"mispronunciations": [
{
"canonical-phone": "L",
"index": 0,
"pronounced-phone": "D"
}
],
"stress": 10,
"total": 6
}
```
The file `scores.json` is processed from `scores-detail.json`.
The two JSON files are almost the same, but `scores-detail.json` has the five experts' original scores, while the scores of scores.json were the average or median scores.
An example item in `scores-detail.json`:
```
{
"000010011": {
"text": "WE CALL IT BEAR",
"accuracy": [7.0, 9.0, 8.0, 8.0, 9.0],
"completeness": [1.0, 1.0, 1.0, 1.0, 1.0],
"fluency": [10.0, 9.0, 8.0, 8.0, 10.0],
"prosodic": [10.0, 9.0, 7.0, 8.0, 9.0],
"total": [7.6, 9.0, 7.9, 8.0, 9.1],
"words": [
{
"accuracy": [10.0, 10.0, 10.0, 10.0, 10.0],
"stress": [10.0, 10.0, 10.0, 10.0, 10.0],
"total": [10.0, 10.0, 10.0, 10.0, 10.0],
"text": "WE",
"ref-phones": "W IY0",
"phones": ["W IY0", "W IY0", "W IY0", "W IY0", "W IY0"]
},
{
"accuracy": [10.0, 8.0, 10.0, 10.0, 8.0],
"stress": [10.0, 10.0, 10.0, 10.0, 10.0],
"total": [10.0, 8.4, 10.0, 10.0, 8.4],
"text": "CALL",
"ref-phones": "K AO0 L",
"phones": ["K AO0 L", "K {AO0} L", "K AO0 L", "K AO0 L", "K AO0 {L}"],
},
{
"accuracy": [10.0, 10.0, 10.0, 10.0, 10.0],
"stress": [10.0, 10.0, 10.0, 10.0, 10.0],
"total": [10.0, 10.0, 10.0, 10.0, 10.0],
"text": "IT",
"ref-phones": "IH0 T",
"phones": ["IH0 T", "IH0 T", "IH0 T", "IH0 T", "IH0 T"]
},
{
"accuracy": [3.0, 7.0, 10.0, 2.0, 6.0],
"stress": [10.0, 10.0, 10.0, 10.0, 10.0],
"phones": ["B (EH0) (R)", "B {EH0} {R}", "B EH0 R", "B (EH0) (R)", "B EH0 [L] R"],
"total": [4.4, 7.6, 10.0, 3.6, 6.8],
"text": "BEAR",
"ref-phones": "B EH0 R"
}
],
},
...
}
```
In `scores-detail.json`, the phoneme-level scores are notated in the following convenient notation:
* for score 2, do not use any symbol
* for score 1, use "{}" symbol
* for score 0, use "()" symbol
* for the inserted phone, use the "[]" symbol
For example, "B (EH) R" means the score of EH is 0 while the scores of B and R are both 2,
"B EH [L] R" mean there is an unexpected phone "L" and the other phones are scored 2.
## Citation
Please cite our paper if you find this work useful:
```bibtext
@inproceedings{speechocean762,
title={speechocean762: An Open-Source Non-native English Speech Corpus For Pronunciation Assessment},
booktitle={Proc. Interspeech 2021},
year=2021,
author={Junbo Zhang, Zhiwen Zhang, Yongqing Wang, Zhiyong Yan, Qiong Song, Yukai Huang, Ke Li, Daniel Povey, Yujun Wang}
}
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atom-in-the-universe/bild-deduped-131 | atom-in-the-universe | 2023-11-25T16:26:34Z | 0 | 0 | null | [
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atom-in-the-universe/bild-deduped-96_101_102_103_104_105_106 | atom-in-the-universe | 2023-11-25T16:14:05Z | 0 | 0 | null | [
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atom-in-the-universe/bild-deduped-143 | atom-in-the-universe | 2023-11-26T00:12:41Z | 0 | 0 | null | [
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atom-in-the-universe/bild-deduped-153 | atom-in-the-universe | 2023-11-25T17:02:12Z | 0 | 0 | null | [
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atom-in-the-universe/bild-deduped-122 | atom-in-the-universe | 2023-11-25T17:16:58Z | 0 | 0 | null | [
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FpOliveira/TuPi-Portuguese-Hate-Speech-Dataset-Binary | FpOliveira | 2023-11-25T20:57:17Z | 0 | 0 | null | [
"license:mit",
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license: mit
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atom-in-the-universe/bild-deduped-158_101 | atom-in-the-universe | 2023-11-25T16:42:00Z | 0 | 0 | null | [
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atom-in-the-universe/bild-deduped-158 | atom-in-the-universe | 2023-11-25T17:12:38Z | 0 | 0 | null | [
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atom-in-the-universe/bild-deduped-146_101 | atom-in-the-universe | 2023-11-26T14:06:57Z | 0 | 0 | null | [
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LND-EDUCATION/Synthetic_audio_bambara | LND-EDUCATION | 2023-11-25T16:57:22Z | 0 | 0 | null | [
"license:apache-2.0",
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] | 2023-11-25T16:57:22Z | 2023-11-25T16:51:23.000Z | 2023-11-25T16:51:23 | ---
license: apache-2.0
---
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Fogore/gufo | Fogore | 2023-11-27T21:18:41Z | 0 | 0 | null | [
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