datasetId
stringlengths 2
117
| author
stringlengths 2
42
⌀ | last_modified
unknown | downloads
int64 0
9.36M
| likes
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| tags
sequence | task_categories
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unknown | card
stringlengths 19
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| embedding
sequence |
|---|---|---|---|---|---|---|---|---|---|
AppleHarem/kroos_arknights | AppleHarem | "2023-12-13T23:24:47Z" | 0 | 0 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | [
"text-to-image"
] | "2023-12-13T23:24:32Z" | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of kroos (Arknights)
This is the dataset of kroos (Arknights), containing 196 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
This is a WebUI contains crawlers and other thing: ([LittleAppleWebUI](https://github.com/LittleApple-fp16/LittleAppleWebUI))
| Name | Images | Download | Description |
|:----------------|---------:|:----------------------------------------|:-----------------------------------------------------------------------------------------|
| raw | 196 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 497 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| raw-stage3-eyes | 533 | [Download](dataset-raw-stage3-eyes.zip) | 3-stage cropped (with eye-focus) raw data with meta information. |
| 384x512 | 196 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x704 | 196 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x880 | 196 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 497 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 497 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-p512-640 | 346 | [Download](dataset-stage3-p512-640.zip) | 3-stage cropped dataset with the area not less than 512x512 pixels. |
| stage3-eyes-640 | 533 | [Download](dataset-stage3-eyes-640.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 640 pixels. |
| stage3-eyes-800 | 533 | [Download](dataset-stage3-eyes-800.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 800 pixels. |
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marcones/locutoroficial | marcones | "2023-12-13T23:37:05Z" | 0 | 0 | [
"license:openrail",
"region:us"
] | null | "2023-12-13T23:36:40Z" | ---
license: openrail
---
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eslamisaeid58/saeid_mihkhfhjgg | eslamisaeid58 | "2023-12-13T23:38:01Z" | 0 | 0 | [
"license:apache-2.0",
"region:us"
] | null | "2023-12-13T23:38:01Z" | ---
license: apache-2.0
---
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AppleHarem/adnachiel_arknights | AppleHarem | "2023-12-13T23:49:07Z" | 0 | 0 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | [
"text-to-image"
] | "2023-12-13T23:49:04Z" | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of adnachiel (Arknights)
This is the dataset of adnachiel (Arknights), containing 19 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
This is a WebUI contains crawlers and other thing: ([LittleAppleWebUI](https://github.com/LittleApple-fp16/LittleAppleWebUI))
| Name | Images | Download | Description |
|:----------------|---------:|:----------------------------------------|:-----------------------------------------------------------------------------------------|
| raw | 19 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 45 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| raw-stage3-eyes | 46 | [Download](dataset-raw-stage3-eyes.zip) | 3-stage cropped (with eye-focus) raw data with meta information. |
| 384x512 | 19 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x704 | 19 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x880 | 19 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 45 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 45 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-p512-640 | 22 | [Download](dataset-stage3-p512-640.zip) | 3-stage cropped dataset with the area not less than 512x512 pixels. |
| stage3-eyes-640 | 46 | [Download](dataset-stage3-eyes-640.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 640 pixels. |
| stage3-eyes-800 | 46 | [Download](dataset-stage3-eyes-800.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 800 pixels. |
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lunarlist/thai_ner | lunarlist | "2023-12-14T00:01:52Z" | 0 | 0 | [
"license:apache-2.0",
"region:us"
] | null | "2023-12-13T23:50:44Z" | ---
license: apache-2.0
dataset_info:
features:
- name: id
dtype: int64
- name: ner_tags
sequence: int64
- name: tokens
sequence: string
- name: tags
sequence: string
splits:
- name: train
num_bytes: 28713732
num_examples: 10409
- name: validation
num_bytes: 9633693
num_examples: 3486
download_size: 1106191
dataset_size: 38347425
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
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Ediudo/modelo | Ediudo | "2023-12-14T00:03:12Z" | 0 | 0 | [
"license:openrail++",
"region:us"
] | null | "2023-12-14T00:02:30Z" | ---
license: openrail++
---
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BangumiBase/orenokanojotoosananajimigashurabasugiru | BangumiBase | "2023-12-14T01:36:25Z" | 0 | 0 | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | null | "2023-12-14T00:13:54Z" | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Ore No Kanojo To Osananajimi Ga Shuraba Sugiru
This is the image base of bangumi Ore No Kanojo To Osananajimi Ga Shuraba Sugiru, we detected 19 characters, 1620 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 | 221 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 36 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 13 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 35 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 28 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 10 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 18 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 590 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 19 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 22 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 103 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 9 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 282 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 10 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 5 | [Download](14/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| 15 | 9 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 127 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 6 | [Download](17/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| noise | 77 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
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jp4prezi/jp4prezi | jp4prezi | "2023-12-14T00:19:34Z" | 0 | 0 | [
"license:mit",
"region:us"
] | null | "2023-12-14T00:19:34Z" | ---
license: mit
---
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marcones/mulhervozsex | marcones | "2023-12-14T00:48:06Z" | 0 | 0 | [
"license:openrail",
"region:us"
] | null | "2023-12-14T00:47:52Z" | ---
license: openrail
---
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82magnolia/N-ImageNet | 82magnolia | "2023-12-15T07:15:53Z" | 0 | 0 | [
"license:agpl-3.0",
"region:us"
] | null | "2023-12-14T00:58:38Z" | ---
license: agpl-3.0
---
Please refer to the following instructions for more details about the dataset: [link](https://docs.google.com/document/d/1bliFASar5S7t1Ws_wORhUZA4KzF7bZ9fvyNaLt_n9U0/edit?usp=sharing).
Also, note that the code for the accompanying ICCV 2021 paper is available [here](https://github.com/82magnolia/n_imagenet). | [
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Kelvy/vozteste | Kelvy | "2023-12-14T01:20:39Z" | 0 | 0 | [
"license:openrail",
"region:us"
] | null | "2023-12-14T01:19:25Z" | ---
license: openrail
---
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] |
nguyenthanhdo/safety_en_100k | nguyenthanhdo | "2023-12-14T01:38:54Z" | 0 | 0 | [
"region:us"
] | null | "2023-12-14T01:38:41Z" | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
- name: type
dtype: string
- name: split
dtype: string
- name: translated
dtype: bool
splits:
- name: train
num_bytes: 77405834
num_examples: 100000
download_size: 40110568
dataset_size: 77405834
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "safety_en_100k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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Umbrellos/nitebearmsm | Umbrellos | "2023-12-17T15:24:21Z" | 0 | 0 | [
"license:openrail",
"region:us"
] | null | "2023-12-14T01:49:26Z" | ---
license: openrail
---
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gaizerick/tyballtv3 | gaizerick | "2023-12-14T02:21:21Z" | 0 | 0 | [
"license:openrail",
"region:us"
] | null | "2023-12-14T02:15:02Z" | ---
license: openrail
---
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] |
AppleHarem/hibiscus_arknights | AppleHarem | "2023-12-14T02:28:45Z" | 0 | 0 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | [
"text-to-image"
] | "2023-12-14T02:28:36Z" | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of hibiscus (Arknights)
This is the dataset of hibiscus (Arknights), containing 99 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
This is a WebUI contains crawlers and other thing: ([LittleAppleWebUI](https://github.com/LittleApple-fp16/LittleAppleWebUI))
| Name | Images | Download | Description |
|:----------------|---------:|:----------------------------------------|:-----------------------------------------------------------------------------------------|
| raw | 99 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 247 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| raw-stage3-eyes | 268 | [Download](dataset-raw-stage3-eyes.zip) | 3-stage cropped (with eye-focus) raw data with meta information. |
| 384x512 | 99 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x704 | 99 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x880 | 99 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 247 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 247 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-p512-640 | 168 | [Download](dataset-stage3-p512-640.zip) | 3-stage cropped dataset with the area not less than 512x512 pixels. |
| stage3-eyes-640 | 268 | [Download](dataset-stage3-eyes-640.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 640 pixels. |
| stage3-eyes-800 | 268 | [Download](dataset-stage3-eyes-800.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 800 pixels. |
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Sitedemerde/projet | Sitedemerde | "2023-12-14T02:34:05Z" | 0 | 0 | [
"license:other",
"region:us"
] | null | "2023-12-14T02:34:05Z" | ---
license: other
license_name: jesaispas
license_link: LICENSE
---
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hbin0701/MATH_PRM | hbin0701 | "2023-12-14T02:39:48Z" | 0 | 0 | [
"license:apache-2.0",
"region:us"
] | null | "2023-12-14T02:37:03Z" | ---
license: apache-2.0
---
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Vinnyyw/Miacolucci | Vinnyyw | "2023-12-14T03:08:33Z" | 0 | 0 | [
"license:openrail",
"region:us"
] | null | "2023-12-14T03:07:57Z" | ---
license: openrail
---
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AppleHarem/gitano_arknights | AppleHarem | "2023-12-14T03:17:51Z" | 0 | 0 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | [
"text-to-image"
] | "2023-12-14T03:17:48Z" | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of gitano (Arknights)
This is the dataset of gitano (Arknights), containing 33 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
This is a WebUI contains crawlers and other thing: ([LittleAppleWebUI](https://github.com/LittleApple-fp16/LittleAppleWebUI))
| Name | Images | Download | Description |
|:----------------|---------:|:----------------------------------------|:-----------------------------------------------------------------------------------------|
| raw | 33 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 85 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| raw-stage3-eyes | 90 | [Download](dataset-raw-stage3-eyes.zip) | 3-stage cropped (with eye-focus) raw data with meta information. |
| 384x512 | 33 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x704 | 33 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x880 | 33 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 85 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 85 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-p512-640 | 54 | [Download](dataset-stage3-p512-640.zip) | 3-stage cropped dataset with the area not less than 512x512 pixels. |
| stage3-eyes-640 | 90 | [Download](dataset-stage3-eyes-640.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 640 pixels. |
| stage3-eyes-800 | 90 | [Download](dataset-stage3-eyes-800.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 800 pixels. |
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mj96/subject_lionel_messi_resized | mj96 | "2023-12-14T03:43:39Z" | 0 | 0 | [
"region:us"
] | null | "2023-12-14T03:42:55Z" | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 3698784.0
num_examples: 14
download_size: 3700180
dataset_size: 3698784.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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] |
Asad321/irfan-junejo-tweerts333 | Asad321 | "2023-12-14T04:16:02Z" | 0 | 0 | [
"region:us"
] | null | "2023-12-14T04:15:51Z" | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 42301
num_examples: 126
download_size: 14643
dataset_size: 42301
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "irfan-junejo-tweerts333"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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autumnjohnson/ceti_audio_small | autumnjohnson | "2023-12-14T04:30:54Z" | 0 | 0 | [
"region:us"
] | null | "2023-12-14T04:28:08Z" | ---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: coda_type
dtype: string
- name: path
dtype: string
- name: sampling_rate
dtype: int64
splits:
- name: train
num_bytes: 289366946.0
num_examples: 3072
download_size: 142853607
dataset_size: 289366946.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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zhuhu00/AIG3D | zhuhu00 | "2023-12-14T04:36:15Z" | 0 | 0 | [
"license:mit",
"region:us"
] | null | "2023-12-14T04:36:15Z" | ---
license: mit
---
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WmVernon/nfl-wk14-2023-stats | WmVernon | "2023-12-14T04:41:00Z" | 0 | 0 | [
"license:apache-2.0",
"region:us"
] | null | "2023-12-14T04:39:42Z" | ---
license: apache-2.0
---
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WmVernon/nfl-boxscores | WmVernon | "2023-12-14T04:44:27Z" | 0 | 0 | [
"license:apache-2.0",
"region:us"
] | null | "2023-12-14T04:44:11Z" | ---
license: apache-2.0
---
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CantoINC/caption-decomposition | CantoINC | "2023-12-14T04:48:11Z" | 0 | 0 | [
"region:us"
] | null | "2023-12-14T04:48:01Z" | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: caption
dtype: string
- name: metadata
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 34315538
num_examples: 19899
- name: test
num_bytes: 179470
num_examples: 100
download_size: 15476944
dataset_size: 34495008
---
# Dataset Card for "caption-decomposition"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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koochikoo25/jilani_ds | koochikoo25 | "2023-12-14T05:19:03Z" | 0 | 0 | [
"license:mit",
"region:us"
] | null | "2023-12-14T05:17:05Z" | ---
license: mit
dataset_info:
features:
- name: instructions
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 88566301
num_examples: 52002
download_size: 48394293
dataset_size: 88566301
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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z7ye/alpaca_test | z7ye | "2023-12-14T05:34:54Z" | 0 | 0 | [
"license:apache-2.0",
"region:us"
] | null | "2023-12-14T05:24:58Z" | ---
license: apache-2.0
---
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] |
Asad321/Irfan-Junejoscraped-data | Asad321 | "2023-12-14T05:44:11Z" | 0 | 0 | [
"region:us"
] | null | "2023-12-14T05:44:08Z" | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 829
num_examples: 2
download_size: 3686
dataset_size: 829
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "Irfan-Junejoscraped-data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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swarnavoroopya/demo | swarnavoroopya | "2023-12-14T06:30:57Z" | 0 | 0 | [
"license:mit",
"region:us"
] | null | "2023-12-14T06:29:29Z" | ---
license: mit
---
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] |
Seongill/NQ_5_uns-adv-test | Seongill | "2023-12-14T06:35:01Z" | 0 | 0 | [
"region:us"
] | null | "2023-12-14T06:34:58Z" | ---
dataset_info:
features:
- name: question
dtype: string
- name: answers
sequence: string
- name: has_answer
dtype: bool
- name: similar_sub
dtype: string
- name: ctxs
list:
- name: answer_sent
sequence: string
- name: hasanswer
dtype: bool
- name: id
dtype: string
- name: is_adv
dtype: bool
- name: new_answer_sent
dtype: string
- name: original_text
dtype: string
- name: score
dtype: float64
- name: text
dtype: string
- name: title
dtype: string
- name: status
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 1993029
num_examples: 450
download_size: 1138217
dataset_size: 1993029
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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AMead10/Universal-Verified-Camel | AMead10 | "2023-12-14T07:46:38Z" | 0 | 0 | [
"region:us"
] | null | "2023-12-14T07:19:44Z" | ---
dataset_info:
features:
- name: conversation
list:
- name: input
dtype: string
- name: output
dtype: string
- name: system
dtype: string
splits:
- name: train
num_bytes: 326725
num_examples: 127
download_size: 168364
dataset_size: 326725
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "Universal-Verified-Camel"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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arieg/cluster09_large_150 | arieg | "2023-12-14T07:31:52Z" | 0 | 0 | [
"region:us"
] | null | "2023-12-14T07:30:49Z" | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
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splits:
- name: train
num_bytes: 1330348523.4
num_examples: 23400
download_size: 1301547352
dataset_size: 1330348523.4
configs:
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data_files:
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path: data/train-*
---
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] |
lorinma/Chinese_Evol_Instruct_3.5 | lorinma | "2023-12-14T08:10:18Z" | 0 | 1 | [
"task_categories:conversational",
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:zh",
"region:us"
] | [
"conversational",
"text-generation"
] | "2023-12-14T07:31:33Z" | ---
task_categories:
- conversational
- text-generation
language:
- zh
size_categories:
- 10K<n<100K
---
因为WizardLM系列长期霸榜LLM开源榜，一直很好奇EvolInstruct在英文世界表现出的对于复杂prompt的应对能力。
目前中文没有原生的EvolInstruct，仅有两个翻译版本 [1](https://huggingface.co/datasets/FreedomIntelligence/Evol-Instruct-Chinese-GPT4) [2](https://huggingface.co/datasets/silk-road/Wizard-LM-Chinese-instruct-evol)。
故浅浅尝试复现中文版本。代码参照 [3](https://github.com/h2oai/h2o-wizardlm/blob/main/wizardlm.py)
但无奈接口实在是太贵，且生成的时间很长。所以如果有能够提供GPT-4 API资源的，我很乐意将这个量级撑到50K+并进行公开。
一共有3个文件：
combined_seed_correct.json 是使用的基础种子任务371条，alpaca格式。使用了 [Belle的中文种子任务175条](https://github.com/LianjiaTech/BELLE)。并且参照了 [4](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k) 增加了ShareGPT的数据以更接近真实世界的用法，掺入了 [Wildchat-zh抽样196条](https://huggingface.co/datasets/lorinma/Wildchat_zh_sharegpt_Subsample_20K) ，多轮对话只采用第一个有意义的问答对。
231213_ChineseEvolInstruct_140_gpt-4-1106-preview.json 使用gpt-4-1106-preview，因为太贵且接口不稳定，故只生成了140条。这里犯了一个错误，只使用了instruction而忽略了input，所以evol的基础不完整。接口花费约几百人民币。
231214_ChineseEvolInstruction_11k_3.5-turbo-0613.json 修正了错误，即将instruction和input进行concat，使用3.5-turbo-0613接口生成了共计1.1万个alpaca格式的问答对。接口花费约一千人民币，生成时间约24小时。
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peterkros/COFOG-feedback | peterkros | "2023-12-15T08:03:58Z" | 0 | 0 | [
"license:apache-2.0",
"region:us"
] | null | "2023-12-14T07:41:52Z" | ---
license: apache-2.0
configs:
- config_name: default
data_files:
- split: train
path: data.csv
---
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ShiqiDing/testdata | ShiqiDing | "2023-12-14T07:45:33Z" | 0 | 0 | [
"license:apache-2.0",
"region:us"
] | null | "2023-12-14T07:45:33Z" | ---
license: apache-2.0
---
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] |
AMead10/Universal-Magicoder-Evol-Instruct-110K | AMead10 | "2023-12-14T07:55:03Z" | 0 | 0 | [
"region:us"
] | null | "2023-12-14T07:50:35Z" | ---
dataset_info:
features:
- name: system
dtype: string
- name: conversation
list:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 247866350
num_examples: 111183
download_size: 136772975
dataset_size: 247866350
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "Universal-Magicoder-Evol-Instruct-110K"
[Magicoder-Evol-Instruct-110K](https://huggingface.co/datasets/ise-uiuc/Magicoder-Evol-Instruct-110K) reformatted to in the universal data format. | [
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arieg/cluster10_large_150 | arieg | "2023-12-14T07:52:06Z" | 0 | 0 | [
"region:us"
] | null | "2023-12-14T07:51:44Z" | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': 001069
'1': '010377'
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splits:
- name: train
num_bytes: 450056909.5
num_examples: 8550
download_size: 442987288
dataset_size: 450056909.5
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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Thomas2312/Pepper | Thomas2312 | "2023-12-14T07:53:38Z" | 0 | 0 | [
"size_categories:1K<n<10K",
"language:en",
"region:us"
] | null | "2023-12-14T07:52:15Z" | ---
language:
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size_categories:
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yashyadav/guanaco-llama2-1k | yashyadav | "2023-12-14T08:11:37Z" | 0 | 0 | [
"region:us"
] | null | "2023-12-14T08:11:35Z" | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 4710307
num_examples: 3000
download_size: 2785880
dataset_size: 4710307
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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AppleHarem/meteor_arknights | AppleHarem | "2023-12-14T08:15:44Z" | 0 | 0 | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | [
"text-to-image"
] | "2023-12-14T08:15:23Z" | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of meteor (Arknights)
This is the dataset of meteor (Arknights), containing 161 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
This is a WebUI contains crawlers and other thing: ([LittleAppleWebUI](https://github.com/LittleApple-fp16/LittleAppleWebUI))
| Name | Images | Download | Description |
|:----------------|---------:|:----------------------------------------|:-----------------------------------------------------------------------------------------|
| raw | 161 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 409 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| raw-stage3-eyes | 446 | [Download](dataset-raw-stage3-eyes.zip) | 3-stage cropped (with eye-focus) raw data with meta information. |
| 384x512 | 161 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x704 | 161 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x880 | 161 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 409 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 409 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-p512-640 | 308 | [Download](dataset-stage3-p512-640.zip) | 3-stage cropped dataset with the area not less than 512x512 pixels. |
| stage3-eyes-640 | 446 | [Download](dataset-stage3-eyes-640.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 640 pixels. |
| stage3-eyes-800 | 446 | [Download](dataset-stage3-eyes-800.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 800 pixels. |
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TinyPixel/wiki-en | TinyPixel | "2023-12-14T08:20:21Z" | 0 | 0 | [
"region:us"
] | null | "2023-12-14T08:19:10Z" | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 2448198176
num_examples: 208279
download_size: 1378174394
dataset_size: 2448198176
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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AMead10/Universal-glaive-function-calling-v2 | AMead10 | "2023-12-14T08:21:15Z" | 0 | 0 | [
"region:us"
] | null | "2023-12-14T08:20:53Z" | ---
dataset_info:
features:
- name: system
dtype: string
- name: conversation
list:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 251613737
num_examples: 112960
download_size: 99291787
dataset_size: 251613737
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "Universal-glaive-function-calling-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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CCZZJJ/AGIN | CCZZJJ | "2023-12-14T08:22:15Z" | 0 | 0 | [
"license:cc-by-4.0",
"region:us"
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license: cc-by-4.0
---
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arieg/cluster11_large_150 | arieg | "2023-12-14T08:26:07Z" | 0 | 0 | [
"region:us"
] | null | "2023-12-14T08:25:32Z" | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
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splits:
- name: train
num_bytes: 728489341.1
num_examples: 14700
download_size: 720192838
dataset_size: 728489341.1
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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huseinbud/test | huseinbud | "2023-12-14T08:39:01Z" | 0 | 0 | [
"license:apache-2.0",
"region:us"
] | null | "2023-12-14T08:39:01Z" | ---
license: apache-2.0
---
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alvarobartt/Anthropic_HH_Golden_Extended | alvarobartt | "2023-12-14T10:01:29Z" | 0 | 0 | [
"task_categories:conversational",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"not-for-all-audiences",
"region:us"
] | [
"conversational"
] | "2023-12-14T08:47:02Z" | ---
tags:
- not-for-all-audiences
dataset_info:
features:
- name: prompt_id
dtype: string
- name: prompt
dtype: string
- name: chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: rejected
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 128690951
num_examples: 85074
- name: test
num_bytes: 7201288
num_examples: 4624
download_size: 44628148
dataset_size: 135892239
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
license: apache-2.0
task_categories:
- conversational
language:
- en
size_categories:
- 10K<n<100K
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TriadParty/deepsex-RP | TriadParty | "2023-12-14T09:12:00Z" | 0 | 3 | [
"license:apache-2.0",
"region:us"
] | null | "2023-12-14T09:10:46Z" | ---
license: apache-2.0
---
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arieg/cluster12_large_150 | arieg | "2023-12-14T09:18:10Z" | 0 | 0 | [
"region:us"
] | null | "2023-12-14T09:17:13Z" | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
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splits:
- name: train
num_bytes: 1194341424.0
num_examples: 24000
download_size: 1192704712
dataset_size: 1194341424.0
configs:
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data_files:
- split: train
path: data/train-*
---
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open-llm-leaderboard/details_microsoft__phi-2 | open-llm-leaderboard | "2023-12-14T09:33:46Z" | 0 | 0 | [
"region:us"
] | null | "2023-12-14T09:33:33Z" | ---
pretty_name: Evaluation run of microsoft/phi-2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_microsoft__phi-2_private\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-14T09:31:24.484620](https://huggingface.co/datasets/open-llm-leaderboard/details_microsoft__phi-2_private/blob/main/results_2023-12-14T09-31-24.484620.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\": 0.5830682154788298,\n\
\ \"acc_stderr\": 0.033691700321669536,\n \"acc_norm\": 0.5845100617204194,\n\
\ \"acc_norm_stderr\": 0.03438023397765123,\n \"mc1\": 0.30966952264381886,\n\
\ \"mc1_stderr\": 0.016185744355144912,\n \"mc2\": 0.4446839625467275,\n\
\ \"mc2_stderr\": 0.015123741169164418\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5861774744027304,\n \"acc_stderr\": 0.014392730009221005,\n\
\ \"acc_norm\": 0.6109215017064846,\n \"acc_norm_stderr\": 0.014247309976045605\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5653256323441546,\n\
\ \"acc_stderr\": 0.00494701093745534,\n \"acc_norm\": 0.7511451902011551,\n\
\ \"acc_norm_stderr\": 0.004314659034649402\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.3,\n \"acc_stderr\": 0.04605661864718381,\n \
\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.04605661864718381\n },\n\
\ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4444444444444444,\n\
\ \"acc_stderr\": 0.04292596718256981,\n \"acc_norm\": 0.4444444444444444,\n\
\ \"acc_norm_stderr\": 0.04292596718256981\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.5855263157894737,\n \"acc_stderr\": 0.040089737857792046,\n\
\ \"acc_norm\": 0.5855263157894737,\n \"acc_norm_stderr\": 0.040089737857792046\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.59,\n\
\ \"acc_stderr\": 0.04943110704237101,\n \"acc_norm\": 0.59,\n \
\ \"acc_norm_stderr\": 0.04943110704237101\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6113207547169811,\n \"acc_stderr\": 0.030000485448675986,\n\
\ \"acc_norm\": 0.6113207547169811,\n \"acc_norm_stderr\": 0.030000485448675986\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6666666666666666,\n\
\ \"acc_stderr\": 0.03942082639927213,\n \"acc_norm\": 0.6666666666666666,\n\
\ \"acc_norm_stderr\": 0.03942082639927213\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \
\ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\"\
: 0.43,\n \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \
\ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5953757225433526,\n\
\ \"acc_stderr\": 0.03742461193887248,\n \"acc_norm\": 0.5953757225433526,\n\
\ \"acc_norm_stderr\": 0.03742461193887248\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.3627450980392157,\n \"acc_stderr\": 0.04784060704105654,\n\
\ \"acc_norm\": 0.3627450980392157,\n \"acc_norm_stderr\": 0.04784060704105654\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.74,\n \"acc_stderr\": 0.04408440022768078,\n \"acc_norm\": 0.74,\n\
\ \"acc_norm_stderr\": 0.04408440022768078\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.502127659574468,\n \"acc_stderr\": 0.03268572658667492,\n\
\ \"acc_norm\": 0.502127659574468,\n \"acc_norm_stderr\": 0.03268572658667492\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.37719298245614036,\n\
\ \"acc_stderr\": 0.04559522141958216,\n \"acc_norm\": 0.37719298245614036,\n\
\ \"acc_norm_stderr\": 0.04559522141958216\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5379310344827586,\n \"acc_stderr\": 0.04154659671707548,\n\
\ \"acc_norm\": 0.5379310344827586,\n \"acc_norm_stderr\": 0.04154659671707548\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.455026455026455,\n \"acc_stderr\": 0.025646928361049395,\n \"\
acc_norm\": 0.455026455026455,\n \"acc_norm_stderr\": 0.025646928361049395\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.35714285714285715,\n\
\ \"acc_stderr\": 0.04285714285714281,\n \"acc_norm\": 0.35714285714285715,\n\
\ \"acc_norm_stderr\": 0.04285714285714281\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \
\ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7,\n\
\ \"acc_stderr\": 0.02606936229533513,\n \"acc_norm\": 0.7,\n \
\ \"acc_norm_stderr\": 0.02606936229533513\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.4876847290640394,\n \"acc_stderr\": 0.035169204442208966,\n\
\ \"acc_norm\": 0.4876847290640394,\n \"acc_norm_stderr\": 0.035169204442208966\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.64,\n \"acc_stderr\": 0.048241815132442176,\n \"acc_norm\"\
: 0.64,\n \"acc_norm_stderr\": 0.048241815132442176\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.6424242424242425,\n \"acc_stderr\": 0.03742597043806586,\n\
\ \"acc_norm\": 0.6424242424242425,\n \"acc_norm_stderr\": 0.03742597043806586\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7373737373737373,\n \"acc_stderr\": 0.031353050095330855,\n \"\
acc_norm\": 0.7373737373737373,\n \"acc_norm_stderr\": 0.031353050095330855\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8082901554404145,\n \"acc_stderr\": 0.028408953626245282,\n\
\ \"acc_norm\": 0.8082901554404145,\n \"acc_norm_stderr\": 0.028408953626245282\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.5692307692307692,\n \"acc_stderr\": 0.025106820660539753,\n\
\ \"acc_norm\": 0.5692307692307692,\n \"acc_norm_stderr\": 0.025106820660539753\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.32222222222222224,\n \"acc_stderr\": 0.0284934650910286,\n \
\ \"acc_norm\": 0.32222222222222224,\n \"acc_norm_stderr\": 0.0284934650910286\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6092436974789915,\n \"acc_stderr\": 0.031693802357129965,\n\
\ \"acc_norm\": 0.6092436974789915,\n \"acc_norm_stderr\": 0.031693802357129965\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.37748344370860926,\n \"acc_stderr\": 0.0395802723112157,\n \"\
acc_norm\": 0.37748344370860926,\n \"acc_norm_stderr\": 0.0395802723112157\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.7981651376146789,\n \"acc_stderr\": 0.017208579357787575,\n \"\
acc_norm\": 0.7981651376146789,\n \"acc_norm_stderr\": 0.017208579357787575\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.49537037037037035,\n \"acc_stderr\": 0.03409825519163572,\n \"\
acc_norm\": 0.49537037037037035,\n \"acc_norm_stderr\": 0.03409825519163572\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.6617647058823529,\n \"acc_stderr\": 0.03320574612945431,\n \"\
acc_norm\": 0.6617647058823529,\n \"acc_norm_stderr\": 0.03320574612945431\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7383966244725738,\n \"acc_stderr\": 0.028609516716994934,\n \
\ \"acc_norm\": 0.7383966244725738,\n \"acc_norm_stderr\": 0.028609516716994934\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6547085201793722,\n\
\ \"acc_stderr\": 0.03191100192835794,\n \"acc_norm\": 0.6547085201793722,\n\
\ \"acc_norm_stderr\": 0.03191100192835794\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7099236641221374,\n \"acc_stderr\": 0.03980066246467765,\n\
\ \"acc_norm\": 0.7099236641221374,\n \"acc_norm_stderr\": 0.03980066246467765\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.743801652892562,\n \"acc_stderr\": 0.03984979653302872,\n \"acc_norm\"\
: 0.743801652892562,\n \"acc_norm_stderr\": 0.03984979653302872\n },\n\
\ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7129629629629629,\n\
\ \"acc_stderr\": 0.043733130409147614,\n \"acc_norm\": 0.7129629629629629,\n\
\ \"acc_norm_stderr\": 0.043733130409147614\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7484662576687117,\n \"acc_stderr\": 0.03408997886857529,\n\
\ \"acc_norm\": 0.7484662576687117,\n \"acc_norm_stderr\": 0.03408997886857529\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.49107142857142855,\n\
\ \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.49107142857142855,\n\
\ \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7378640776699029,\n \"acc_stderr\": 0.04354631077260595,\n\
\ \"acc_norm\": 0.7378640776699029,\n \"acc_norm_stderr\": 0.04354631077260595\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8205128205128205,\n\
\ \"acc_stderr\": 0.025140935950335435,\n \"acc_norm\": 0.8205128205128205,\n\
\ \"acc_norm_stderr\": 0.025140935950335435\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.63,\n \"acc_stderr\": 0.04852365870939099,\n \
\ \"acc_norm\": 0.63,\n \"acc_norm_stderr\": 0.04852365870939099\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.6909323116219668,\n\
\ \"acc_stderr\": 0.016524988919702204,\n \"acc_norm\": 0.6909323116219668,\n\
\ \"acc_norm_stderr\": 0.016524988919702204\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6763005780346821,\n \"acc_stderr\": 0.025190181327608422,\n\
\ \"acc_norm\": 0.6763005780346821,\n \"acc_norm_stderr\": 0.025190181327608422\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.31620111731843575,\n\
\ \"acc_stderr\": 0.015551673652172544,\n \"acc_norm\": 0.31620111731843575,\n\
\ \"acc_norm_stderr\": 0.015551673652172544\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.6143790849673203,\n \"acc_stderr\": 0.027870745278290275,\n\
\ \"acc_norm\": 0.6143790849673203,\n \"acc_norm_stderr\": 0.027870745278290275\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6237942122186495,\n\
\ \"acc_stderr\": 0.02751392568354943,\n \"acc_norm\": 0.6237942122186495,\n\
\ \"acc_norm_stderr\": 0.02751392568354943\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.6203703703703703,\n \"acc_stderr\": 0.02700252103451646,\n\
\ \"acc_norm\": 0.6203703703703703,\n \"acc_norm_stderr\": 0.02700252103451646\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.44680851063829785,\n \"acc_stderr\": 0.029658235097666907,\n \
\ \"acc_norm\": 0.44680851063829785,\n \"acc_norm_stderr\": 0.029658235097666907\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.423728813559322,\n\
\ \"acc_stderr\": 0.012620785155885996,\n \"acc_norm\": 0.423728813559322,\n\
\ \"acc_norm_stderr\": 0.012620785155885996\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.4742647058823529,\n \"acc_stderr\": 0.030332578094555026,\n\
\ \"acc_norm\": 0.4742647058823529,\n \"acc_norm_stderr\": 0.030332578094555026\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.5604575163398693,\n \"acc_stderr\": 0.020079420408087918,\n \
\ \"acc_norm\": 0.5604575163398693,\n \"acc_norm_stderr\": 0.020079420408087918\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\
\ \"acc_stderr\": 0.04494290866252091,\n \"acc_norm\": 0.6727272727272727,\n\
\ \"acc_norm_stderr\": 0.04494290866252091\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7346938775510204,\n \"acc_stderr\": 0.028263889943784596,\n\
\ \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.028263889943784596\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8109452736318408,\n\
\ \"acc_stderr\": 0.02768691358801301,\n \"acc_norm\": 0.8109452736318408,\n\
\ \"acc_norm_stderr\": 0.02768691358801301\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.77,\n \"acc_stderr\": 0.042295258468165065,\n \
\ \"acc_norm\": 0.77,\n \"acc_norm_stderr\": 0.042295258468165065\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4759036144578313,\n\
\ \"acc_stderr\": 0.038879718495972646,\n \"acc_norm\": 0.4759036144578313,\n\
\ \"acc_norm_stderr\": 0.038879718495972646\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.6900584795321637,\n \"acc_stderr\": 0.035469769593931624,\n\
\ \"acc_norm\": 0.6900584795321637,\n \"acc_norm_stderr\": 0.035469769593931624\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.30966952264381886,\n\
\ \"mc1_stderr\": 0.016185744355144912,\n \"mc2\": 0.4446839625467275,\n\
\ \"mc2_stderr\": 0.015123741169164418\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7434885556432518,\n \"acc_stderr\": 0.01227364800875999\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5481425322213799,\n \
\ \"acc_stderr\": 0.013708494995677643\n }\n}\n```"
repo_url: https://huggingface.co/microsoft/phi-2
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|arc:challenge|25_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|gsm8k|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hellaswag|10_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-14T09-31-24.484620.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-14T09-31-24.484620.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- '**/details_harness|winogrande|5_2023-12-14T09-31-24.484620.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-14T09-31-24.484620.parquet'
- config_name: results
data_files:
- split: 2023_12_14T09_31_24.484620
path:
- results_2023-12-14T09-31-24.484620.parquet
- split: latest
path:
- results_2023-12-14T09-31-24.484620.parquet
---
# Dataset Card for Evaluation run of microsoft/phi-2
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_microsoft__phi-2_private",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-14T09:31:24.484620](https://huggingface.co/datasets/open-llm-leaderboard/details_microsoft__phi-2_private/blob/main/results_2023-12-14T09-31-24.484620.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.5830682154788298,
"acc_stderr": 0.033691700321669536,
"acc_norm": 0.5845100617204194,
"acc_norm_stderr": 0.03438023397765123,
"mc1": 0.30966952264381886,
"mc1_stderr": 0.016185744355144912,
"mc2": 0.4446839625467275,
"mc2_stderr": 0.015123741169164418
},
"harness|arc:challenge|25": {
"acc": 0.5861774744027304,
"acc_stderr": 0.014392730009221005,
"acc_norm": 0.6109215017064846,
"acc_norm_stderr": 0.014247309976045605
},
"harness|hellaswag|10": {
"acc": 0.5653256323441546,
"acc_stderr": 0.00494701093745534,
"acc_norm": 0.7511451902011551,
"acc_norm_stderr": 0.004314659034649402
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.3,
"acc_stderr": 0.04605661864718381,
"acc_norm": 0.3,
"acc_norm_stderr": 0.04605661864718381
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.4444444444444444,
"acc_stderr": 0.04292596718256981,
"acc_norm": 0.4444444444444444,
"acc_norm_stderr": 0.04292596718256981
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.5855263157894737,
"acc_stderr": 0.040089737857792046,
"acc_norm": 0.5855263157894737,
"acc_norm_stderr": 0.040089737857792046
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.59,
"acc_stderr": 0.04943110704237101,
"acc_norm": 0.59,
"acc_norm_stderr": 0.04943110704237101
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6113207547169811,
"acc_stderr": 0.030000485448675986,
"acc_norm": 0.6113207547169811,
"acc_norm_stderr": 0.030000485448675986
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.6666666666666666,
"acc_stderr": 0.03942082639927213,
"acc_norm": 0.6666666666666666,
"acc_norm_stderr": 0.03942082639927213
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.41,
"acc_stderr": 0.049431107042371025,
"acc_norm": 0.41,
"acc_norm_stderr": 0.049431107042371025
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.43,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.43,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.36,
"acc_stderr": 0.048241815132442176,
"acc_norm": 0.36,
"acc_norm_stderr": 0.048241815132442176
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.5953757225433526,
"acc_stderr": 0.03742461193887248,
"acc_norm": 0.5953757225433526,
"acc_norm_stderr": 0.03742461193887248
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.3627450980392157,
"acc_stderr": 0.04784060704105654,
"acc_norm": 0.3627450980392157,
"acc_norm_stderr": 0.04784060704105654
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.74,
"acc_stderr": 0.04408440022768078,
"acc_norm": 0.74,
"acc_norm_stderr": 0.04408440022768078
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.502127659574468,
"acc_stderr": 0.03268572658667492,
"acc_norm": 0.502127659574468,
"acc_norm_stderr": 0.03268572658667492
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.37719298245614036,
"acc_stderr": 0.04559522141958216,
"acc_norm": 0.37719298245614036,
"acc_norm_stderr": 0.04559522141958216
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5379310344827586,
"acc_stderr": 0.04154659671707548,
"acc_norm": 0.5379310344827586,
"acc_norm_stderr": 0.04154659671707548
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.455026455026455,
"acc_stderr": 0.025646928361049395,
"acc_norm": 0.455026455026455,
"acc_norm_stderr": 0.025646928361049395
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.35714285714285715,
"acc_stderr": 0.04285714285714281,
"acc_norm": 0.35714285714285715,
"acc_norm_stderr": 0.04285714285714281
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.36,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7,
"acc_stderr": 0.02606936229533513,
"acc_norm": 0.7,
"acc_norm_stderr": 0.02606936229533513
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.4876847290640394,
"acc_stderr": 0.035169204442208966,
"acc_norm": 0.4876847290640394,
"acc_norm_stderr": 0.035169204442208966
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.64,
"acc_stderr": 0.048241815132442176,
"acc_norm": 0.64,
"acc_norm_stderr": 0.048241815132442176
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.6424242424242425,
"acc_stderr": 0.03742597043806586,
"acc_norm": 0.6424242424242425,
"acc_norm_stderr": 0.03742597043806586
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7373737373737373,
"acc_stderr": 0.031353050095330855,
"acc_norm": 0.7373737373737373,
"acc_norm_stderr": 0.031353050095330855
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8082901554404145,
"acc_stderr": 0.028408953626245282,
"acc_norm": 0.8082901554404145,
"acc_norm_stderr": 0.028408953626245282
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.5692307692307692,
"acc_stderr": 0.025106820660539753,
"acc_norm": 0.5692307692307692,
"acc_norm_stderr": 0.025106820660539753
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.32222222222222224,
"acc_stderr": 0.0284934650910286,
"acc_norm": 0.32222222222222224,
"acc_norm_stderr": 0.0284934650910286
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6092436974789915,
"acc_stderr": 0.031693802357129965,
"acc_norm": 0.6092436974789915,
"acc_norm_stderr": 0.031693802357129965
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.37748344370860926,
"acc_stderr": 0.0395802723112157,
"acc_norm": 0.37748344370860926,
"acc_norm_stderr": 0.0395802723112157
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.7981651376146789,
"acc_stderr": 0.017208579357787575,
"acc_norm": 0.7981651376146789,
"acc_norm_stderr": 0.017208579357787575
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.49537037037037035,
"acc_stderr": 0.03409825519163572,
"acc_norm": 0.49537037037037035,
"acc_norm_stderr": 0.03409825519163572
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.6617647058823529,
"acc_stderr": 0.03320574612945431,
"acc_norm": 0.6617647058823529,
"acc_norm_stderr": 0.03320574612945431
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7383966244725738,
"acc_stderr": 0.028609516716994934,
"acc_norm": 0.7383966244725738,
"acc_norm_stderr": 0.028609516716994934
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6547085201793722,
"acc_stderr": 0.03191100192835794,
"acc_norm": 0.6547085201793722,
"acc_norm_stderr": 0.03191100192835794
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7099236641221374,
"acc_stderr": 0.03980066246467765,
"acc_norm": 0.7099236641221374,
"acc_norm_stderr": 0.03980066246467765
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.743801652892562,
"acc_stderr": 0.03984979653302872,
"acc_norm": 0.743801652892562,
"acc_norm_stderr": 0.03984979653302872
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7129629629629629,
"acc_stderr": 0.043733130409147614,
"acc_norm": 0.7129629629629629,
"acc_norm_stderr": 0.043733130409147614
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7484662576687117,
"acc_stderr": 0.03408997886857529,
"acc_norm": 0.7484662576687117,
"acc_norm_stderr": 0.03408997886857529
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.49107142857142855,
"acc_stderr": 0.04745033255489123,
"acc_norm": 0.49107142857142855,
"acc_norm_stderr": 0.04745033255489123
},
"harness|hendrycksTest-management|5": {
"acc": 0.7378640776699029,
"acc_stderr": 0.04354631077260595,
"acc_norm": 0.7378640776699029,
"acc_norm_stderr": 0.04354631077260595
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8205128205128205,
"acc_stderr": 0.025140935950335435,
"acc_norm": 0.8205128205128205,
"acc_norm_stderr": 0.025140935950335435
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.63,
"acc_stderr": 0.04852365870939099,
"acc_norm": 0.63,
"acc_norm_stderr": 0.04852365870939099
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.6909323116219668,
"acc_stderr": 0.016524988919702204,
"acc_norm": 0.6909323116219668,
"acc_norm_stderr": 0.016524988919702204
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.6763005780346821,
"acc_stderr": 0.025190181327608422,
"acc_norm": 0.6763005780346821,
"acc_norm_stderr": 0.025190181327608422
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.31620111731843575,
"acc_stderr": 0.015551673652172544,
"acc_norm": 0.31620111731843575,
"acc_norm_stderr": 0.015551673652172544
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.6143790849673203,
"acc_stderr": 0.027870745278290275,
"acc_norm": 0.6143790849673203,
"acc_norm_stderr": 0.027870745278290275
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.6237942122186495,
"acc_stderr": 0.02751392568354943,
"acc_norm": 0.6237942122186495,
"acc_norm_stderr": 0.02751392568354943
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.6203703703703703,
"acc_stderr": 0.02700252103451646,
"acc_norm": 0.6203703703703703,
"acc_norm_stderr": 0.02700252103451646
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.44680851063829785,
"acc_stderr": 0.029658235097666907,
"acc_norm": 0.44680851063829785,
"acc_norm_stderr": 0.029658235097666907
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.423728813559322,
"acc_stderr": 0.012620785155885996,
"acc_norm": 0.423728813559322,
"acc_norm_stderr": 0.012620785155885996
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.4742647058823529,
"acc_stderr": 0.030332578094555026,
"acc_norm": 0.4742647058823529,
"acc_norm_stderr": 0.030332578094555026
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.5604575163398693,
"acc_stderr": 0.020079420408087918,
"acc_norm": 0.5604575163398693,
"acc_norm_stderr": 0.020079420408087918
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6727272727272727,
"acc_stderr": 0.04494290866252091,
"acc_norm": 0.6727272727272727,
"acc_norm_stderr": 0.04494290866252091
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7346938775510204,
"acc_stderr": 0.028263889943784596,
"acc_norm": 0.7346938775510204,
"acc_norm_stderr": 0.028263889943784596
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8109452736318408,
"acc_stderr": 0.02768691358801301,
"acc_norm": 0.8109452736318408,
"acc_norm_stderr": 0.02768691358801301
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.77,
"acc_stderr": 0.042295258468165065,
"acc_norm": 0.77,
"acc_norm_stderr": 0.042295258468165065
},
"harness|hendrycksTest-virology|5": {
"acc": 0.4759036144578313,
"acc_stderr": 0.038879718495972646,
"acc_norm": 0.4759036144578313,
"acc_norm_stderr": 0.038879718495972646
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.6900584795321637,
"acc_stderr": 0.035469769593931624,
"acc_norm": 0.6900584795321637,
"acc_norm_stderr": 0.035469769593931624
},
"harness|truthfulqa:mc|0": {
"mc1": 0.30966952264381886,
"mc1_stderr": 0.016185744355144912,
"mc2": 0.4446839625467275,
"mc2_stderr": 0.015123741169164418
},
"harness|winogrande|5": {
"acc": 0.7434885556432518,
"acc_stderr": 0.01227364800875999
},
"harness|gsm8k|5": {
"acc": 0.5481425322213799,
"acc_stderr": 0.013708494995677643
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
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[More Information Needed] | [
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Spico/Mirror | Spico | "2023-12-14T16:18:18Z" | 0 | 0 | [
"license:apache-2.0",
"region:us"
] | null | "2023-12-14T09:48:59Z" | ---
license: apache-2.0
---
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arieg/cluster13_large_150 | arieg | "2023-12-14T10:03:16Z" | 0 | 0 | [
"region:us"
] | null | "2023-12-14T10:02:26Z" | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
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splits:
- name: train
num_bytes: 1020081783.6
num_examples: 19800
download_size: 1007223091
dataset_size: 1020081783.6
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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arieg/cluster14_large_150 | arieg | "2023-12-14T10:35:05Z" | 0 | 0 | [
"region:us"
] | null | "2023-12-14T10:34:30Z" | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
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splits:
- name: train
num_bytes: 754012845.25
num_examples: 13950
download_size: 735130963
dataset_size: 754012845.25
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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kaifahmad/Hate-Speech-Tweets | kaifahmad | "2023-12-14T10:44:47Z" | 0 | 0 | [
"license:apache-2.0",
"region:us"
] | null | "2023-12-14T10:43:59Z" | ---
license: apache-2.0
---
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BoooomNing/CNet-set2 | BoooomNing | "2023-12-14T10:57:41Z" | 0 | 0 | [
"region:us"
] | null | "2023-12-14T10:49:48Z" | ---
dataset_info:
features:
- name: id
dtype: int32
- name: superimposed_image
dtype: image
- name: next_frame
dtype: image
splits:
- name: train
num_bytes: 7567984298.96
num_examples: 42920
download_size: 7431454261
dataset_size: 7567984298.96
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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open-llm-leaderboard/details_Open-Orca__Mixtral-SlimOrca-8x7B | open-llm-leaderboard | "2023-12-14T10:58:03Z" | 0 | 0 | [
"region:us"
] | null | "2023-12-14T10:57:25Z" | ---
pretty_name: Evaluation run of Open-Orca/Mixtral-SlimOrca-8x7B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Open-Orca/Mixtral-SlimOrca-8x7B](https://huggingface.co/Open-Orca/Mixtral-SlimOrca-8x7B)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Open-Orca__Mixtral-SlimOrca-8x7B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-14T10:54:31.511638](https://huggingface.co/datasets/open-llm-leaderboard/details_Open-Orca__Mixtral-SlimOrca-8x7B/blob/main/results_2023-12-14T10-54-31.511638.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\": 0.6772838776728083,\n\
\ \"acc_stderr\": 0.031217522187270183,\n \"acc_norm\": 0.6826319719917908,\n\
\ \"acc_norm_stderr\": 0.03182881958823988,\n \"mc1\": 0.3818849449204406,\n\
\ \"mc1_stderr\": 0.017008101939163498,\n \"mc2\": 0.5498097510513819,\n\
\ \"mc2_stderr\": 0.015613516175450912\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6390784982935154,\n \"acc_stderr\": 0.014034761386175452,\n\
\ \"acc_norm\": 0.6766211604095563,\n \"acc_norm_stderr\": 0.013669421630012144\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6669986058554073,\n\
\ \"acc_stderr\": 0.004703238534045805,\n \"acc_norm\": 0.8511252738498307,\n\
\ \"acc_norm_stderr\": 0.003552374531305199\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \
\ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6148148148148148,\n\
\ \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n\
\ \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.7960526315789473,\n \"acc_stderr\": 0.032790004063100495,\n\
\ \"acc_norm\": 0.7960526315789473,\n \"acc_norm_stderr\": 0.032790004063100495\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.66,\n\
\ \"acc_stderr\": 0.04760952285695238,\n \"acc_norm\": 0.66,\n \
\ \"acc_norm_stderr\": 0.04760952285695238\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.769811320754717,\n \"acc_stderr\": 0.025907897122408163,\n\
\ \"acc_norm\": 0.769811320754717,\n \"acc_norm_stderr\": 0.025907897122408163\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7708333333333334,\n\
\ \"acc_stderr\": 0.03514697467862388,\n \"acc_norm\": 0.7708333333333334,\n\
\ \"acc_norm_stderr\": 0.03514697467862388\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \
\ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.6,\n \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n\
\ \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.45,\n \"acc_stderr\": 0.04999999999999999,\n \
\ \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.04999999999999999\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6820809248554913,\n\
\ \"acc_stderr\": 0.03550683989165579,\n \"acc_norm\": 0.6820809248554913,\n\
\ \"acc_norm_stderr\": 0.03550683989165579\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107224,\n\
\ \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107224\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.78,\n \"acc_stderr\": 0.04163331998932264,\n \"acc_norm\": 0.78,\n\
\ \"acc_norm_stderr\": 0.04163331998932264\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.6510638297872341,\n \"acc_stderr\": 0.03115852213135779,\n\
\ \"acc_norm\": 0.6510638297872341,\n \"acc_norm_stderr\": 0.03115852213135779\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5877192982456141,\n\
\ \"acc_stderr\": 0.04630653203366596,\n \"acc_norm\": 0.5877192982456141,\n\
\ \"acc_norm_stderr\": 0.04630653203366596\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5862068965517241,\n \"acc_stderr\": 0.04104269211806232,\n\
\ \"acc_norm\": 0.5862068965517241,\n \"acc_norm_stderr\": 0.04104269211806232\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.47883597883597884,\n \"acc_stderr\": 0.025728230952130726,\n \"\
acc_norm\": 0.47883597883597884,\n \"acc_norm_stderr\": 0.025728230952130726\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5317460317460317,\n\
\ \"acc_stderr\": 0.04463112720677172,\n \"acc_norm\": 0.5317460317460317,\n\
\ \"acc_norm_stderr\": 0.04463112720677172\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \
\ \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.8096774193548387,\n \"acc_stderr\": 0.022331707611823078,\n \"\
acc_norm\": 0.8096774193548387,\n \"acc_norm_stderr\": 0.022331707611823078\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.5320197044334976,\n \"acc_stderr\": 0.03510766597959217,\n \"\
acc_norm\": 0.5320197044334976,\n \"acc_norm_stderr\": 0.03510766597959217\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\"\
: 0.72,\n \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.793939393939394,\n \"acc_stderr\": 0.03158415324047709,\n\
\ \"acc_norm\": 0.793939393939394,\n \"acc_norm_stderr\": 0.03158415324047709\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8333333333333334,\n \"acc_stderr\": 0.026552207828215286,\n \"\
acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.026552207828215286\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9222797927461139,\n \"acc_stderr\": 0.019321805557223144,\n\
\ \"acc_norm\": 0.9222797927461139,\n \"acc_norm_stderr\": 0.019321805557223144\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.676923076923077,\n \"acc_stderr\": 0.02371088850197057,\n \
\ \"acc_norm\": 0.676923076923077,\n \"acc_norm_stderr\": 0.02371088850197057\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.31851851851851853,\n \"acc_stderr\": 0.028406533090608463,\n \
\ \"acc_norm\": 0.31851851851851853,\n \"acc_norm_stderr\": 0.028406533090608463\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.7394957983193278,\n \"acc_stderr\": 0.028510251512341947,\n\
\ \"acc_norm\": 0.7394957983193278,\n \"acc_norm_stderr\": 0.028510251512341947\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.37748344370860926,\n \"acc_stderr\": 0.0395802723112157,\n \"\
acc_norm\": 0.37748344370860926,\n \"acc_norm_stderr\": 0.0395802723112157\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8550458715596331,\n \"acc_stderr\": 0.015094215699700469,\n \"\
acc_norm\": 0.8550458715596331,\n \"acc_norm_stderr\": 0.015094215699700469\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5231481481481481,\n \"acc_stderr\": 0.034063153607115086,\n \"\
acc_norm\": 0.5231481481481481,\n \"acc_norm_stderr\": 0.034063153607115086\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8284313725490197,\n \"acc_stderr\": 0.026460569561240644,\n \"\
acc_norm\": 0.8284313725490197,\n \"acc_norm_stderr\": 0.026460569561240644\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.869198312236287,\n \"acc_stderr\": 0.021948766059470756,\n \
\ \"acc_norm\": 0.869198312236287,\n \"acc_norm_stderr\": 0.021948766059470756\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7443946188340808,\n\
\ \"acc_stderr\": 0.029275891003969923,\n \"acc_norm\": 0.7443946188340808,\n\
\ \"acc_norm_stderr\": 0.029275891003969923\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7938931297709924,\n \"acc_stderr\": 0.03547771004159464,\n\
\ \"acc_norm\": 0.7938931297709924,\n \"acc_norm_stderr\": 0.03547771004159464\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8677685950413223,\n \"acc_stderr\": 0.0309227883204458,\n \"acc_norm\"\
: 0.8677685950413223,\n \"acc_norm_stderr\": 0.0309227883204458\n },\n\
\ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8055555555555556,\n\
\ \"acc_stderr\": 0.03826076324884866,\n \"acc_norm\": 0.8055555555555556,\n\
\ \"acc_norm_stderr\": 0.03826076324884866\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7914110429447853,\n \"acc_stderr\": 0.03192193448934723,\n\
\ \"acc_norm\": 0.7914110429447853,\n \"acc_norm_stderr\": 0.03192193448934723\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5267857142857143,\n\
\ \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.5267857142857143,\n\
\ \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8349514563106796,\n \"acc_stderr\": 0.03675668832233188,\n\
\ \"acc_norm\": 0.8349514563106796,\n \"acc_norm_stderr\": 0.03675668832233188\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.905982905982906,\n\
\ \"acc_stderr\": 0.01911989279892498,\n \"acc_norm\": 0.905982905982906,\n\
\ \"acc_norm_stderr\": 0.01911989279892498\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \
\ \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.042923469599092816\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8671775223499362,\n\
\ \"acc_stderr\": 0.012136303209884564,\n \"acc_norm\": 0.8671775223499362,\n\
\ \"acc_norm_stderr\": 0.012136303209884564\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7745664739884393,\n \"acc_stderr\": 0.02249723019096755,\n\
\ \"acc_norm\": 0.7745664739884393,\n \"acc_norm_stderr\": 0.02249723019096755\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.41787709497206704,\n\
\ \"acc_stderr\": 0.016495400635820084,\n \"acc_norm\": 0.41787709497206704,\n\
\ \"acc_norm_stderr\": 0.016495400635820084\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.738562091503268,\n \"acc_stderr\": 0.025160998214292456,\n\
\ \"acc_norm\": 0.738562091503268,\n \"acc_norm_stderr\": 0.025160998214292456\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7556270096463023,\n\
\ \"acc_stderr\": 0.024406162094668886,\n \"acc_norm\": 0.7556270096463023,\n\
\ \"acc_norm_stderr\": 0.024406162094668886\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7932098765432098,\n \"acc_stderr\": 0.022535006705942835,\n\
\ \"acc_norm\": 0.7932098765432098,\n \"acc_norm_stderr\": 0.022535006705942835\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.5141843971631206,\n \"acc_stderr\": 0.02981549448368206,\n \
\ \"acc_norm\": 0.5141843971631206,\n \"acc_norm_stderr\": 0.02981549448368206\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5052151238591917,\n\
\ \"acc_stderr\": 0.012769541449652547,\n \"acc_norm\": 0.5052151238591917,\n\
\ \"acc_norm_stderr\": 0.012769541449652547\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6948529411764706,\n \"acc_stderr\": 0.0279715413701706,\n\
\ \"acc_norm\": 0.6948529411764706,\n \"acc_norm_stderr\": 0.0279715413701706\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.7107843137254902,\n \"acc_stderr\": 0.018342529845275908,\n \
\ \"acc_norm\": 0.7107843137254902,\n \"acc_norm_stderr\": 0.018342529845275908\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6636363636363637,\n\
\ \"acc_stderr\": 0.04525393596302505,\n \"acc_norm\": 0.6636363636363637,\n\
\ \"acc_norm_stderr\": 0.04525393596302505\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7551020408163265,\n \"acc_stderr\": 0.02752963744017492,\n\
\ \"acc_norm\": 0.7551020408163265,\n \"acc_norm_stderr\": 0.02752963744017492\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8855721393034826,\n\
\ \"acc_stderr\": 0.022509345325101706,\n \"acc_norm\": 0.8855721393034826,\n\
\ \"acc_norm_stderr\": 0.022509345325101706\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.87,\n \"acc_stderr\": 0.033799766898963086,\n \
\ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.033799766898963086\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5180722891566265,\n\
\ \"acc_stderr\": 0.03889951252827216,\n \"acc_norm\": 0.5180722891566265,\n\
\ \"acc_norm_stderr\": 0.03889951252827216\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8713450292397661,\n \"acc_stderr\": 0.025679342723276915,\n\
\ \"acc_norm\": 0.8713450292397661,\n \"acc_norm_stderr\": 0.025679342723276915\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3818849449204406,\n\
\ \"mc1_stderr\": 0.017008101939163498,\n \"mc2\": 0.5498097510513819,\n\
\ \"mc2_stderr\": 0.015613516175450912\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8050513022888713,\n \"acc_stderr\": 0.011134099415938278\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.45564821834723274,\n \
\ \"acc_stderr\": 0.013718194542485601\n }\n}\n```"
repo_url: https://huggingface.co/Open-Orca/Mixtral-SlimOrca-8x7B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|arc:challenge|25_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|gsm8k|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hellaswag|10_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-14T10-54-31.511638.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-14T10-54-31.511638.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- '**/details_harness|winogrande|5_2023-12-14T10-54-31.511638.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-14T10-54-31.511638.parquet'
- config_name: results
data_files:
- split: 2023_12_14T10_54_31.511638
path:
- results_2023-12-14T10-54-31.511638.parquet
- split: latest
path:
- results_2023-12-14T10-54-31.511638.parquet
---
# Dataset Card for Evaluation run of Open-Orca/Mixtral-SlimOrca-8x7B
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Open-Orca/Mixtral-SlimOrca-8x7B](https://huggingface.co/Open-Orca/Mixtral-SlimOrca-8x7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Open-Orca__Mixtral-SlimOrca-8x7B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-14T10:54:31.511638](https://huggingface.co/datasets/open-llm-leaderboard/details_Open-Orca__Mixtral-SlimOrca-8x7B/blob/main/results_2023-12-14T10-54-31.511638.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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"mc1_stderr": 0.017008101939163498,
"mc2": 0.5498097510513819,
"mc2_stderr": 0.015613516175450912
},
"harness|arc:challenge|25": {
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"acc_stderr": 0.014034761386175452,
"acc_norm": 0.6766211604095563,
"acc_norm_stderr": 0.013669421630012144
},
"harness|hellaswag|10": {
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},
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},
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},
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},
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"harness|hendrycksTest-high_school_microeconomics|5": {
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"harness|hendrycksTest-high_school_us_history|5": {
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"harness|hendrycksTest-high_school_world_history|5": {
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"harness|gsm8k|5": {
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}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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### Dataset Sources [optional]
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## Uses
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### Direct Use
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### Out-of-Scope Use
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## Dataset Structure
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## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
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### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
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#### Who are the source data producers?
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### Annotations [optional]
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#### Annotation process
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#### Who are the annotators?
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#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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### Recommendations
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Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
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## Glossary [optional]
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## Dataset Card Authors [optional]
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## Dataset Card Contact
[More Information Needed] | [
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arieg/cluster15_large_150 | arieg | "2023-12-14T11:01:06Z" | 0 | 0 | [
"region:us"
] | null | "2023-12-14T11:00:32Z" | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
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splits:
- name: train
num_bytes: 649342654.5
num_examples: 11250
download_size: 638445636
dataset_size: 649342654.5
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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ConseggioLigure/lijnews-instruct-ita-lij | ConseggioLigure | "2023-12-14T11:08:22Z" | 0 | 0 | [
"task_categories:conversational",
"task_categories:translation",
"license:cc-by-4.0",
"region:us"
] | [
"conversational",
"translation"
] | "2023-12-14T11:02:58Z" | ---
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
splits:
- name: train
num_bytes: 284530
num_examples: 153
- name: dev
num_bytes: 46829
num_examples: 27
- name: test
num_bytes: 66406
num_examples: 36
download_size: 286886
dataset_size: 397765
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
- split: test
path: data/test-*
license: cc-by-4.0
task_categories:
- conversational
- translation
pretty_name: LigurianNews ita-lij translation dataset (instruction-style)
--- | [
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ConseggioLigure/lijnews-instruct-lij-ita | ConseggioLigure | "2023-12-14T11:07:54Z" | 0 | 0 | [
"task_categories:conversational",
"task_categories:translation",
"license:cc-by-4.0",
"region:us"
] | [
"conversational",
"translation"
] | "2023-12-14T11:03:01Z" | ---
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
splits:
- name: train
num_bytes: 285611
num_examples: 153
- name: dev
num_bytes: 46987
num_examples: 27
- name: test
num_bytes: 66713
num_examples: 36
download_size: 288229
dataset_size: 399311
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
- split: test
path: data/test-*
license: cc-by-4.0
task_categories:
- conversational
- translation
pretty_name: LigurianNews lij-ita translation dataset (instruction-style)
--- | [
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MMoin/mini-platypus-two | MMoin | "2023-12-14T11:27:46Z" | 0 | 0 | [
"region:us"
] | null | "2023-12-14T11:27:45Z" | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 4186564
num_examples: 1000
download_size: 2245925
dataset_size: 4186564
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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ConseggioLigure/seed-instruct-eng-lij | ConseggioLigure | "2023-12-14T11:39:42Z" | 0 | 0 | [
"task_categories:conversational",
"task_categories:translation",
"license:cc-by-sa-4.0",
"region:us"
] | [
"conversational",
"translation"
] | "2023-12-14T11:36:42Z" | ---
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
splits:
- name: train
num_bytes: 2237341
num_examples: 5802
- name: dev
num_bytes: 75561
num_examples: 190
- name: test
num_bytes: 82236
num_examples: 201
download_size: 1289851
dataset_size: 2395138
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
- split: test
path: data/test-*
license: cc-by-sa-4.0
task_categories:
- conversational
- translation
pretty_name: OLDI Seed eng-lij translation dataset (instruction-style)
--- | [
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ConseggioLigure/seed-instruct-lij-eng | ConseggioLigure | "2023-12-14T11:39:40Z" | 0 | 0 | [
"task_categories:conversational",
"task_categories:translation",
"license:cc-by-sa-4.0",
"region:us"
] | [
"conversational",
"translation"
] | "2023-12-14T11:36:46Z" | ---
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
splits:
- name: train
num_bytes: 2272574
num_examples: 5802
- name: dev
num_bytes: 76790
num_examples: 190
- name: test
num_bytes: 83309
num_examples: 201
download_size: 1283303
dataset_size: 2432673
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
- split: test
path: data/test-*
license: cc-by-sa-4.0
task_categories:
- conversational
- translation
pretty_name: OLDI Seed lij-eng translation dataset (instruction-style)
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arieg/cluster16_large_150 | arieg | "2023-12-14T11:40:37Z" | 0 | 0 | [
"region:us"
] | null | "2023-12-14T11:39:54Z" | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
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'111': '148208'
'112': '149712'
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splits:
- name: train
num_bytes: 932294770.7
num_examples: 17100
download_size: 912242614
dataset_size: 932294770.7
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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DearTanishq/CustomDataSetByTan | DearTanishq | "2023-12-14T12:07:29Z" | 0 | 0 | [
"license:apache-2.0",
"region:us"
] | null | "2023-12-14T12:06:00Z" | ---
license: apache-2.0
---
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arieg/cluster17_large_150 | arieg | "2023-12-14T12:14:43Z" | 0 | 0 | [
"region:us"
] | null | "2023-12-14T12:14:06Z" | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
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splits:
- name: train
num_bytes: 748930382.0
num_examples: 14700
download_size: 753547762
dataset_size: 748930382.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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arieg/cluster18_large_150 | arieg | "2023-12-14T12:47:36Z" | 0 | 0 | [
"region:us"
] | null | "2023-12-14T12:46:55Z" | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
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splits:
- name: train
num_bytes: 788944881.6
num_examples: 14400
download_size: 795846839
dataset_size: 788944881.6
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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gaizerick/belle | gaizerick | "2023-12-14T13:00:27Z" | 0 | 0 | [
"license:openrail",
"region:us"
] | null | "2023-12-14T12:59:21Z" | ---
license: openrail
---
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sagakarl/ms151 | sagakarl | "2023-12-14T13:15:13Z" | 0 | 0 | [
"license:mit",
"region:us"
] | null | "2023-12-14T13:12:42Z" | ---
license: mit
---
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vmatyukh/test_dataset | vmatyukh | "2023-12-14T13:15:44Z" | 0 | 0 | [
"license:mit",
"region:us"
] | null | "2023-12-14T13:15:44Z" | ---
license: mit
---
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Marcello78/gaussian-splatting | Marcello78 | "2023-12-14T13:21:31Z" | 0 | 0 | [
"license:mit",
"region:us"
] | null | "2023-12-14T13:20:26Z" | ---
license: mit
---
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mkj3085003/Arxiv_two_months_version | mkj3085003 | "2023-12-14T13:20:56Z" | 0 | 0 | [
"license:mit",
"region:us"
] | null | "2023-12-14T13:20:56Z" | ---
license: mit
---
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arieg/cluster19_large_150 | arieg | "2023-12-14T13:30:43Z" | 0 | 0 | [
"region:us"
] | null | "2023-12-14T13:29:50Z" | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
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splits:
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num_bytes: 994536368.0
num_examples: 19500
download_size: 1025002189
dataset_size: 994536368.0
configs:
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data_files:
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path: data/train-*
---
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JairoDanielMT/manzanas | JairoDanielMT | "2023-12-14T14:49:30Z" | 0 | 0 | [
"license:creativeml-openrail-m",
"region:us"
] | null | "2023-12-14T13:31:00Z" | ---
license: creativeml-openrail-m
---
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valurank/Topic_Classification | valurank | "2023-12-14T15:36:46Z" | 0 | 0 | [
"license:other",
"region:us"
] | null | "2023-12-14T13:44:05Z" | ---
license: other
license_name: valurank
license_link: LICENSE
---
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Seongill/NQ_5_missing_adv_top7 | Seongill | "2023-12-14T13:54:44Z" | 0 | 0 | [
"region:us"
] | null | "2023-12-14T13:54:42Z" | ---
dataset_info:
features:
- name: question
dtype: string
- name: answers
sequence: string
- name: has_answer
dtype: bool
- name: similar_sub
dtype: string
- name: ctxs
list:
- name: answer_sent
sequence: string
- name: hasanswer
dtype: bool
- name: id
dtype: string
- name: is_adv
dtype: bool
- name: new_answer_sent
dtype: string
- name: original_text
dtype: string
- name: score
dtype: float64
- name: text
dtype: string
- name: title
dtype: string
- name: status
dtype: string
splits:
- name: train
num_bytes: 14540839
num_examples: 3610
download_size: 8130563
dataset_size: 14540839
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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arieg/cluster20_large_150 | arieg | "2023-12-14T13:59:41Z" | 0 | 0 | [
"region:us"
] | null | "2023-12-14T13:59:12Z" | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': 000197
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splits:
- name: train
num_bytes: 652831276.0
num_examples: 12750
download_size: 610809116
dataset_size: 652831276.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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cowlag/sd-webui-config | cowlag | "2023-12-14T14:39:24Z" | 0 | 0 | [
"license:unknown",
"region:us"
] | null | "2023-12-14T14:10:56Z" | ---
license: unknown
---
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Siar-Akbayin/sat-history-multiple-choice | Siar-Akbayin | "2023-12-14T14:14:23Z" | 0 | 0 | [
"license:apache-2.0",
"region:us"
] | null | "2023-12-14T14:12:46Z" | ---
license: apache-2.0
---
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arieg/cluster21_large_150 | arieg | "2023-12-14T14:17:30Z" | 0 | 0 | [
"region:us"
] | null | "2023-12-14T14:17:07Z" | ---
dataset_info:
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'37': '064005'
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'44': 080695
'45': 080773
'46': 081523
'47': 083718
'48': 083719
splits:
- name: train
num_bytes: 436223051.5
num_examples: 7350
download_size: 429796286
dataset_size: 436223051.5
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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FadiBoutros/IDiff-Face | FadiBoutros | "2023-12-14T14:26:59Z" | 0 | 0 | [
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | "2023-12-14T14:26:59Z" | ---
license: cc-by-nc-sa-4.0
---
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DragonLine/ksponspeech_eval_clean | DragonLine | "2023-12-14T14:29:51Z" | 0 | 0 | [
"region:us"
] | null | "2023-12-14T14:29:34Z" | ---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: transcripts
dtype: string
splits:
- name: train
num_bytes: 232494990.4
num_examples: 2400
- name: test
num_bytes: 28591870.6
num_examples: 300
- name: valid
num_bytes: 31790718.6
num_examples: 300
download_size: 300204624
dataset_size: 292877579.6
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: valid
path: data/valid-*
---
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speed1/lordecoreto | speed1 | "2023-12-14T14:32:54Z" | 0 | 0 | [
"license:openrail",
"region:us"
] | null | "2023-12-14T14:31:46Z" | ---
license: openrail
---
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DragonLine/ksponspeech_eval_clean_preprocess | DragonLine | "2023-12-14T14:32:35Z" | 0 | 0 | [
"region:us"
] | null | "2023-12-14T14:31:59Z" | ---
dataset_info:
features:
- name: input_features
sequence:
sequence: float32
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 2305165384
num_examples: 2400
- name: test
num_bytes: 288144608
num_examples: 300
- name: valid
num_bytes: 288145888
num_examples: 300
download_size: 424520861
dataset_size: 2881455880
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: valid
path: data/valid-*
---
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13Mai13/BuzzFeed | 13Mai13 | "2023-12-14T14:41:18Z" | 0 | 0 | [
"language:en",
"license:mit",
"region:us"
] | null | "2023-12-14T14:40:42Z" | ---
license: mit
language:
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Vinibarcley/Anahii | Vinibarcley | "2023-12-14T14:53:11Z" | 0 | 0 | [
"license:openrail",
"region:us"
] | null | "2023-12-14T14:52:30Z" | ---
license: openrail
---
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maruf7705/chosen-impasto-mix | maruf7705 | "2023-12-14T17:48:14Z" | 0 | 0 | [
"license:apache-2.0",
"region:us"
] | null | "2023-12-14T15:13:23Z" | ---
license: apache-2.0
---
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HaoYun34/test | HaoYun34 | "2023-12-14T15:26:47Z" | 0 | 0 | [
"license:unknown",
"region:us"
] | null | "2023-12-14T15:26:47Z" | ---
license: unknown
---
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NomeIncrivel/genos | NomeIncrivel | "2023-12-14T15:31:53Z" | 0 | 0 | [
"license:openrail",
"region:us"
] | null | "2023-12-14T15:31:03Z" | ---
license: openrail
---
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NomeIncrivel/Marty | NomeIncrivel | "2023-12-14T15:34:13Z" | 0 | 0 | [
"license:openrail",
"region:us"
] | null | "2023-12-14T15:33:01Z" | ---
license: openrail
---
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NomeIncrivel/Joeveno | NomeIncrivel | "2023-12-14T15:41:01Z" | 0 | 0 | [
"license:openrail",
"region:us"
] | null | "2023-12-14T15:39:56Z" | ---
license: openrail
---
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NomeIncrivel/Scooby-doo | NomeIncrivel | "2023-12-14T15:47:43Z" | 0 | 0 | [
"license:openrail",
"region:us"
] | null | "2023-12-14T15:46:10Z" | ---
license: openrail
---
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HaoYun34/hao_test | HaoYun34 | "2023-12-14T16:03:11Z" | 0 | 0 | [
"region:us"
] | null | "2023-12-14T16:03:08Z" | ---
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 2761811.0
num_examples: 18
download_size: 2763169
dataset_size: 2761811.0
---
# Dataset Card for "hao_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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jbilcke-hf/ai-tube-selmer-band | jbilcke-hf | "2023-12-17T16:41:35Z" | 0 | 0 | [
"license:cc-by-nc-4.0",
"region:us"
] | null | "2023-12-14T16:43:16Z" | ---
license: cc-by-nc-4.0
pretty_name: "𝔰𝔢𝔩𝔪𝔢𝔯"
---
## Description
𝔰𝔢𝔩𝔪𝔢𝔯 (pronounced "selmer") is a latent doom metal band.
The style is hypnotic, with deep electronic influence.
Influences range from drone and doom metal bands, to electronic drone music.
## Model
SVD
## Voice
Muted
## Music
hypnotic instrumental doom metal and ambient techno loop
## Prompt
𝔰𝔢𝔩𝔪𝔢𝔯 (pronounced "selmer") is a latent doom metal band.
Like for Sunn O))), the name is inspired by an old guitar amp manufacturer.
𝔰𝔢𝔩𝔪𝔢𝔯 music often feature gloomy themes, with keywords such as "hooded monk figure", "fog, mist", "dim lit", "forest" etc.
It is mostly instrumental, hypnotic, and electronic. | [
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hippocrates/Ophtho_Instruction_FT_test | hippocrates | "2023-12-14T16:47:01Z" | 0 | 0 | [
"region:us"
] | null | "2023-12-14T16:46:48Z" | ---
dataset_info:
features:
- name: id
dtype: string
- name: query
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 175638580
num_examples: 112820
- name: valid
num_bytes: 22040735
num_examples: 14101
- name: test
num_bytes: 21966708
num_examples: 14106
download_size: 109216454
dataset_size: 219646023
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
---
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sumitj39/openhathi-7b-base-q4_0.ggml | sumitj39 | "2023-12-14T17:27:52Z" | 0 | 0 | [
"license:llama2",
"region:us"
] | null | "2023-12-14T17:06:05Z" | ---
license: llama2
---
This dataset contains the ggml version of OpenHathi model released by Sarvam AI. [Link to original model](https://huggingface.co/sarvamai/OpenHathi-7B-Hi-v0.1-Base).
The ggml file provided is 4 bit quantized version, it can be run on local devices such as an M1 MacBook or other hardware.
### How to use?
1. Download llama.cpp from [here](https://github.com/ggerganov/llama.cpp)
```bash
git clone https://github.com/ggerganov/llama.cpp
```
3. Note: The ggml support has been deprecated, new file format is gguf. But since this repository contains ggml file, we have to switch back to an older commit of llama.cpp that worked with ggml files.
Execute this command to switch to the commit that worked with ggml files:
```
bash git checkout dadbed9
```
3. Read the instructions mentioned [here](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#build) to create an executable file in the llama.cpp directory.
4. Run the model:
```bash
./main -t 4 -m ~/ggml-models/openhathi-7b-base-q4_0.ggml -p "tell me about india in hindi: - भारत" --ctx-size 1024 -ngl 1 2>/dev/null
```
5. The model prints output:
>भारत दुनिया के सबसे पुराने देशों में से एक है और दुनिया की 7वीं सबसे बड़ी आबादी वाला देश है। The nation has a rich and diverse history, dating back to ancient times when it was ruled by various empires and kingdoms. भारत में दो मुख्य भौगोलिक क्षेत्र शामिल हैंः एक द्वीपसमूह जिसमें कई बड़े द्वीपों के साथ-साथ छोटे द्वीप भी शामिल हैं और दूसरा समतल क्षेत्रों से घिरा हुआ है। भारत की अनूठी सांस्कृतिक विरासत, विविध धर्मों और भाषाओं को बढ़ावा देता है जो देश की समृद्ध विविधता का प्रमाण हैं। भारत में सबसे अधिक बोली जाने वाली भाषाएँ हिंदी, बंगाली, तमिल, मराठी, कन्नड़, उड़िया और मलयालम हैं। 40 प्रतिशत आबादी हिंदू है, जबकि अन्य प्रमुख धर्म इस्लाम, बौद्ध धर्म, ईसाई धर्म और सिख धर्म हैं। भारत अपनी समृद्ध कृषि अर्थव्यवस्था के लिए जाना जाता है और यह अपने विविध व्यंजनों, समृद्ध इतिहास और जीवंत सांस्कृतिक विरासत के लिए भी मनाया जाता है। The country has made remarkable strides in areas such as information technology and manufacturing, which have contributed to its global economic position. एक बड़े देश होने के बावजूद, भारत में सभी को एक स्थान से दूसरे स्थान पर जाने की आवश्यकता नहीं है। India's transport infrastructure is extensive, with a well-developed road network that connects most major cities and towns. इसके अलावा, मुंबई और दिल्ली जैसे प्रमुख शहरों में अंतर्राष्ट्रीय हवाई अड्डे हैं जो दुनिया भर के गंतव्यों के लिए उड़ान भरते हैं। India has also gained prominence as a popular tourist destination in recent years. देश अपने अनूठे अनुभवों, आश्चर्यजनक प्राकृतिक परिदृश्यों, विविध संस्कृतियों और समृद्ध इतिहास की पेशकश करता है। From world-famous sites such as the Taj Mahal to lesser- .....
> Note: This is a base model, to use it in your applications you need to fine tune it.
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hf-100/sb-in | hf-100 | "2023-12-14T17:24:01Z" | 0 | 0 | [
"license:cc-by-nc-nd-4.0",
"region:us"
] | null | "2023-12-14T17:23:23Z" | ---
license: cc-by-nc-nd-4.0
---
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ihaflix1/pauloloko | ihaflix1 | "2023-12-14T17:41:54Z" | 0 | 0 | [
"license:openrail",
"region:us"
] | null | "2023-12-14T17:41:27Z" | ---
license: openrail
---
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blutjens/hrmelt | blutjens | "2023-12-15T03:04:51Z" | 0 | 0 | [
"task_categories:image-to-image",
"task_categories:image-segmentation",
"size_categories:10K<n<100K",
"license:cc-by-4.0",
"climate",
"region:us"
] | [
"image-to-image",
"image-segmentation"
] | "2023-12-14T17:50:04Z" | ---
license: cc-by-4.0
task_categories:
- image-to-image
- image-segmentation
tags:
- climate
pretty_name: hrmelt
size_categories:
- 10K<n<100K
---
See instructions on how to use this dataset at
https://github.com/blutjens/hrmelt
To be released in Q1-2/2023 | [
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] |
aitamilnadu/tamil_ner_data_wikiann | aitamilnadu | "2023-12-17T08:58:03Z" | 0 | 0 | [
"task_categories:token-classification",
"size_categories:10K<n<100K",
"language:ta",
"license:unknown",
"region:us"
] | [
"token-classification"
] | "2023-12-14T18:17:03Z" | ---
dataset_info:
features:
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-PER
'2': I-PER
'3': B-ORG
'4': I-ORG
'5': B-LOC
'6': I-LOC
- name: langs
sequence: string
- name: spans
sequence: string
splits:
- name: validation
num_bytes: 354929
num_examples: 1000
- name: test
num_bytes: 357639
num_examples: 1000
- name: train
num_bytes: 5275703
num_examples: 15000
download_size: 1527096
dataset_size: 5988271
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
- split: test
path: data/test-*
- split: train
path: data/train-*
license: unknown
task_categories:
- token-classification
language:
- ta
size_categories:
- 10K<n<100K
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mondaugen/IMBD | mondaugen | "2023-12-14T18:32:11Z" | 0 | 0 | [
"license:other",
"region:us"
] | null | "2023-12-14T18:22:04Z" | ---
license: other
license_name: imdb
license_link: https://developer.imdb.com/non-commercial-datasets/
---
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Subsets and Splits