datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
KentoTsu/KOKO | ---
license: openrail
---
|
dllllb/libru-poetry | ---
license: apache-2.0
task_categories:
- text2text-generation
tags:
- art
language:
- ru
--- |
bot-yaya/UN_PDF_SUBSET_PREPROCESSED | ---
dataset_info:
features:
- name: zh
dtype: string
- name: en
dtype: string
- name: fr
dtype: string
- name: es
dtype: string
- name: ru
dtype: string
- name: record
dtype: string
splits:
- name: train
num_bytes: 589332110
num_examples: 2950
download_size: 279887483
dataset_size: 589332110
---
# Dataset Card for "UN_PDF_SUBSET_PREPROCESSED"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ruanchaves/nru_hse | ---
annotations_creators:
- expert-generated
language_creators:
- machine-generated
language:
- ru
license:
- unknown
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- structure-prediction
task_ids: []
pretty_name: NRU-HSE
tags:
- word-segmentation
---
# Dataset Card for NRU-HSE
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Dataset Creation](#dataset-creation)
- [Additional Information](#additional-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [glushkovato/hashtag_segmentation](https://github.com/glushkovato/hashtag_segmentation/)
- **Paper:** [Char-RNN and Active Learning for Hashtag Segmentation](https://arxiv.org/abs/1911.03270)
### Dataset Summary
Real hashtags collected from several pages about civil services on vk.com (a Russian social network) and then segmented manually.
### Languages
Russian
## Dataset Structure
### Data Instances
```
{
"index": 0,
"hashtag": "ЁлкаВЗазеркалье",
"segmentation": "Ёлка В Зазеркалье"
}
```
### Data Fields
- `index`: a numerical index.
- `hashtag`: the original hashtag.
- `segmentation`: the gold segmentation for the hashtag.
## Dataset Creation
- All hashtag segmentation and identifier splitting datasets on this profile have the same basic fields: `hashtag` and `segmentation` or `identifier` and `segmentation`.
- The only difference between `hashtag` and `segmentation` or between `identifier` and `segmentation` are the whitespace characters. Spell checking, expanding abbreviations or correcting characters to uppercase go into other fields.
- There is always whitespace between an alphanumeric character and a sequence of any special characters ( such as `_` , `:`, `~` ).
- If there are any annotations for named entity recognition and other token classification tasks, they are given in a `spans` field.
## Additional Information
### Citation Information
```
@article{glushkova2019char,
title={Char-RNN and Active Learning for Hashtag Segmentation},
author={Glushkova, Taisiya and Artemova, Ekaterina},
journal={arXiv preprint arXiv:1911.03270},
year={2019}
}
```
### Contributions
This dataset was added by [@ruanchaves](https://github.com/ruanchaves) while developing the [hashformers](https://github.com/ruanchaves/hashformers) library. |
kgiamalis/Llama-2-train | ---
license: cc-by-nc-sa-4.0
---
|
SantiCalde/santi | ---
license: unknown
---
|
haonanqqq/AgriSFT | ---
license: apache-2.0
task_categories:
- question-answering
- conversational
- text2text-generation
- text-generation
size_categories:
- 10K<n<100K
---
## 数据集描述
这是一个基于Agricultural-dataset构建的农业指令跟随数据集。由于Agricultural-dataset是一个比较脏的数据集，并且包含了大量印度相关的内容。所以此数据集也是不干净的。干净版本将会在未来上传。
## Dataset Description
This is an agricultural instruction-following dataset built upon the Agricultural-dataset. Since the Agricultural-dataset is somewhat messy and contains a significant amount of content related to India, this dataset is also not entirely clean. A clean version will be uploaded in the future.
## 构建方法
本数据集使用gpt-3.5-turbo构建
this dataset was created by gpt-3.5-turbo |
mii-llm/discorsi-vari | ---
dataset_info:
features:
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 63794757.0
num_examples: 8125
download_size: 29458789
dataset_size: 63794757.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "discorsi-vari"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
dmrau/cqadupstack-unix-qrels | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
dataset_info:
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 44636
num_examples: 1693
download_size: 23577
dataset_size: 44636
---
# Dataset Card for "cqadupstack-unix-qrels"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Francesco/bone-fracture-7fylg | ---
dataset_info:
features:
- name: image_id
dtype: int64
- name: image
dtype: image
- name: width
dtype: int32
- name: height
dtype: int32
- name: objects
sequence:
- name: id
dtype: int64
- name: area
dtype: int64
- name: bbox
sequence: float32
length: 4
- name: category
dtype:
class_label:
names:
'0': bone-fracture
'1': angle
'2': fracture
'3': line
'4': messed_up_angle
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- object-detection
task_ids: []
pretty_name: bone-fracture-7fylg
tags:
- rf100
---
# Dataset Card for bone-fracture-7fylg
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/bone-fracture-7fylg
- **Point of Contact:** francesco.zuppichini@gmail.com
### Dataset Summary
bone-fracture-7fylg
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/bone-fracture-7fylg
### Citation Information
```
@misc{ bone-fracture-7fylg,
title = { bone fracture 7fylg Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/bone-fracture-7fylg } },
url = { https://universe.roboflow.com/object-detection/bone-fracture-7fylg },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. |
CyberHarem/kousaka_honoka_lovelive | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of kousaka_honoka/高坂穂乃果/코사카호노카 (Love Live!)
This is the dataset of kousaka_honoka/高坂穂乃果/코사카호노카 (Love Live!), containing 500 images and their tags.
The core tags of this character are `blue_eyes, orange_hair, one_side_up, bow, short_hair, bangs, hair_bow, hair_ornament`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 670.05 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kousaka_honoka_lovelive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 368.56 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kousaka_honoka_lovelive/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1227 | 818.02 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kousaka_honoka_lovelive/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 583.72 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kousaka_honoka_lovelive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1227 | 1.16 GiB | [Download](https://huggingface.co/datasets/CyberHarem/kousaka_honoka_lovelive/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/kousaka_honoka_lovelive',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 21 |  |  |  |  |  | 1girl, otonokizaka_school_uniform, solo, striped_bowtie, looking_at_viewer, pleated_skirt, white_shirt, blue_skirt, yellow_bow, blush, red_bowtie, open_mouth, blazer, collared_shirt, long_sleeves, winter_uniform, plaid_skirt, :d, medium_hair, miniskirt, blue_jacket, hair_between_eyes, summer_uniform |
| 1 | 11 |  |  |  |  |  | 1girl, looking_at_viewer, otonokizaka_school_uniform, solo, blazer, smile, winter_uniform, blush, upper_body, bowtie, brown_hair, character_name, one_eye_closed, open_mouth, white_background |
| 2 | 7 |  |  |  |  |  | 1girl, skirt, smile, solo, looking_at_viewer, otonokizaka_school_uniform, sweater_vest, open_mouth, summer_uniform, blush |
| 3 | 6 |  |  |  |  |  | 1girl, :d, hair_flower, looking_at_viewer, open_mouth, solo, blush, brown_hair, dress |
| 4 | 15 |  |  |  |  |  | 1girl, hairclip, solo, earrings, looking_at_viewer, skirt, open_mouth, :d, blush, brown_hair |
| 5 | 11 |  |  |  |  |  | 1girl, smile, solo, brown_hair, hat, white_gloves, looking_at_viewer, blush, clenched_hands, heart_earrings, parody |
| 6 | 6 |  |  |  |  |  | 1girl, bokura_wa_ima_no_naka_de, fingerless_gloves, looking_at_viewer, navel, open_mouth, skirt, solo, choker, earrings, red_gloves, :d, blush, brown_hair, character_name, happy_birthday, suspenders |
| 7 | 14 |  |  |  |  |  | 1girl, solo, smile, cleavage, hair_flower, looking_at_viewer, medium_breasts, blush, navel, bracelet, striped_bikini, brown_hair, day, hibiscus, open_mouth, front-tie_top, bikini_skirt, outdoors, sky |
| 8 | 5 |  |  |  |  |  | 1girl, earrings, elbow_gloves, hair_flower, hairband, looking_at_viewer, smile, solo, white_gloves, dress, ribbon, blush, heart, brown_hair, large_breasts, single_side_bun, starry_sky, striped_bowtie |
| 9 | 14 |  |  |  |  |  | 1girl, hair_flower, smile, solo, looking_at_viewer, short_sleeves, medium_hair, blush, upper_body, white_dress, wrist_scrunchie, sunflower, bowtie, hair_between_eyes, simple_background, white_background, character_name, dated, happy_birthday, open_mouth, pink_bow, pink_scrunchie, sailor_collar, shiny_hair, shirt |
| 10 | 7 |  |  |  |  |  | 1girl, braid, solo, hair_flower, looking_at_viewer, short_sleeves, blush, hair_ribbon, open_mouth, pleated_skirt, white_skirt, black_footwear, :d, brown_hair, knee_boots, red_bowtie, white_background |
| 11 | 5 |  |  |  |  |  | 1girl, solo, blue_sky, blush, cloud, hair_between_eyes, looking_at_viewer, medium_breasts, star_print, yellow_bikini, cleavage, collarbone, halterneck, ocean, open_mouth, orange_bikini, outdoors, shiny_hair, upper_body, :d, front-tie_top, hair_scrunchie, happy_birthday, heart_earrings, high_ponytail, innertube, navel, one_eye_closed, print_bikini, side_ponytail, water |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | otonokizaka_school_uniform | solo | striped_bowtie | looking_at_viewer | pleated_skirt | white_shirt | blue_skirt | yellow_bow | blush | red_bowtie | open_mouth | blazer | collared_shirt | long_sleeves | winter_uniform | plaid_skirt | :d | medium_hair | miniskirt | blue_jacket | hair_between_eyes | summer_uniform | smile | upper_body | bowtie | brown_hair | character_name | one_eye_closed | white_background | skirt | sweater_vest | hair_flower | dress | hairclip | earrings | hat | white_gloves | clenched_hands | heart_earrings | parody | bokura_wa_ima_no_naka_de | fingerless_gloves | navel | choker | red_gloves | happy_birthday | suspenders | cleavage | medium_breasts | bracelet | striped_bikini | day | hibiscus | front-tie_top | bikini_skirt | outdoors | sky | elbow_gloves | hairband | ribbon | heart | large_breasts | single_side_bun | starry_sky | short_sleeves | white_dress | wrist_scrunchie | sunflower | simple_background | dated | pink_bow | pink_scrunchie | sailor_collar | shiny_hair | shirt | braid | hair_ribbon | white_skirt | black_footwear | knee_boots | blue_sky | cloud | star_print | yellow_bikini | collarbone | halterneck | ocean | orange_bikini | hair_scrunchie | high_ponytail | innertube | print_bikini | side_ponytail | water |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:-----------------------------|:-------|:-----------------|:--------------------|:----------------|:--------------|:-------------|:-------------|:--------|:-------------|:-------------|:---------|:-----------------|:---------------|:-----------------|:--------------|:-----|:--------------|:------------|:--------------|:--------------------|:-----------------|:--------|:-------------|:---------|:-------------|:-----------------|:-----------------|:-------------------|:--------|:---------------|:--------------|:--------|:-----------|:-----------|:------|:---------------|:-----------------|:-----------------|:---------|:---------------------------|:--------------------|:--------|:---------|:-------------|:-----------------|:-------------|:-----------|:-----------------|:-----------|:-----------------|:------|:-----------|:----------------|:---------------|:-----------|:------|:---------------|:-----------|:---------|:--------|:----------------|:------------------|:-------------|:----------------|:--------------|:------------------|:------------|:--------------------|:--------|:-----------|:-----------------|:----------------|:-------------|:--------|:--------|:--------------|:--------------|:-----------------|:-------------|:-----------|:--------|:-------------|:----------------|:-------------|:-------------|:--------|:----------------|:-----------------|:----------------|:------------|:---------------|:----------------|:--------|
| 0 | 21 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 11 |  |  |  |  |  | X | X | X | | X | | | | | X | | X | X | | | X | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 7 |  |  |  |  |  | X | X | X | | X | | | | | X | | X | | | | | | | | | | | X | X | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 6 |  |  |  |  |  | X | | X | | X | | | | | X | | X | | | | | | X | | | | | | | | | X | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 15 |  |  |  |  |  | X | | X | | X | | | | | X | | X | | | | | | X | | | | | | | | | X | | | | X | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 11 |  |  |  |  |  | X | | X | | X | | | | | X | | | | | | | | | | | | | | X | | | X | | | | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 6 |  |  |  |  |  | X | | X | | X | | | | | X | | X | | | | | | X | | | | | | | | | X | X | | | X | | | | | X | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 14 |  |  |  |  |  | X | | X | | X | | | | | X | | X | | | | | | | | | | | | X | | | X | | | | | | X | | | | | | | | | | | X | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 5 |  |  |  |  |  | X | | X | X | X | | | | | X | | | | | | | | | | | | | | X | | | X | | | | | | X | X | | X | | X | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 9 | 14 |  |  |  |  |  | X | | X | | X | | | | | X | | X | | | | | | | X | | | X | | X | X | X | | X | | X | | | X | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | |
| 10 | 7 |  |  |  |  |  | X | | X | | X | X | | | | X | X | X | | | | | | X | | | | | | | | | X | | | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | |
| 11 | 5 |  |  |  |  |  | X | | X | | X | | | | | X | | X | | | | | | X | | | | X | | | X | | | | X | | | | | | | | | | | X | | | | X | | | X | | X | X | | | | | X | | X | | | | | | | | | | | | | | | | | | X | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
dchatca/economic-for-llama2-ft | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 12940373.97403685
num_examples: 955
- name: test
num_bytes: 3238481.025963149
num_examples: 239
download_size: 8613970
dataset_size: 16178855.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
emozilla/booksum-summary-analysis_llama-8192 | ---
dataset_info:
features:
- name: chapter
dtype: string
- name: text
dtype: string
- name: type
dtype: string
splits:
- name: train
num_bytes: 181882155.9809025
num_examples: 10201
- name: validation
num_bytes: 33836910.18621307
num_examples: 1724
- name: test
num_bytes: 25274232.87394451
num_examples: 1545
download_size: 84868415
dataset_size: 240993299.0410601
---
# Dataset Card for "booksum-summary-analysis_llama-8192"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
virtualvoidsteve/code_correction_dataset_2207 | ---
dataset_info:
features:
- name: corrupted
dtype: string
- name: corrected
dtype: string
splits:
- name: train
num_bytes: 190719
num_examples: 214
download_size: 60671
dataset_size: 190719
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
iambestfeed/vnexpress-filter-word-seg | ---
license: apache-2.0
---
|
IlyaGusev/rulm_human_preferences | ---
dataset_info:
features:
- name: result
dtype: string
- name: worker_id
dtype: string
- name: assignment_id
dtype: string
- name: pool_id
dtype: int64
- name: instruction
dtype: string
- name: input
dtype: string
- name: left_answer
dtype: string
- name: right_answer
dtype: string
- name: left_model
dtype: string
- name: right_model
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 104434766
num_examples: 34520
download_size: 12663395
dataset_size: 104434766
---
# Dataset Card for "rulm_human_preferences"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Matias12f/cats_dogs | ---
license: apache-2.0
---
|
AdapterOcean/med_alpaca_standardized_cluster_73_std | ---
dataset_info:
features:
- name: message
dtype: string
- name: message_type
dtype: string
- name: message_id
dtype: int64
- name: conversation_id
dtype: int64
- name: cluster
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 22335204
num_examples: 35454
download_size: 11162828
dataset_size: 22335204
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "med_alpaca_standardized_cluster_73_std"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
BeIR/fever | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
- 10K<n<100K
arguana:
- 1K<n<10K
touche-2020:
- 100K<n<1M
cqadupstack:
- 100K<n<1M
quora:
- 100K<n<1M
dbpedia:
- 1M<n<10M
scidocs:
- 10K<n<100K
fever:
- 1M<n<10M
climate-fever:
- 1M<n<10M
scifact:
- 1K<n<10K
source_datasets: []
task_categories:
- text-retrieval
- zero-shot-retrieval
- information-retrieval
- zero-shot-information-retrieval
task_ids:
- passage-retrieval
- entity-linking-retrieval
- fact-checking-retrieval
- tweet-retrieval
- citation-prediction-retrieval
- duplication-question-retrieval
- argument-retrieval
- news-retrieval
- biomedical-information-retrieval
- question-answering-retrieval
---
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** nandan.thakur@uwaterloo.ca
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. |
heliosprime/twitter_dataset_1712988938 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 9505
num_examples: 20
download_size: 9906
dataset_size: 9505
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "twitter_dataset_1712988938"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
liuyanchen1015/MULTI_VALUE_mnli_dont | ---
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: score
dtype: int64
splits:
- name: dev_matched
num_bytes: 20304
num_examples: 97
- name: dev_mismatched
num_bytes: 14784
num_examples: 73
- name: test_matched
num_bytes: 31104
num_examples: 145
- name: test_mismatched
num_bytes: 14833
num_examples: 72
- name: train
num_bytes: 1082378
num_examples: 4682
download_size: 686587
dataset_size: 1163403
---
# Dataset Card for "MULTI_VALUE_mnli_dont"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
indiejoseph/wikipedia-translate-zhhk-zhcn | ---
dataset_info:
features:
- name: zh
dtype: string
- name: yue
dtype: string
splits:
- name: train
num_bytes: 1368062
num_examples: 1301
download_size: 1033502
dataset_size: 1368062
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "wikipedia-translate-zhhk-zhcn"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
liuyanchen1015/MULTI_VALUE_rte_doubly_filled_comp | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: test
num_bytes: 12188
num_examples: 20
- name: train
num_bytes: 7381
num_examples: 12
download_size: 24427
dataset_size: 19569
---
# Dataset Card for "MULTI_VALUE_rte_doubly_filled_comp"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
adrianex00/Wodecki | ---
license: openrail
---
|
fydhfzh/arabic_sl_asr | ---
dataset_info:
features:
- name: audio
struct:
- name: array
sequence: float32
- name: path
dtype: string
- name: sampling_rate
dtype: int64
- name: path
dtype: string
- name: transcription
dtype: string
splits:
- name: train
num_bytes: 550131445
num_examples: 6229
download_size: 426911483
dataset_size: 550131445
---
# Dataset Card for "arabic_sl_asr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jxu9001/cs6301project50k | ---
dataset_info:
features:
- name: image
dtype: image
- name: expression
dtype: string
- name: img_width
dtype: int64
- name: img_height
dtype: int64
- name: x
dtype: float64
- name: y
dtype: float64
- name: w
dtype: float64
- name: h
dtype: float64
splits:
- name: train
num_bytes: 7128143566.0
num_examples: 40000
- name: test
num_bytes: 1723596306.0
num_examples: 10000
download_size: 4714944672
dataset_size: 8851739872.0
---
# Dataset Card for "cs6301project50k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
madagisa/llama2_custom_kor | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 7786
num_examples: 32
download_size: 4172
dataset_size: 7786
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
pankajemplay/mistral-intent-1K | ---
dataset_info:
features:
- name: User Query
dtype: string
- name: Intent
dtype: string
- name: id type
dtype: string
- name: id value
dtype: string
- name: id slot filled
dtype: bool
- name: Task
dtype: string
- name: task slot filled
dtype: bool
- name: Bot Response
dtype: string
- name: text
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 992882
num_examples: 1308
download_size: 218767
dataset_size: 992882
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "mistral-intent-1K"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/yuna_kumakumakumabear | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of Yuna
This is the dataset of Yuna, containing 300 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)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 300 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 615 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 300 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 300 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 300 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 300 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 300 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 615 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 615 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 615 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
Cognitive-Lab/Indic-ARC-Challenge | ---
configs:
- config_name: kn
data_files:
- split: train
path: kn/arc_kan-train.json
- split: test
path: kn/arc_kan-test.json
- split: validation
path: kn/arc_kan-validation.json
- config_name: hi
data_files:
- split: train
path: hi/arc_hi-train.json
- split: test
path: hi/arc_hi-test.json
- split: validation
path: hi/arc_hi-validation.json
- config_name: ta
data_files:
- split: train
path: ta/arc_ta-train.json
- split: test
path: ta/arc_ta-test.json
- split: validation
path: ta/arc_ta-validation.json
- config_name: te
data_files:
- split: train
path: te/arc_tel-train.json
- split: test
path: te/arc_tel-test.json
- split: validation
path: te/arc_tel-validation.json
- config_name: ml
data_files:
- split: train
path: ml/arc_ml-train.json
- split: test
path: ml/arc_ml-test.json
- split: validation
path: ml/arc_ml-validation.json
- config_name: gu
data_files:
- split: train
path: gu/arc_gu-train.json
- split: test
path: gu/arc_gu-test.json
- split: validation
path: gu/arc_gu-validation.json
- config_name: mr
data_files:
- split: train
path: mr/arc_mr-train.json
- split: test
path: mr/arc_mr-test.json
- split: validation
path: mr/arc_mr-validation.json
---
# ARC Challenge Translated
Citation:
```
@article{allenai:arc,
author = {Peter Clark and Isaac Cowhey and Oren Etzioni and Tushar Khot and
Ashish Sabharwal and Carissa Schoenick and Oyvind Tafjord},
title = {Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge},
journal = {arXiv:1803.05457v1},
year = {2018},
}
```
Contributions:\
Thanks to [@Srinidhi9113](https://huggingface.co/Srinidhi9113) and [@Achala Nayak](https://huggingface.co/achalanayak) for adding the dataset. |
ought/raft | ---
annotations_creators:
- expert-generated
- crowdsourced
language_creators:
- expert-generated
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
- extended|ade_corpus_v2
- extended|banking77
task_categories:
- text-classification
task_ids:
- multi-class-classification
pretty_name: 'Real-world Annotated Few-shot Tasks: RAFT'
language_bcp47:
- en-US
---
# Dataset Card for RAFT
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://raft.elicit.org
- **Repository:** https://huggingface.co/datasets/ought/raft
- **Paper:** [arxiv.org](https://arxiv.org/abs/2109.14076)
- **Leaderboard:** https://huggingface.co/spaces/ought/raft-leaderboard
- **Point of Contact:** [Eli Lifland](eli.d.lifland@gmail.com)
### Dataset Summary
The Real-world Annotated Few-shot Tasks (RAFT) dataset is an aggregation of English-language datasets found in the real world. Associated with each dataset is a binary or multiclass classification task, intended to improve our understanding of how language models perform on tasks that have concrete, real-world value. Only 50 labeled examples are provided in each dataset.
### Supported Tasks and Leaderboards
- `text-classification`: Each subtask in RAFT is a text classification task, and the provided train and test sets can be used to submit to the [RAFT Leaderboard](https://huggingface.co/spaces/ought/raft-leaderboard) To prevent overfitting and tuning on a held-out test set, the leaderboard is only evaluated once per week. Each task has its macro-f1 score calculated, then those scores are averaged to produce the overall leaderboard score.
### Languages
RAFT is entirely in American English (en-US).
## Dataset Structure
### Data Instances
| Dataset | First Example |
| ----------- | ----------- |
| Ade Corpus V2 | <pre>Sentence: No regional side effects were noted.<br>ID: 0<br>Label: 2</pre> |
| Banking 77 | <pre>Query: Is it possible for me to change my PIN number?<br>ID: 0<br>Label: 23<br></pre> |
| NeurIPS Impact Statement Risks | <pre>Paper title: Auto-Panoptic: Cooperative Multi-Component Architecture Search for Panoptic Segmentation...<br>Paper link: https://proceedings.neurips.cc/paper/2020/file/ec1f764517b7ffb52057af6df18142b7-Paper.pdf...<br>Impact statement: This work makes the first attempt to search for all key components of panoptic pipeline and manages to accomplish this via the p...<br>ID: 0<br>Label: 1</pre> |
| One Stop English | <pre>Article: For 85 years, it was just a grey blob on classroom maps of the solar system. But, on 15 July, Pluto was seen in high resolution ...<br>ID: 0<br>Label: 3<br></pre> |
| Overruling | <pre>Sentence: in light of both our holding today and previous rulings in johnson, dueser, and gronroos, we now explicitly overrule dupree....<br>ID: 0<br>Label: 2<br></pre> |
| Semiconductor Org Types | <pre>Paper title: 3Gb/s AC-coupled chip-to-chip communication using a low-swing pulse receiver...<br>Organization name: North Carolina State Univ.,Raleigh,NC,USA<br>ID: 0<br>Label: 3<br></pre> |
| Systematic Review Inclusion | <pre>Title: Prototyping and transforming facial textures for perception research...<br>Abstract: Wavelet based methods for prototyping facial textures for artificially transforming the age of facial images were described. Pro...<br>Authors: Tiddeman, B.; Burt, M.; Perrett, D.<br>Journal: IEEE Comput Graphics Appl<br>ID: 0<br>Label: 2</pre> |
| TAI Safety Research | <pre>Title: Malign generalization without internal search<br>Abstract Note: In my last post, I challenged the idea that inner alignment failures should be explained by appealing to agents which perform ex...<br>Url: https://www.alignmentforum.org/posts/ynt9TD6PrYw6iT49m/malign-generalization-without-internal-search...<br>Publication Year: 2020<br>Item Type: blogPost<br>Author: Barnett, Matthew<br>Publication Title: AI Alignment Forum<br>ID: 0<br>Label: 1</pre> |
| Terms Of Service | <pre>Sentence: Crowdtangle may change these terms of service, as described above, notwithstanding any provision to the contrary in any agreemen...<br>ID: 0<br>Label: 2<br></pre> |
| Tweet Eval Hate | <pre>Tweet: New to Twitter-- any men on here know what the process is to get #verified?...<br>ID: 0<br>Label: 2<br></pre> |
| Twitter Complaints | <pre>Tweet text: @HMRCcustomers No this is my first job<br>ID: 0<br>Label: 2</pre> |
### Data Fields
The ID field is used for indexing data points. It will be used to match your submissions with the true test labels, so you must include it in your submission. All other columns contain textual data. Some contain links and URLs to websites on the internet.
All output fields are designated with the "Label" column header. The 0 value in this column indicates that the entry is unlabeled, and should only appear in the unlabeled test set. Other values in this column are various other labels. To get their textual value for a given dataset:
```
# Load the dataset
dataset = datasets.load_dataset("ought/raft", "ade_corpus_v2")
# First, get the object that holds information about the "Label" feature in the dataset.
label_info = dataset.features["Label"]
# Use the int2str method to access the textual labels.
print([label_info.int2str(i) for i in (0, 1, 2)])
# ['Unlabeled', 'ADE-related', 'not ADE-related']
```
### Data Splits
There are two splits provided: train data and unlabeled test data.
The training examples were chosen at random. No attempt was made to ensure that classes were balanced or proportional in the training data -- indeed, the Banking 77 task with 77 different classes if used cannot fit all of its classes into the 50 training examples.
| Dataset | Train Size | Test Size | |
|--------------------------------|------------|-----------|---|
| Ade Corpus V2 | 50 | 5000 | |
| Banking 77 | 50 | 5000 | |
| NeurIPS Impact Statement Risks | 50 | 150 | |
| One Stop English | 50 | 516 | |
| Overruling | 50 | 2350 | |
| Semiconductor Org Types | 50 | 449 | |
| Systematic Review Inclusion | 50 | 2243 | |
| TAI Safety Research | 50 | 1639 | |
| Terms Of Service | 50 | 5000 | |
| Tweet Eval Hate | 50 | 2966 | |
| Twitter Complaints | 50 | 3399 | |
| **Total** | **550** | **28712** | |
## Dataset Creation
### Curation Rationale
Generally speaking, the rationale behind RAFT was to create a benchmark for evaluating NLP models that didn't consist of contrived or artificial data sources, for which the tasks weren't originally assembled for the purpose of testing NLP models. However, each individual dataset in RAFT was collected independently. For the majority of datasets, we only collected them second-hand from existing curated sources. The datasets that we curated are:
* NeurIPS impact statement risks
* Semiconductor org types
* TAI Safety Research
Each of these three datasets was sourced from our existing collaborators at Ought. They had used our service, Elicit, to analyze their dataset in the past, and we contact them to include their dataset and the associated classification task in the benchmark. For all datasets, more information is provided in our paper. For the ones which we did not curate, we provide a link to the dataset. For the ones which we did, we provide a datasheet that elaborates on many of the topics here in greater detail.
For the three datasets that we introduced:
* **NeurIPS impact statement risks** The dataset was created to evaluate the then new requirement for authors to include an "impact statement" in their 2020 NeurIPS papers. Had it been successful? What kind of things did authors mention the most? How long were impact statements on average? Etc.
* **Semiconductor org types** The dataset was originally created to understand better which countries’ organisations have contributed most to semiconductor R\&D over the past 25 years using three main conferences. Moreover, to estimate the share of academic and private sector contributions, the organisations were classified as “university”, “research institute” or “company”.
* **TAI Safety Research** The primary motivations for assembling this database were to: (1) Aid potential donors in assessing organizations focusing on TAI safety by collecting and analyzing their research output. (2) Assemble a comprehensive bibliographic database that can be used as a base for future projects, such as a living review of the field.
**For the following sections, we will only describe the datasets we introduce. All other dataset details, and more details on the ones described here, can be found in our paper.**
### Source Data
#### Initial Data Collection and Normalization
* **NeurIPS impact statement risks** The data was directly observable (raw text scraped) for the most part; although some data was taken from previous datasets (which themselves had taken it from raw text). The data was validated, but only in part, by human reviewers. Cf this link for full details:
* **Semiconductor org types** We used the IEEE API to obtain institutions that contributed papers to semiconductor conferences in the last 25 years. This is a random sample of 500 of them with a corresponding conference paper title. The three conferences were the International Solid-State Circuits Conference (ISSCC), the Symposia on VLSI Technology and Circuits (VLSI) and the International Electron Devices Meeting (IEDM).
* **TAI Safety Research** We asked TAI safety organizations for what their employees had written, emailed some individual authors, and searched Google Scholar. See the LessWrong post for more details: https://www.lesswrong.com/posts/4DegbDJJiMX2b3EKm/tai-safety-bibliographic-database
#### Who are the source language producers?
* **NeurIPS impact statement risks** Language generated from NeurIPS 2020 impact statement authors, generally the authors of submission papers.
* **Semiconductor org types** Language generated from IEEE API. Generally machine-formatted names, and title of academic papers.
* **TAI Safety Research** Language generated by authors of TAI safety research publications.
### Annotations
#### Annotation process
* **NeurIPS impact statement risks** Annotations were entered directly into a Google Spreadsheet with instructions, labeled training examples, and unlabeled testing examples.
* **Semiconductor org types** Annotations were entered directly into a Google Spreadsheet with instructions, labeled training examples, and unlabeled testing examples.
* **TAI Safety Research** N/A
#### Who are the annotators?
* **NeurIPS impact statement risks** Contractors paid by Ought performed the labeling of whether impact statements mention harmful applications. A majority vote was taken from 3 annotators.
* **Semiconductor org types** Contractors paid by Ought performed the labeling of organization types. A majority vote was taken from 3 annotators.
* **TAI Safety Research** The dataset curators annotated the dataset by hand.
### Personal and Sensitive Information
It is worth mentioning that the Tweet Eval Hate, by necessity, contains highly offensive content.
* **NeurIPS impact statement risks** The dataset contains authors' names. These were scraped from publicly available scientific papers submitted to NeurIPS 2020.
* **Semiconductor org types** N/A
* **TAI Safety Research** N/A
## Considerations for Using the Data
### Social Impact of Dataset
* **NeurIPS impact statement risks** N/A
* **Semiconductor org types** N/A
* **TAI Safety Research** N/A
### Discussion of Biases
* **NeurIPS impact statement risks** N/A
* **Semiconductor org types** N/A
* **TAI Safety Research** N/A
### Other Known Limitations
* **NeurIPS impact statement risks** This dataset has limitations that should be taken into consideration when using it. In particular, the method used to collect broader impact statements involved automated downloads, conversions and scraping and was not error-proof. Although care has been taken to identify and correct as many errors as possible, not all texts have been reviewed by a human. This means it is possible some of the broader impact statements contained in the dataset are truncated or otherwise incorrectly extracted from their original article.
* **Semiconductor org types** N/A
* **TAI Safety Research** Don't use it to create a dangerous AI that could bring the end of days.
## Additional Information
### Dataset Curators
The overall RAFT curators are Neel Alex, Eli Lifland, and Andreas Stuhlmüller.
* **NeurIPS impact statement risks** Volunteers working with researchers affiliated to Oxford's Future of Humanity Institute (Carolyn Ashurst, now at The Alan Turing Institute) created the impact statements dataset.
* **Semiconductor org types** The data science unit of Stiftung Neue Verantwortung (Berlin).
* **TAI Safety Research** Angelica Deibel and Jess Riedel. We did not do it on behalf of any entity.
### Licensing Information
RAFT aggregates many other datasets, each of which is provided under its own license. Generally, those licenses permit research and commercial use.
| Dataset | License |
| ----------- | ----------- |
| Ade Corpus V2 | Unlicensed |
| Banking 77 | CC BY 4.0 |
| NeurIPS Impact Statement Risks | MIT License/CC BY 4.0 |
| One Stop English | CC BY-SA 4.0 |
| Overruling | Unlicensed |
| Semiconductor Org Types | CC BY-NC 4.0 |
| Systematic Review Inclusion | CC BY 4.0 |
| TAI Safety Research | CC BY-SA 4.0 |
| Terms Of Service | Unlicensed |
| Tweet Eval Hate | Unlicensed |
| Twitter Complaints | Unlicensed |
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@neel-alex](https://github.com/neel-alex), [@uvafan](https://github.com/uvafan), and [@lewtun](https://github.com/lewtun) for adding this dataset. |
cmu-mlsp/hubert_layer9-librispeech-asr100h | ---
dataset_info:
features:
- name: file
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 24000
- name: text
dtype: string
- name: speaker_id
dtype: int64
- name: chapter_id
dtype: int64
- name: id
dtype: string
- name: audio_codes
sequence: string
splits:
- name: train
num_bytes: 17519233058.625
num_examples: 28539
- name: validation
num_bytes: 938649953.125
num_examples: 2703
- name: test
num_bytes: 941348688.5
num_examples: 2620
download_size: 18862891148
dataset_size: 19399231700.25
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
# Dataset Card for "hubert_layer9-librispeech-asr100h"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ekolasky/NQLongAnswersForCustomLEDForQA | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: input_ids
sequence: int32
- name: start_positions
sequence: int64
- name: end_positions
sequence: int64
- name: global_attention_mask
sequence: int64
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 1000140417
num_examples: 12323
- name: validation
num_bytes: 47307511
num_examples: 588
download_size: 119671635
dataset_size: 1047447928
---
# Dataset Card for "NQLongAnswersForCustomLEDForQA"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/dorothy_granbluefantasy | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of dorothy (Granblue Fantasy)
This is the dataset of dorothy (Granblue Fantasy), containing 14 images and their tags.
The core tags of this character are `maid_headdress, long_hair, bangs, brown_hair, twintails, blue_eyes, blunt_bangs, very_long_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:-------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 14 | 16.09 MiB | [Download](https://huggingface.co/datasets/CyberHarem/dorothy_granbluefantasy/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 14 | 9.33 MiB | [Download](https://huggingface.co/datasets/CyberHarem/dorothy_granbluefantasy/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 23 | 15.76 MiB | [Download](https://huggingface.co/datasets/CyberHarem/dorothy_granbluefantasy/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 14 | 14.74 MiB | [Download](https://huggingface.co/datasets/CyberHarem/dorothy_granbluefantasy/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 23 | 22.37 MiB | [Download](https://huggingface.co/datasets/CyberHarem/dorothy_granbluefantasy/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/dorothy_granbluefantasy',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 14 |  |  |  |  |  | 1girl, solo, looking_at_viewer, dress, holding, maid_apron, frills, full_body, open_mouth, juliet_sleeves, white_background, :d, blush, food, shoes, simple_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | looking_at_viewer | dress | holding | maid_apron | frills | full_body | open_mouth | juliet_sleeves | white_background | :d | blush | food | shoes | simple_background |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------------------|:--------|:----------|:-------------|:---------|:------------|:-------------|:-----------------|:-------------------|:-----|:--------|:-------|:--------|:--------------------|
| 0 | 14 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
SichaoHu/small_dataset_for_testing | ---
license: apache-2.0
---
|
ioclab/grayscale_image_aesthetic_10k | ---
dataset_info:
features:
- name: image
dtype: image
- name: grayscale_image
dtype: image
- name: caption
dtype: string
splits:
- name: train
num_bytes: 2155299692.0
num_examples: 10000
download_size: 2150374908
dataset_size: 2155299692.0
---
# Dataset Card for "grayscale_image_aesthetic_10k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
RaviSheel04/Psychology-Data2 | ---
license: mit
---
|
irds/codesearchnet_challenge | ---
pretty_name: '`codesearchnet/challenge`'
viewer: false
source_datasets: ['irds/codesearchnet']
task_categories:
- text-retrieval
---
# Dataset Card for `codesearchnet/challenge`
The `codesearchnet/challenge` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/codesearchnet#codesearchnet/challenge).
# Data
This dataset provides:
- `queries` (i.e., topics); count=99
- `qrels`: (relevance assessments); count=4,006
- For `docs`, use [`irds/codesearchnet`](https://huggingface.co/datasets/irds/codesearchnet)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/codesearchnet_challenge', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/codesearchnet_challenge', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'note': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Husain2019CodeSearchNet,
title={CodeSearchNet Challenge: Evaluating the State of Semantic Code Search},
author={Hamel Husain and Ho-Hsiang Wu and Tiferet Gazit and Miltiadis Allamanis and Marc Brockschmidt},
journal={ArXiv},
year={2019}
}
```
|
VishalMysore/cookGPT | ---
license: apache-2.0
---
This is sample dataset for cookGPT , it has around 6000 Indian Food receipe in guanaco format trading data
"### Human: give me recipe for Masala Karela Recipe ### Assistant: To begin making the Masala Karela Recipe,de-seed the karela and slice.
Ingredients 6 Karela (Bitter Gourd/ Pavakkai) - deseeded,Salt - to taste,1 Onion - thinly sliced,3 tablespoon Gram flour (besan),2 teaspoo...
Cook Time: 30
Cuisine: Indian
Diet: Diabetic Friendly" |
speed1/chato | ---
license: openrail
---
|
benayas/massive_chatgpt_10pct_v2 | ---
dataset_info:
features:
- name: text
dtype: string
- name: category
dtype: string
splits:
- name: train
num_bytes: 781269
num_examples: 11514
download_size: 272041
dataset_size: 781269
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
paiyun-huang/autotrain-data-analytics-intent-reasoning | ---
language:
- zh
task_categories:
- text-classification
---
# AutoTrain Dataset for project: analytics-intent-reasoning
## Dataset Description
This dataset has been automatically processed by AutoTrain for project analytics-intent-reasoning.
### Languages
The BCP-47 code for the dataset's language is zh.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "\u9500\u552e\u91d1\u989d\u7684\u540c\u6bd4",
"target": 1
},
{
"text": "\u676d\u5dde\u54ea\u4e2a\u533a\u7684\u9500\u552e\u91d1\u989d\u6700\u9ad8",
"target": 1
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "ClassLabel(names=['\u62a5\u8868\u6784\u5efa', '\u67e5\u8be2\u7c7b', '\u67e5\u8be2\u7c7b\u67e5\u8be2\u7c7b'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 72 |
| valid | 20 |
|
HyperionHF/winogenerated | ---
license: cc-by-4.0
---
|
zhangcen456/subject | ---
license: mit
---
|
Pipper/Solcoder_QA | ---
dataset_info:
features:
- name: 'Unnamed: 0'
dtype: int64
- name: comments
dtype: string
- name: code_string
dtype: string
- name: code
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splits:
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- name: valid
num_bytes: 285421368
num_examples: 295592
download_size: 1224253169
dataset_size: 2823388493
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: valid
path: data/valid-*
---
|
Multimodal-Fatima/LLM_Description_Vocab_opt_Multimodal_Fatima_opt_175b_downstream_tasks | ---
dataset_info:
features:
- name: vocab
dtype: string
- name: descriptions
sequence: string
splits:
- name: test
num_bytes: 696475
num_examples: 3426
download_size: 381428
dataset_size: 696475
---
# Dataset Card for "LLM_Description_Vocab_opt_Multimodal_Fatima_opt_175b_downstream_tasks"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ChuGyouk/openorca_cot_filtered | ---
dataset_info:
features:
- name: id
dtype: string
- name: system_prompt
dtype: string
- name: question
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 135167104.42415047
num_examples: 127540
download_size: 58014350
dataset_size: 135167104.42415047
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
killah-t-cell/multinose_train_controlnet_dataset | ---
dataset_info:
features:
- name: image
dtype: image
- name: conditioning_image
dtype: image
- name: caption
dtype: string
splits:
- name: train
num_bytes: 2326065485.613
num_examples: 44263
download_size: 2126832094
dataset_size: 2326065485.613
---
# Dataset Card for "multinose_train_controlnet_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
tydymy/150bp_human_vs_microbial_dna | ---
dataset_info:
features:
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dtype: string
- name: asm_name
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dtype: int64
- name: autotrain_text
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- name: autotrain_label
dtype:
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names:
'0': 0
'1': 1
splits:
- name: train
num_bytes: 70411052
num_examples: 100000
- name: validation
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num_examples: 5000
download_size: 15423840
dataset_size: 73939997
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
# Dataset Card for "autotrain-data-human_dna_classify_150bp"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Norod78/futurama-blip2-captions-512 | ---
language: en
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 233975670.0
num_examples: 834
download_size: 233996558
dataset_size: 233975670.0
---
# Dataset Card for "futurama-blip-captions-512"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
kaleemWaheed/twitter_dataset_1713120082 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
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splits:
- name: train
num_bytes: 8475
num_examples: 20
download_size: 8898
dataset_size: 8475
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Polo123/Open_Orca_Shorter_dedupe_v2 | ---
dataset_info:
features:
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dtype: string
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num_examples: 24980
download_size: 29642624
dataset_size: 53089835.1272
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
TheHolyPacman/test_dataset | ---
dataset_info:
features:
- name: file_name
dtype: string
- name: accent
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struct:
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sequence: float32
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splits:
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download_size: 774698090
dataset_size: 772347014
configs:
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data_files:
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path: data/train-*
---
|
reichenbach/drug_combi_instruct_test | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: doc_id
dtype: string
- name: sentence
dtype: string
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list:
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sequence: int64
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- name: instruction
dtype: string
splits:
- name: train
num_bytes: 1230393
num_examples: 272
download_size: 633198
dataset_size: 1230393
---
# Dataset Card for "drug_combi_instruct_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
swaroopajit/next-dataset-refined-batch-11000 | ---
dataset_info:
features:
- name: caption
dtype: string
- name: image
dtype: image
splits:
- name: train
num_bytes: 315028784.0
num_examples: 1000
download_size: 287078371
dataset_size: 315028784.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "next-dataset-refined-batch-11000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
zalladin/template | ---
license: unknown
---
|
GETALP/FLUE_VSD | ---
license: gpl-3.0
multilinguality:
- monolingual
language:
- fr
task_categories:
- other
task_ids:
- word-sense-disambiguation
dataset_info:
features:
- name: document_id
dtype: string
- name: sentence_id
dtype: string
- name: surface_forms
sequence: string
- name: fine_pos
sequence: string
- name: lemmas
sequence: string
- name: pos
sequence: string
- name: instance_surface_forms
sequence: string
- name: instance_fine_pos
sequence: string
- name: instance_lemmas
sequence: string
- name: instance_pos
sequence: string
splits:
- name: FSE
num_bytes: 2781427
num_examples: 3121
- name: wiki_FSE
num_bytes: 43227879
num_examples: 58508
download_size: 0
dataset_size: 46009306
---
# FrenchSemEval
## Dataset Description
- **Homepage:**
- **Repository:**
- **https://aclanthology.org/W19-0422.pdf**
- **Leaderboard:**
- **vincent.segonne@univ-grenoble-alpes.fr**
### Dataset Summary
This dataset correspond to the FrenchSemEval, in which verb occurences where manually annotated with Wiktionary senses.
### Supported Tasks and Leaderboards
Verb Sense Disambiguation for French verbs.
### Language
French
## Dataset Structure
### Data Instances
Each instance of the dataset has the following fields and these following types of field.
```json
{
"document_id": "d001",
"sentence_id": "d001.s001",
"surface_forms": ['Il', 'rend', 'hommage', 'au', 'roi', 'de', 'France', 'et', 'des', 'négociations', 'au', 'traité', 'du', 'Goulet', ',', 'formalisant', 'la', 'paix', 'entre', 'les', 'deux', 'pays', '.'],
"fine_pos": ['CLS', 'V', 'NC', 'P+D', 'NC', 'P', 'NPP', 'CC', 'DET', 'NC', 'P+D', 'NC', 'P+D', 'NPP', 'PONCT', 'VPR', 'DET', 'NC', 'P', 'DET', 'ADJ', 'NC', 'PONCT'],
"lemmas": ['il', 'rendre', 'hommage', 'à', 'roi', 'de', 'France', 'et', 'un', 'négociation', 'à', 'traité', 'de', 'Goulet', ',', 'formaliser', 'le', 'paix', 'entre', 'le', 'deux', 'pays', '.'],
"pos": ['CL', 'V', 'N', 'P+D', 'N', 'P', 'N', 'C', 'D', 'N', 'P+D', 'N', 'P+D', 'N', 'PONCT', 'V', 'D', 'N', 'P', 'D', 'A', 'N', 'PONCT'],
"instance_surface_forms":['aboutissent'],
"instance_fine_pos":['V'],
"instance_lemmas":['aboutir'],
"instance_pos":['V']
}
```
### Data Fields
Each sentence has the following fields: **document_id**, **sentence_id**, **surface_forms**, **fine_pos**, **lemmas**, **pos**, **instance_surface_forms**, **instance_fine_pos**, **instance_lemmas**, **instance_pos**.
### Data Splits
No splits provided.
## Dataset Creation
### Source Data
#### Initial Data Collection and Normalization
To build the FrenchSemEval dataset, the authors focused on annotating moderately frequent and moderately ambiguous verbs by selecting verbs appearing between 50 and 1000 times into the French Wikipedia (2016-12-12 fr dump). For those verbs, the authors extracted 50 occurences with other annotations thanks to the French TreeBank [Abeillé and Barrier, 2004](http://ftb.linguist.univ-paris-diderot.fr/index.php?langue=en) and the Sequoia Treebank [Candito and Seddah, 2012](https://www.rocq.inria.fr/alpage-wiki/tiki-index.php?page=CorpusSequoia).
### Annotations
#### Annotation process
To annotate FrenchSemEval, the annotators used [WebAnno](https://webanno.github.io/webanno/) an open-source adaptable annotation tool. Sentences have been pre-processed into CoNLL format and then annotated into WebAnno. The annotators where asked to only annotate marked occurences using the sense inventory from Wiktionnary.
#### Who are the annotators?
The annotation has been performed by 3 French students, with no prior experience in dataset annotation.
### Dataset statistics
|Type|#|
|---|---|
|Number of sentences|3121|
| Number of annoatated verb tokens | 3199 |
| Number of annotated verb types | 66 |
| Mean number of annotations per verb type | 48.47 |
| Mean number of senses per verb type | 3.83 |
### Licensing Information
```
GNU Lesser General Public License
```
### Citation Information
```bibtex
@inproceedings{segonne-etal-2019-using,
title = "Using {W}iktionary as a resource for {WSD} : the case of {F}rench verbs",
author = "Segonne, Vincent and
Candito, Marie and
Crabb{\'e}, Beno{\^\i}t",
booktitle = "Proceedings of the 13th International Conference on Computational Semantics - Long Papers",
month = may,
year = "2019",
address = "Gothenburg, Sweden",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-0422",
doi = "10.18653/v1/W19-0422",
pages = "259--270",
abstract = "As opposed to word sense induction, word sense disambiguation (WSD) has the advantage of us-ing interpretable senses, but requires annotated data, which are quite rare for most languages except English (Miller et al. 1993; Fellbaum, 1998). In this paper, we investigate which strategy to adopt to achieve WSD for languages lacking data that was annotated specifically for the task, focusing on the particular case of verb disambiguation in French. We first study the usability of Eurosense (Bovi et al. 2017) , a multilingual corpus extracted from Europarl (Kohen, 2005) and automatically annotated with BabelNet (Navigli and Ponzetto, 2010) senses. Such a resource opened up the way to supervised and semi-supervised WSD for resourceless languages like French. While this perspective looked promising, our evaluation on French verbs was inconclusive and showed the annotated senses{'} quality was not sufficient for supervised WSD on French verbs. Instead, we propose to use Wiktionary, a collaboratively edited, multilingual online dictionary, as a resource for WSD. Wiktionary provides both sense inventory and manually sense tagged examples which can be used to train supervised and semi-supervised WSD systems. Yet, because senses{'} distribution differ in lexicographic examples found in Wiktionary with respect to natural text, we then focus on studying the impact on WSD of the training data size and senses{'} distribution. Using state-of-the art semi-supervised systems, we report experiments of Wiktionary-based WSD for French verbs, evaluated on FrenchSemEval (FSE), a new dataset of French verbs manually annotated with wiktionary senses.",
}
```
### Contributions
* vincent.segonne@univ-grenoble-alpes.fr
* marie.candito@linguist.univ-paris-diderot.fr
* benoit.crabbe@linguist.univ-paris-diderot.fr |
nielsr/datacomp-small-with-embeddings-ca-filtered | ---
dataset_info:
features:
- name: uid
dtype: string
- name: url
dtype: string
- name: text
dtype: string
- name: original_width
dtype: int64
- name: original_height
dtype: int64
- name: clip_b32_similarity_score
dtype: float32
- name: clip_l14_similarity_score
dtype: float32
- name: face_bboxes
sequence:
sequence: float64
- name: sha256
dtype: string
- name: clip_l14_embedding
sequence: float64
splits:
- name: train
num_bytes: 9771986006.919222
num_examples: 1513398
download_size: 2799089442
dataset_size: 9771986006.919222
---
# Dataset Card for "datacomp-small-with-embeddings-ca-filtered"
This is the [datacomp-small](https://huggingface.co/datasets/mlfoundations/datacomp_small) dataset, with CLIP-large-patch14 image embeddings added, as well as CA filtering:
- minimum caption complexity of 1
- minimum 1 action in the caption |
open-llm-leaderboard/details_Locutusque__Hyperion-2.0-Mistral-7B | ---
pretty_name: Evaluation run of Locutusque/Hyperion-2.0-Mistral-7B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Locutusque/Hyperion-2.0-Mistral-7B](https://huggingface.co/Locutusque/Hyperion-2.0-Mistral-7B)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the 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_Locutusque__Hyperion-2.0-Mistral-7B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-10T05:52:30.143262](https://huggingface.co/datasets/open-llm-leaderboard/details_Locutusque__Hyperion-2.0-Mistral-7B/blob/main/results_2024-03-10T05-52-30.143262.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\": 0.6346692637770753,\n\
\ \"acc_stderr\": 0.03232834743290968,\n \"acc_norm\": 0.6397577306836747,\n\
\ \"acc_norm_stderr\": 0.03297845242054893,\n \"mc1\": 0.27539779681762544,\n\
\ \"mc1_stderr\": 0.01563813566777552,\n \"mc2\": 0.4197149652162468,\n\
\ \"mc2_stderr\": 0.014030449483056798\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5725255972696246,\n \"acc_stderr\": 0.014456862944650649,\n\
\ \"acc_norm\": 0.6109215017064846,\n \"acc_norm_stderr\": 0.014247309976045607\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6323441545508863,\n\
\ \"acc_stderr\": 0.004811815959388832,\n \"acc_norm\": 0.8349930292770364,\n\
\ \"acc_norm_stderr\": 0.0037042823907817183\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252606,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252606\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6592592592592592,\n\
\ \"acc_stderr\": 0.04094376269996793,\n \"acc_norm\": 0.6592592592592592,\n\
\ \"acc_norm_stderr\": 0.04094376269996793\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6710526315789473,\n \"acc_stderr\": 0.03823428969926604,\n\
\ \"acc_norm\": 0.6710526315789473,\n \"acc_norm_stderr\": 0.03823428969926604\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.59,\n\
\ \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\": 0.59,\n \
\ \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.690566037735849,\n \"acc_stderr\": 0.028450154794118637,\n\
\ \"acc_norm\": 0.690566037735849,\n \"acc_norm_stderr\": 0.028450154794118637\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7291666666666666,\n\
\ \"acc_stderr\": 0.03716177437566017,\n \"acc_norm\": 0.7291666666666666,\n\
\ \"acc_norm_stderr\": 0.03716177437566017\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \
\ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n \"\
acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \
\ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6647398843930635,\n\
\ \"acc_stderr\": 0.03599586301247078,\n \"acc_norm\": 0.6647398843930635,\n\
\ \"acc_norm_stderr\": 0.03599586301247078\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.3431372549019608,\n \"acc_stderr\": 0.04724007352383887,\n\
\ \"acc_norm\": 0.3431372549019608,\n \"acc_norm_stderr\": 0.04724007352383887\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.8,\n \"acc_stderr\": 0.04020151261036846,\n \"acc_norm\": 0.8,\n\
\ \"acc_norm_stderr\": 0.04020151261036846\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.574468085106383,\n \"acc_stderr\": 0.03232146916224468,\n\
\ \"acc_norm\": 0.574468085106383,\n \"acc_norm_stderr\": 0.03232146916224468\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.49122807017543857,\n\
\ \"acc_stderr\": 0.047028804320496165,\n \"acc_norm\": 0.49122807017543857,\n\
\ \"acc_norm_stderr\": 0.047028804320496165\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5724137931034483,\n \"acc_stderr\": 0.04122737111370332,\n\
\ \"acc_norm\": 0.5724137931034483,\n \"acc_norm_stderr\": 0.04122737111370332\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.3835978835978836,\n \"acc_stderr\": 0.025043757318520196,\n \"\
acc_norm\": 0.3835978835978836,\n \"acc_norm_stderr\": 0.025043757318520196\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.38095238095238093,\n\
\ \"acc_stderr\": 0.043435254289490965,\n \"acc_norm\": 0.38095238095238093,\n\
\ \"acc_norm_stderr\": 0.043435254289490965\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145632,\n \
\ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145632\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7645161290322581,\n\
\ \"acc_stderr\": 0.024137632429337714,\n \"acc_norm\": 0.7645161290322581,\n\
\ \"acc_norm_stderr\": 0.024137632429337714\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.46798029556650245,\n \"acc_stderr\": 0.03510766597959215,\n\
\ \"acc_norm\": 0.46798029556650245,\n \"acc_norm_stderr\": 0.03510766597959215\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621504,\n \"acc_norm\"\
: 0.68,\n \"acc_norm_stderr\": 0.04688261722621504\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7515151515151515,\n \"acc_stderr\": 0.033744026441394036,\n\
\ \"acc_norm\": 0.7515151515151515,\n \"acc_norm_stderr\": 0.033744026441394036\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7929292929292929,\n \"acc_stderr\": 0.028869778460267045,\n \"\
acc_norm\": 0.7929292929292929,\n \"acc_norm_stderr\": 0.028869778460267045\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8704663212435233,\n \"acc_stderr\": 0.024233532297758733,\n\
\ \"acc_norm\": 0.8704663212435233,\n \"acc_norm_stderr\": 0.024233532297758733\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.658974358974359,\n \"acc_stderr\": 0.02403548967633508,\n \
\ \"acc_norm\": 0.658974358974359,\n \"acc_norm_stderr\": 0.02403548967633508\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3592592592592593,\n \"acc_stderr\": 0.029252905927251976,\n \
\ \"acc_norm\": 0.3592592592592593,\n \"acc_norm_stderr\": 0.029252905927251976\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6512605042016807,\n \"acc_stderr\": 0.030956636328566548,\n\
\ \"acc_norm\": 0.6512605042016807,\n \"acc_norm_stderr\": 0.030956636328566548\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.2980132450331126,\n \"acc_stderr\": 0.037345356767871984,\n \"\
acc_norm\": 0.2980132450331126,\n \"acc_norm_stderr\": 0.037345356767871984\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8275229357798165,\n \"acc_stderr\": 0.01619780795684805,\n \"\
acc_norm\": 0.8275229357798165,\n \"acc_norm_stderr\": 0.01619780795684805\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5092592592592593,\n \"acc_stderr\": 0.034093869469927006,\n \"\
acc_norm\": 0.5092592592592593,\n \"acc_norm_stderr\": 0.034093869469927006\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8137254901960784,\n \"acc_stderr\": 0.027325470966716312,\n \"\
acc_norm\": 0.8137254901960784,\n \"acc_norm_stderr\": 0.027325470966716312\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7679324894514767,\n \"acc_stderr\": 0.02747974455080851,\n \
\ \"acc_norm\": 0.7679324894514767,\n \"acc_norm_stderr\": 0.02747974455080851\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6995515695067265,\n\
\ \"acc_stderr\": 0.03076935200822915,\n \"acc_norm\": 0.6995515695067265,\n\
\ \"acc_norm_stderr\": 0.03076935200822915\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8015267175572519,\n \"acc_stderr\": 0.034981493854624714,\n\
\ \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.034981493854624714\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228732,\n \"\
acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228732\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7592592592592593,\n\
\ \"acc_stderr\": 0.04133119440243838,\n \"acc_norm\": 0.7592592592592593,\n\
\ \"acc_norm_stderr\": 0.04133119440243838\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.8098159509202454,\n \"acc_stderr\": 0.030833491146281245,\n\
\ \"acc_norm\": 0.8098159509202454,\n \"acc_norm_stderr\": 0.030833491146281245\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\": 0.7864077669902912,\n \"acc_stderr\": 0.040580420156460344,\n\
\ \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.040580420156460344\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8675213675213675,\n\
\ \"acc_stderr\": 0.022209309073165612,\n \"acc_norm\": 0.8675213675213675,\n\
\ \"acc_norm_stderr\": 0.022209309073165612\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \
\ \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.04512608598542128\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8122605363984674,\n\
\ \"acc_stderr\": 0.013964393769899136,\n \"acc_norm\": 0.8122605363984674,\n\
\ \"acc_norm_stderr\": 0.013964393769899136\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.708092485549133,\n \"acc_stderr\": 0.02447699407624733,\n\
\ \"acc_norm\": 0.708092485549133,\n \"acc_norm_stderr\": 0.02447699407624733\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2994413407821229,\n\
\ \"acc_stderr\": 0.015318257745976708,\n \"acc_norm\": 0.2994413407821229,\n\
\ \"acc_norm_stderr\": 0.015318257745976708\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7549019607843137,\n \"acc_stderr\": 0.024630048979824775,\n\
\ \"acc_norm\": 0.7549019607843137,\n \"acc_norm_stderr\": 0.024630048979824775\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7041800643086816,\n\
\ \"acc_stderr\": 0.025922371788818774,\n \"acc_norm\": 0.7041800643086816,\n\
\ \"acc_norm_stderr\": 0.025922371788818774\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7191358024691358,\n \"acc_stderr\": 0.025006469755799208,\n\
\ \"acc_norm\": 0.7191358024691358,\n \"acc_norm_stderr\": 0.025006469755799208\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4929078014184397,\n \"acc_stderr\": 0.02982449855912901,\n \
\ \"acc_norm\": 0.4929078014184397,\n \"acc_norm_stderr\": 0.02982449855912901\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4576271186440678,\n\
\ \"acc_stderr\": 0.012724296550980188,\n \"acc_norm\": 0.4576271186440678,\n\
\ \"acc_norm_stderr\": 0.012724296550980188\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6433823529411765,\n \"acc_stderr\": 0.029097209568411952,\n\
\ \"acc_norm\": 0.6433823529411765,\n \"acc_norm_stderr\": 0.029097209568411952\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6503267973856209,\n \"acc_stderr\": 0.01929196189506638,\n \
\ \"acc_norm\": 0.6503267973856209,\n \"acc_norm_stderr\": 0.01929196189506638\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6545454545454545,\n\
\ \"acc_stderr\": 0.04554619617541054,\n \"acc_norm\": 0.6545454545454545,\n\
\ \"acc_norm_stderr\": 0.04554619617541054\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7306122448979592,\n \"acc_stderr\": 0.02840125202902294,\n\
\ \"acc_norm\": 0.7306122448979592,\n \"acc_norm_stderr\": 0.02840125202902294\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8557213930348259,\n\
\ \"acc_stderr\": 0.024845753212306046,\n \"acc_norm\": 0.8557213930348259,\n\
\ \"acc_norm_stderr\": 0.024845753212306046\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.83,\n \"acc_stderr\": 0.0377525168068637,\n \
\ \"acc_norm\": 0.83,\n \"acc_norm_stderr\": 0.0377525168068637\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.536144578313253,\n\
\ \"acc_stderr\": 0.038823108508905954,\n \"acc_norm\": 0.536144578313253,\n\
\ \"acc_norm_stderr\": 0.038823108508905954\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8245614035087719,\n \"acc_stderr\": 0.029170885500727665,\n\
\ \"acc_norm\": 0.8245614035087719,\n \"acc_norm_stderr\": 0.029170885500727665\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.27539779681762544,\n\
\ \"mc1_stderr\": 0.01563813566777552,\n \"mc2\": 0.4197149652162468,\n\
\ \"mc2_stderr\": 0.014030449483056798\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7924230465666929,\n \"acc_stderr\": 0.011398593419386772\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.4177407126611069,\n \
\ \"acc_stderr\": 0.013584820638504832\n }\n}\n```"
repo_url: https://huggingface.co/Locutusque/Hyperion-2.0-Mistral-7B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|arc:challenge|25_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|arc:challenge|25_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|gsm8k|5_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|gsm8k|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hellaswag|10_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|hellaswag|10_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-10T04-55-15.610547.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-10T05-52-30.143262.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-10T04-55-15.610547.parquet'
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path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-10T04-55-15.610547.parquet'
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path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
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path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-10T04-55-15.610547.parquet'
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path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
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path:
- '**/details_harness|hendrycksTest-management|5_2024-03-10T04-55-15.610547.parquet'
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path:
- '**/details_harness|hendrycksTest-management|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
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path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-10T04-55-15.610547.parquet'
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path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
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path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-10T04-55-15.610547.parquet'
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path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
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path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-10T04-55-15.610547.parquet'
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path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-10T04-55-15.610547.parquet'
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path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
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path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-10T04-55-15.610547.parquet'
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path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-10T04-55-15.610547.parquet'
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path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
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path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-10T04-55-15.610547.parquet'
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path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
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path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-10T04-55-15.610547.parquet'
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path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-10T04-55-15.610547.parquet'
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path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
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path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-10T04-55-15.610547.parquet'
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path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
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path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-10T04-55-15.610547.parquet'
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path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-10T04-55-15.610547.parquet'
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path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-10T05-52-30.143262.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- '**/details_harness|winogrande|5_2024-03-10T04-55-15.610547.parquet'
- split: 2024_03_10T05_52_30.143262
path:
- '**/details_harness|winogrande|5_2024-03-10T05-52-30.143262.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-10T05-52-30.143262.parquet'
- config_name: results
data_files:
- split: 2024_03_10T04_55_15.610547
path:
- results_2024-03-10T04-55-15.610547.parquet
- split: 2024_03_10T05_52_30.143262
path:
- results_2024-03-10T05-52-30.143262.parquet
- split: latest
path:
- results_2024-03-10T05-52-30.143262.parquet
---
# Dataset Card for Evaluation run of Locutusque/Hyperion-2.0-Mistral-7B
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Locutusque/Hyperion-2.0-Mistral-7B](https://huggingface.co/Locutusque/Hyperion-2.0-Mistral-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the 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_Locutusque__Hyperion-2.0-Mistral-7B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-10T05:52:30.143262](https://huggingface.co/datasets/open-llm-leaderboard/details_Locutusque__Hyperion-2.0-Mistral-7B/blob/main/results_2024-03-10T05-52-30.143262.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": 0.6346692637770753,
"acc_stderr": 0.03232834743290968,
"acc_norm": 0.6397577306836747,
"acc_norm_stderr": 0.03297845242054893,
"mc1": 0.27539779681762544,
"mc1_stderr": 0.01563813566777552,
"mc2": 0.4197149652162468,
"mc2_stderr": 0.014030449483056798
},
"harness|arc:challenge|25": {
"acc": 0.5725255972696246,
"acc_stderr": 0.014456862944650649,
"acc_norm": 0.6109215017064846,
"acc_norm_stderr": 0.014247309976045607
},
"harness|hellaswag|10": {
"acc": 0.6323441545508863,
"acc_stderr": 0.004811815959388832,
"acc_norm": 0.8349930292770364,
"acc_norm_stderr": 0.0037042823907817183
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.33,
"acc_stderr": 0.04725815626252606,
"acc_norm": 0.33,
"acc_norm_stderr": 0.04725815626252606
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6592592592592592,
"acc_stderr": 0.04094376269996793,
"acc_norm": 0.6592592592592592,
"acc_norm_stderr": 0.04094376269996793
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6710526315789473,
"acc_stderr": 0.03823428969926604,
"acc_norm": 0.6710526315789473,
"acc_norm_stderr": 0.03823428969926604
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.59,
"acc_stderr": 0.04943110704237102,
"acc_norm": 0.59,
"acc_norm_stderr": 0.04943110704237102
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.690566037735849,
"acc_stderr": 0.028450154794118637,
"acc_norm": 0.690566037735849,
"acc_norm_stderr": 0.028450154794118637
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7291666666666666,
"acc_stderr": 0.03716177437566017,
"acc_norm": 0.7291666666666666,
"acc_norm_stderr": 0.03716177437566017
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.5,
"acc_stderr": 0.050251890762960605,
"acc_norm": 0.5,
"acc_norm_stderr": 0.050251890762960605
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.55,
"acc_stderr": 0.05,
"acc_norm": 0.55,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.39,
"acc_stderr": 0.04902071300001975,
"acc_norm": 0.39,
"acc_norm_stderr": 0.04902071300001975
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6647398843930635,
"acc_stderr": 0.03599586301247078,
"acc_norm": 0.6647398843930635,
"acc_norm_stderr": 0.03599586301247078
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.3431372549019608,
"acc_stderr": 0.04724007352383887,
"acc_norm": 0.3431372549019608,
"acc_norm_stderr": 0.04724007352383887
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.8,
"acc_stderr": 0.04020151261036846,
"acc_norm": 0.8,
"acc_norm_stderr": 0.04020151261036846
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.574468085106383,
"acc_stderr": 0.03232146916224468,
"acc_norm": 0.574468085106383,
"acc_norm_stderr": 0.03232146916224468
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.49122807017543857,
"acc_stderr": 0.047028804320496165,
"acc_norm": 0.49122807017543857,
"acc_norm_stderr": 0.047028804320496165
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5724137931034483,
"acc_stderr": 0.04122737111370332,
"acc_norm": 0.5724137931034483,
"acc_norm_stderr": 0.04122737111370332
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.3835978835978836,
"acc_stderr": 0.025043757318520196,
"acc_norm": 0.3835978835978836,
"acc_norm_stderr": 0.025043757318520196
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.38095238095238093,
"acc_stderr": 0.043435254289490965,
"acc_norm": 0.38095238095238093,
"acc_norm_stderr": 0.043435254289490965
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.38,
"acc_stderr": 0.04878317312145632,
"acc_norm": 0.38,
"acc_norm_stderr": 0.04878317312145632
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7645161290322581,
"acc_stderr": 0.024137632429337714,
"acc_norm": 0.7645161290322581,
"acc_norm_stderr": 0.024137632429337714
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.46798029556650245,
"acc_stderr": 0.03510766597959215,
"acc_norm": 0.46798029556650245,
"acc_norm_stderr": 0.03510766597959215
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.68,
"acc_stderr": 0.04688261722621504,
"acc_norm": 0.68,
"acc_norm_stderr": 0.04688261722621504
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7515151515151515,
"acc_stderr": 0.033744026441394036,
"acc_norm": 0.7515151515151515,
"acc_norm_stderr": 0.033744026441394036
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7929292929292929,
"acc_stderr": 0.028869778460267045,
"acc_norm": 0.7929292929292929,
"acc_norm_stderr": 0.028869778460267045
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8704663212435233,
"acc_stderr": 0.024233532297758733,
"acc_norm": 0.8704663212435233,
"acc_norm_stderr": 0.024233532297758733
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.658974358974359,
"acc_stderr": 0.02403548967633508,
"acc_norm": 0.658974358974359,
"acc_norm_stderr": 0.02403548967633508
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.3592592592592593,
"acc_stderr": 0.029252905927251976,
"acc_norm": 0.3592592592592593,
"acc_norm_stderr": 0.029252905927251976
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6512605042016807,
"acc_stderr": 0.030956636328566548,
"acc_norm": 0.6512605042016807,
"acc_norm_stderr": 0.030956636328566548
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.2980132450331126,
"acc_stderr": 0.037345356767871984,
"acc_norm": 0.2980132450331126,
"acc_norm_stderr": 0.037345356767871984
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8275229357798165,
"acc_stderr": 0.01619780795684805,
"acc_norm": 0.8275229357798165,
"acc_norm_stderr": 0.01619780795684805
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5092592592592593,
"acc_stderr": 0.034093869469927006,
"acc_norm": 0.5092592592592593,
"acc_norm_stderr": 0.034093869469927006
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8137254901960784,
"acc_stderr": 0.027325470966716312,
"acc_norm": 0.8137254901960784,
"acc_norm_stderr": 0.027325470966716312
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7679324894514767,
"acc_stderr": 0.02747974455080851,
"acc_norm": 0.7679324894514767,
"acc_norm_stderr": 0.02747974455080851
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6995515695067265,
"acc_stderr": 0.03076935200822915,
"acc_norm": 0.6995515695067265,
"acc_norm_stderr": 0.03076935200822915
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.8015267175572519,
"acc_stderr": 0.034981493854624714,
"acc_norm": 0.8015267175572519,
"acc_norm_stderr": 0.034981493854624714
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7768595041322314,
"acc_stderr": 0.03800754475228732,
"acc_norm": 0.7768595041322314,
"acc_norm_stderr": 0.03800754475228732
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7592592592592593,
"acc_stderr": 0.04133119440243838,
"acc_norm": 0.7592592592592593,
"acc_norm_stderr": 0.04133119440243838
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.8098159509202454,
"acc_stderr": 0.030833491146281245,
"acc_norm": 0.8098159509202454,
"acc_norm_stderr": 0.030833491146281245
},
"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": 0.7864077669902912,
"acc_stderr": 0.040580420156460344,
"acc_norm": 0.7864077669902912,
"acc_norm_stderr": 0.040580420156460344
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8675213675213675,
"acc_stderr": 0.022209309073165612,
"acc_norm": 0.8675213675213675,
"acc_norm_stderr": 0.022209309073165612
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.72,
"acc_stderr": 0.04512608598542128,
"acc_norm": 0.72,
"acc_norm_stderr": 0.04512608598542128
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8122605363984674,
"acc_stderr": 0.013964393769899136,
"acc_norm": 0.8122605363984674,
"acc_norm_stderr": 0.013964393769899136
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.708092485549133,
"acc_stderr": 0.02447699407624733,
"acc_norm": 0.708092485549133,
"acc_norm_stderr": 0.02447699407624733
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.2994413407821229,
"acc_stderr": 0.015318257745976708,
"acc_norm": 0.2994413407821229,
"acc_norm_stderr": 0.015318257745976708
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7549019607843137,
"acc_stderr": 0.024630048979824775,
"acc_norm": 0.7549019607843137,
"acc_norm_stderr": 0.024630048979824775
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7041800643086816,
"acc_stderr": 0.025922371788818774,
"acc_norm": 0.7041800643086816,
"acc_norm_stderr": 0.025922371788818774
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7191358024691358,
"acc_stderr": 0.025006469755799208,
"acc_norm": 0.7191358024691358,
"acc_norm_stderr": 0.025006469755799208
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.4929078014184397,
"acc_stderr": 0.02982449855912901,
"acc_norm": 0.4929078014184397,
"acc_norm_stderr": 0.02982449855912901
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4576271186440678,
"acc_stderr": 0.012724296550980188,
"acc_norm": 0.4576271186440678,
"acc_norm_stderr": 0.012724296550980188
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6433823529411765,
"acc_stderr": 0.029097209568411952,
"acc_norm": 0.6433823529411765,
"acc_norm_stderr": 0.029097209568411952
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6503267973856209,
"acc_stderr": 0.01929196189506638,
"acc_norm": 0.6503267973856209,
"acc_norm_stderr": 0.01929196189506638
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6545454545454545,
"acc_stderr": 0.04554619617541054,
"acc_norm": 0.6545454545454545,
"acc_norm_stderr": 0.04554619617541054
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7306122448979592,
"acc_stderr": 0.02840125202902294,
"acc_norm": 0.7306122448979592,
"acc_norm_stderr": 0.02840125202902294
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8557213930348259,
"acc_stderr": 0.024845753212306046,
"acc_norm": 0.8557213930348259,
"acc_norm_stderr": 0.024845753212306046
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.83,
"acc_stderr": 0.0377525168068637,
"acc_norm": 0.83,
"acc_norm_stderr": 0.0377525168068637
},
"harness|hendrycksTest-virology|5": {
"acc": 0.536144578313253,
"acc_stderr": 0.038823108508905954,
"acc_norm": 0.536144578313253,
"acc_norm_stderr": 0.038823108508905954
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8245614035087719,
"acc_stderr": 0.029170885500727665,
"acc_norm": 0.8245614035087719,
"acc_norm_stderr": 0.029170885500727665
},
"harness|truthfulqa:mc|0": {
"mc1": 0.27539779681762544,
"mc1_stderr": 0.01563813566777552,
"mc2": 0.4197149652162468,
"mc2_stderr": 0.014030449483056798
},
"harness|winogrande|5": {
"acc": 0.7924230465666929,
"acc_stderr": 0.011398593419386772
},
"harness|gsm8k|5": {
"acc": 0.4177407126611069,
"acc_stderr": 0.013584820638504832
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
CVasNLPExperiments/FGVC_Aircraft_test_google_flan_t5_xxl_mode_A_ns_100 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: prompt
dtype: string
- name: true_label
dtype: string
- name: prediction
dtype: string
splits:
- name: fewshot_0_clip_tags_ViT_L_14_Attributes_ViT_L_14_text_davinci_003_full_clip_tags_ViT_L_14_simple_specific_rices
num_bytes: 42164
num_examples: 100
download_size: 0
dataset_size: 42164
---
# Dataset Card for "FGVC_Aircraft_test_google_flan_t5_xxl_mode_A_ns_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ping98k/dolly-rag-instruct-th | ---
language:
- en
- th
license: apache-2.0
--- |
open-llm-leaderboard/details_abacusai__Smaug-Mixtral-v0.1 | ---
pretty_name: Evaluation run of abacusai/Smaug-Mixtral-v0.1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [abacusai/Smaug-Mixtral-v0.1](https://huggingface.co/abacusai/Smaug-Mixtral-v0.1)\
\ 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 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the 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_abacusai__Smaug-Mixtral-v0.1\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-02T07:05:17.382753](https://huggingface.co/datasets/open-llm-leaderboard/details_abacusai__Smaug-Mixtral-v0.1/blob/main/results_2024-03-02T07-05-17.382753.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\": 0.7030495843228143,\n\
\ \"acc_stderr\": 0.030533873119951097,\n \"acc_norm\": 0.7044128822322689,\n\
\ \"acc_norm_stderr\": 0.031144412436827272,\n \"mc1\": 0.5030599755201959,\n\
\ \"mc1_stderr\": 0.01750317326096063,\n \"mc2\": 0.6694851107267487,\n\
\ \"mc2_stderr\": 0.014706509050408262\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.7150170648464164,\n \"acc_stderr\": 0.013191348179838793,\n\
\ \"acc_norm\": 0.7465870307167235,\n \"acc_norm_stderr\": 0.012710896778378607\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.698864767974507,\n\
\ \"acc_stderr\": 0.004578137949298177,\n \"acc_norm\": 0.8772156940848437,\n\
\ \"acc_norm_stderr\": 0.003275187310785844\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6666666666666666,\n\
\ \"acc_stderr\": 0.04072314811876837,\n \"acc_norm\": 0.6666666666666666,\n\
\ \"acc_norm_stderr\": 0.04072314811876837\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\": 0.7849056603773585,\n \"acc_stderr\": 0.025288394502891366,\n\
\ \"acc_norm\": 0.7849056603773585,\n \"acc_norm_stderr\": 0.025288394502891366\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8194444444444444,\n\
\ \"acc_stderr\": 0.032166008088022675,\n \"acc_norm\": 0.8194444444444444,\n\
\ \"acc_norm_stderr\": 0.032166008088022675\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.56,\n \"acc_stderr\": 0.049888765156985884,\n \
\ \"acc_norm\": 0.56,\n \"acc_norm_stderr\": 0.049888765156985884\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.62,\n \"acc_stderr\": 0.04878317312145633,\n \"acc_norm\"\
: 0.62,\n \"acc_norm_stderr\": 0.04878317312145633\n },\n \"harness|hendrycksTest-college_mathematics|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_medicine|5\": {\n \"acc\": 0.7225433526011561,\n\
\ \"acc_stderr\": 0.03414014007044037,\n \"acc_norm\": 0.7225433526011561,\n\
\ \"acc_norm_stderr\": 0.03414014007044037\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.5,\n \"acc_stderr\": 0.04975185951049946,\n \
\ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.04975185951049946\n },\n\
\ \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.81,\n\
\ \"acc_stderr\": 0.03942772444036624,\n \"acc_norm\": 0.81,\n \
\ \"acc_norm_stderr\": 0.03942772444036624\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.6680851063829787,\n \"acc_stderr\": 0.030783736757745647,\n\
\ \"acc_norm\": 0.6680851063829787,\n \"acc_norm_stderr\": 0.030783736757745647\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5964912280701754,\n\
\ \"acc_stderr\": 0.04615186962583707,\n \"acc_norm\": 0.5964912280701754,\n\
\ \"acc_norm_stderr\": 0.04615186962583707\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.6344827586206897,\n \"acc_stderr\": 0.04013124195424385,\n\
\ \"acc_norm\": 0.6344827586206897,\n \"acc_norm_stderr\": 0.04013124195424385\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.46296296296296297,\n \"acc_stderr\": 0.025680564640056882,\n \"\
acc_norm\": 0.46296296296296297,\n \"acc_norm_stderr\": 0.025680564640056882\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5079365079365079,\n\
\ \"acc_stderr\": 0.044715725362943486,\n \"acc_norm\": 0.5079365079365079,\n\
\ \"acc_norm_stderr\": 0.044715725362943486\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\": 0.8483870967741935,\n\
\ \"acc_stderr\": 0.020402616654416762,\n \"acc_norm\": 0.8483870967741935,\n\
\ \"acc_norm_stderr\": 0.020402616654416762\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5812807881773399,\n \"acc_stderr\": 0.03471192860518468,\n\
\ \"acc_norm\": 0.5812807881773399,\n \"acc_norm_stderr\": 0.03471192860518468\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768077,\n \"acc_norm\"\
: 0.74,\n \"acc_norm_stderr\": 0.04408440022768077\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7878787878787878,\n \"acc_stderr\": 0.031922715695483,\n\
\ \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.031922715695483\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8535353535353535,\n \"acc_stderr\": 0.025190921114603918,\n \"\
acc_norm\": 0.8535353535353535,\n \"acc_norm_stderr\": 0.025190921114603918\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9533678756476683,\n \"acc_stderr\": 0.015216761819262572,\n\
\ \"acc_norm\": 0.9533678756476683,\n \"acc_norm_stderr\": 0.015216761819262572\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.7102564102564103,\n \"acc_stderr\": 0.023000628243687968,\n\
\ \"acc_norm\": 0.7102564102564103,\n \"acc_norm_stderr\": 0.023000628243687968\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3925925925925926,\n \"acc_stderr\": 0.02977384701253297,\n \
\ \"acc_norm\": 0.3925925925925926,\n \"acc_norm_stderr\": 0.02977384701253297\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.7815126050420168,\n \"acc_stderr\": 0.026841514322958938,\n\
\ \"acc_norm\": 0.7815126050420168,\n \"acc_norm_stderr\": 0.026841514322958938\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.4304635761589404,\n \"acc_stderr\": 0.040428099613956346,\n \"\
acc_norm\": 0.4304635761589404,\n \"acc_norm_stderr\": 0.040428099613956346\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8788990825688073,\n \"acc_stderr\": 0.013987618292389713,\n \"\
acc_norm\": 0.8788990825688073,\n \"acc_norm_stderr\": 0.013987618292389713\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5555555555555556,\n \"acc_stderr\": 0.03388857118502325,\n \"\
acc_norm\": 0.5555555555555556,\n \"acc_norm_stderr\": 0.03388857118502325\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8578431372549019,\n \"acc_stderr\": 0.024509803921568603,\n \"\
acc_norm\": 0.8578431372549019,\n \"acc_norm_stderr\": 0.024509803921568603\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8607594936708861,\n \"acc_stderr\": 0.022535526352692705,\n \
\ \"acc_norm\": 0.8607594936708861,\n \"acc_norm_stderr\": 0.022535526352692705\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7174887892376681,\n\
\ \"acc_stderr\": 0.030216831011508762,\n \"acc_norm\": 0.7174887892376681,\n\
\ \"acc_norm_stderr\": 0.030216831011508762\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\":\
\ 0.8760330578512396,\n \"acc_stderr\": 0.030083098716035206,\n \"\
acc_norm\": 0.8760330578512396,\n \"acc_norm_stderr\": 0.030083098716035206\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8333333333333334,\n\
\ \"acc_stderr\": 0.036028141763926456,\n \"acc_norm\": 0.8333333333333334,\n\
\ \"acc_norm_stderr\": 0.036028141763926456\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7975460122699386,\n \"acc_stderr\": 0.031570650789119,\n\
\ \"acc_norm\": 0.7975460122699386,\n \"acc_norm_stderr\": 0.031570650789119\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5,\n\
\ \"acc_stderr\": 0.04745789978762494,\n \"acc_norm\": 0.5,\n \
\ \"acc_norm_stderr\": 0.04745789978762494\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8155339805825242,\n \"acc_stderr\": 0.03840423627288276,\n\
\ \"acc_norm\": 0.8155339805825242,\n \"acc_norm_stderr\": 0.03840423627288276\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9017094017094017,\n\
\ \"acc_stderr\": 0.019503444900757567,\n \"acc_norm\": 0.9017094017094017,\n\
\ \"acc_norm_stderr\": 0.019503444900757567\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \
\ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8697318007662835,\n\
\ \"acc_stderr\": 0.012036729568216054,\n \"acc_norm\": 0.8697318007662835,\n\
\ \"acc_norm_stderr\": 0.012036729568216054\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7890173410404624,\n \"acc_stderr\": 0.021966309947043117,\n\
\ \"acc_norm\": 0.7890173410404624,\n \"acc_norm_stderr\": 0.021966309947043117\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4558659217877095,\n\
\ \"acc_stderr\": 0.01665722942458631,\n \"acc_norm\": 0.4558659217877095,\n\
\ \"acc_norm_stderr\": 0.01665722942458631\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.8071895424836601,\n \"acc_stderr\": 0.022589318888176703,\n\
\ \"acc_norm\": 0.8071895424836601,\n \"acc_norm_stderr\": 0.022589318888176703\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7781350482315113,\n\
\ \"acc_stderr\": 0.023598858292863047,\n \"acc_norm\": 0.7781350482315113,\n\
\ \"acc_norm_stderr\": 0.023598858292863047\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.8055555555555556,\n \"acc_stderr\": 0.0220213661002202,\n\
\ \"acc_norm\": 0.8055555555555556,\n \"acc_norm_stderr\": 0.0220213661002202\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.549645390070922,\n \"acc_stderr\": 0.02968010556502904,\n \
\ \"acc_norm\": 0.549645390070922,\n \"acc_norm_stderr\": 0.02968010556502904\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5319426336375489,\n\
\ \"acc_stderr\": 0.012744149704869647,\n \"acc_norm\": 0.5319426336375489,\n\
\ \"acc_norm_stderr\": 0.012744149704869647\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.7794117647058824,\n \"acc_stderr\": 0.02518778666022726,\n\
\ \"acc_norm\": 0.7794117647058824,\n \"acc_norm_stderr\": 0.02518778666022726\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.738562091503268,\n \"acc_stderr\": 0.017776947157528037,\n \
\ \"acc_norm\": 0.738562091503268,\n \"acc_norm_stderr\": 0.017776947157528037\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7181818181818181,\n\
\ \"acc_stderr\": 0.04309118709946458,\n \"acc_norm\": 0.7181818181818181,\n\
\ \"acc_norm_stderr\": 0.04309118709946458\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7959183673469388,\n \"acc_stderr\": 0.0258012834750905,\n\
\ \"acc_norm\": 0.7959183673469388,\n \"acc_norm_stderr\": 0.0258012834750905\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8805970149253731,\n\
\ \"acc_stderr\": 0.02292879327721974,\n \"acc_norm\": 0.8805970149253731,\n\
\ \"acc_norm_stderr\": 0.02292879327721974\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.9,\n \"acc_stderr\": 0.030151134457776348,\n \
\ \"acc_norm\": 0.9,\n \"acc_norm_stderr\": 0.030151134457776348\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5421686746987951,\n\
\ \"acc_stderr\": 0.03878626771002361,\n \"acc_norm\": 0.5421686746987951,\n\
\ \"acc_norm_stderr\": 0.03878626771002361\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8771929824561403,\n \"acc_stderr\": 0.02517298435015577,\n\
\ \"acc_norm\": 0.8771929824561403,\n \"acc_norm_stderr\": 0.02517298435015577\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5030599755201959,\n\
\ \"mc1_stderr\": 0.01750317326096063,\n \"mc2\": 0.6694851107267487,\n\
\ \"mc2_stderr\": 0.014706509050408262\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8161010260457774,\n \"acc_stderr\": 0.010887916013305887\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7194844579226687,\n \
\ \"acc_stderr\": 0.012374608490929547\n }\n}\n```"
repo_url: https://huggingface.co/abacusai/Smaug-Mixtral-v0.1
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|arc:challenge|25_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|arc:challenge|25_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|gsm8k|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|gsm8k|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hellaswag|10_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hellaswag|10_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-20T07-34-45.297724.parquet'
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- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-20T07-34-45.297724.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-02T07-05-17.382753.parquet'
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- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-02T07-05-17.382753.parquet'
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- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-02T07-05-17.382753.parquet'
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- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-02T07-05-17.382753.parquet'
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- '**/details_harness|hendrycksTest-sociology|5_2024-03-02T07-05-17.382753.parquet'
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- '**/details_harness|hendrycksTest-virology|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-02T07-05-17.382753.parquet'
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- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-02T07-05-17.382753.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
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path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-02T07-05-17.382753.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- '**/details_harness|winogrande|5_2024-02-20T07-34-45.297724.parquet'
- split: 2024_03_02T07_05_17.382753
path:
- '**/details_harness|winogrande|5_2024-03-02T07-05-17.382753.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-02T07-05-17.382753.parquet'
- config_name: results
data_files:
- split: 2024_02_20T07_34_45.297724
path:
- results_2024-02-20T07-34-45.297724.parquet
- split: 2024_03_02T07_05_17.382753
path:
- results_2024-03-02T07-05-17.382753.parquet
- split: latest
path:
- results_2024-03-02T07-05-17.382753.parquet
---
# Dataset Card for Evaluation run of abacusai/Smaug-Mixtral-v0.1
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [abacusai/Smaug-Mixtral-v0.1](https://huggingface.co/abacusai/Smaug-Mixtral-v0.1) 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 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the 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_abacusai__Smaug-Mixtral-v0.1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-02T07:05:17.382753](https://huggingface.co/datasets/open-llm-leaderboard/details_abacusai__Smaug-Mixtral-v0.1/blob/main/results_2024-03-02T07-05-17.382753.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": 0.7030495843228143,
"acc_stderr": 0.030533873119951097,
"acc_norm": 0.7044128822322689,
"acc_norm_stderr": 0.031144412436827272,
"mc1": 0.5030599755201959,
"mc1_stderr": 0.01750317326096063,
"mc2": 0.6694851107267487,
"mc2_stderr": 0.014706509050408262
},
"harness|arc:challenge|25": {
"acc": 0.7150170648464164,
"acc_stderr": 0.013191348179838793,
"acc_norm": 0.7465870307167235,
"acc_norm_stderr": 0.012710896778378607
},
"harness|hellaswag|10": {
"acc": 0.698864767974507,
"acc_stderr": 0.004578137949298177,
"acc_norm": 0.8772156940848437,
"acc_norm_stderr": 0.003275187310785844
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.32,
"acc_stderr": 0.04688261722621504,
"acc_norm": 0.32,
"acc_norm_stderr": 0.04688261722621504
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6666666666666666,
"acc_stderr": 0.04072314811876837,
"acc_norm": 0.6666666666666666,
"acc_norm_stderr": 0.04072314811876837
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7960526315789473,
"acc_stderr": 0.032790004063100495,
"acc_norm": 0.7960526315789473,
"acc_norm_stderr": 0.032790004063100495
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.66,
"acc_stderr": 0.04760952285695238,
"acc_norm": 0.66,
"acc_norm_stderr": 0.04760952285695238
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7849056603773585,
"acc_stderr": 0.025288394502891366,
"acc_norm": 0.7849056603773585,
"acc_norm_stderr": 0.025288394502891366
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.8194444444444444,
"acc_stderr": 0.032166008088022675,
"acc_norm": 0.8194444444444444,
"acc_norm_stderr": 0.032166008088022675
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.56,
"acc_stderr": 0.049888765156985884,
"acc_norm": 0.56,
"acc_norm_stderr": 0.049888765156985884
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.62,
"acc_stderr": 0.04878317312145633,
"acc_norm": 0.62,
"acc_norm_stderr": 0.04878317312145633
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.41,
"acc_stderr": 0.049431107042371025,
"acc_norm": 0.41,
"acc_norm_stderr": 0.049431107042371025
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.7225433526011561,
"acc_stderr": 0.03414014007044037,
"acc_norm": 0.7225433526011561,
"acc_norm_stderr": 0.03414014007044037
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.5,
"acc_stderr": 0.04975185951049946,
"acc_norm": 0.5,
"acc_norm_stderr": 0.04975185951049946
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.81,
"acc_stderr": 0.03942772444036624,
"acc_norm": 0.81,
"acc_norm_stderr": 0.03942772444036624
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.6680851063829787,
"acc_stderr": 0.030783736757745647,
"acc_norm": 0.6680851063829787,
"acc_norm_stderr": 0.030783736757745647
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5964912280701754,
"acc_stderr": 0.04615186962583707,
"acc_norm": 0.5964912280701754,
"acc_norm_stderr": 0.04615186962583707
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.6344827586206897,
"acc_stderr": 0.04013124195424385,
"acc_norm": 0.6344827586206897,
"acc_norm_stderr": 0.04013124195424385
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.46296296296296297,
"acc_stderr": 0.025680564640056882,
"acc_norm": 0.46296296296296297,
"acc_norm_stderr": 0.025680564640056882
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.5079365079365079,
"acc_stderr": 0.044715725362943486,
"acc_norm": 0.5079365079365079,
"acc_norm_stderr": 0.044715725362943486
},
"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": 0.8483870967741935,
"acc_stderr": 0.020402616654416762,
"acc_norm": 0.8483870967741935,
"acc_norm_stderr": 0.020402616654416762
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5812807881773399,
"acc_stderr": 0.03471192860518468,
"acc_norm": 0.5812807881773399,
"acc_norm_stderr": 0.03471192860518468
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.74,
"acc_stderr": 0.04408440022768077,
"acc_norm": 0.74,
"acc_norm_stderr": 0.04408440022768077
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7878787878787878,
"acc_stderr": 0.031922715695483,
"acc_norm": 0.7878787878787878,
"acc_norm_stderr": 0.031922715695483
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.8535353535353535,
"acc_stderr": 0.025190921114603918,
"acc_norm": 0.8535353535353535,
"acc_norm_stderr": 0.025190921114603918
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9533678756476683,
"acc_stderr": 0.015216761819262572,
"acc_norm": 0.9533678756476683,
"acc_norm_stderr": 0.015216761819262572
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.7102564102564103,
"acc_stderr": 0.023000628243687968,
"acc_norm": 0.7102564102564103,
"acc_norm_stderr": 0.023000628243687968
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.3925925925925926,
"acc_stderr": 0.02977384701253297,
"acc_norm": 0.3925925925925926,
"acc_norm_stderr": 0.02977384701253297
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.7815126050420168,
"acc_stderr": 0.026841514322958938,
"acc_norm": 0.7815126050420168,
"acc_norm_stderr": 0.026841514322958938
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.4304635761589404,
"acc_stderr": 0.040428099613956346,
"acc_norm": 0.4304635761589404,
"acc_norm_stderr": 0.040428099613956346
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8788990825688073,
"acc_stderr": 0.013987618292389713,
"acc_norm": 0.8788990825688073,
"acc_norm_stderr": 0.013987618292389713
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5555555555555556,
"acc_stderr": 0.03388857118502325,
"acc_norm": 0.5555555555555556,
"acc_norm_stderr": 0.03388857118502325
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8578431372549019,
"acc_stderr": 0.024509803921568603,
"acc_norm": 0.8578431372549019,
"acc_norm_stderr": 0.024509803921568603
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8607594936708861,
"acc_stderr": 0.022535526352692705,
"acc_norm": 0.8607594936708861,
"acc_norm_stderr": 0.022535526352692705
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.7174887892376681,
"acc_stderr": 0.030216831011508762,
"acc_norm": 0.7174887892376681,
"acc_norm_stderr": 0.030216831011508762
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7938931297709924,
"acc_stderr": 0.03547771004159464,
"acc_norm": 0.7938931297709924,
"acc_norm_stderr": 0.03547771004159464
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8760330578512396,
"acc_stderr": 0.030083098716035206,
"acc_norm": 0.8760330578512396,
"acc_norm_stderr": 0.030083098716035206
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.8333333333333334,
"acc_stderr": 0.036028141763926456,
"acc_norm": 0.8333333333333334,
"acc_norm_stderr": 0.036028141763926456
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7975460122699386,
"acc_stderr": 0.031570650789119,
"acc_norm": 0.7975460122699386,
"acc_norm_stderr": 0.031570650789119
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.5,
"acc_stderr": 0.04745789978762494,
"acc_norm": 0.5,
"acc_norm_stderr": 0.04745789978762494
},
"harness|hendrycksTest-management|5": {
"acc": 0.8155339805825242,
"acc_stderr": 0.03840423627288276,
"acc_norm": 0.8155339805825242,
"acc_norm_stderr": 0.03840423627288276
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.9017094017094017,
"acc_stderr": 0.019503444900757567,
"acc_norm": 0.9017094017094017,
"acc_norm_stderr": 0.019503444900757567
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.75,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8697318007662835,
"acc_stderr": 0.012036729568216054,
"acc_norm": 0.8697318007662835,
"acc_norm_stderr": 0.012036729568216054
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7890173410404624,
"acc_stderr": 0.021966309947043117,
"acc_norm": 0.7890173410404624,
"acc_norm_stderr": 0.021966309947043117
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.4558659217877095,
"acc_stderr": 0.01665722942458631,
"acc_norm": 0.4558659217877095,
"acc_norm_stderr": 0.01665722942458631
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.8071895424836601,
"acc_stderr": 0.022589318888176703,
"acc_norm": 0.8071895424836601,
"acc_norm_stderr": 0.022589318888176703
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7781350482315113,
"acc_stderr": 0.023598858292863047,
"acc_norm": 0.7781350482315113,
"acc_norm_stderr": 0.023598858292863047
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.8055555555555556,
"acc_stderr": 0.0220213661002202,
"acc_norm": 0.8055555555555556,
"acc_norm_stderr": 0.0220213661002202
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.549645390070922,
"acc_stderr": 0.02968010556502904,
"acc_norm": 0.549645390070922,
"acc_norm_stderr": 0.02968010556502904
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.5319426336375489,
"acc_stderr": 0.012744149704869647,
"acc_norm": 0.5319426336375489,
"acc_norm_stderr": 0.012744149704869647
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.7794117647058824,
"acc_stderr": 0.02518778666022726,
"acc_norm": 0.7794117647058824,
"acc_norm_stderr": 0.02518778666022726
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.738562091503268,
"acc_stderr": 0.017776947157528037,
"acc_norm": 0.738562091503268,
"acc_norm_stderr": 0.017776947157528037
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.7181818181818181,
"acc_stderr": 0.04309118709946458,
"acc_norm": 0.7181818181818181,
"acc_norm_stderr": 0.04309118709946458
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7959183673469388,
"acc_stderr": 0.0258012834750905,
"acc_norm": 0.7959183673469388,
"acc_norm_stderr": 0.0258012834750905
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8805970149253731,
"acc_stderr": 0.02292879327721974,
"acc_norm": 0.8805970149253731,
"acc_norm_stderr": 0.02292879327721974
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.9,
"acc_stderr": 0.030151134457776348,
"acc_norm": 0.9,
"acc_norm_stderr": 0.030151134457776348
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5421686746987951,
"acc_stderr": 0.03878626771002361,
"acc_norm": 0.5421686746987951,
"acc_norm_stderr": 0.03878626771002361
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8771929824561403,
"acc_stderr": 0.02517298435015577,
"acc_norm": 0.8771929824561403,
"acc_norm_stderr": 0.02517298435015577
},
"harness|truthfulqa:mc|0": {
"mc1": 0.5030599755201959,
"mc1_stderr": 0.01750317326096063,
"mc2": 0.6694851107267487,
"mc2_stderr": 0.014706509050408262
},
"harness|winogrande|5": {
"acc": 0.8161010260457774,
"acc_stderr": 0.010887916013305887
},
"harness|gsm8k|5": {
"acc": 0.7194844579226687,
"acc_stderr": 0.012374608490929547
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
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## Dataset Card Authors [optional]
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## Dataset Card Contact
[More Information Needed] |
Rimyy/problemMath-Llama3.5K | ---
dataset_info:
features:
- name: texte
dtype: string
splits:
- name: train
num_bytes: 2780768
num_examples: 3500
download_size: 1221998
dataset_size: 2780768
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
alxfng/noisycommonvoice | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 1293491394.0
num_examples: 5000
- name: test
num_bytes: 638561647.5
num_examples: 2500
download_size: 2034213156
dataset_size: 1932053041.5
---
# Dataset Card for "noisycommonvoice"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
shidowake/philschmid_guanaco-sharegpt-style_split_0 | ---
dataset_info:
features:
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
splits:
- name: train
num_bytes: 3496121.7309863833
num_examples: 2259
download_size: 2044490
dataset_size: 3496121.7309863833
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
olegka/fine-tune | ---
license: other
---
|
vip201/sft_zh_del | ---
license: apache-2.0
---
|
open-llm-leaderboard/details_4season__alignment-model-test9 | ---
pretty_name: Evaluation run of 4season/alignment-model-test9
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [4season/alignment-model-test9](https://huggingface.co/4season/alignment-model-test9)\
\ 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_4season__alignment-model-test9\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-04-08T15:04:12.877958](https://huggingface.co/datasets/open-llm-leaderboard/details_4season__alignment-model-test9/blob/main/results_2024-04-08T15-04-12.877958.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\": 0.6834330163002643,\n\
\ \"acc_stderr\": 0.031540209576181304,\n \"acc_norm\": 0.6858087556068694,\n\
\ \"acc_norm_stderr\": 0.03219476958988141,\n \"mc1\": 0.609547123623011,\n\
\ \"mc1_stderr\": 0.017078230743431455,\n \"mc2\": 0.7524744031917101,\n\
\ \"mc2_stderr\": 0.014224281719560656\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.764505119453925,\n \"acc_stderr\": 0.012399451855004741,\n\
\ \"acc_norm\": 0.7755972696245734,\n \"acc_norm_stderr\": 0.012191404938603831\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7879904401513643,\n\
\ \"acc_stderr\": 0.004078962503408514,\n \"acc_norm\": 0.9068910575582553,\n\
\ \"acc_norm_stderr\": 0.0028999116931072897\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.4,\n \"acc_stderr\": 0.04923659639173309,\n \
\ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n\
\ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6518518518518519,\n\
\ \"acc_stderr\": 0.041153246103369526,\n \"acc_norm\": 0.6518518518518519,\n\
\ \"acc_norm_stderr\": 0.041153246103369526\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.7631578947368421,\n \"acc_stderr\": 0.03459777606810536,\n\
\ \"acc_norm\": 0.7631578947368421,\n \"acc_norm_stderr\": 0.03459777606810536\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.75,\n\
\ \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n \
\ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7396226415094339,\n \"acc_stderr\": 0.027008766090708052,\n\
\ \"acc_norm\": 0.7396226415094339,\n \"acc_norm_stderr\": 0.027008766090708052\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8055555555555556,\n\
\ \"acc_stderr\": 0.03309615177059004,\n \"acc_norm\": 0.8055555555555556,\n\
\ \"acc_norm_stderr\": 0.03309615177059004\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956911,\n \
\ \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956911\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n\
\ \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6705202312138728,\n\
\ \"acc_stderr\": 0.035839017547364134,\n \"acc_norm\": 0.6705202312138728,\n\
\ \"acc_norm_stderr\": 0.035839017547364134\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.46078431372549017,\n \"acc_stderr\": 0.049598599663841815,\n\
\ \"acc_norm\": 0.46078431372549017,\n \"acc_norm_stderr\": 0.049598599663841815\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.81,\n \"acc_stderr\": 0.039427724440366234,\n \"acc_norm\": 0.81,\n\
\ \"acc_norm_stderr\": 0.039427724440366234\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.6382978723404256,\n \"acc_stderr\": 0.03141082197596239,\n\
\ \"acc_norm\": 0.6382978723404256,\n \"acc_norm_stderr\": 0.03141082197596239\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5087719298245614,\n\
\ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.5087719298245614,\n\
\ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.6413793103448275,\n \"acc_stderr\": 0.039966295748767186,\n\
\ \"acc_norm\": 0.6413793103448275,\n \"acc_norm_stderr\": 0.039966295748767186\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.5185185185185185,\n \"acc_stderr\": 0.025733641991838994,\n \"\
acc_norm\": 0.5185185185185185,\n \"acc_norm_stderr\": 0.025733641991838994\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.48412698412698413,\n\
\ \"acc_stderr\": 0.04469881854072606,\n \"acc_norm\": 0.48412698412698413,\n\
\ \"acc_norm_stderr\": 0.04469881854072606\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.8354838709677419,\n \"acc_stderr\": 0.021090847745939313,\n \"\
acc_norm\": 0.8354838709677419,\n \"acc_norm_stderr\": 0.021090847745939313\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.5862068965517241,\n \"acc_stderr\": 0.03465304488406796,\n \"\
acc_norm\": 0.5862068965517241,\n \"acc_norm_stderr\": 0.03465304488406796\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\"\
: 0.73,\n \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.8242424242424242,\n \"acc_stderr\": 0.02972094300622445,\n\
\ \"acc_norm\": 0.8242424242424242,\n \"acc_norm_stderr\": 0.02972094300622445\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8333333333333334,\n \"acc_stderr\": 0.02655220782821529,\n \"\
acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.02655220782821529\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8756476683937824,\n \"acc_stderr\": 0.023814477086593556,\n\
\ \"acc_norm\": 0.8756476683937824,\n \"acc_norm_stderr\": 0.023814477086593556\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.7025641025641025,\n \"acc_stderr\": 0.023177408131465953,\n\
\ \"acc_norm\": 0.7025641025641025,\n \"acc_norm_stderr\": 0.023177408131465953\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3925925925925926,\n \"acc_stderr\": 0.029773847012532967,\n \
\ \"acc_norm\": 0.3925925925925926,\n \"acc_norm_stderr\": 0.029773847012532967\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.7857142857142857,\n \"acc_stderr\": 0.026653531596715484,\n\
\ \"acc_norm\": 0.7857142857142857,\n \"acc_norm_stderr\": 0.026653531596715484\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.5099337748344371,\n \"acc_stderr\": 0.04081677107248437,\n \"\
acc_norm\": 0.5099337748344371,\n \"acc_norm_stderr\": 0.04081677107248437\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8733944954128441,\n \"acc_stderr\": 0.014257128686165167,\n \"\
acc_norm\": 0.8733944954128441,\n \"acc_norm_stderr\": 0.014257128686165167\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5879629629629629,\n \"acc_stderr\": 0.03356787758160831,\n \"\
acc_norm\": 0.5879629629629629,\n \"acc_norm_stderr\": 0.03356787758160831\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8725490196078431,\n \"acc_stderr\": 0.02340553048084631,\n \"\
acc_norm\": 0.8725490196078431,\n \"acc_norm_stderr\": 0.02340553048084631\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8438818565400844,\n \"acc_stderr\": 0.02362715946031868,\n \
\ \"acc_norm\": 0.8438818565400844,\n \"acc_norm_stderr\": 0.02362715946031868\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7309417040358744,\n\
\ \"acc_stderr\": 0.02976377940687497,\n \"acc_norm\": 0.7309417040358744,\n\
\ \"acc_norm_stderr\": 0.02976377940687497\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.6870229007633588,\n \"acc_stderr\": 0.04066962905677698,\n\
\ \"acc_norm\": 0.6870229007633588,\n \"acc_norm_stderr\": 0.04066962905677698\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8264462809917356,\n \"acc_stderr\": 0.0345727283691767,\n \"acc_norm\"\
: 0.8264462809917356,\n \"acc_norm_stderr\": 0.0345727283691767\n },\n\
\ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7962962962962963,\n\
\ \"acc_stderr\": 0.03893542518824847,\n \"acc_norm\": 0.7962962962962963,\n\
\ \"acc_norm_stderr\": 0.03893542518824847\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n\
\ \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.48214285714285715,\n\
\ \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.48214285714285715,\n\
\ \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8155339805825242,\n \"acc_stderr\": 0.03840423627288276,\n\
\ \"acc_norm\": 0.8155339805825242,\n \"acc_norm_stderr\": 0.03840423627288276\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9017094017094017,\n\
\ \"acc_stderr\": 0.019503444900757567,\n \"acc_norm\": 0.9017094017094017,\n\
\ \"acc_norm_stderr\": 0.019503444900757567\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8109833971902938,\n\
\ \"acc_stderr\": 0.014000791294407,\n \"acc_norm\": 0.8109833971902938,\n\
\ \"acc_norm_stderr\": 0.014000791294407\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7225433526011561,\n \"acc_stderr\": 0.02410571260775431,\n\
\ \"acc_norm\": 0.7225433526011561,\n \"acc_norm_stderr\": 0.02410571260775431\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4972067039106145,\n\
\ \"acc_stderr\": 0.016722240595491714,\n \"acc_norm\": 0.4972067039106145,\n\
\ \"acc_norm_stderr\": 0.016722240595491714\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.803921568627451,\n \"acc_stderr\": 0.0227337894054476,\n\
\ \"acc_norm\": 0.803921568627451,\n \"acc_norm_stderr\": 0.0227337894054476\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7106109324758842,\n\
\ \"acc_stderr\": 0.025755865922632945,\n \"acc_norm\": 0.7106109324758842,\n\
\ \"acc_norm_stderr\": 0.025755865922632945\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7808641975308642,\n \"acc_stderr\": 0.02301670564026219,\n\
\ \"acc_norm\": 0.7808641975308642,\n \"acc_norm_stderr\": 0.02301670564026219\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.5425531914893617,\n \"acc_stderr\": 0.02971928127223684,\n \
\ \"acc_norm\": 0.5425531914893617,\n \"acc_norm_stderr\": 0.02971928127223684\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4876140808344198,\n\
\ \"acc_stderr\": 0.01276631731547356,\n \"acc_norm\": 0.4876140808344198,\n\
\ \"acc_norm_stderr\": 0.01276631731547356\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.7169117647058824,\n \"acc_stderr\": 0.027365861131513812,\n\
\ \"acc_norm\": 0.7169117647058824,\n \"acc_norm_stderr\": 0.027365861131513812\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.684640522875817,\n \"acc_stderr\": 0.018798086284886887,\n \
\ \"acc_norm\": 0.684640522875817,\n \"acc_norm_stderr\": 0.018798086284886887\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6545454545454545,\n\
\ \"acc_stderr\": 0.04554619617541054,\n \"acc_norm\": 0.6545454545454545,\n\
\ \"acc_norm_stderr\": 0.04554619617541054\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7551020408163265,\n \"acc_stderr\": 0.027529637440174923,\n\
\ \"acc_norm\": 0.7551020408163265,\n \"acc_norm_stderr\": 0.027529637440174923\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8258706467661692,\n\
\ \"acc_stderr\": 0.026814951200421603,\n \"acc_norm\": 0.8258706467661692,\n\
\ \"acc_norm_stderr\": 0.026814951200421603\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.83,\n \"acc_stderr\": 0.0377525168068637,\n \
\ \"acc_norm\": 0.83,\n \"acc_norm_stderr\": 0.0377525168068637\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5481927710843374,\n\
\ \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.5481927710843374,\n\
\ \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.7894736842105263,\n \"acc_stderr\": 0.03126781714663179,\n\
\ \"acc_norm\": 0.7894736842105263,\n \"acc_norm_stderr\": 0.03126781714663179\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.609547123623011,\n\
\ \"mc1_stderr\": 0.017078230743431455,\n \"mc2\": 0.7524744031917101,\n\
\ \"mc2_stderr\": 0.014224281719560656\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8745067087608525,\n \"acc_stderr\": 0.00931054223748618\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.48218347232752085,\n \
\ \"acc_stderr\": 0.013763738379867925\n }\n}\n```"
repo_url: https://huggingface.co/4season/alignment-model-test9
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|arc:challenge|25_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|gsm8k|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hellaswag|10_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-08T15-04-12.877958.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-08T15-04-12.877958.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- '**/details_harness|winogrande|5_2024-04-08T15-04-12.877958.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-04-08T15-04-12.877958.parquet'
- config_name: results
data_files:
- split: 2024_04_08T15_04_12.877958
path:
- results_2024-04-08T15-04-12.877958.parquet
- split: latest
path:
- results_2024-04-08T15-04-12.877958.parquet
---
# Dataset Card for Evaluation run of 4season/alignment-model-test9
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [4season/alignment-model-test9](https://huggingface.co/4season/alignment-model-test9) 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_4season__alignment-model-test9",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-04-08T15:04:12.877958](https://huggingface.co/datasets/open-llm-leaderboard/details_4season__alignment-model-test9/blob/main/results_2024-04-08T15-04-12.877958.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": 0.6834330163002643,
"acc_stderr": 0.031540209576181304,
"acc_norm": 0.6858087556068694,
"acc_norm_stderr": 0.03219476958988141,
"mc1": 0.609547123623011,
"mc1_stderr": 0.017078230743431455,
"mc2": 0.7524744031917101,
"mc2_stderr": 0.014224281719560656
},
"harness|arc:challenge|25": {
"acc": 0.764505119453925,
"acc_stderr": 0.012399451855004741,
"acc_norm": 0.7755972696245734,
"acc_norm_stderr": 0.012191404938603831
},
"harness|hellaswag|10": {
"acc": 0.7879904401513643,
"acc_stderr": 0.004078962503408514,
"acc_norm": 0.9068910575582553,
"acc_norm_stderr": 0.0028999116931072897
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.4,
"acc_stderr": 0.04923659639173309,
"acc_norm": 0.4,
"acc_norm_stderr": 0.04923659639173309
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6518518518518519,
"acc_stderr": 0.041153246103369526,
"acc_norm": 0.6518518518518519,
"acc_norm_stderr": 0.041153246103369526
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7631578947368421,
"acc_stderr": 0.03459777606810536,
"acc_norm": 0.7631578947368421,
"acc_norm_stderr": 0.03459777606810536
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.75,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7396226415094339,
"acc_stderr": 0.027008766090708052,
"acc_norm": 0.7396226415094339,
"acc_norm_stderr": 0.027008766090708052
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.8055555555555556,
"acc_stderr": 0.03309615177059004,
"acc_norm": 0.8055555555555556,
"acc_norm_stderr": 0.03309615177059004
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.51,
"acc_stderr": 0.05024183937956911,
"acc_norm": 0.51,
"acc_norm_stderr": 0.05024183937956911
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.61,
"acc_stderr": 0.04902071300001975,
"acc_norm": 0.61,
"acc_norm_stderr": 0.04902071300001975
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.37,
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```
## 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. -->
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### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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**APA:**
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## Glossary [optional]
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## Dataset Card Contact
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wagnergrangeiro/edlima | ---
license: openrail
---
|
rlasseri/test-OrangeSum-small | ---
pretty_name: OrangeSum
annotations_creators:
- found
language_creators:
- found
language:
- fr
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- summarization
task_ids:
- news-articles-headline-generation
- news-articles-summarization
paperswithcode_id: orangesum
dataset_info:
- config_name: abstract
features:
- name: text
dtype: string
- name: summary
dtype: string
splits:
- name: train
num_bytes: 53531651
num_examples: 21401
- name: test
num_bytes: 3785207
num_examples: 1500
- name: validation
num_bytes: 3698650
num_examples: 1500
download_size: 23058350
dataset_size: 61015508
- config_name: title
features:
- name: text
dtype: string
- name: summary
dtype: string
splits:
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num_examples: 30659
- name: test
num_bytes: 3176690
num_examples: 1500
- name: validation
num_bytes: 3276713
num_examples: 1500
download_size: 27321627
dataset_size: 71678539
---
# Dataset Card for OrangeSum
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [OrangeSum repository](https://github.com/Tixierae/OrangeSum)
- **Paper:** [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://arxiv.org/abs/2010.12321)
- **Point of Contact:** [Antoine J.-P. Tixier](Antoine.Tixier-1@colorado.edu)
### Dataset Summary
The OrangeSum dataset was inspired by the XSum dataset. It was created by scraping the "Orange Actu" website: https://actu.orange.fr/. Orange S.A. is a large French multinational telecommunications corporation, with 266M customers worldwide. Scraped pages cover almost a decade from Feb 2011 to Sep 2020. They belong to five main categories: France, world, politics, automotive, and society. The society category is itself divided into 8 subcategories: health, environment, people, culture, media, high-tech, unsual ("insolite" in French), and miscellaneous.
Each article featured a single-sentence title as well as a very brief abstract, both professionally written by the author of the article. These two fields were extracted from each page, thus creating two summarization tasks: OrangeSum Title and OrangeSum Abstract.
### Supported Tasks and Leaderboards
**Tasks:** OrangeSum Title and OrangeSum Abstract.
To this day, there is no Leaderboard for this dataset.
### Languages
The text in the dataset is in French.
## Dataset Structure
### Data Instances
A data instance consists of a news article and a summary. The summary can be a short abstract or a title depending on the configuration.
Example:
**Document:** Le temps sera pluvieux sur huit départements de la France ces prochaines heures : outre les trois départements bretons placés en vigilance orange jeudi matin, cinq autres départements du sud du Massif Central ont été à leur tour placés en alerte orange pluie et inondation. Il s'agit de l'Aveyron, du Cantal, du Gard, de la Lozère, et de la Haute-Loire. Sur l'ensemble de l'épisode, les cumuls de pluies attendus en Bretagne sont compris entre 40 et 60 mm en 24 heures et peuvent atteindre localement les 70 mm en 24 heures.Par la suite, la dégradation qui va se mettre en place cette nuit sur le Languedoc et le sud du Massif Central va donner sur l'Aveyron une première salve intense de pluie. Des cumuls entre 70 et 100 mm voir 120 mm localement sont attendus sur une durée de 24 heures. Sur le relief des Cévennes on attend de 150 à 200 mm, voire 250 mm très ponctuellement sur l'ouest du Gard et l'est de la Lozère. Cet épisode va s'estomper dans la soirée avec le décalage des orages vers les régions plus au nord. Un aspect orageux se mêlera à ces précipitations, avec de la grêle possible, des rafales de vent et une forte activité électrique.
**Abstract:** Outre les trois départements bretons, cinq autres départements du centre de la France ont été placés en vigilance orange pluie-inondation.
**Title:** Pluie-inondations : 8 départements en alerte orange.
### Data Fields
`text`: the document to be summarized. \
`summary`: the summary of the source document.
### Data Splits
The data is split into a training, validation and test in both configuration.
| | train | validation | test |
|----------|------:|-----------:|-----:|
| Abstract | 21400 | 1500 | 1500 |
| Title | 30658 | 1500 | 1500 |
## Dataset Creation
### Curation Rationale
The goal here was to create a French equivalent of the recently introduced [XSum](https://github.com/EdinburghNLP/XSum/tree/master/XSum-Dataset) dataset. Unlike the historical summarization datasets, CNN, DailyMail, and NY Times, which favor extractive strategies, XSum, as well as OrangeSum require the models to display a high degree of abstractivity to perform well. The summaries in OrangeSum are not catchy headlines, but rather capture the gist of the articles.
### Source Data
#### Initial Data Collection and Normalization
Each article features a single-sentence title as well as a very brief abstract. Extracting these two fields from each news article page, creates two summarization tasks: OrangeSum Title and OrangeSum Abstract. As a post-processing step, all empty articles and those whose summaries were shorter than 5 words were removed. For OrangeSum Abstract, the top 10% articles in terms of proportion of novel unigrams in the abstracts were removed, as it was observed that such abstracts tend to be introductions rather than real abstracts. This corresponded to a threshold of 57% novel unigrams. For both OrangeSum Title and OrangeSum Abstract, 1500 pairs for testing and 1500 for validation are set aside, and all the remaining ones are used for training.
#### Who are the source language producers?
The authors of the artiles.
### Annotations
#### Annotation process
The smmaries are professionally written by the author of the articles.
#### Who are the annotators?
The authors of the artiles.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The dataset was initially created by Antoine J.-P. Tixier.
### Licensing Information
[More Information Needed]
### Citation Information
```
@article{eddine2020barthez,
title={BARThez: a Skilled Pretrained French Sequence-to-Sequence Model},
author={Eddine, Moussa Kamal and Tixier, Antoine J-P and Vazirgiannis, Michalis},
journal={arXiv preprint arXiv:2010.12321},
year={2020}
}
```
### Contributions
Thanks to [@moussaKam](https://github.com/moussaKam) for adding this dataset. |
Marceli/polish_medical_dataset | ---
license: unknown
---
|
trajesh/language_translation_samples | ---
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- split: train
path: en-tg/train-*
- config_name: en-tm
data_files:
- split: train
path: en-tm/train-*
- split: test
path: en-tm/test-*
---
|
distilled-from-one-sec-cv12/chunk_148 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 979580764
num_examples: 190877
download_size: 1000200428
dataset_size: 979580764
---
# Dataset Card for "chunk_148"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
zolak/twitter_dataset_79_1713167249 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 493209
num_examples: 1180
download_size: 244383
dataset_size: 493209
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
P3ps/condition_to_drug | ---
dataset_info:
features:
- name: drugName
dtype: string
- name: condition
dtype: string
splits:
- name: train
num_bytes: 3438399.8732642303
num_examples: 100587
- name: validation
num_bytes: 534429.7245546674
num_examples: 15399
- name: test
num_bytes: 1111779.385698873
num_examples: 32553
download_size: 1068870
dataset_size: 5084608.983517771
---
# Dataset Card for "condition_to_drug"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
openclimatefix/arco-era5 | ---
license: apache-2.0
---
This dataset simply loads Google's Analysis-Ready Cloud Optimized ERA5 Reanalysis dataset from Google Public Datasets. |
gabrielmbmb/distilabel-test | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: completion
dtype: string
- name: meta
struct:
- name: category
dtype: string
- name: completion
dtype: string
- name: id
dtype: int64
- name: input
dtype: string
- name: motivation_app
dtype: string
- name: prompt
dtype: string
- name: source
dtype: string
- name: subcategory
dtype: string
splits:
- name: train
num_bytes: 430365
num_examples: 327
download_size: 290506
dataset_size: 430365
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
ProfessorBob/train_florian | ---
dataset_info:
features:
- name: triplets
sequence: string
- name: passage
dtype: string
- name: label_str
dtype: string
- name: passage_id
dtype: float64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 95365899
num_examples: 75843
download_size: 11578478
dataset_size: 95365899
---
# Dataset Card for "train_florian"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
xezpeleta/oasst2_eu_top1_sharegpt_test | ---
dataset_info:
features:
- name: conversations
dtype: string
- name: langs
dtype: string
splits:
- name: train
num_bytes: 3010
num_examples: 2
download_size: 12042
dataset_size: 3010
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
strombergnlp/bornholmsk_parallel | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- da
- da-bornholm
license:
- cc-by-4.0
multilinguality:
- translation
pretty_name: Bornholmsk/Danish Parallel Texts
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: bornholmsk-parallel
---
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [https://github.com/StrombergNLP/bornholmsk](https://github.com/StrombergNLP/bornholmsk)
- **Repository:** [https://github.com/StrombergNLP/bornholmsk](https://github.com/StrombergNLP/bornholmsk)
- **Paper:** [https://aclanthology.org/W19-6138/](https://aclanthology.org/W19-6138/)
- **Point of Contact:** [Leon Derczynski](https://github.com/leondz)
- **Size of downloaded dataset files:** 490 KB
- **Size of the generated dataset:** 582 KB
- **Total amount of disk used:** 1072 KB
### Dataset Summary
This dataset is parallel text for Bornholmsk and Danish.
For more details, see the paper [Bornholmsk Natural Language Processing: Resources and Tools](https://aclanthology.org/W19-6138/).
### Supported Tasks and Leaderboards
*
### Languages
Bornholmsk, a language variant of Danish spoken on the island of Bornholm, and Danish. bcp47: `da-bornholm` and `da-DK`
## Dataset Structure
### Data Instances
### Data Fields
`id`: the sentence ID, `int`
`da-bornholm`: the Bornholmsk text, `string`
`da`: the Danish translation, `string`
### Data Splits
* Train: 5785 sentence pairs
* Validation: 500 sentence pairs
* Test: 500 sentence pairs
## Dataset Creation
### Curation Rationale
To gather as much parallel Bornholmsk together as possible
### Source Data
#### Initial Data Collection and Normalization
From a translation of Kuhre's Sansager, a selection of colloquial resources, and a prototype Bornholmsk/Danish dictionary
#### Who are the source language producers?
Native speakers of Bornholmsk who have produced works in their native language, or translated them to Danish. Much of the data is the result of a community of Bornholmsk speakers volunteering their time across the island in an effort to capture this endangered language.
### Annotations
#### Annotation process
No annotations
#### Who are the annotators?
Native speakers of Bornholmsk, mostly aged 60+.
### Personal and Sensitive Information
Unknown, but low risk of presence, given the source material
## Considerations for Using the Data
### Social Impact of Dataset
The hope behind this data is to enable people to learn and use Bornholmsk
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
This collection of Bornholmsk is curated by Leon Derczynski and Alex Speed Kjeldsen
### Licensing Information
Creative Commons Attribution 4.0
### Citation Information
```
@inproceedings{derczynski-kjeldsen-2019-bornholmsk,
title = "Bornholmsk Natural Language Processing: Resources and Tools",
author = "Derczynski, Leon and
Kjeldsen, Alex Speed",
booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
month = sep # "{--}" # oct,
year = "2019",
address = "Turku, Finland",
publisher = {Link{\"o}ping University Electronic Press},
url = "https://aclanthology.org/W19-6138",
pages = "338--344",
}
``` |
autoevaluate/autoeval-eval-glue-mnli_matched-c9e0cb-1508854842 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- glue
eval_info:
task: natural_language_inference
model: JeremiahZ/bert-base-uncased-mnli
metrics: []
dataset_name: glue
dataset_config: mnli_matched
dataset_split: validation
col_mapping:
text1: premise
text2: hypothesis
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Natural Language Inference
* Model: JeremiahZ/bert-base-uncased-mnli
* Dataset: glue
* Config: mnli_matched
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@JeremiahZ](https://huggingface.co/JeremiahZ) for evaluating this model. |
anyastrophic/jbvalues-automod-dataset | ---
dataset_info:
features:
- name: topic
dtype: string
- name: content
dtype: string
splits:
- name: train
num_bytes: 476966
num_examples: 5379
download_size: 129906
dataset_size: 476966
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "jbvalues-automod-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
RajuKandasamy/venba5k | ---
license: gpl-3.0
task_categories:
- text-generation
language:
- ta
size_categories:
- 1K<n<10K
---
தமிழ் வெண்பாக்கள் ~5000, பதவுரை குறிப்புரையுடன். |
jiuyuan/mind_10k | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 56720773
num_examples: 9778
download_size: 25546197
dataset_size: 56720773
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "mind_10k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
allenai/ms2_dense_oracle | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other-MS^2
- extended|other-Cochrane
task_categories:
- summarization
- text2text-generation
paperswithcode_id: multi-document-summarization
pretty_name: MSLR Shared Task
---
This is a copy of the [MS^2](https://huggingface.co/datasets/allenai/mslr2022) dataset, except the input source documents of the `train`, `validation`, and `test` splits have been replaced by a __dense__ retriever.
- __query__: The `background` field of each example
- __corpus__: The union of all documents in the `train`, `validation` and `test` splits. A document is the concatenation of the `title` and `abstract`.
- __retriever__: [`facebook/contriever-msmarco`](https://huggingface.co/facebook/contriever-msmarco) via [PyTerrier](https://pyterrier.readthedocs.io/en/latest/) with default settings
- __top-k strategy__: `"oracle"`, i.e. the number of documents retrieved, `k`, is set as the original number of input documents for each example
Retrieval results on the `validation` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.4764 | 0.2395 | 0.2395 | 0.2395 |
Retrieval results on the `validation` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.4364 | 0.2125 | 0.2125 | 0.2125 |
Retrieval results on the `test` set:
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.4481 | 0.2224 | 0.2224 | 0.2224 | |
threite/github-issues | ---
dataset_info:
features:
- name: url
dtype: string
- name: repository_url
dtype: string
- name: labels_url
dtype: string
- name: comments_url
dtype: string
- name: events_url
dtype: string
- name: html_url
dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
- name: number
dtype: int64
- name: title
dtype: string
- name: user
struct:
- name: avatar_url
dtype: string
- name: events_url
dtype: string
- name: followers_url
dtype: string
- name: following_url
dtype: string
- name: gists_url
dtype: string
- name: gravatar_id
dtype: string
- name: html_url
dtype: string
- name: id
dtype: int64
- name: login
dtype: string
- name: node_id
dtype: string
- name: organizations_url
dtype: string
- name: received_events_url
dtype: string
- name: repos_url
dtype: string
- name: site_admin
dtype: bool
- name: starred_url
dtype: string
- name: subscriptions_url
dtype: string
- name: type
dtype: string
- name: url
dtype: string
- name: labels
list:
- name: color
dtype: string
- name: default
dtype: bool
- name: description
dtype: string
- name: id
dtype: int64
- name: name
dtype: string
- name: node_id
dtype: string
- name: url
dtype: string
- name: state
dtype: string
- name: locked
dtype: bool
- name: assignee
struct:
- name: avatar_url
dtype: string
- name: events_url
dtype: string
- name: followers_url
dtype: string
- name: following_url
dtype: string
- name: gists_url
dtype: string
- name: gravatar_id
dtype: string
- name: html_url
dtype: string
- name: id
dtype: int64
- name: login
dtype: string
- name: node_id
dtype: string
- name: organizations_url
dtype: string
- name: received_events_url
dtype: string
- name: repos_url
dtype: string
- name: site_admin
dtype: bool
- name: starred_url
dtype: string
- name: subscriptions_url
dtype: string
- name: type
dtype: string
- name: url
dtype: string
- name: assignees
list:
- name: avatar_url
dtype: string
- name: events_url
dtype: string
- name: followers_url
dtype: string
- name: following_url
dtype: string
- name: gists_url
dtype: string
- name: gravatar_id
dtype: string
- name: html_url
dtype: string
- name: id
dtype: int64
- name: login
dtype: string
- name: node_id
dtype: string
- name: organizations_url
dtype: string
- name: received_events_url
dtype: string
- name: repos_url
dtype: string
- name: site_admin
dtype: bool
- name: starred_url
dtype: string
- name: subscriptions_url
dtype: string
- name: type
dtype: string
- name: url
dtype: string
- name: milestone
struct:
- name: closed_at
dtype: string
- name: closed_issues
dtype: int64
- name: created_at
dtype: string
- name: creator
struct:
- name: avatar_url
dtype: string
- name: events_url
dtype: string
- name: followers_url
dtype: string
- name: following_url
dtype: string
- name: gists_url
dtype: string
- name: gravatar_id
dtype: string
- name: html_url
dtype: string
- name: id
dtype: int64
- name: login
dtype: string
- name: node_id
dtype: string
- name: organizations_url
dtype: string
- name: received_events_url
dtype: string
- name: repos_url
dtype: string
- name: site_admin
dtype: bool
- name: starred_url
dtype: string
- name: subscriptions_url
dtype: string
- name: type
dtype: string
- name: url
dtype: string
- name: description
dtype: string
- name: due_on
dtype: string
- name: html_url
dtype: string
- name: id
dtype: int64
- name: labels_url
dtype: string
- name: node_id
dtype: string
- name: number
dtype: int64
- name: open_issues
dtype: int64
- name: state
dtype: string
- name: title
dtype: string
- name: updated_at
dtype: string
- name: url
dtype: string
- name: comments
sequence: 'null'
- name: created_at
dtype: string
- name: updated_at
dtype: string
- name: closed_at
dtype: string
- name: author_association
dtype: string
- name: active_lock_reason
dtype: 'null'
- name: draft
dtype: bool
- name: pull_request
struct:
- name: diff_url
dtype: string
- name: html_url
dtype: string
- name: merged_at
dtype: string
- name: patch_url
dtype: string
- name: url
dtype: string
- name: body
dtype: string
- name: reactions
struct:
- name: '+1'
dtype: int64
- name: '-1'
dtype: int64
- name: confused
dtype: int64
- name: eyes
dtype: int64
- name: heart
dtype: int64
- name: hooray
dtype: int64
- name: laugh
dtype: int64
- name: rocket
dtype: int64
- name: total_count
dtype: int64
- name: url
dtype: string
- name: timeline_url
dtype: string
- name: performed_via_github_app
dtype: 'null'
- name: state_reason
dtype: string
- name: is_pull_request
dtype: bool
splits:
- name: train
num_bytes: 16275865
num_examples: 5392
download_size: 3809038
dataset_size: 16275865
---
# Dataset Card for "github-issues"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mariagrandury/databricks-dolly-15k-curated-es | ---
size_categories: 10K<n<100K
tags:
- rlfh
- argilla
- human-feedback
---
# Dataset Card for databricks-dolly-15k-curated-es
This dataset has been created with [Argilla](https://docs.argilla.io).
As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets).
## Dataset Description
- **Homepage:** https://argilla.io
- **Repository:** https://github.com/argilla-io/argilla
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset contains:
* A dataset configuration file conforming to the Argilla dataset format named `argilla.cfg`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla.
* Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` and can be loaded independently using the `datasets` library via `load_dataset`.
* The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla.
### Load with Argilla
To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code:
```python
import argilla as rg
ds = rg.FeedbackDataset.from_huggingface("mariagrandury/databricks-dolly-15k-curated-es")
```
### Load with `datasets`
To load this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code:
```python
from datasets import load_dataset
ds = load_dataset("mariagrandury/databricks-dolly-15k-curated-es")
```
### Supported Tasks and Leaderboards
This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/guides/llms/conceptual_guides/data_model.html) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure).
There are no leaderboards associated with this dataset.
### Languages
[More Information Needed]
## Dataset Structure
### Data in Argilla
The dataset is created in Argilla with: **fields**, **questions**, and **guidelines**.
The **fields** are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.
| Field Name | Title | Type | Required | Markdown |
| ---------- | ----- | ---- | -------- | -------- |
| category | Task category | TextField | True | False |
| instruction | Instruction | TextField | True | False |
| context | Input | TextField | True | False |
| response | Response | TextField | True | False |
The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, single choice, or multiple choice.
| Question Name | Title | Type | Required | Description | Values/Labels |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| new-instruction | Final instruction: | TextQuestion | True | Write the final version of the instruction, making sure that it matches the task category. If the original instruction is ok, copy and paste it here. | N/A |
| new-input | Final input: | TextQuestion | True | Write the final version of the input, making sure that it makes sense with the task category. If the original input is ok, copy and paste it here. If an input is not needed, leave this empty. | N/A |
| new-response | Final response: | TextQuestion | True | Write the final version of the response, making sure that it matches the task category and makes sense for the instruction (and input) provided. If the original response is ok, copy and paste it here. | N/A |
Finally, the **guidelines** are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section.
### Data Instances
An example of a dataset instance in Argilla looks as follows:
```json
{
"external_id": "0",
"fields": {
"category": "closed_qa",
"context": "Virgin Australia, nombre comercial de Virgin Australia Airlines Pty Ltd, es una compa\u00f1\u00eda a\u00e9rea con sede en Australia. Es la mayor aerol\u00ednea por tama\u00f1o de flota que utiliza la marca Virgin. Inici\u00f3 sus servicios el 31 de agosto de 2000 como Virgin Blue, con dos aviones en una \u00fanica ruta. Se encontr\u00f3 de repente como una importante aerol\u00ednea en el mercado nacional australiano tras la quiebra de Ansett Australia en septiembre de 2001. Desde entonces, la aerol\u00ednea ha crecido hasta prestar servicio directo a 32 ciudades de Australia, desde los centros de Brisbane, Melbourne y Sydney.",
"instruction": "\u00bfCu\u00e1ndo empez\u00f3 a operar Virgin Australia?",
"response": "Virgin Australia inici\u00f3 sus servicios el 31 de agosto de 2000 como Virgin Blue, con dos aviones en una sola ruta."
},
"metadata": null,
"responses": []
}
```
While the same record in HuggingFace `datasets` looks as follows:
```json
{
"category": "closed_qa",
"context": "Virgin Australia, nombre comercial de Virgin Australia Airlines Pty Ltd, es una compa\u00f1\u00eda a\u00e9rea con sede en Australia. Es la mayor aerol\u00ednea por tama\u00f1o de flota que utiliza la marca Virgin. Inici\u00f3 sus servicios el 31 de agosto de 2000 como Virgin Blue, con dos aviones en una \u00fanica ruta. Se encontr\u00f3 de repente como una importante aerol\u00ednea en el mercado nacional australiano tras la quiebra de Ansett Australia en septiembre de 2001. Desde entonces, la aerol\u00ednea ha crecido hasta prestar servicio directo a 32 ciudades de Australia, desde los centros de Brisbane, Melbourne y Sydney.",
"external_id": "0",
"instruction": "\u00bfCu\u00e1ndo empez\u00f3 a operar Virgin Australia?",
"metadata": null,
"new-input": null,
"new-instruction": null,
"new-response": null,
"response": "Virgin Australia inici\u00f3 sus servicios el 31 de agosto de 2000 como Virgin Blue, con dos aviones en una sola ruta."
}
```
### Data Fields
Among the dataset fields, we differentiate between the following:
* **Fields:** These are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.
* **category** is of type `TextField`.
* **instruction** is of type `TextField`.
* (optional) **context** is of type `TextField`.
* **response** is of type `TextField`.
* **Questions:** These are the questions that will be asked to the annotators. They can be of different types, such as rating, text, single choice, or multiple choice.
* **new-instruction** is of type `TextQuestion`, and description "Write the final version of the instruction, making sure that it matches the task category. If the original instruction is ok, copy and paste it here.".
* (optional) **new-input** is of type `TextQuestion`, and description "Write the final version of the input, making sure that it makes sense with the task category. If the original input is ok, copy and paste it here. If an input is not needed, leave this empty.".
* **new-response** is of type `TextQuestion`, and description "Write the final version of the response, making sure that it matches the task category and makes sense for the instruction (and input) provided. If the original response is ok, copy and paste it here.".
Additionally, we also have one more field which is optional and is the following:
* **external_id:** This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.
### Data Splits
The dataset contains a single split, which is `train`.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation guidelines
In this dataset, you will find a collection of records that show a category, an instruction, an input and a response to that instruction. The aim of the project is to correct the instructions, intput and responses to make sure they are of the highest quality and that they match the task category that they belong to. All three texts should be clear and include real information. In addition, the response should be as complete but concise as possible.
To curate the dataset, you will need to provide an answer to the following text fields:
1 - Final instruction:
The final version of the instruction field. You may copy it using the copy icon in the instruction field. Leave it as it is if it's ok or apply any necessary corrections. Remember to change the instruction if it doesn't represent well the task category of the record.
2 - Final input:
The final version of the instruction field. You may copy it using the copy icon in the input field. Leave it as it is if it's ok or apply any necessary corrections. If the task category and instruction don't need of an input to be completed, leave this question blank.
3 - Final response:
The final version of the response field. You may copy it using the copy icon in the response field. Leave it as it is if it's ok or apply any necessary corrections. Check that the response makes sense given all the fields above.
You will need to provide at least an instruction and a response for all records. If you are not sure about a record and you prefer not to provide a response, click Discard.
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
parksez/superalloy1 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 11901
num_examples: 6
download_size: 14998
dataset_size: 11901
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "superalloy1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mteb/medrxiv-clustering-p2p | ---
language:
- en
--- |
AgntPerseus/Hadesstl | ---
license: creativeml-openrail-m
---
Textual Inversion trained on Hades game art. Tested on Anything V3 model. Recommend to use words "cartoon","comic","realistic","dark outlines" in prompt to get better results.
|
autoevaluate/autoeval-eval-inverse-scaling__NeQA-inverse-scaling__NeQA-4df82b-1769161494 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- inverse-scaling/NeQA
eval_info:
task: text_zero_shot_classification
model: gpt2-xl
metrics: []
dataset_name: inverse-scaling/NeQA
dataset_config: inverse-scaling--NeQA
dataset_split: train
col_mapping:
text: prompt
classes: classes
target: answer_index
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: gpt2-xl
* Dataset: inverse-scaling/NeQA
* Config: inverse-scaling--NeQA
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@rololbot](https://huggingface.co/rololbot) for evaluating this model. |
open-llm-leaderboard/details_wannaphong__han-llm-7b-v2 | ---
pretty_name: Evaluation run of wannaphong/han-llm-7b-v2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [wannaphong/han-llm-7b-v2](https://huggingface.co/wannaphong/han-llm-7b-v2) 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_wannaphong__han-llm-7b-v2\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-04T20:24:26.117217](https://huggingface.co/datasets/open-llm-leaderboard/details_wannaphong__han-llm-7b-v2/blob/main/results_2024-03-04T20-24-26.117217.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\": 0.5972728257098081,\n\
\ \"acc_stderr\": 0.03311148975202147,\n \"acc_norm\": 0.6028244973496223,\n\
\ \"acc_norm_stderr\": 0.03379622943577808,\n \"mc1\": 0.2741738066095471,\n\
\ \"mc1_stderr\": 0.015616518497219376,\n \"mc2\": 0.4237773900118851,\n\
\ \"mc2_stderr\": 0.014244420047515118\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5452218430034129,\n \"acc_stderr\": 0.014551507060836353,\n\
\ \"acc_norm\": 0.5878839590443686,\n \"acc_norm_stderr\": 0.014383915302225403\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6122286397132045,\n\
\ \"acc_stderr\": 0.004862461799370391,\n \"acc_norm\": 0.8174666401115316,\n\
\ \"acc_norm_stderr\": 0.0038549403270910316\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252606,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252606\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5407407407407407,\n\
\ \"acc_stderr\": 0.04304979692464241,\n \"acc_norm\": 0.5407407407407407,\n\
\ \"acc_norm_stderr\": 0.04304979692464241\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6447368421052632,\n \"acc_stderr\": 0.038947344870133176,\n\
\ \"acc_norm\": 0.6447368421052632,\n \"acc_norm_stderr\": 0.038947344870133176\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.56,\n\
\ \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.56,\n \
\ \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6452830188679245,\n \"acc_stderr\": 0.029445175328199583,\n\
\ \"acc_norm\": 0.6452830188679245,\n \"acc_norm_stderr\": 0.029445175328199583\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6736111111111112,\n\
\ \"acc_stderr\": 0.03921067198982266,\n \"acc_norm\": 0.6736111111111112,\n\
\ \"acc_norm_stderr\": 0.03921067198982266\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \
\ \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.53,\n\
\ \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6069364161849711,\n\
\ \"acc_stderr\": 0.037242495958177295,\n \"acc_norm\": 0.6069364161849711,\n\
\ \"acc_norm_stderr\": 0.037242495958177295\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4019607843137255,\n \"acc_stderr\": 0.048786087144669955,\n\
\ \"acc_norm\": 0.4019607843137255,\n \"acc_norm_stderr\": 0.048786087144669955\n\
\ },\n \"harness|hendrycksTest-computer_security|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-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\": 0.45614035087719296,\n\
\ \"acc_stderr\": 0.046854730419077895,\n \"acc_norm\": 0.45614035087719296,\n\
\ \"acc_norm_stderr\": 0.046854730419077895\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\"\
: 0.3888888888888889,\n \"acc_stderr\": 0.02510742548113728,\n \"\
acc_norm\": 0.3888888888888889,\n \"acc_norm_stderr\": 0.02510742548113728\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3412698412698413,\n\
\ \"acc_stderr\": 0.04240799327574924,\n \"acc_norm\": 0.3412698412698413,\n\
\ \"acc_norm_stderr\": 0.04240799327574924\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \
\ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.7161290322580646,\n \"acc_stderr\": 0.025649381063029265,\n \"\
acc_norm\": 0.7161290322580646,\n \"acc_norm_stderr\": 0.025649381063029265\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.4729064039408867,\n \"acc_stderr\": 0.03512819077876106,\n \"\
acc_norm\": 0.4729064039408867,\n \"acc_norm_stderr\": 0.03512819077876106\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.64,\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\": 0.6909090909090909,\n \"acc_stderr\": 0.036085410115739666,\n\
\ \"acc_norm\": 0.6909090909090909,\n \"acc_norm_stderr\": 0.036085410115739666\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7575757575757576,\n \"acc_stderr\": 0.030532892233932026,\n \"\
acc_norm\": 0.7575757575757576,\n \"acc_norm_stderr\": 0.030532892233932026\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8393782383419689,\n \"acc_stderr\": 0.026499057701397457,\n\
\ \"acc_norm\": 0.8393782383419689,\n \"acc_norm_stderr\": 0.026499057701397457\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6051282051282051,\n \"acc_stderr\": 0.024784316942156395,\n\
\ \"acc_norm\": 0.6051282051282051,\n \"acc_norm_stderr\": 0.024784316942156395\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.32222222222222224,\n \"acc_stderr\": 0.028493465091028597,\n \
\ \"acc_norm\": 0.32222222222222224,\n \"acc_norm_stderr\": 0.028493465091028597\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.5840336134453782,\n \"acc_stderr\": 0.03201650100739611,\n \
\ \"acc_norm\": 0.5840336134453782,\n \"acc_norm_stderr\": 0.03201650100739611\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3443708609271523,\n \"acc_stderr\": 0.038796870240733264,\n \"\
acc_norm\": 0.3443708609271523,\n \"acc_norm_stderr\": 0.038796870240733264\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.7761467889908257,\n \"acc_stderr\": 0.017871217767790236,\n \"\
acc_norm\": 0.7761467889908257,\n \"acc_norm_stderr\": 0.017871217767790236\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.46296296296296297,\n \"acc_stderr\": 0.03400603625538271,\n \"\
acc_norm\": 0.46296296296296297,\n \"acc_norm_stderr\": 0.03400603625538271\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.75,\n \"acc_stderr\": 0.03039153369274154,\n \"acc_norm\": 0.75,\n\
\ \"acc_norm_stderr\": 0.03039153369274154\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\
: {\n \"acc\": 0.7172995780590717,\n \"acc_stderr\": 0.029312814153955927,\n\
\ \"acc_norm\": 0.7172995780590717,\n \"acc_norm_stderr\": 0.029312814153955927\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.672645739910314,\n\
\ \"acc_stderr\": 0.03149384670994131,\n \"acc_norm\": 0.672645739910314,\n\
\ \"acc_norm_stderr\": 0.03149384670994131\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n\
\ \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7355371900826446,\n \"acc_stderr\": 0.04026187527591205,\n \"\
acc_norm\": 0.7355371900826446,\n \"acc_norm_stderr\": 0.04026187527591205\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.6851851851851852,\n\
\ \"acc_stderr\": 0.04489931073591312,\n \"acc_norm\": 0.6851851851851852,\n\
\ \"acc_norm_stderr\": 0.04489931073591312\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7668711656441718,\n \"acc_stderr\": 0.0332201579577674,\n\
\ \"acc_norm\": 0.7668711656441718,\n \"acc_norm_stderr\": 0.0332201579577674\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.38392857142857145,\n\
\ \"acc_stderr\": 0.04616143075028547,\n \"acc_norm\": 0.38392857142857145,\n\
\ \"acc_norm_stderr\": 0.04616143075028547\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n\
\ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8547008547008547,\n\
\ \"acc_stderr\": 0.02308663508684141,\n \"acc_norm\": 0.8547008547008547,\n\
\ \"acc_norm_stderr\": 0.02308663508684141\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252609,\n \
\ \"acc_norm\": 0.67,\n \"acc_norm_stderr\": 0.04725815626252609\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7879948914431673,\n\
\ \"acc_stderr\": 0.014616099385833688,\n \"acc_norm\": 0.7879948914431673,\n\
\ \"acc_norm_stderr\": 0.014616099385833688\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6763005780346821,\n \"acc_stderr\": 0.025190181327608405,\n\
\ \"acc_norm\": 0.6763005780346821,\n \"acc_norm_stderr\": 0.025190181327608405\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3027932960893855,\n\
\ \"acc_stderr\": 0.015366860386397112,\n \"acc_norm\": 0.3027932960893855,\n\
\ \"acc_norm_stderr\": 0.015366860386397112\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.6830065359477124,\n \"acc_stderr\": 0.02664327847450875,\n\
\ \"acc_norm\": 0.6830065359477124,\n \"acc_norm_stderr\": 0.02664327847450875\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6784565916398714,\n\
\ \"acc_stderr\": 0.026527724079528872,\n \"acc_norm\": 0.6784565916398714,\n\
\ \"acc_norm_stderr\": 0.026527724079528872\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.6604938271604939,\n \"acc_stderr\": 0.02634856441201162,\n\
\ \"acc_norm\": 0.6604938271604939,\n \"acc_norm_stderr\": 0.02634856441201162\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4326241134751773,\n \"acc_stderr\": 0.029555454236778855,\n \
\ \"acc_norm\": 0.4326241134751773,\n \"acc_norm_stderr\": 0.029555454236778855\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.41460234680573665,\n\
\ \"acc_stderr\": 0.012582597058908284,\n \"acc_norm\": 0.41460234680573665,\n\
\ \"acc_norm_stderr\": 0.012582597058908284\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.5992647058823529,\n \"acc_stderr\": 0.029768263528933105,\n\
\ \"acc_norm\": 0.5992647058823529,\n \"acc_norm_stderr\": 0.029768263528933105\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6339869281045751,\n \"acc_stderr\": 0.01948802574552967,\n \
\ \"acc_norm\": 0.6339869281045751,\n \"acc_norm_stderr\": 0.01948802574552967\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6272727272727273,\n\
\ \"acc_stderr\": 0.04631381319425465,\n \"acc_norm\": 0.6272727272727273,\n\
\ \"acc_norm_stderr\": 0.04631381319425465\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7224489795918367,\n \"acc_stderr\": 0.028666857790274648,\n\
\ \"acc_norm\": 0.7224489795918367,\n \"acc_norm_stderr\": 0.028666857790274648\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7810945273631841,\n\
\ \"acc_stderr\": 0.029239174636647,\n \"acc_norm\": 0.7810945273631841,\n\
\ \"acc_norm_stderr\": 0.029239174636647\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774709,\n \
\ \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774709\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5301204819277109,\n\
\ \"acc_stderr\": 0.03885425420866767,\n \"acc_norm\": 0.5301204819277109,\n\
\ \"acc_norm_stderr\": 0.03885425420866767\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8070175438596491,\n \"acc_stderr\": 0.030267457554898458,\n\
\ \"acc_norm\": 0.8070175438596491,\n \"acc_norm_stderr\": 0.030267457554898458\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2741738066095471,\n\
\ \"mc1_stderr\": 0.015616518497219376,\n \"mc2\": 0.4237773900118851,\n\
\ \"mc2_stderr\": 0.014244420047515118\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7797947908445146,\n \"acc_stderr\": 0.01164627675508968\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.33510235026535257,\n \
\ \"acc_stderr\": 0.013001948176422957\n }\n}\n```"
repo_url: https://huggingface.co/wannaphong/han-llm-7b-v2
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|arc:challenge|25_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|gsm8k|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hellaswag|10_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-04T20-24-26.117217.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-04T20-24-26.117217.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- '**/details_harness|winogrande|5_2024-03-04T20-24-26.117217.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-04T20-24-26.117217.parquet'
- config_name: results
data_files:
- split: 2024_03_04T20_24_26.117217
path:
- results_2024-03-04T20-24-26.117217.parquet
- split: latest
path:
- results_2024-03-04T20-24-26.117217.parquet
---
# Dataset Card for Evaluation run of wannaphong/han-llm-7b-v2
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [wannaphong/han-llm-7b-v2](https://huggingface.co/wannaphong/han-llm-7b-v2) 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_wannaphong__han-llm-7b-v2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-04T20:24:26.117217](https://huggingface.co/datasets/open-llm-leaderboard/details_wannaphong__han-llm-7b-v2/blob/main/results_2024-03-04T20-24-26.117217.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": 0.5972728257098081,
"acc_stderr": 0.03311148975202147,
"acc_norm": 0.6028244973496223,
"acc_norm_stderr": 0.03379622943577808,
"mc1": 0.2741738066095471,
"mc1_stderr": 0.015616518497219376,
"mc2": 0.4237773900118851,
"mc2_stderr": 0.014244420047515118
},
"harness|arc:challenge|25": {
"acc": 0.5452218430034129,
"acc_stderr": 0.014551507060836353,
"acc_norm": 0.5878839590443686,
"acc_norm_stderr": 0.014383915302225403
},
"harness|hellaswag|10": {
"acc": 0.6122286397132045,
"acc_stderr": 0.004862461799370391,
"acc_norm": 0.8174666401115316,
"acc_norm_stderr": 0.0038549403270910316
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.33,
"acc_stderr": 0.04725815626252606,
"acc_norm": 0.33,
"acc_norm_stderr": 0.04725815626252606
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5407407407407407,
"acc_stderr": 0.04304979692464241,
"acc_norm": 0.5407407407407407,
"acc_norm_stderr": 0.04304979692464241
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6447368421052632,
"acc_stderr": 0.038947344870133176,
"acc_norm": 0.6447368421052632,
"acc_norm_stderr": 0.038947344870133176
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.56,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.56,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6452830188679245,
"acc_stderr": 0.029445175328199583,
"acc_norm": 0.6452830188679245,
"acc_norm_stderr": 0.029445175328199583
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.6736111111111112,
"acc_stderr": 0.03921067198982266,
"acc_norm": 0.6736111111111112,
"acc_norm_stderr": 0.03921067198982266
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.44,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.44,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.53,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.53,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6069364161849711,
"acc_stderr": 0.037242495958177295,
"acc_norm": 0.6069364161849711,
"acc_norm_stderr": 0.037242495958177295
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4019607843137255,
"acc_stderr": 0.048786087144669955,
"acc_norm": 0.4019607843137255,
"acc_norm_stderr": 0.048786087144669955
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.76,
"acc_stderr": 0.042923469599092816,
"acc_norm": 0.76,
"acc_norm_stderr": 0.042923469599092816
},
"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": 0.45614035087719296,
"acc_stderr": 0.046854730419077895,
"acc_norm": 0.45614035087719296,
"acc_norm_stderr": 0.046854730419077895
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5862068965517241,
"acc_stderr": 0.04104269211806232,
"acc_norm": 0.5862068965517241,
"acc_norm_stderr": 0.04104269211806232
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.3888888888888889,
"acc_stderr": 0.02510742548113728,
"acc_norm": 0.3888888888888889,
"acc_norm_stderr": 0.02510742548113728
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.3412698412698413,
"acc_stderr": 0.04240799327574924,
"acc_norm": 0.3412698412698413,
"acc_norm_stderr": 0.04240799327574924
},
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```
## 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. -->
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- **Paper [optional]:** [More Information Needed]
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## 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. -->
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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]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
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#### 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
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## Citation [optional]
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## Glossary [optional]
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Epiculous/Gnosis | ---
license: agpl-3.0
language:
- en
---
# Gnosis
This dataset was provided by jeiku |
RaiBP/openwebtext2-first-30-chunks-nonenglish-examples | ---
license: mit
dataset_info:
features:
- name: text
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 1173553162
num_examples: 520038
download_size: 774487748
dataset_size: 1173553162
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
hitachi-nlp/proofwriter_processed_OWA | ---
dataset_info:
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configs:
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path: NatLang/dev-*
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---
|
Nexdata/Italian_Speech_Data_by_Mobile_Phone | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for Nexdata/Italian_Speech_Data_by_Mobile_Phone
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://www.nexdata.ai/datasets/948?source=Huggingface
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The data were recorded by 3,109 native Italian speakers with authentic Italian accents. The recorded content covers a wide range of categories such as general purpose, interactive, in car commands, home commands, etc. The recorded text is designed by a language expert, and the text is manually proofread with high accuracy. Match mainstream Android, Apple system phones.
For more details, please refer to the link: https://www.nexdata.ai/datasets/948?source=Huggingface
### Supported Tasks and Leaderboards
automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).
### Languages
Italian
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
### Citation Information
[More Information Needed]
### Contributions
|
feynman-integrals-nn/heavycrossbox | ---
license: cc-by-4.0
---
# heavycrossbox
* [data](https://huggingface.co/datasets/feynman-integrals-nn/heavycrossbox)
* [source](https://gitlab.com/feynman-integrals-nn/feynman-integrals-nn/-/tree/main/heavycrossbox)
|
ctu-aic/csfever_v2_pvi | ---
license: cc-by-sa-3.0
task_categories:
- text-classification
task_ids:
- natural-language-inference
language:
- cs
tags:
- Fact-checking
pretty_name: CsFEVERv2-PVI
multilinguality: monolingual
source_datasets: fever
size_categories:
- 100K<n<1M
---
# Dataset Card for "CsFEVERv2"
## Dataset Description
CsFEVERv2_pvi is a dataset for Czech fact-checking (NLI) developed as part of a bachelor thesis at the Artificial Intelligence Center of the Faculty of Electrical Engineering of
the Czech technical university in Prague.
### Languages
Czech
## Dataset Usage Example
```python
from datasets import load_dataset
dataset = load_dataset("/home/mlynatom/csfever_v2_pvi")
```
## Dataset Structure
### Data Instances
An example of 'train' looks as follows.
```json
{'id': 155439,
'label': 2,
'claim': 'Newcastle United FC vyhrál pět ligových titulů.',
'evidence': "Ronnie Simpson. Ronnie Simpson (21. října 1930, Glasgow – 19. dubna 2004, Edinburgh) byl skotský fotbalový brankář..."}
```
### Data Fields
- `id`: a `int32` feature.
- `label`: a `int32` feature.
- `claim`: a `string` feature.
- `evidence`: a `string` feature.
### Data Splits
| | train | dev | test |
|----------|-------:|-----:|------:|
| num_rows | 106209 | 6319 | 6261 |
# Citation
```bibtex
@article{Ullrich_2023,
doi = {10.1007/s10579-023-09654-3},
url = {https://doi.org/10.1007%2Fs10579-023-09654-3},
year = 2023,
month = {may},
publisher = {Springer Science and Business Media {LLC}},
author = {Herbert Ullrich and Jan Drchal and Martin Rýpar and Hana Vincourová and Václav Moravec},
title = {{CsFEVER} and {CTKFacts}: acquiring Czech data for fact verification},
journal = {Language Resources and Evaluation},
archivePrefix={arXiv},
eprint={2201.11115},
}
```
```bibtex
@misc{ethayarajh2022understanding,
title={Understanding Dataset Difficulty with $\mathcal{V}$-Usable Information},
author={Kawin Ethayarajh and Yejin Choi and Swabha Swayamdipta},
year={2022},
eprint={2110.08420},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```bibtex
@thesis{Mlynar_2023,
author = {Mlynář, Tomáš},
type = {Bachelor's Thesis}
title = {Automated Fact Checking Based on Czech Wikipedia},
institution = {Czech Technical University in Prague, Faculty of Electrical Engineering},
date = {2023},
url = {http://hdl.handle.net/10467/109219}
}
``` |
julian-cuadra-g/impsheet | ---
license: apache-2.0
---
|
TomTBT/pmc_open_access_xml | ---
pretty_name: XML-parsed PMC
task_categories:
- text-classification
- summarization
- other
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- en
size_categories:
- 1M<n<10M
source_datasets:
- original
license:
- cc0-1.0
- cc-by-4.0
- cc-by-sa-4.0
- cc-by-nc-4.0
- cc-by-nd-4.0
- cc-by-nc-nd-4.0
- cc-by-nc-sa-4.0
- unknown
- other
multilinguality:
- monolingual
task_ids: []
tags:
- research papers
- biology
- medecine
---
# Dataset Card for PMC Open Access XML
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://www.ncbi.nlm.nih.gov/pmc/tools/openftlist/
- **Repository:** [Needs More Information]
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
The XML Open Access includes more than 3.4 million journal articles and preprints that are made available under
license terms that allow reuse.
Not all articles in PMC are available for text mining and other reuse, many have copyright protection, however articles
in the PMC Open Access Subset are made available under Creative Commons or similar licenses that generally allow more
liberal redistribution and reuse than a traditional copyrighted work.
The PMC Open Access Subset is one part of the PMC Article Datasets
This version takes XML version as source, benefiting from the structured text
to split the articles in parts, naming the introduction, methods, results,
discussion and conclusion, and reference with keywords in the text to external or internal
resources (articles, figures, tables, formulas, boxed-text, quotes, code, footnotes, chemicals, graphics, medias).
The dataset was initially created with relation-extraction tasks in mind, between the references in text and the content of the
references (e.g. for PMID, by joining the refered article abstract from the pubmed dataset), but aims in a larger extent to provide
a corpus of pre-annotated text for other tasks (e.g. figure caption to graphic, glossary definition detection, summarization).
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
[Needs More Information]
## Dataset Structure
### Data Fields
- "accession_id": The PMC ID of the article
- "pmid": The PubMed ID of the article
- "introduction": List of \<title\> and \<p\> elements in \<body\>, sharing their root with a \<title\> containing "introduction" or "background".
- "methods": Same as introduction with "method" keyword.
- "results": Same as introduction with "result" keyword.
- "discussion": Same as introduction with "discussion" keyword.
- "conclusion": Same as introduction with "conclusion" keyword.
- "front": List of \<title\> and \<p\> elements in \<front\> after everything else has been searched.
- "body": List of \<title\> and \<p\> elements in \<body\> after everything else has been searched.
- "back": List of \<title\> and \<p\> elements in \<back\> after everything else has been searched.
- "figure": List of \<fig\> elements of the article.
- "table": List of \<table-wrap\> and \<array\> elements of the article.
- "formula": List of \<disp-formula\> and \<inline-formula\> elements of the article.
- "box": List of \<boxed-text\> elements of the article.
- "code": List of \<code\> elements of the article.
- "quote": List of \<disp-quote\> and \<speech\> elements of the article.
- "chemical": List of \<chem-struct-wrap\> elements of the article.
- "supplementary": List of \<supplementary-material\> and \<inline-supplementary-material\> elements of the article.
- "footnote": List of \<fn-group\> and \<table-wrap-foot\> elements of the article.
- "graphic": List of \<graphic\> and \<inline-graphic\> elements of the article.
- "media": List of \<media\> and \<inline-media\> elements of the article.
- "glossary": Glossary if found in the XML
- "unknown_references": JSON of a dictionnary of each "tag":"text" for the reference that did not indicate a PMID
- "n_references": Total number of references and unknown references
- "license": The licence of the article
- "retracted": If the article was retracted or not
- "last_updated": Last update of the article
- "citation": Citation of the article
- "package_file": path to the folder containing the graphics and media files of the article (to append to the base URL: ftp.ncbi.nlm.nih.gov/pub/pmc/)
In text, the references are in the form ##KEYWORD##IDX_REF##OLD_TEXT##, with keywords (REF, UREF, FIG, TAB, FORMU, BOX, CODE, QUOTE, CHEM, SUPPL, FOOTN, GRAPH, MEDIA) referencing respectively to "pubmed articles" (external), "unknown_references", "figure", "table", "formula", "box", "code", "quote", "chem", "supplementary", "footnote", "graphic" and "media".
### Data Splits
[Needs More Information]
## Dataset Creation
### Curation Rationale
Internal references (figures, tables, ...) were found using specific tags. Deciding on those tags was done by testing and by looking in the documentation
for the different kind of possible usage.
Then, to split the article into introduction, methods, results, discussion and conclusion, specific keywords in titles were used. Because there are no rules
in this xml to tag those sections, finding the keyword seemed like the most reliable approach to do so. A drawback is that many section do not have those
keywords in the titles but could be assimilated to those. However, the huge diversity in the titles makes it harder to label such sections. This could be the
work of further versions of this dataset.
### Source Data
#### Initial Data Collection and Normalization
Data was obtained from:
- ftp.ncbi.nlm.nih.gov/pub/pmc/oa_bulk/oa_noncomm/xml/
- ftp.ncbi.nlm.nih.gov/pub/pmc/oa_bulk/oa_comm/xml/
- ftp.ncbi.nlm.nih.gov/pub/pmc/oa_bulk/oa_other/xml/
Additional content for individual articles (graphics, media) can be obtained from:
- ftp.ncbi.nlm.nih.gov/pub/pmc + "package_file"
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
The articles XML are similar accross collections. This means that if a certain collection handles the structure in unusual ways, the whole collection might not be as
well annotated than others. This concerns all the sections (intro, methods, ...), the external references (pmids) and the internal references (tables, figures, ...).
To illustrate that, references are sometime given as a range (e.g. 10-15). In that case, only reference 10 and 15 are linked. This could potentially be handled in a
future version.
### Other Known Limitations
[Needs More Information]
### Preprocessing recommendations
- Filter out empty contents.
- Remove unwanted references from the text, and replace either by the "references_text" or by the reference content itself.
- Unescape HTML special characters: `import html; html.unescape(my_text)`
- Remove superfluous line break in text.
- Remove XML tags (\<italic\>, \<sup\>, \<sub\>, ...), replace by special tokens?
- Join the items of the contents' lists.
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
https://www.ncbi.nlm.nih.gov/pmc/about/copyright/
Within the PMC Open Access Subset, there are three groupings:
Commercial Use Allowed - CC0, CC BY, CC BY-SA, CC BY-ND licenses
Non-Commercial Use Only - CC BY-NC, CC BY-NC-SA, CC BY-NC-ND licenses; and
Other - no machine-readable Creative Commons license, no license, or a custom license.
### Citation Information
[Needs More Information] |
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