datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
lilobe8614/oxford-flowers | ---
pretty_name: Oxford Flowers Dataset
source_datasets: https://www.robots.ox.ac.uk/~vgg/data/flowers
tags:
- flowers
- oxford
task_categories:
- image-classification
- unconditional-image-generation
license:
- unknown
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': '1'
'1': '10'
'2': '100'
'3': '101'
'4': '102'
'5': '11'
'6': '12'
'7': '13'
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'10': '16'
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'24': '29'
'25': '3'
'26': '30'
'27': '31'
'28': '32'
'29': '33'
'30': '34'
'31': '35'
'32': '36'
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'39': '42'
'40': '43'
'41': '44'
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'78': '78'
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'80': '8'
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'82': '81'
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'84': '83'
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'86': '85'
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'89': '88'
'90': '89'
'91': '9'
'92': '90'
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'94': '92'
'95': '93'
'96': '94'
'97': '95'
'98': '96'
'99': '97'
'100': '98'
'101': '99'
splits:
- name: train
num_bytes: 308119477.446
num_examples: 7169
- name: test
num_bytes: 43247670.14
num_examples: 1020
download_size: 346597973
dataset_size: 351367147.58599997
---
# Dataset Card for "oxford-flowers"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
McSpicyWithMilo/target-element-add-cv | ---
dataset_info:
features:
- name: target_element
dtype: string
- name: instruction
dtype: string
splits:
- name: train
num_bytes: 18474
num_examples: 180
download_size: 11044
dataset_size: 18474
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "target-element-add-cv"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Uilham/Charlie | ---
license: openrail
---
|
dipteshkanojia/t5-qe-2023-ente-da-test | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
- name: task
dtype: string
splits:
- name: train
num_bytes: 814660
num_examples: 1075
download_size: 265541
dataset_size: 814660
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
language:
- en
- te
---
# Dataset Card for "t5-qe-2023-ente-da-test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Minglii/r_pv4_wiz_all | ---
dataset_info:
features:
- name: data
struct:
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 215231808
num_examples: 76836
download_size: 103377449
dataset_size: 215231808
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "r_pv4_wiz_all"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
bejaeger/filled_stacks_metadata | ---
dataset_info:
features:
- name: videoId
dtype: string
- name: title
dtype: string
- name: channelId
dtype: string
- name: description
dtype: string
- name: publishedAt
dtype: string
- name: likes
dtype: string
- name: views
dtype: string
splits:
- name: train
num_bytes: 45766
num_examples: 98
download_size: 0
dataset_size: 45766
---
# Dataset Card for "filled_stacks_metadata"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
kfahn/fractal_flame | ---
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 40495438.0
num_examples: 100
download_size: 40499080
dataset_size: 40495438.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
HumanF-MarkrAI/WIKI_QA_Near_dedup | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: input
dtype: float64
- name: output
dtype: string
- name: instruction
dtype: string
splits:
- name: train
num_bytes: 145724140
num_examples: 137505
download_size: 87382170
dataset_size: 145724140
license: cc-by-nc-sa-4.0
---
# Dataset Card for "WIKI_QA_Near_dedup"
**The license is `cc-by-nc-sa`.**
## Datasets Details
**Developers** SeungyooLee (DopeorNopeLee)
WIKI_QA_Near_dedup dataset was created by Near dedup algorithm to reduce similarity.
**It's original source is [maywell/wikidata_QA](maywell/wikidata_QA), which created by an innovative developer named [maywell(Jeonghwan Park)](https://huggingface.co/maywell).**
It follows "cc-by-nc-sa-4.0 lisence" policy. |
kye/all-openai-github-code | ---
license: mit
---
|
fimu-docproc-research/CIVQA-TesseractOCR | ---
dataset_info:
features:
- name: id
dtype: string
- name: words
sequence: string
- name: answers
dtype: string
- name: bboxes
sequence:
sequence: float64
- name: answers_bboxes
sequence:
sequence: float64
- name: questions
dtype: string
- name: image
dtype: string
splits:
- name: train
num_bytes: 1838637418
num_examples: 160645
- name: validation
num_bytes: 177768370
num_examples: 16220
download_size: 6274981
dataset_size: 2016405788
license: mit
language:
- cs
tags:
- finance
---
# CIVQA TesseractOCR Dataset
The Czech Invoice Visual Question Answering dataset was created with Tesseract OCR, and it is suitable for adding labels for the chosen model.
The encoded dataset for LayoutLM model can be found on this link: https://huggingface.co/datasets/fimu-docproc-research/CIVQA-TesseractOCR-LayoutLM
All invoices used in this dataset were obtained from public sources. Over these invoices, we were focusing on 15 different entities, which are crucial for processing the invoices.
- Invoice number
- Variable symbol
- Specific symbol
- Constant symbol
- Bank code
- Account number
- ICO
- Total amount
- Invoice date
- Due date
- Name of supplier
- IBAN
- DIC
- QR code
- Supplier's address
The invoices included in this dataset were gathered from the internet. We understand that privacy is of utmost importance. Therefore, we sincerely apologise for any inconvenience caused by including your identifiable information in this dataset. If you have identified your data in this dataset and wish to have it removed from research purposes, we request you kindly to access the following URL: https://forms.gle/tUVJKoB22oeTncUD6
We profoundly appreciate your cooperation and understanding in this matter. |
Erynan/eval_just_100 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response_a
dtype: string
- name: response_b
dtype: string
- name: more_reasonable
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 29403
num_examples: 100
download_size: 16405
dataset_size: 29403
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
markbotterill/olist_encoded | ---
dataset_info:
features:
- name: label
dtype: int64
- name: text
dtype: string
- name: Words Per Review
dtype: int64
- name: __index_level_0__
dtype: int64
- name: input_ids
sequence: int32
- name: token_type_ids
sequence: int8
- name: attention_mask
sequence: int8
- name: hidden_state
sequence: float32
splits:
- name: train
num_bytes: 99759813
num_examples: 23860
- name: test
num_bytes: 25686431
num_examples: 5965
download_size: 112853840
dataset_size: 125446244
---
# Dataset Card for "olist_encoded"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ycchen/oaast_seed | ---
dataset_info:
features:
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 4258491
num_examples: 3359
download_size: 2403423
dataset_size: 4258491
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "oaast_seed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
zolak/twitter_dataset_79_1713181861 | ---
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: 342178
num_examples: 829
download_size: 168377
dataset_size: 342178
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
CyberHarem/hunter_azurlane | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of hunter/ハンター/猎人 (Azur Lane)
This is the dataset of hunter/ハンター/猎人 (Azur Lane), containing 24 images and their tags.
The core tags of this character are `hat, long_hair, red_eyes, brown_hair, bangs, blonde_hair, 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 | 24 | 26.20 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hunter_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 24 | 15.70 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hunter_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 48 | 31.62 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hunter_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 24 | 23.52 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hunter_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 48 | 43.88 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hunter_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/hunter_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:----------------------------------------------------------------------------------------------------------------------|
| 0 | 24 |  |  |  |  |  | 1girl, solo, tricorne, gloves, scarf, navel, shorts, belt, gun, looking_at_viewer, midriff, thighhighs, boots, jacket |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | tricorne | gloves | scarf | navel | shorts | belt | gun | looking_at_viewer | midriff | thighhighs | boots | jacket |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:-----------|:---------|:--------|:--------|:---------|:-------|:------|:--------------------|:----------|:-------------|:--------|:---------|
| 0 | 24 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
joey234/mmlu-international_law-neg-answer | ---
dataset_info:
features:
- name: question
dtype: string
- name: choices
sequence: string
- name: answer
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
- name: neg_answer
dtype: string
splits:
- name: test
num_bytes: 67365
num_examples: 121
download_size: 38742
dataset_size: 67365
---
# Dataset Card for "mmlu-international_law-neg-answer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mask-distilled-one-sec-cv12/chunk_44 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1241572176
num_examples: 243828
download_size: 1260638110
dataset_size: 1241572176
---
# Dataset Card for "chunk_44"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
lamini/product-catalog-questions | ---
license: cc-by-4.0
task_categories:
- text-classification
- question-answering
- text-generation
language:
- en
tags:
- ecommerce
- shopping
- products
size_categories:
- 10K<n<50k
---
# Lamini Product Catalog QA Dataset
## Description
This dataset contains questions about products and their corresonding product information like product id, product name, product description, etc. This questions catalog has been built on top of open-source product catalog from [kaggle.](https://www.kaggle.com/c/instacart-market-basket-analysis)
## Format
The questions and product information are in the form of jsonlines file.
## Data Pipeline Code
The entire data pipeline used to create this dataset is open source at: [https://github.com/lamini-ai/instacart-greg](https://github.com/lamini-ai/instacart-greg/blob/main/instacart/generate_data_pipeline.py)
It can be used to reproduce this dataset, or add new products to it.
## License
The dataset is released under the CC-BY license.
## Citation
If you use this dataset in your research, please cite us. lamini.ai
## Contributing
If you would like to contribute to this dataset, please submit a pull request with your changes. |
CyberHarem/plumeri_pokemon | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of plumeri/プルメリ (Pokémon)
This is the dataset of plumeri/プルメリ (Pokémon), containing 215 images and their tags.
The core tags of this character are `pink_hair, multicolored_hair, blonde_hair, two-tone_hair, long_hair, yellow_eyes, hair_ornament, quad_tails, breasts, eyeshadow, skull_hair_ornament, dark_skin`, 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 | 215 | 201.58 MiB | [Download](https://huggingface.co/datasets/CyberHarem/plumeri_pokemon/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 215 | 128.70 MiB | [Download](https://huggingface.co/datasets/CyberHarem/plumeri_pokemon/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 452 | 240.21 MiB | [Download](https://huggingface.co/datasets/CyberHarem/plumeri_pokemon/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 215 | 183.37 MiB | [Download](https://huggingface.co/datasets/CyberHarem/plumeri_pokemon/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 452 | 313.55 MiB | [Download](https://huggingface.co/datasets/CyberHarem/plumeri_pokemon/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/plumeri_pokemon',
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 | 30 |  |  |  |  |  | 1girl, crop_top, makeup, tank_top, navel, midriff, stomach_tattoo, black_pants, solo, wristband, looking_at_viewer, closed_mouth, skull_necklace, pubic_tattoo, bare_shoulders, frown |
| 1 | 5 |  |  |  |  |  | 1boy, 1girl, blush, hetero, open_mouth, cum_in_pussy, makeup, pokephilia, solo_focus, sweat, tank_top, tongue_out, ahegao, doggystyle, necklace, penis, pokemon_(creature), saliva, sex_from_behind, uncensored, vaginal, anus, ass_grab, bestiality, fucked_silly, medium_breasts, rolling_eyes, spread_legs |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | crop_top | makeup | tank_top | navel | midriff | stomach_tattoo | black_pants | solo | wristband | looking_at_viewer | closed_mouth | skull_necklace | pubic_tattoo | bare_shoulders | frown | 1boy | blush | hetero | open_mouth | cum_in_pussy | pokephilia | solo_focus | sweat | tongue_out | ahegao | doggystyle | necklace | penis | pokemon_(creature) | saliva | sex_from_behind | uncensored | vaginal | anus | ass_grab | bestiality | fucked_silly | medium_breasts | rolling_eyes | spread_legs |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------|:---------|:-----------|:--------|:----------|:-----------------|:--------------|:-------|:------------|:--------------------|:---------------|:-----------------|:---------------|:-----------------|:--------|:-------|:--------|:---------|:-------------|:---------------|:-------------|:-------------|:--------|:-------------|:---------|:-------------|:-----------|:--------|:---------------------|:---------|:------------------|:-------------|:----------|:-------|:-----------|:-------------|:---------------|:-----------------|:---------------|:--------------|
| 0 | 30 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 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 |
|
cakiki/python_paths | ---
dataset_info:
features:
- name: repository_name
dtype: string
splits:
- name: train
num_bytes: 636121755
num_examples: 23578465
download_size: 550836738
dataset_size: 636121755
---
# Dataset Card for "python_paths"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Transform21/Godardv1 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 228793812.668
num_examples: 9623
download_size: 1122951385
dataset_size: 228793812.668
---
# Dataset Card for "Godardv1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
one-sec-cv12/chunk_0 | ---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
splits:
- name: train
num_bytes: 21981833424.125
num_examples: 228863
download_size: 18831760350
dataset_size: 21981833424.125
---
# Dataset Card for "chunk_0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
MariaIsabel/FR_NFR_Spanish_requirements_classification | ---
annotations_creators:
- other
language:
- es
language_creators:
- other
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: ReSpa - Spanish requirements labeled in functional and non-functional classes.
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
---
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
ReSpa: Published version of dataset used for paper 'Towards an automatic requirements classification in a new Spanish dataset'
### Languages
Spanish
## Dataset Structure
### Data Fields
Project: Project's Identifier from which the requirements were obtained.
Requirement: Description of the software requirement.
Final label: Label of the requirement: F (functional requirement) and NF (non-functional requirement).
## Dataset Creation
### Initial Data Collection and Normalization
This dataset was created from a collection of functional and non-functional requirements extracted from 13 final degree and 2 master’s projects carried out from the University of A Coruna. It consist in 300 functional and 89 non-funtcional requirements.
## Additional Information
### Citation Information
https://doi.org/10.1109/RE54965.2022.00039
|
open-llm-leaderboard/details_Weyaxi__OpenHermes-2.5-neural-chat-7b-v3-1-7B | ---
pretty_name: Evaluation run of Weyaxi/OpenHermes-2.5-neural-chat-7b-v3-1-7B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Weyaxi/OpenHermes-2.5-neural-chat-7b-v3-1-7B](https://huggingface.co/Weyaxi/OpenHermes-2.5-neural-chat-7b-v3-1-7B)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 1 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Weyaxi__OpenHermes-2.5-neural-chat-7b-v3-1-7B\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T13:00:25.330681](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__OpenHermes-2.5-neural-chat-7b-v3-1-7B/blob/main/results_2023-12-02T13-00-25.330681.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\": 0.530705079605762,\n\
\ \"acc_stderr\": 0.013746490739560035\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.530705079605762,\n \"acc_stderr\": 0.013746490739560035\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Weyaxi/OpenHermes-2.5-neural-chat-7b-v3-1-7B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_02T13_00_25.330681
path:
- '**/details_harness|gsm8k|5_2023-12-02T13-00-25.330681.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T13-00-25.330681.parquet'
- config_name: results
data_files:
- split: 2023_12_02T13_00_25.330681
path:
- results_2023-12-02T13-00-25.330681.parquet
- split: latest
path:
- results_2023-12-02T13-00-25.330681.parquet
---
# Dataset Card for Evaluation run of Weyaxi/OpenHermes-2.5-neural-chat-7b-v3-1-7B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Weyaxi/OpenHermes-2.5-neural-chat-7b-v3-1-7B
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [Weyaxi/OpenHermes-2.5-neural-chat-7b-v3-1-7B](https://huggingface.co/Weyaxi/OpenHermes-2.5-neural-chat-7b-v3-1-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 1 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Weyaxi__OpenHermes-2.5-neural-chat-7b-v3-1-7B",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T13:00:25.330681](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__OpenHermes-2.5-neural-chat-7b-v3-1-7B/blob/main/results_2023-12-02T13-00-25.330681.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": 0.530705079605762,
"acc_stderr": 0.013746490739560035
},
"harness|gsm8k|5": {
"acc": 0.530705079605762,
"acc_stderr": 0.013746490739560035
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
open-llm-leaderboard/details_luffycodes__vicuna-mmlu-val-only-correct-mcq-7b-ep2 | ---
pretty_name: Evaluation run of luffycodes/vicuna-mmlu-val-only-correct-mcq-7b-ep2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [luffycodes/vicuna-mmlu-val-only-correct-mcq-7b-ep2](https://huggingface.co/luffycodes/vicuna-mmlu-val-only-correct-mcq-7b-ep2)\
\ 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_luffycodes__vicuna-mmlu-val-only-correct-mcq-7b-ep2\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-18T08:54:03.410174](https://huggingface.co/datasets/open-llm-leaderboard/details_luffycodes__vicuna-mmlu-val-only-correct-mcq-7b-ep2/blob/main/results_2023-12-18T08-54-03.410174.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\": 0.4771667109240717,\n\
\ \"acc_stderr\": 0.034233110377777674,\n \"acc_norm\": 0.485115350864936,\n\
\ \"acc_norm_stderr\": 0.035153131866548476,\n \"mc1\": 0.2827417380660955,\n\
\ \"mc1_stderr\": 0.015764770836777305,\n \"mc2\": 0.4317214117939303,\n\
\ \"mc2_stderr\": 0.01510139555175088\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.492320819112628,\n \"acc_stderr\": 0.01460966744089257,\n\
\ \"acc_norm\": 0.5298634812286689,\n \"acc_norm_stderr\": 0.014585305840007102\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5798645688109938,\n\
\ \"acc_stderr\": 0.004925717008099711,\n \"acc_norm\": 0.7767377016530571,\n\
\ \"acc_norm_stderr\": 0.004155816900505157\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \
\ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4740740740740741,\n\
\ \"acc_stderr\": 0.04313531696750574,\n \"acc_norm\": 0.4740740740740741,\n\
\ \"acc_norm_stderr\": 0.04313531696750574\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.5131578947368421,\n \"acc_stderr\": 0.04067533136309174,\n\
\ \"acc_norm\": 0.5131578947368421,\n \"acc_norm_stderr\": 0.04067533136309174\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.46,\n\
\ \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\": 0.46,\n \
\ \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.4641509433962264,\n \"acc_stderr\": 0.030693675018458006,\n\
\ \"acc_norm\": 0.4641509433962264,\n \"acc_norm_stderr\": 0.030693675018458006\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.4930555555555556,\n\
\ \"acc_stderr\": 0.041808067502949374,\n \"acc_norm\": 0.4930555555555556,\n\
\ \"acc_norm_stderr\": 0.041808067502949374\n },\n \"harness|hendrycksTest-college_chemistry|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-college_computer_science|5\": {\n \"\
acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\"\
: 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.45664739884393063,\n\
\ \"acc_stderr\": 0.03798106566014498,\n \"acc_norm\": 0.45664739884393063,\n\
\ \"acc_norm_stderr\": 0.03798106566014498\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.1568627450980392,\n \"acc_stderr\": 0.036186648199362466,\n\
\ \"acc_norm\": 0.1568627450980392,\n \"acc_norm_stderr\": 0.036186648199362466\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.6,\n \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\": 0.6,\n\
\ \"acc_norm_stderr\": 0.049236596391733084\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.40425531914893614,\n \"acc_stderr\": 0.032081157507886836,\n\
\ \"acc_norm\": 0.40425531914893614,\n \"acc_norm_stderr\": 0.032081157507886836\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.3508771929824561,\n\
\ \"acc_stderr\": 0.04489539350270699,\n \"acc_norm\": 0.3508771929824561,\n\
\ \"acc_norm_stderr\": 0.04489539350270699\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.47586206896551725,\n \"acc_stderr\": 0.041618085035015295,\n\
\ \"acc_norm\": 0.47586206896551725,\n \"acc_norm_stderr\": 0.041618085035015295\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.30423280423280424,\n \"acc_stderr\": 0.023695415009463087,\n \"\
acc_norm\": 0.30423280423280424,\n \"acc_norm_stderr\": 0.023695415009463087\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.29365079365079366,\n\
\ \"acc_stderr\": 0.04073524322147125,\n \"acc_norm\": 0.29365079365079366,\n\
\ \"acc_norm_stderr\": 0.04073524322147125\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.04793724854411019,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.04793724854411019\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.532258064516129,\n\
\ \"acc_stderr\": 0.02838474778881333,\n \"acc_norm\": 0.532258064516129,\n\
\ \"acc_norm_stderr\": 0.02838474778881333\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.4039408866995074,\n \"acc_stderr\": 0.0345245390382204,\n\
\ \"acc_norm\": 0.4039408866995074,\n \"acc_norm_stderr\": 0.0345245390382204\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.45,\n \"acc_stderr\": 0.049999999999999996,\n \"acc_norm\"\
: 0.45,\n \"acc_norm_stderr\": 0.049999999999999996\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.6242424242424243,\n \"acc_stderr\": 0.03781887353205982,\n\
\ \"acc_norm\": 0.6242424242424243,\n \"acc_norm_stderr\": 0.03781887353205982\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.601010101010101,\n \"acc_stderr\": 0.03488901616852731,\n \"acc_norm\"\
: 0.601010101010101,\n \"acc_norm_stderr\": 0.03488901616852731\n },\n\
\ \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \
\ \"acc\": 0.6683937823834197,\n \"acc_stderr\": 0.03397636541089118,\n\
\ \"acc_norm\": 0.6683937823834197,\n \"acc_norm_stderr\": 0.03397636541089118\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.41025641025641024,\n \"acc_stderr\": 0.024939313906940788,\n\
\ \"acc_norm\": 0.41025641025641024,\n \"acc_norm_stderr\": 0.024939313906940788\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3074074074074074,\n \"acc_stderr\": 0.028133252578815635,\n \
\ \"acc_norm\": 0.3074074074074074,\n \"acc_norm_stderr\": 0.028133252578815635\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.39915966386554624,\n \"acc_stderr\": 0.03181110032413925,\n\
\ \"acc_norm\": 0.39915966386554624,\n \"acc_norm_stderr\": 0.03181110032413925\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\"\
: 0.634862385321101,\n \"acc_stderr\": 0.020642801454384005,\n \"\
acc_norm\": 0.634862385321101,\n \"acc_norm_stderr\": 0.020642801454384005\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.39351851851851855,\n \"acc_stderr\": 0.03331747876370312,\n \"\
acc_norm\": 0.39351851851851855,\n \"acc_norm_stderr\": 0.03331747876370312\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.6862745098039216,\n \"acc_stderr\": 0.032566854844603886,\n \"\
acc_norm\": 0.6862745098039216,\n \"acc_norm_stderr\": 0.032566854844603886\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.6708860759493671,\n \"acc_stderr\": 0.030587326294702365,\n \
\ \"acc_norm\": 0.6708860759493671,\n \"acc_norm_stderr\": 0.030587326294702365\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.5246636771300448,\n\
\ \"acc_stderr\": 0.03351695167652628,\n \"acc_norm\": 0.5246636771300448,\n\
\ \"acc_norm_stderr\": 0.03351695167652628\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.5648854961832062,\n \"acc_stderr\": 0.04348208051644858,\n\
\ \"acc_norm\": 0.5648854961832062,\n \"acc_norm_stderr\": 0.04348208051644858\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.6115702479338843,\n \"acc_stderr\": 0.04449270350068382,\n \"\
acc_norm\": 0.6115702479338843,\n \"acc_norm_stderr\": 0.04449270350068382\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.4722222222222222,\n\
\ \"acc_stderr\": 0.04826217294139894,\n \"acc_norm\": 0.4722222222222222,\n\
\ \"acc_norm_stderr\": 0.04826217294139894\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.49079754601226994,\n \"acc_stderr\": 0.03927705600787443,\n\
\ \"acc_norm\": 0.49079754601226994,\n \"acc_norm_stderr\": 0.03927705600787443\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.33035714285714285,\n\
\ \"acc_stderr\": 0.04464285714285712,\n \"acc_norm\": 0.33035714285714285,\n\
\ \"acc_norm_stderr\": 0.04464285714285712\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.6601941747572816,\n \"acc_stderr\": 0.04689765937278135,\n\
\ \"acc_norm\": 0.6601941747572816,\n \"acc_norm_stderr\": 0.04689765937278135\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.6623931623931624,\n\
\ \"acc_stderr\": 0.030980296992618558,\n \"acc_norm\": 0.6623931623931624,\n\
\ \"acc_norm_stderr\": 0.030980296992618558\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \
\ \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620332\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.6666666666666666,\n\
\ \"acc_stderr\": 0.01685739124747255,\n \"acc_norm\": 0.6666666666666666,\n\
\ \"acc_norm_stderr\": 0.01685739124747255\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.5028901734104047,\n \"acc_stderr\": 0.026918645383239004,\n\
\ \"acc_norm\": 0.5028901734104047,\n \"acc_norm_stderr\": 0.026918645383239004\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2536312849162011,\n\
\ \"acc_stderr\": 0.014551553659369923,\n \"acc_norm\": 0.2536312849162011,\n\
\ \"acc_norm_stderr\": 0.014551553659369923\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.5261437908496732,\n \"acc_stderr\": 0.028590752958852394,\n\
\ \"acc_norm\": 0.5261437908496732,\n \"acc_norm_stderr\": 0.028590752958852394\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.5369774919614148,\n\
\ \"acc_stderr\": 0.028320325830105915,\n \"acc_norm\": 0.5369774919614148,\n\
\ \"acc_norm_stderr\": 0.028320325830105915\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.5061728395061729,\n \"acc_stderr\": 0.02781862396258329,\n\
\ \"acc_norm\": 0.5061728395061729,\n \"acc_norm_stderr\": 0.02781862396258329\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.3475177304964539,\n \"acc_stderr\": 0.028406627809590954,\n \
\ \"acc_norm\": 0.3475177304964539,\n \"acc_norm_stderr\": 0.028406627809590954\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.33702737940026073,\n\
\ \"acc_stderr\": 0.012072836273691323,\n \"acc_norm\": 0.33702737940026073,\n\
\ \"acc_norm_stderr\": 0.012072836273691323\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.49264705882352944,\n \"acc_stderr\": 0.030369552523902173,\n\
\ \"acc_norm\": 0.49264705882352944,\n \"acc_norm_stderr\": 0.030369552523902173\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.4526143790849673,\n \"acc_stderr\": 0.02013679091849254,\n \
\ \"acc_norm\": 0.4526143790849673,\n \"acc_norm_stderr\": 0.02013679091849254\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5727272727272728,\n\
\ \"acc_stderr\": 0.047381987035454834,\n \"acc_norm\": 0.5727272727272728,\n\
\ \"acc_norm_stderr\": 0.047381987035454834\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.6244897959183674,\n \"acc_stderr\": 0.03100120903989484,\n\
\ \"acc_norm\": 0.6244897959183674,\n \"acc_norm_stderr\": 0.03100120903989484\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6616915422885572,\n\
\ \"acc_stderr\": 0.03345563070339193,\n \"acc_norm\": 0.6616915422885572,\n\
\ \"acc_norm_stderr\": 0.03345563070339193\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.41566265060240964,\n\
\ \"acc_stderr\": 0.03836722176598052,\n \"acc_norm\": 0.41566265060240964,\n\
\ \"acc_norm_stderr\": 0.03836722176598052\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.6842105263157895,\n \"acc_stderr\": 0.03565079670708312,\n\
\ \"acc_norm\": 0.6842105263157895,\n \"acc_norm_stderr\": 0.03565079670708312\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2827417380660955,\n\
\ \"mc1_stderr\": 0.015764770836777305,\n \"mc2\": 0.4317214117939303,\n\
\ \"mc2_stderr\": 0.01510139555175088\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7190213101815311,\n \"acc_stderr\": 0.012632541095875824\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.000758150113722517,\n \
\ \"acc_stderr\": 0.0007581501137225296\n }\n}\n```"
repo_url: https://huggingface.co/luffycodes/vicuna-mmlu-val-only-correct-mcq-7b-ep2
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|arc:challenge|25_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|gsm8k|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hellaswag|10_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-18T08-54-03.410174.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-18T08-54-03.410174.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- '**/details_harness|winogrande|5_2023-12-18T08-54-03.410174.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-18T08-54-03.410174.parquet'
- config_name: results
data_files:
- split: 2023_12_18T08_54_03.410174
path:
- results_2023-12-18T08-54-03.410174.parquet
- split: latest
path:
- results_2023-12-18T08-54-03.410174.parquet
---
# Dataset Card for Evaluation run of luffycodes/vicuna-mmlu-val-only-correct-mcq-7b-ep2
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [luffycodes/vicuna-mmlu-val-only-correct-mcq-7b-ep2](https://huggingface.co/luffycodes/vicuna-mmlu-val-only-correct-mcq-7b-ep2) 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_luffycodes__vicuna-mmlu-val-only-correct-mcq-7b-ep2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-18T08:54:03.410174](https://huggingface.co/datasets/open-llm-leaderboard/details_luffycodes__vicuna-mmlu-val-only-correct-mcq-7b-ep2/blob/main/results_2023-12-18T08-54-03.410174.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": 0.4771667109240717,
"acc_stderr": 0.034233110377777674,
"acc_norm": 0.485115350864936,
"acc_norm_stderr": 0.035153131866548476,
"mc1": 0.2827417380660955,
"mc1_stderr": 0.015764770836777305,
"mc2": 0.4317214117939303,
"mc2_stderr": 0.01510139555175088
},
"harness|arc:challenge|25": {
"acc": 0.492320819112628,
"acc_stderr": 0.01460966744089257,
"acc_norm": 0.5298634812286689,
"acc_norm_stderr": 0.014585305840007102
},
"harness|hellaswag|10": {
"acc": 0.5798645688109938,
"acc_stderr": 0.004925717008099711,
"acc_norm": 0.7767377016530571,
"acc_norm_stderr": 0.004155816900505157
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.29,
"acc_stderr": 0.04560480215720684,
"acc_norm": 0.29,
"acc_norm_stderr": 0.04560480215720684
},
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```
## Dataset Details
### Dataset Description
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- **Curated by:** [More Information Needed]
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CyberHarem/negev_girlsfrontline | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of negev/ネゲヴ/内格夫 (Girls' Frontline)
This is the dataset of negev/ネゲヴ/内格夫 (Girls' Frontline), containing 407 images and their tags.
The core tags of this character are `long_hair, pink_hair, red_eyes, hair_ornament, braid, bow, bangs, hair_bow, hairclip, red_bow, ribbon, one_side_up, hair_between_eyes, hair_ribbon, breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 407 | 563.25 MiB | [Download](https://huggingface.co/datasets/CyberHarem/negev_girlsfrontline/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 407 | 298.79 MiB | [Download](https://huggingface.co/datasets/CyberHarem/negev_girlsfrontline/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1012 | 673.01 MiB | [Download](https://huggingface.co/datasets/CyberHarem/negev_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 407 | 483.04 MiB | [Download](https://huggingface.co/datasets/CyberHarem/negev_girlsfrontline/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1012 | 992.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/negev_girlsfrontline/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/negev_girlsfrontline',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 19 |  |  |  |  |  | 1girl, blush, star_of_david, 1boy, collarbone, hetero, nude, simple_background, solo_focus, penis, white_background, smile, looking_at_viewer, mosaic_censoring, nipples, open_mouth, medium_breasts, navel, pussy, tongue_out, large_breasts |
| 1 | 6 |  |  |  |  |  | 1girl, blush, looking_at_viewer, red_ribbon, simple_background, solo, star_of_david, white_gloves, white_skirt, jacket, smile, white_background, closed_mouth, black_pantyhose |
| 2 | 9 |  |  |  |  |  | 1girl, blush, long_sleeves, looking_at_viewer, solo, star_of_david, white_gloves, white_skirt, black_pantyhose, jacket, open_mouth, red_ribbon, simple_background, pleated_skirt, shirt, side_braid, :d, bowtie, cowboy_shot, hand_on_hip |
| 3 | 11 |  |  |  |  |  | 1girl, black_pantyhose, holding_gun, looking_at_viewer, machine_gun, smile, solo, star_of_david, white_gloves, white_skirt, blush, jacket, pleated_skirt, simple_background, white_background, red_ribbon, ammunition_belt, full_body, leg_ribbon, shoes |
| 4 | 11 |  |  |  |  |  | 1girl, shirt, simple_background, smile, solo, star_of_david, upper_body, blush, looking_at_viewer, red_ribbon, white_background, bowtie, closed_mouth, collared_jacket |
| 5 | 10 |  |  |  |  |  | 1girl, blush, looking_at_viewer, navel, smile, solo, collarbone, simple_background, star_of_david, medium_breasts, white_background, white_bikini, bare_shoulders, cleavage, open_mouth, stomach, halterneck, thighs |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | star_of_david | 1boy | collarbone | hetero | nude | simple_background | solo_focus | penis | white_background | smile | looking_at_viewer | mosaic_censoring | nipples | open_mouth | medium_breasts | navel | pussy | tongue_out | large_breasts | red_ribbon | solo | white_gloves | white_skirt | jacket | closed_mouth | black_pantyhose | long_sleeves | pleated_skirt | shirt | side_braid | :d | bowtie | cowboy_shot | hand_on_hip | holding_gun | machine_gun | ammunition_belt | full_body | leg_ribbon | shoes | upper_body | collared_jacket | white_bikini | bare_shoulders | cleavage | stomach | halterneck | thighs |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:----------------|:-------|:-------------|:---------|:-------|:--------------------|:-------------|:--------|:-------------------|:--------|:--------------------|:-------------------|:----------|:-------------|:-----------------|:--------|:--------|:-------------|:----------------|:-------------|:-------|:---------------|:--------------|:---------|:---------------|:------------------|:---------------|:----------------|:--------|:-------------|:-----|:---------|:--------------|:--------------|:--------------|:--------------|:------------------|:------------|:-------------|:--------|:-------------|:------------------|:---------------|:-----------------|:-----------|:----------|:-------------|:---------|
| 0 | 19 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 6 |  |  |  |  |  | X | X | X | | | | | X | | | X | X | X | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 9 |  |  |  |  |  | X | X | X | | | | | X | | | | | X | | | X | | | | | | X | X | X | X | X | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | |
| 3 | 11 |  |  |  |  |  | X | X | X | | | | | X | | | X | X | X | | | | | | | | | X | X | X | X | X | | X | | X | | | | | | | X | X | X | X | X | X | | | | | | | | |
| 4 | 11 |  |  |  |  |  | X | X | X | | | | | X | | | X | X | X | | | | | | | | | X | X | | | | X | | | | X | | | X | | | | | | | | | X | X | | | | | | |
| 5 | 10 |  |  |  |  |  | X | X | X | | X | | | X | | | X | X | X | | | X | X | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X |
|
result-kand2-sdxl-wuerst-karlo/52b331a6 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 189
num_examples: 10
download_size: 1383
dataset_size: 189
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "52b331a6"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Aunsiels/InfantBooks | ---
annotations_creators:
- no-annotation
language:
- en
language_creators:
- crowdsourced
license:
- gpl
multilinguality:
- monolingual
pretty_name: InfantBooks
size_categories:
- 1M<n<10M
source_datasets:
- original
tags:
- research paper
- kids
- children
- books
task_categories:
- text-generation
task_ids:
- language-modeling
---
# Dataset Card for InfantBooks
## 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)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Additional Information](#additional-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [https://www.mpi-inf.mpg.de/children-texts-for-commonsense](https://www.mpi-inf.mpg.de/children-texts-for-commonsense)
- **Paper:** Do Children Texts Hold The Key To Commonsense Knowledge?
### Dataset Summary
A dataset of infants/children's books.
### Languages
All the books are in English;
## Dataset Structure
### Data Instances
malis-friend_BookDash-FKB.txt,"Then a taxi driver, hooting around the yard with his wire car. Mali enjoys playing by himself..."
### Data Fields
- title: The title of the book
- content: The content of the book
## Dataset Creation
### Curation Rationale
The goal of the dataset is to study infant books, which are supposed to be easier to understand than normal texts. In particular, the original goal was to study if these texts contain more commonsense knowledge.
### Source Data
#### Initial Data Collection and Normalization
We automatically collected kids' books on the web.
#### Who are the source language producers?
Native speakers.
### Citation Information
```
Romero, J., & Razniewski, S. (2022).
Do Children Texts Hold The Key To Commonsense Knowledge?
In Proceedings of the 2022 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning.
```
|
lordseidon/gf_uncensored_10k | ---
license: apache-2.0
---
|
LambdaTests/VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_4_1000 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: response
dtype: string
splits:
- name: train
num_bytes: 925
num_examples: 32
download_size: 2034
dataset_size: 925
---
# Dataset Card for "VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_4_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
rajpurkar/squad | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- found
language:
- en
license: cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|wikipedia
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: squad
pretty_name: SQuAD
dataset_info:
config_name: plain_text
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
splits:
- name: train
num_bytes: 79346108
num_examples: 87599
- name: validation
num_bytes: 10472984
num_examples: 10570
download_size: 16278203
dataset_size: 89819092
configs:
- config_name: plain_text
data_files:
- split: train
path: plain_text/train-*
- split: validation
path: plain_text/validation-*
default: true
train-eval-index:
- config: plain_text
task: question-answering
task_id: extractive_question_answering
splits:
train_split: train
eval_split: validation
col_mapping:
question: question
context: context
answers:
text: text
answer_start: answer_start
metrics:
- type: squad
name: SQuAD
---
# Dataset Card for SQuAD
## Table of Contents
- [Dataset Card for "squad"](#dataset-card-for-squad)
- [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)
- [plain_text](#plain_text)
- [Data Fields](#data-fields)
- [plain_text](#plain_text-1)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [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://rajpurkar.github.io/SQuAD-explorer/
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** https://arxiv.org/abs/1606.05250
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Dataset Summary
Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.
SQuAD 1.1 contains 100,000+ question-answer pairs on 500+ articles.
### Supported Tasks and Leaderboards
Question Answering.
### Languages
English (`en`).
## Dataset Structure
### Data Instances
#### plain_text
- **Size of downloaded dataset files:** 35.14 MB
- **Size of the generated dataset:** 89.92 MB
- **Total amount of disk used:** 125.06 MB
An example of 'train' looks as follows.
```
{
"answers": {
"answer_start": [1],
"text": ["This is a test text"]
},
"context": "This is a test context.",
"id": "1",
"question": "Is this a test?",
"title": "train test"
}
```
### Data Fields
The data fields are the same among all splits.
#### plain_text
- `id`: a `string` feature.
- `title`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
### Data Splits
| name |train|validation|
|----------|----:|---------:|
|plain_text|87599| 10570|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
The dataset is distributed under the CC BY-SA 4.0 license.
### Citation Information
```
@inproceedings{rajpurkar-etal-2016-squad,
title = "{SQ}u{AD}: 100,000+ Questions for Machine Comprehension of Text",
author = "Rajpurkar, Pranav and
Zhang, Jian and
Lopyrev, Konstantin and
Liang, Percy",
editor = "Su, Jian and
Duh, Kevin and
Carreras, Xavier",
booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2016",
address = "Austin, Texas",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D16-1264",
doi = "10.18653/v1/D16-1264",
pages = "2383--2392",
eprint={1606.05250},
archivePrefix={arXiv},
primaryClass={cs.CL},
}
```
### Contributions
Thanks to [@lewtun](https://github.com/lewtun), [@albertvillanova](https://github.com/albertvillanova), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset. |
liuyanchen1015/MULTI_VALUE_mrpc_non_coordinated_subj_obj | ---
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: 21573
num_examples: 79
- name: train
num_bytes: 51626
num_examples: 186
- name: validation
num_bytes: 6023
num_examples: 22
download_size: 64370
dataset_size: 79222
---
# Dataset Card for "MULTI_VALUE_mrpc_non_coordinated_subj_obj"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
xmj2002/genshin_ch_10npc | ---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: language
dtype: string
- name: npcName
dtype: string
- name: text
dtype: string
- name: type
dtype: string
splits:
- name: train
num_bytes: 2459515323.168046
num_examples: 17293
- name: test
num_bytes: 273358494.8319542
num_examples: 1922
download_size: 2154942775
dataset_size: 2732873818
license: apache-2.0
task_categories:
- text-to-speech
language:
- zh
---
# Dataset Card for "genshin_ch_10npc"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
aai530-group6/pmdata-sleep_scores | ---
license: cc-by-4.0
---
|
Lohse/alpaca-dummy | ---
license: mit
---
|
weaviate/WithoutRetrieval-SchemaSplit-Train-20 | ---
license: apache-2.0
---
|
Kevinger/hub-report-classlabels | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: valid
path: data/valid-*
dataset_info:
features:
- name: id
dtype: string
- name: score
dtype: float64
- name: label
dtype:
class_label:
names:
'0': business
'1': crime
'2': culture
'3': entertainment
'4': politics
'5': science
'6': sports
'7': weather
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 6068451
num_examples: 2211
- name: test
num_bytes: 1197234
num_examples: 474
- name: valid
num_bytes: 1288652
num_examples: 474
download_size: 5225035
dataset_size: 8554337
---
# Dataset Card for "hub-report-classlabels"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
aimankem32/mrdcs | ---
license: unknown
---
|
Mitsuki-Sakamoto/alpaca_farm-deberta-re-preference-64-nsample-16_filter_gold_thr_0.0_self_160m | ---
dataset_info:
config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1
features:
- name: instruction
dtype: string
- name: input
dtype: string
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- name: output_2
dtype: string
- name: reward_model_prompt_format
dtype: string
- name: gen_prompt_format
dtype: string
- name: gen_kwargs
struct:
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dtype: bool
- name: max_new_tokens
dtype: int64
- name: pad_token_id
dtype: int64
- name: top_k
dtype: int64
- name: top_p
dtype: float64
- name: reward_1
dtype: float64
- name: reward_2
dtype: float64
- name: n_samples
dtype: int64
- name: reject_select
dtype: string
- name: index
dtype: int64
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
- name: filtered_epoch
dtype: int64
- name: gen_reward
dtype: float64
- name: gen_response
dtype: string
splits:
- name: epoch_0
num_bytes: 43739689
num_examples: 18928
- name: epoch_1
num_bytes: 44387101
num_examples: 18928
- name: epoch_2
num_bytes: 44442616
num_examples: 18928
- name: epoch_3
num_bytes: 44465665
num_examples: 18928
- name: epoch_4
num_bytes: 44474648
num_examples: 18928
- name: epoch_5
num_bytes: 44478169
num_examples: 18928
- name: epoch_6
num_bytes: 44445111
num_examples: 18928
- name: epoch_7
num_bytes: 44428537
num_examples: 18928
- name: epoch_8
num_bytes: 44413870
num_examples: 18928
- name: epoch_9
num_bytes: 44390595
num_examples: 18928
download_size: 700749470
dataset_size: 443666001
configs:
- config_name: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1
data_files:
- split: epoch_0
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_0-*
- split: epoch_1
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_1-*
- split: epoch_2
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_2-*
- split: epoch_3
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_3-*
- split: epoch_4
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_4-*
- split: epoch_5
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_5-*
- split: epoch_6
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_6-*
- split: epoch_7
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_7-*
- split: epoch_8
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_8-*
- split: epoch_9
path: alpaca_instructions-pythia_160m_alpaca_farm_instructions_sft_constant_pa_seed_1/epoch_9-*
---
|
CIRAL/ciral | ---
license: apache-2.0
language:
- ha
- so
- sw
- yo
task_categories:
- text-retrieval
mutilinguality:
- multilingual
viewer: true
---
# Dataset Summary
CIRAL is a collection for cross-lingual information retrieval research across four (4) African languages. The collection comprises English queries and query-passage relevance judgements for passages in the African languages.
This dataset repo contains only the queries and relevance judgements. The corpus collection can be found here [here](https://huggingface.co/datasets/CIRAL/ciral-corpus)
# Dataset Structure
1. To download the files: The queries can be found under `ciral-{lang}/topics` and are in `.tsv` formats with each line in the form:
```
qid\tquery
```
while the judgements are in the folder `ciral-{lang}/qrels`, with each file in the standard TREC format:
```
qid Q0 docid relevance
```
2. To access the dataset via `datasets`:
```
ciral_dataset = load_dataset("ciral/ciral", "hausa") #or swahili, somali, yoruba
for data in ciral_dataset['dev']: # or 'testA' or 'testB'
query_id = data['query_id']
query = data['query']
pos_qrels = data['positive_passages']
neg_qrels = data['negative_passages']
# To load test set A's pool judgments
pools_pos_qrels = data['pools_positive_passages']
pools_neg_qrels = data['pools_negative_passages']
for qrel in pos_qrels:
docid = qrel['docid']
text = qrel['text']
```
## Citation
```
@misc{CiralHfCite,
title = {{CIRAL: A Test Suite for {CLIR} in {A}frican Languages}},
author = {Mofetoluwa Adeyemi and
Akintunde Oladipo and
Xinyu Zhang and
David Alfonso-Hermelo and
Mehdi Rezagholizadeh and
Boxing Chen and
Jimmy Lin},
year = 2023,
url = {https://huggingface.co/datasets/CIRAL/ciral},
urldate = {2023-12-19}
}
``` |
AngelBottomless/Gelbooru-Post-Dump | ---
license: mit
---
|
Multimodal-Fatima/VQAv2_test_split_3 | ---
dataset_info:
features:
- name: question_type
dtype: string
- name: multiple_choice_answer
dtype: string
- name: answers
sequence: string
- name: answers_original
list:
- name: answer
dtype: string
- name: answer_confidence
dtype: string
- name: answer_id
dtype: int64
- name: id_image
dtype: int64
- name: answer_type
dtype: string
- name: question_id
dtype: int64
- name: question
dtype: string
- name: image
dtype: image
- name: id
dtype: int64
- name: clip_tags_ViT_L_14
sequence: string
- name: blip_caption
dtype: string
- name: LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14
sequence: string
- name: DETA_detections_deta_swin_large_o365_coco_classes
list:
- name: attribute
dtype: string
- name: box
sequence: float32
- name: label
dtype: string
- name: location
dtype: string
- name: ratio
dtype: float32
- name: size
dtype: string
- name: tag
dtype: string
- name: Attributes_ViT_L_14_descriptors_text_davinci_003_full
sequence: string
- name: clip_tags_ViT_L_14_wo_openai
sequence: string
- name: clip_tags_ViT_L_14_with_openai
sequence: string
- name: clip_tags_LAION_ViT_H_14_2B_wo_openai
sequence: string
- name: clip_tags_LAION_ViT_H_14_2B_with_openai
sequence: string
- name: clip_tags_LAION_ViT_bigG_14_2B_wo_openai
sequence: string
- name: clip_tags_LAION_ViT_bigG_14_2B_with_openai
sequence: string
- name: Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full
sequence: string
- name: Attributes_LAION_ViT_bigG_14_2B_descriptors_text_davinci_003_full
sequence: string
- name: clip_tags_ViT_B_16_with_openai
sequence: string
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splits:
- name: test
num_bytes: 9127511526.0
num_examples: 44779
download_size: 1825926763
dataset_size: 9127511526.0
---
# Dataset Card for "VQAv2_test_split_3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
zolak/twitter_dataset_81_1713040541 | ---
dataset_info:
features:
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dtype: string
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download_size: 1577716
dataset_size: 3158016
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
vidhikatkoria/DA_MultiWOZ_hotel | ---
dataset_info:
features:
- name: domain
dtype: string
- name: context
dtype: string
- name: response
dtype: string
- name: act
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- name: speaker
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splits:
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num_examples: 4953
- name: test
num_bytes: 366
num_examples: 1
download_size: 653642
dataset_size: 1627674.4499394428
---
# Dataset Card for "DA_MultiWOZ_hotel"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
doniz/jecaroline | ---
license: openrail
---
|
cleanrl/summarize_from_feedback_oai_preprocessing_1704578687 | ---
dataset_info:
features:
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struct:
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- name: validation_cnndm
num_bytes: 153419887
num_examples: 2284
download_size: 277994823
dataset_size: 4489455425
---
# Dataset Card for "summarize_from_feedback_oai_preprocessing_1704578687"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ravithejads/telugu_gpteacher | ---
dataset_info:
features:
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dtype: string
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- name: output
dtype: string
splits:
- name: train
num_bytes: 337517
num_examples: 202
download_size: 161927
dataset_size: 337517
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Danocas/CUPINTOROLA | ---
license: openrail
---
|
vietgpt/databricks_dolly15k_en | ---
language: en
dataset_info:
features:
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 12413076
num_examples: 15014
download_size: 7321407
dataset_size: 12413076
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
--- |
ziq/ingredient_to_good_or_bad | ---
dataset_info:
features:
- name: src
dtype: string
- name: ingredients
dtype: string
- name: Good
dtype: bool
splits:
- name: train
num_bytes: 12018794
num_examples: 24889
download_size: 5553609
dataset_size: 12018794
---
# Dataset Card for "ingredient_to_good_or_bad"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Conan-Lao/github-issues | ---
dataset_info:
features:
- name: url
dtype: string
- name: repository_url
dtype: string
- name: labels_url
dtype: string
- name: comments_url
dtype: string
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dtype: string
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dtype: string
- name: id
dtype: int64
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dtype: string
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dtype: string
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struct:
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dtype: string
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dtype: string
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dtype: bool
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dtype: int64
- name: timeline_url
dtype: string
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dtype: 'null'
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dtype: bool
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download_size: 4977402
dataset_size: 17609883
configs:
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data_files:
- split: train
path: data/train-*
---
# Dataset Card for "github-issues"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
LucasMagnana/aactext_large | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: token_type_ids
sequence: int8
- name: attention_mask
sequence: int8
- name: word_ids
sequence: int64
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 1239965
num_examples: 2681
- name: test
num_bytes: 287675
num_examples: 622
download_size: 247500
dataset_size: 1527640
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
SauravMaheshkar/congress-bills | ---
license: unknown
task_categories:
- graph-ml
tags:
- chemistry
configs:
- config_name: transductive
data_files:
- split: train
path: "processed/transductive/train_df.csv"
- split: valid
path: "processed/transductive/val_df.csv"
- split: test
path: "processed/transductive/test_df.csv"
- config_name: inductive
data_files:
- split: train
path: "processed/inductive/train_df.csv"
- split: valid
path: "processed/inductive/val_df.csv"
- split: test
path: "processed/inductive/test_df.csv"
- config_name: raw
data_files: "raw/*.txt"
---
Source Paper: https://arxiv.org/abs/1802.06916
### Usage
```
from torch_geometric.datasets.cornell import CornellTemporalHyperGraphDataset
dataset = CornellTemporalHyperGraphDataset(root = "./", name="congress-bills", split="train")
```
### Citation
```misc
@article{Benson-2018-simplicial,
author = {Benson, Austin R. and Abebe, Rediet and Schaub, Michael T. and Jadbabaie, Ali and Kleinberg, Jon},
title = {Simplicial closure and higher-order link prediction},
year = {2018},
doi = {10.1073/pnas.1800683115},
publisher = {National Academy of Sciences},
issn = {0027-8424},
journal = {Proceedings of the National Academy of Sciences}
}
``` |
heliosprime/twitter_dataset_1713096019 | ---
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: 6395
num_examples: 18
download_size: 10116
dataset_size: 6395
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "twitter_dataset_1713096019"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ChiranjibMohapatra/dataset1 | ---
dataset_info:
features:
- name: USER
dtype: string
- name: ASSISTANT
dtype: string
- name: concatenated_text
dtype: string
splits:
- name: train
num_bytes: 830251635
num_examples: 200157
download_size: 393415122
dataset_size: 830251635
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "dataset1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
JoeyCheng/story_analogy | ---
license: mit
language:
- en
pretty_name: StoryAnalogy
size_categories:
- 1K<n<10K
---
<h1 align="center">StoryAnalogy: Deriving Story-level Analogies from Large Language Models to Unlock Analogical Understanding</h1>
<p align="center">
<a href="https://arxiv.org/abs/2310.12874"><img src="https://img.shields.io/badge/arXiv-2310.12874-b31b1b.svg" alt="Paper" style="display:inline"></a>
<a href="https://aclanthology.org/2023.emnlp-main.706/"> <img alt="License" src="https://img.shields.io/static/v1?label=Pub&message=EMNLP%2723&color=blue" style="display:inline"> </a>
<a href="https://github.com/LFhase/PAIR"><img src="https://img.shields.io/badge/-Github-grey?logo=github" alt="Github" style="display:inline"></a>
<a href="https://github.com/LFhase/PAIR/blob/main/LICENSE"> <img alt="License" src="https://img.shields.io/github/license/LFhase/PAIR?color=blue" style="display:inline"> </a>
<a href="https://github.com/loginaway/StoryAnalogy/blob/main/raw/Poster%20-%20StoryAnalogy%20Deriving%20Story-level%20Analogies%20from%20Large%20Language%20Models%20to%20Unlock%20Analogical%20Understanding.pdf"> <img src="https://img.shields.io/badge/Poster-grey?logo=airplayvideo&logoColor=white" alt="Poster" style="display:inline"></a>
</div>
This is the StoryAnalogy dataset in the EMNLP'23 paper: *[StoryAnalogy: Deriving Story-level Analogies from Large Language Models to Unlock Analogical Understanding](https://arxiv.org/abs/2310.12874)*.
If you use this research, please cite us:
```bibtex
@inproceedings{jiayang2023storyanalogy,
title={StoryAnalogy: Deriving Story-level Analogies from Large Language Models to Unlock Analogical Understanding},
author={Jiayang, Cheng and Qiu, Lin and Chan, Tsz and Fang, Tianqing and Wang, Weiqi and Chan, Chunkit and Ru, Dongyu and Guo, Qipeng and Zhang, Hongming and Song, Yangqiu and others},
booktitle={Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing},
pages={11518--11537},
year={2023}
}
``` |
open-llm-leaderboard/details_AI-Sweden-Models__gpt-sw3-6.7b-v2-instruct | ---
pretty_name: Evaluation run of AI-Sweden-Models/gpt-sw3-6.7b-v2-instruct
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [AI-Sweden-Models/gpt-sw3-6.7b-v2-instruct](https://huggingface.co/AI-Sweden-Models/gpt-sw3-6.7b-v2-instruct)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_AI-Sweden-Models__gpt-sw3-6.7b-v2-instruct_public\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-11-18T21:04:21.939404](https://huggingface.co/datasets/open-llm-leaderboard/details_AI-Sweden-Models__gpt-sw3-6.7b-v2-instruct_public/blob/main/results_2023-11-18T21-04-21.939404.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\": 0.32058974654497724,\n\
\ \"acc_stderr\": 0.03287256745618845,\n \"acc_norm\": 0.3233939935906761,\n\
\ \"acc_norm_stderr\": 0.03364411678813401,\n \"mc1\": 0.26193390452876375,\n\
\ \"mc1_stderr\": 0.015392118805015023,\n \"mc2\": 0.4032485125499964,\n\
\ \"mc2_stderr\": 0.014292284301112663,\n \"em\": 0.22766359060402686,\n\
\ \"em_stderr\": 0.004294273453162853,\n \"f1\": 0.266680998322148,\n\
\ \"f1_stderr\": 0.00428696034436648\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.3575085324232082,\n \"acc_stderr\": 0.014005494275916576,\n\
\ \"acc_norm\": 0.40784982935153585,\n \"acc_norm_stderr\": 0.014361097288449707\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5046803425612428,\n\
\ \"acc_stderr\": 0.004989562798280523,\n \"acc_norm\": 0.6776538538139812,\n\
\ \"acc_norm_stderr\": 0.004664195159393912\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.27,\n \"acc_stderr\": 0.04461960433384741,\n \
\ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.04461960433384741\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.362962962962963,\n\
\ \"acc_stderr\": 0.04153948404742398,\n \"acc_norm\": 0.362962962962963,\n\
\ \"acc_norm_stderr\": 0.04153948404742398\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.32894736842105265,\n \"acc_stderr\": 0.03823428969926604,\n\
\ \"acc_norm\": 0.32894736842105265,\n \"acc_norm_stderr\": 0.03823428969926604\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.35,\n\
\ \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\": 0.35,\n \
\ \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.33962264150943394,\n \"acc_stderr\": 0.029146904747798335,\n\
\ \"acc_norm\": 0.33962264150943394,\n \"acc_norm_stderr\": 0.029146904747798335\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.3680555555555556,\n\
\ \"acc_stderr\": 0.04032999053960718,\n \"acc_norm\": 0.3680555555555556,\n\
\ \"acc_norm_stderr\": 0.04032999053960718\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \
\ \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.26,\n \"acc_stderr\": 0.04408440022768079,\n \"acc_norm\"\
: 0.26,\n \"acc_norm_stderr\": 0.04408440022768079\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847415,\n \
\ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.044619604333847415\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.3352601156069364,\n\
\ \"acc_stderr\": 0.03599586301247078,\n \"acc_norm\": 0.3352601156069364,\n\
\ \"acc_norm_stderr\": 0.03599586301247078\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.2549019607843137,\n \"acc_stderr\": 0.0433643270799318,\n\
\ \"acc_norm\": 0.2549019607843137,\n \"acc_norm_stderr\": 0.0433643270799318\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.35,\n\
\ \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.3191489361702128,\n \"acc_stderr\": 0.030472973363380045,\n\
\ \"acc_norm\": 0.3191489361702128,\n \"acc_norm_stderr\": 0.030472973363380045\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.24561403508771928,\n\
\ \"acc_stderr\": 0.040493392977481404,\n \"acc_norm\": 0.24561403508771928,\n\
\ \"acc_norm_stderr\": 0.040493392977481404\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.27586206896551724,\n \"acc_stderr\": 0.03724563619774634,\n\
\ \"acc_norm\": 0.27586206896551724,\n \"acc_norm_stderr\": 0.03724563619774634\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.29894179894179895,\n \"acc_stderr\": 0.023577604791655805,\n \"\
acc_norm\": 0.29894179894179895,\n \"acc_norm_stderr\": 0.023577604791655805\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.2619047619047619,\n\
\ \"acc_stderr\": 0.039325376803928704,\n \"acc_norm\": 0.2619047619047619,\n\
\ \"acc_norm_stderr\": 0.039325376803928704\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.22,\n \"acc_stderr\": 0.04163331998932269,\n \
\ \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.04163331998932269\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.3161290322580645,\n\
\ \"acc_stderr\": 0.02645087448904276,\n \"acc_norm\": 0.3161290322580645,\n\
\ \"acc_norm_stderr\": 0.02645087448904276\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.1921182266009852,\n \"acc_stderr\": 0.027719315709614775,\n\
\ \"acc_norm\": 0.1921182266009852,\n \"acc_norm_stderr\": 0.027719315709614775\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\"\
: 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.3393939393939394,\n \"acc_stderr\": 0.03697442205031596,\n\
\ \"acc_norm\": 0.3393939393939394,\n \"acc_norm_stderr\": 0.03697442205031596\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.31313131313131315,\n \"acc_stderr\": 0.03304205087813653,\n \"\
acc_norm\": 0.31313131313131315,\n \"acc_norm_stderr\": 0.03304205087813653\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.35233160621761656,\n \"acc_stderr\": 0.034474782864143565,\n\
\ \"acc_norm\": 0.35233160621761656,\n \"acc_norm_stderr\": 0.034474782864143565\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.2564102564102564,\n \"acc_stderr\": 0.02213908110397155,\n \
\ \"acc_norm\": 0.2564102564102564,\n \"acc_norm_stderr\": 0.02213908110397155\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.25925925925925924,\n \"acc_stderr\": 0.026719240783712163,\n \
\ \"acc_norm\": 0.25925925925925924,\n \"acc_norm_stderr\": 0.026719240783712163\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.2689075630252101,\n \"acc_stderr\": 0.028801392193631276,\n\
\ \"acc_norm\": 0.2689075630252101,\n \"acc_norm_stderr\": 0.028801392193631276\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.2251655629139073,\n \"acc_stderr\": 0.03410435282008936,\n \"\
acc_norm\": 0.2251655629139073,\n \"acc_norm_stderr\": 0.03410435282008936\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.42385321100917434,\n \"acc_stderr\": 0.021187263209087516,\n \"\
acc_norm\": 0.42385321100917434,\n \"acc_norm_stderr\": 0.021187263209087516\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.2037037037037037,\n \"acc_stderr\": 0.027467401804058017,\n \"\
acc_norm\": 0.2037037037037037,\n \"acc_norm_stderr\": 0.027467401804058017\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.3088235294117647,\n \"acc_stderr\": 0.03242661719827218,\n \"\
acc_norm\": 0.3088235294117647,\n \"acc_norm_stderr\": 0.03242661719827218\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.4050632911392405,\n \"acc_stderr\": 0.03195514741370673,\n \
\ \"acc_norm\": 0.4050632911392405,\n \"acc_norm_stderr\": 0.03195514741370673\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.38565022421524664,\n\
\ \"acc_stderr\": 0.03266842214289201,\n \"acc_norm\": 0.38565022421524664,\n\
\ \"acc_norm_stderr\": 0.03266842214289201\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.40458015267175573,\n \"acc_stderr\": 0.043046937953806645,\n\
\ \"acc_norm\": 0.40458015267175573,\n \"acc_norm_stderr\": 0.043046937953806645\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.4214876033057851,\n \"acc_stderr\": 0.045077322787750944,\n \"\
acc_norm\": 0.4214876033057851,\n \"acc_norm_stderr\": 0.045077322787750944\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.35185185185185186,\n\
\ \"acc_stderr\": 0.04616631111801714,\n \"acc_norm\": 0.35185185185185186,\n\
\ \"acc_norm_stderr\": 0.04616631111801714\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.26993865030674846,\n \"acc_stderr\": 0.034878251684978906,\n\
\ \"acc_norm\": 0.26993865030674846,\n \"acc_norm_stderr\": 0.034878251684978906\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.35714285714285715,\n\
\ \"acc_stderr\": 0.04547960999764376,\n \"acc_norm\": 0.35714285714285715,\n\
\ \"acc_norm_stderr\": 0.04547960999764376\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.34951456310679613,\n \"acc_stderr\": 0.047211885060971716,\n\
\ \"acc_norm\": 0.34951456310679613,\n \"acc_norm_stderr\": 0.047211885060971716\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.43162393162393164,\n\
\ \"acc_stderr\": 0.0324483553531149,\n \"acc_norm\": 0.43162393162393164,\n\
\ \"acc_norm_stderr\": 0.0324483553531149\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \
\ \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.39080459770114945,\n\
\ \"acc_stderr\": 0.01744836606706253,\n \"acc_norm\": 0.39080459770114945,\n\
\ \"acc_norm_stderr\": 0.01744836606706253\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.315028901734104,\n \"acc_stderr\": 0.025009313790069713,\n\
\ \"acc_norm\": 0.315028901734104,\n \"acc_norm_stderr\": 0.025009313790069713\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2424581005586592,\n\
\ \"acc_stderr\": 0.01433352205921789,\n \"acc_norm\": 0.2424581005586592,\n\
\ \"acc_norm_stderr\": 0.01433352205921789\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.34967320261437906,\n \"acc_stderr\": 0.027305308076274702,\n\
\ \"acc_norm\": 0.34967320261437906,\n \"acc_norm_stderr\": 0.027305308076274702\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.28938906752411575,\n\
\ \"acc_stderr\": 0.02575586592263294,\n \"acc_norm\": 0.28938906752411575,\n\
\ \"acc_norm_stderr\": 0.02575586592263294\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.32407407407407407,\n \"acc_stderr\": 0.026041766202717167,\n\
\ \"acc_norm\": 0.32407407407407407,\n \"acc_norm_stderr\": 0.026041766202717167\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.3262411347517731,\n \"acc_stderr\": 0.027968453043563168,\n \
\ \"acc_norm\": 0.3262411347517731,\n \"acc_norm_stderr\": 0.027968453043563168\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.273142112125163,\n\
\ \"acc_stderr\": 0.01138015056783041,\n \"acc_norm\": 0.273142112125163,\n\
\ \"acc_norm_stderr\": 0.01138015056783041\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.375,\n \"acc_stderr\": 0.029408372932278746,\n \
\ \"acc_norm\": 0.375,\n \"acc_norm_stderr\": 0.029408372932278746\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.30392156862745096,\n \"acc_stderr\": 0.018607552131279834,\n \
\ \"acc_norm\": 0.30392156862745096,\n \"acc_norm_stderr\": 0.018607552131279834\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.36363636363636365,\n\
\ \"acc_stderr\": 0.04607582090719976,\n \"acc_norm\": 0.36363636363636365,\n\
\ \"acc_norm_stderr\": 0.04607582090719976\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.22448979591836735,\n \"acc_stderr\": 0.026711430555538408,\n\
\ \"acc_norm\": 0.22448979591836735,\n \"acc_norm_stderr\": 0.026711430555538408\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.3482587064676617,\n\
\ \"acc_stderr\": 0.033687874661154596,\n \"acc_norm\": 0.3482587064676617,\n\
\ \"acc_norm_stderr\": 0.033687874661154596\n },\n \"harness|hendrycksTest-us_foreign_policy|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-virology|5\": {\n \"acc\": 0.30120481927710846,\n\
\ \"acc_stderr\": 0.0357160923005348,\n \"acc_norm\": 0.30120481927710846,\n\
\ \"acc_norm_stderr\": 0.0357160923005348\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.38011695906432746,\n \"acc_stderr\": 0.037229657413855394,\n\
\ \"acc_norm\": 0.38011695906432746,\n \"acc_norm_stderr\": 0.037229657413855394\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.26193390452876375,\n\
\ \"mc1_stderr\": 0.015392118805015023,\n \"mc2\": 0.4032485125499964,\n\
\ \"mc2_stderr\": 0.014292284301112663\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6353591160220995,\n \"acc_stderr\": 0.013527746622429844\n\
\ },\n \"harness|drop|3\": {\n \"em\": 0.22766359060402686,\n \
\ \"em_stderr\": 0.004294273453162853,\n \"f1\": 0.266680998322148,\n \
\ \"f1_stderr\": 0.00428696034436648\n },\n \"harness|gsm8k|5\": {\n\
\ \"acc\": 0.06368460955269144,\n \"acc_stderr\": 0.006726213078805701\n\
\ }\n}\n```"
repo_url: https://huggingface.co/AI-Sweden-Models/gpt-sw3-6.7b-v2-instruct
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|arc:challenge|25_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|drop|3_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|gsm8k|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hellaswag|10_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-11-18T21-04-21.939404.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-management|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-virology|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|truthfulqa:mc|0_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-11-18T21-04-21.939404.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- '**/details_harness|winogrande|5_2023-11-18T21-04-21.939404.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-11-18T21-04-21.939404.parquet'
- config_name: results
data_files:
- split: 2023_11_18T21_04_21.939404
path:
- results_2023-11-18T21-04-21.939404.parquet
- split: latest
path:
- results_2023-11-18T21-04-21.939404.parquet
---
# Dataset Card for Evaluation run of AI-Sweden-Models/gpt-sw3-6.7b-v2-instruct
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/AI-Sweden-Models/gpt-sw3-6.7b-v2-instruct
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [AI-Sweden-Models/gpt-sw3-6.7b-v2-instruct](https://huggingface.co/AI-Sweden-Models/gpt-sw3-6.7b-v2-instruct) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_AI-Sweden-Models__gpt-sw3-6.7b-v2-instruct_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-18T21:04:21.939404](https://huggingface.co/datasets/open-llm-leaderboard/details_AI-Sweden-Models__gpt-sw3-6.7b-v2-instruct_public/blob/main/results_2023-11-18T21-04-21.939404.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": 0.32058974654497724,
"acc_stderr": 0.03287256745618845,
"acc_norm": 0.3233939935906761,
"acc_norm_stderr": 0.03364411678813401,
"mc1": 0.26193390452876375,
"mc1_stderr": 0.015392118805015023,
"mc2": 0.4032485125499964,
"mc2_stderr": 0.014292284301112663,
"em": 0.22766359060402686,
"em_stderr": 0.004294273453162853,
"f1": 0.266680998322148,
"f1_stderr": 0.00428696034436648
},
"harness|arc:challenge|25": {
"acc": 0.3575085324232082,
"acc_stderr": 0.014005494275916576,
"acc_norm": 0.40784982935153585,
"acc_norm_stderr": 0.014361097288449707
},
"harness|hellaswag|10": {
"acc": 0.5046803425612428,
"acc_stderr": 0.004989562798280523,
"acc_norm": 0.6776538538139812,
"acc_norm_stderr": 0.004664195159393912
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.27,
"acc_stderr": 0.04461960433384741,
"acc_norm": 0.27,
"acc_norm_stderr": 0.04461960433384741
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.362962962962963,
"acc_stderr": 0.04153948404742398,
"acc_norm": 0.362962962962963,
"acc_norm_stderr": 0.04153948404742398
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.32894736842105265,
"acc_stderr": 0.03823428969926604,
"acc_norm": 0.32894736842105265,
"acc_norm_stderr": 0.03823428969926604
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.35,
"acc_stderr": 0.047937248544110196,
"acc_norm": 0.35,
"acc_norm_stderr": 0.047937248544110196
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.33962264150943394,
"acc_stderr": 0.029146904747798335,
"acc_norm": 0.33962264150943394,
"acc_norm_stderr": 0.029146904747798335
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.3680555555555556,
"acc_stderr": 0.04032999053960718,
"acc_norm": 0.3680555555555556,
"acc_norm_stderr": 0.04032999053960718
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.21,
"acc_stderr": 0.040936018074033256,
"acc_norm": 0.21,
"acc_norm_stderr": 0.040936018074033256
},
"harness|hendrycksTest-college_computer_science|5": {
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"acc_stderr": 0.04408440022768079,
"acc_norm": 0.26,
"acc_norm_stderr": 0.04408440022768079
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.27,
"acc_stderr": 0.044619604333847415,
"acc_norm": 0.27,
"acc_norm_stderr": 0.044619604333847415
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.3352601156069364,
"acc_stderr": 0.03599586301247078,
"acc_norm": 0.3352601156069364,
"acc_norm_stderr": 0.03599586301247078
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.2549019607843137,
"acc_stderr": 0.0433643270799318,
"acc_norm": 0.2549019607843137,
"acc_norm_stderr": 0.0433643270799318
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.35,
"acc_stderr": 0.0479372485441102,
"acc_norm": 0.35,
"acc_norm_stderr": 0.0479372485441102
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.3191489361702128,
"acc_stderr": 0.030472973363380045,
"acc_norm": 0.3191489361702128,
"acc_norm_stderr": 0.030472973363380045
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.24561403508771928,
"acc_stderr": 0.040493392977481404,
"acc_norm": 0.24561403508771928,
"acc_norm_stderr": 0.040493392977481404
},
"harness|hendrycksTest-electrical_engineering|5": {
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"acc_stderr": 0.03724563619774634,
"acc_norm": 0.27586206896551724,
"acc_norm_stderr": 0.03724563619774634
},
"harness|hendrycksTest-elementary_mathematics|5": {
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"acc_stderr": 0.023577604791655805,
"acc_norm": 0.29894179894179895,
"acc_norm_stderr": 0.023577604791655805
},
"harness|hendrycksTest-formal_logic|5": {
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"acc_stderr": 0.039325376803928704,
"acc_norm": 0.2619047619047619,
"acc_norm_stderr": 0.039325376803928704
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.22,
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"acc_norm": 0.22,
"acc_norm_stderr": 0.04163331998932269
},
"harness|hendrycksTest-high_school_biology|5": {
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"acc_stderr": 0.02645087448904276,
"acc_norm": 0.3161290322580645,
"acc_norm_stderr": 0.02645087448904276
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.1921182266009852,
"acc_stderr": 0.027719315709614775,
"acc_norm": 0.1921182266009852,
"acc_norm_stderr": 0.027719315709614775
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.25,
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"acc_norm": 0.25,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-high_school_european_history|5": {
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"acc_norm": 0.3393939393939394,
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},
"harness|hendrycksTest-high_school_geography|5": {
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},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.35233160621761656,
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"acc_norm": 0.35233160621761656,
"acc_norm_stderr": 0.034474782864143565
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.2564102564102564,
"acc_stderr": 0.02213908110397155,
"acc_norm": 0.2564102564102564,
"acc_norm_stderr": 0.02213908110397155
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.25925925925925924,
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"acc_norm": 0.25925925925925924,
"acc_norm_stderr": 0.026719240783712163
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.2689075630252101,
"acc_stderr": 0.028801392193631276,
"acc_norm": 0.2689075630252101,
"acc_norm_stderr": 0.028801392193631276
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.2251655629139073,
"acc_stderr": 0.03410435282008936,
"acc_norm": 0.2251655629139073,
"acc_norm_stderr": 0.03410435282008936
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.42385321100917434,
"acc_stderr": 0.021187263209087516,
"acc_norm": 0.42385321100917434,
"acc_norm_stderr": 0.021187263209087516
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.2037037037037037,
"acc_stderr": 0.027467401804058017,
"acc_norm": 0.2037037037037037,
"acc_norm_stderr": 0.027467401804058017
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.3088235294117647,
"acc_stderr": 0.03242661719827218,
"acc_norm": 0.3088235294117647,
"acc_norm_stderr": 0.03242661719827218
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.4050632911392405,
"acc_stderr": 0.03195514741370673,
"acc_norm": 0.4050632911392405,
"acc_norm_stderr": 0.03195514741370673
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.38565022421524664,
"acc_stderr": 0.03266842214289201,
"acc_norm": 0.38565022421524664,
"acc_norm_stderr": 0.03266842214289201
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.40458015267175573,
"acc_stderr": 0.043046937953806645,
"acc_norm": 0.40458015267175573,
"acc_norm_stderr": 0.043046937953806645
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.4214876033057851,
"acc_stderr": 0.045077322787750944,
"acc_norm": 0.4214876033057851,
"acc_norm_stderr": 0.045077322787750944
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.35185185185185186,
"acc_stderr": 0.04616631111801714,
"acc_norm": 0.35185185185185186,
"acc_norm_stderr": 0.04616631111801714
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.26993865030674846,
"acc_stderr": 0.034878251684978906,
"acc_norm": 0.26993865030674846,
"acc_norm_stderr": 0.034878251684978906
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.35714285714285715,
"acc_stderr": 0.04547960999764376,
"acc_norm": 0.35714285714285715,
"acc_norm_stderr": 0.04547960999764376
},
"harness|hendrycksTest-management|5": {
"acc": 0.34951456310679613,
"acc_stderr": 0.047211885060971716,
"acc_norm": 0.34951456310679613,
"acc_norm_stderr": 0.047211885060971716
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.43162393162393164,
"acc_stderr": 0.0324483553531149,
"acc_norm": 0.43162393162393164,
"acc_norm_stderr": 0.0324483553531149
},
"harness|hendrycksTest-medical_genetics|5": {
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"acc_norm": 0.42,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-miscellaneous|5": {
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"acc_norm": 0.39080459770114945,
"acc_norm_stderr": 0.01744836606706253
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.315028901734104,
"acc_stderr": 0.025009313790069713,
"acc_norm": 0.315028901734104,
"acc_norm_stderr": 0.025009313790069713
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.2424581005586592,
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"acc_norm": 0.2424581005586592,
"acc_norm_stderr": 0.01433352205921789
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.34967320261437906,
"acc_stderr": 0.027305308076274702,
"acc_norm": 0.34967320261437906,
"acc_norm_stderr": 0.027305308076274702
},
"harness|hendrycksTest-philosophy|5": {
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"acc_norm": 0.28938906752411575,
"acc_norm_stderr": 0.02575586592263294
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.32407407407407407,
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"acc_norm": 0.32407407407407407,
"acc_norm_stderr": 0.026041766202717167
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.3262411347517731,
"acc_stderr": 0.027968453043563168,
"acc_norm": 0.3262411347517731,
"acc_norm_stderr": 0.027968453043563168
},
"harness|hendrycksTest-professional_law|5": {
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"acc_norm": 0.273142112125163,
"acc_norm_stderr": 0.01138015056783041
},
"harness|hendrycksTest-professional_medicine|5": {
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"acc_norm": 0.375,
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},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.30392156862745096,
"acc_stderr": 0.018607552131279834,
"acc_norm": 0.30392156862745096,
"acc_norm_stderr": 0.018607552131279834
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.36363636363636365,
"acc_stderr": 0.04607582090719976,
"acc_norm": 0.36363636363636365,
"acc_norm_stderr": 0.04607582090719976
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.22448979591836735,
"acc_stderr": 0.026711430555538408,
"acc_norm": 0.22448979591836735,
"acc_norm_stderr": 0.026711430555538408
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.3482587064676617,
"acc_stderr": 0.033687874661154596,
"acc_norm": 0.3482587064676617,
"acc_norm_stderr": 0.033687874661154596
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.36,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-virology|5": {
"acc": 0.30120481927710846,
"acc_stderr": 0.0357160923005348,
"acc_norm": 0.30120481927710846,
"acc_norm_stderr": 0.0357160923005348
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.38011695906432746,
"acc_stderr": 0.037229657413855394,
"acc_norm": 0.38011695906432746,
"acc_norm_stderr": 0.037229657413855394
},
"harness|truthfulqa:mc|0": {
"mc1": 0.26193390452876375,
"mc1_stderr": 0.015392118805015023,
"mc2": 0.4032485125499964,
"mc2_stderr": 0.014292284301112663
},
"harness|winogrande|5": {
"acc": 0.6353591160220995,
"acc_stderr": 0.013527746622429844
},
"harness|drop|3": {
"em": 0.22766359060402686,
"em_stderr": 0.004294273453162853,
"f1": 0.266680998322148,
"f1_stderr": 0.00428696034436648
},
"harness|gsm8k|5": {
"acc": 0.06368460955269144,
"acc_stderr": 0.006726213078805701
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
ralshinibr/SyntheticProtocolQA | ---
license: apache-2.0
---
|
JaepaX/jaepa_dataset | ---
language:
- kor
task_categories:
- automatic-speech-recognition
tags:
- speech-recognition
dataset_info:
features:
- name: audio
dtype: audio
- name: 'Unnamed: 0'
dtype: int64
- name: transcription
dtype: string
splits:
- name: train
num_bytes: 2023669729.6
num_examples: 1370
download_size: 1994634885
dataset_size: 2023669729.6
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Ali-C137/ultrafeedback-arabic | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: prompt_id
dtype: string
- name: chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: rejected
list:
- name: content
dtype: string
- name: role
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: score_chosen
dtype: float64
- name: score_rejected
dtype: float64
splits:
- name: train
num_bytes: 592459639
num_examples: 61135
- name: test
num_bytes: 19249550
num_examples: 2000
download_size: 286659820
dataset_size: 611709189
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
statworx/leipzip-swiss | ---
annotations_creators: []
language:
- ch
language_creators:
- found
license:
- cc
multilinguality:
- monolingual
pretty_name: Leipzig Corpora Swiss German
size_categories:
- 100K<n<1M
source_datasets: []
tags:
- wikipedia
task_categories:
- text-generation
task_ids:
- language-modeling
---
# Dataset Card for Leipzig Corpora Swiss German
## Dataset Description
- **Homepage:** https://wortschatz.uni-leipzig.de/en/download/Swiss%20German
- **Repository:** https://huggingface.co/datasets/statworx/leipzip-swiss
### Dataset Summary
Swiss German Wikipedia corpus based on material from 2021.
The corpus gsw_wikipedia_2021 is a Swiss German Wikipedia corpus based on material from 2021. It contains 232,933 sentences and 3,824,547 tokens.
### Languages
Swiss-German
## Dataset Structure
### Data Instances
Single sentences.
### Data Fields
`sentence`: Text as string.
### Data Splits
[More Information Needed]
## Dataset Creation
### Source Data
#### Initial Data Collection and Normalization
https://corpora.uni-leipzig.de/en?corpusId=gsw_wikipedia_2021
## Additional Information
### Licensing Information
Creative-Commons-Lizenz CC BY-NC
### Citation Information
Leipzig Corpora Collection: Swiss German Wikipedia corpus based on material from 2021. Leipzig Corpora Collection. Dataset. https://corpora.uni-leipzig.de?corpusId=gsw_wikipedia_2021
|
v2ray/geldump | ---
license: mit
tags:
- not-for-all-audiences
---
# GelDump
Scripts to dump, tag, and validate gelbooru images. |
Fancellu/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: login
dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
- name: avatar_url
dtype: string
- name: gravatar_id
dtype: string
- name: url
dtype: string
- name: html_url
dtype: string
- name: followers_url
dtype: string
- name: following_url
dtype: string
- name: gists_url
dtype: string
- name: starred_url
dtype: string
- name: subscriptions_url
dtype: string
- name: organizations_url
dtype: string
- name: repos_url
dtype: string
- name: events_url
dtype: string
- name: received_events_url
dtype: string
- name: type
dtype: string
- name: site_admin
dtype: bool
- name: labels
list:
- name: id
dtype: int64
- name: node_id
dtype: string
- name: url
dtype: string
- name: name
dtype: string
- name: color
dtype: string
- name: default
dtype: bool
- name: description
dtype: string
- name: state
dtype: string
- name: locked
dtype: bool
- name: assignee
struct:
- name: login
dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
- name: avatar_url
dtype: string
- name: gravatar_id
dtype: string
- name: url
dtype: string
- name: html_url
dtype: string
- name: followers_url
dtype: string
- name: following_url
dtype: string
- name: gists_url
dtype: string
- name: starred_url
dtype: string
- name: subscriptions_url
dtype: string
- name: organizations_url
dtype: string
- name: repos_url
dtype: string
- name: events_url
dtype: string
- name: received_events_url
dtype: string
- name: type
dtype: string
- name: site_admin
dtype: bool
- name: assignees
list:
- name: login
dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
- name: avatar_url
dtype: string
- name: gravatar_id
dtype: string
- name: url
dtype: string
- name: html_url
dtype: string
- name: followers_url
dtype: string
- name: following_url
dtype: string
- name: gists_url
dtype: string
- name: starred_url
dtype: string
- name: subscriptions_url
dtype: string
- name: organizations_url
dtype: string
- name: repos_url
dtype: string
- name: events_url
dtype: string
- name: received_events_url
dtype: string
- name: type
dtype: string
- name: site_admin
dtype: bool
- name: milestone
struct:
- name: url
dtype: string
- name: html_url
dtype: string
- name: labels_url
dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
- name: number
dtype: int64
- name: title
dtype: string
- name: description
dtype: string
- name: creator
struct:
- name: login
dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
- name: avatar_url
dtype: string
- name: gravatar_id
dtype: string
- name: url
dtype: string
- name: html_url
dtype: string
- name: followers_url
dtype: string
- name: following_url
dtype: string
- name: gists_url
dtype: string
- name: starred_url
dtype: string
- name: subscriptions_url
dtype: string
- name: organizations_url
dtype: string
- name: repos_url
dtype: string
- name: events_url
dtype: string
- name: received_events_url
dtype: string
- name: type
dtype: string
- name: site_admin
dtype: bool
- name: open_issues
dtype: int64
- name: closed_issues
dtype: int64
- name: state
dtype: string
- name: created_at
dtype: timestamp[s]
- name: updated_at
dtype: timestamp[s]
- name: due_on
dtype: timestamp[s]
- name: closed_at
dtype: timestamp[s]
- name: comments
sequence: string
- name: created_at
dtype: timestamp[s]
- name: updated_at
dtype: timestamp[s]
- name: closed_at
dtype: timestamp[s]
- name: author_association
dtype: string
- name: active_lock_reason
dtype: 'null'
- name: draft
dtype: 'null'
- name: pull_request
dtype: 'null'
- name: body
dtype: string
- name: reactions
struct:
- name: url
dtype: string
- name: total_count
dtype: int64
- name: '+1'
dtype: int64
- name: '-1'
dtype: int64
- name: laugh
dtype: int64
- name: hooray
dtype: int64
- name: confused
dtype: int64
- name: heart
dtype: int64
- name: rocket
dtype: int64
- name: eyes
dtype: int64
- name: timeline_url
dtype: string
- name: performed_via_github_app
dtype: 'null'
- name: state_reason
dtype: string
splits:
- name: train
num_bytes: 15167310
num_examples: 2719
download_size: 4983927
dataset_size: 15167310
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
maghrane/data | ---
license: afl-3.0
---
|
premai-io/zero-planets | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 82762688.0
num_examples: 36
download_size: 82725019
dataset_size: 82762688.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
muellerzr/RAG-accelerate | ---
license: apache-2.0
language:
- en
---
## Preparing the dataset
### NOTICE:
All code is owned by Hugging Face and uses the Apache 2.0 Licence. While I clean and strip the dataset for processing, do note that this dataset is under the same scruteny as the original Apache 2.0 License.
## Clone Repo
Data souce used is the [accelerate](https://github.com/huggingface/accelerate) repository. I'm using the latest version, v0.25.0
```bash
git clone https://github.com/huggingface/accelerate
cd accelerate
git checkout v0.25.0
cd ..
mkdir docs src
mv accelerate/src/accelerate/* src
mv accelerate/docs/* docs
cd src
rm __init__.py commands/__init__.py test_utils/__init__.py utils/__init__.py
```
### Cleaning the dataset
Using `regex` in VSCODE, use the following replacement:
```regex
# Copyright(.*\n)+# limitations under the license.
```
```regex
<!--Copyright(.*\n)+-->
```
In the source:
```regex
"""
```
To:
```regex
"""
```
Then remove all import statements (as we only care about the content).
Strip all blank spaces/whitespace:
```regex
^(?:[\t ]*(?:\r?\n|\r))+
```
**WARNING**: It is known that this will seperate out the `_inner()` in the source code and use it as a seperate function losing the context. Trying out with this issue for now. |
FaalSa/data11 | ---
dataset_info:
features:
- name: start
dtype: timestamp[s]
- name: target
sequence: float32
- name: item_id
dtype: string
- name: feat_static_cat
sequence: uint64
splits:
- name: train
num_bytes: 17310
num_examples: 1
- name: validation
num_bytes: 17790
num_examples: 1
- name: test
num_bytes: 18270
num_examples: 1
download_size: 18429
dataset_size: 53370
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
mwalmsley/gz_rings | ---
dataset_info:
- config_name: classification
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': not_ring
'1': ring
splits:
- name: train
num_bytes: 3188303696.84
num_examples: 73356
- name: test
num_bytes: 797909277.38
num_examples: 18340
download_size: 3994376190
dataset_size: 3986212974.2200003
- config_name: regression
features:
- name: image
dtype: image
- name: label
dtype: float32
splits:
- name: train
num_bytes: 3188010272.84
num_examples: 73356
- name: test
num_bytes: 797835917.38
num_examples: 18340
download_size: 3994477060
dataset_size: 3985846190.2200003
configs:
- config_name: classification
data_files:
- split: train
path: classification/train-*
- split: test
path: classification/test-*
- config_name: regression
data_files:
- split: train
path: regression/train-*
- split: test
path: regression/test-*
---
|
NovusResearch/OpenHermes-2.5-Translated-TR | ---
license: mit
dataset_info:
features:
- name: custom_instruction
dtype: 'null'
- name: language
dtype: 'null'
- name: idx
dtype: 'null'
- name: source
dtype: string
- name: model_name
dtype: 'null'
- name: skip_prompt_formatting
dtype: bool
- name: category
dtype: string
- name: views
dtype: 'null'
- name: title
dtype: 'null'
- name: topic
dtype: 'null'
- name: id
dtype: 'null'
- name: hash
dtype: 'null'
- name: avatarUrl
dtype: 'null'
- name: system_prompt
dtype: 'null'
- name: model
dtype: 'null'
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: weight
dtype: 'null'
- name: conversations_translated
list:
- name: from
dtype: string
- name: value
dtype: string
splits:
- name: train
num_bytes: 15997466
num_examples: 5000
download_size: 9031862
dataset_size: 15997466
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
dmrau/cqudupstack-gis | ---
configs:
- config_name: default
data_files:
- split: queries
path: data/queries-*
- split: corpus
path: data/corpus-*
dataset_info:
features:
- name: _id
dtype: string
- name: text
dtype: string
- name: title
dtype: string
splits:
- name: queries
num_bytes: 61244
num_examples: 885
- name: corpus
num_bytes: 36704924
num_examples: 37637
download_size: 20083359
dataset_size: 36766168
---
# Dataset Card for "cqudupstack-gis"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
distilled-one-sec-cv12-each-chunk-uniq/chunk_96 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1257930180.0
num_examples: 245115
download_size: 1288788635
dataset_size: 1257930180.0
---
# Dataset Card for "chunk_96"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Acidmanic/DK-FA-Cosmetics | ---
license: mit
language:
- fa
tags:
- e-commerce
- Online-Shop
- Comments
pretty_name: Dk-Fa-Cosmetics
size_categories:
- 100K<n<1M
---
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** Mani Moayedi
- **Language(s) (NLP):** Farsi (Persian)
- **License:** MIT
## Uses
The samples of this dataset are user comments about products of an online shop website. Each comment contains some
additional data alongside the comments body, like star-rating value (0-5). This dataset can be used to train or generate
different data-models for NLP tasks like opinion mining and sentiment analysis.
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
This dataset is the result of crawling 7 categories of cosmetic products from a perisan online-shop's product pages. The vocabulary
mostly revolves around the cosmetics subjects, therefore it might not be suitable for use cases which needs a generic collection of
words and phrases.
## Dataset Structure
Each comment is represented
in structured format and contains comment's body, comment's title, star-rating value (0-5), Other users reactions to
each comment in terms of number-of-likes and number-of-dislikes. and a list of advantages and dis-advantages that user
might have specified. title field and advantages/disadvantages fields can be null or empty in many comments.
For more details please check out the file [Dataset Description](https://huggingface.co/datasets/Acidmanic/DK-FA-Cosmetics/resolve/main/Dataset%20Description.pdf).
## Dataset Creation
The dataset is created using a crawler agains an online shop's website. Comments are scraped from product pages and stored as json, jsonl and csv files.
#### Personal and Sensitive Information
The dataset contains the username of userse who has posted the comments. All the information in the dataset, including these usernames, are
present on the products web-page whithout any login or authentication.
## Bias, Risks, and Limitations
From Npl prespective, the dataset might mostly contain information about the the consmetic products and the quality
of sellers and resellers service. Therefore considering this dataset as a general source of language might introduce
some issues, depending on the use-case.
## Glossary
| Set | Number Of Comments | Number Of Products | Average Comments Per Product |
| ----------- | ----------- | ----------- |----------- |
| **dk-fa-cosmetics** (Full dataset) | 421078 | 8325 | 51 |
| dkfacs-eyeliner (sub-set) | 30824 | 284 | 109 |
| dkfacs-stand (sub-set) | 83197 | 1738 | 48 |
| dkfacs-mascara (sub-set) | 47961 | 338 | 142 |
| dkfacs-sun-screen (sub-set) | 118699 | 772 | 154 |
| dkfacs-eye-shadow (sub-set) | 14532 | 634 | 23 |
| dkfacs-nails (sub-set) | 75209 | 3260 | 23 |
| dkfacs-lipsticks (sub-set) | 50656 | 1299 | 39 |
## Dataset Card Authors
Mani Moayedi
## Dataset Card Contact
acidmanic.moayedi@gmail.com
https://github.com/Acidmanic |
marup/RWBY-RVC | ---
license: openrail
---
|
nakcnx/bad-topics | ---
license: cc-by-sa-4.0
---
Bad Topics is Thai Dataset from Topic Modeling with 2 Bad Topic Website(Bet=16025 , Porn=39237). |
Luizinu823497832/mjbruneilv | ---
license: openrail
---
|
mstz/sonar | ---
language:
- en
tags:
- adult
- tabular_classification
- binary_classification
- UCI
pretty_name: Sonar
size_categories:
- n<1K
task_categories:
- tabular-classification
configs:
- sonar
license: cc
---
# Sonar
The [Sonar dataset](https://archive-beta.ics.uci.edu/dataset/151/connectionist+bench+sonar+mines+vs+rocks) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
Dataset to discriminate between sonar signals bounced off a metal cylinder and those bounced off a roughly cylindrical rock.
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|-----------------------------------------------------------------|
| sonar | Binary classification | Is the sonar detecting a rock? |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/sonar")["train"]
``` |
PariPaliwal/DeDuplication | ---
license: openrail
task_categories:
- text-generation
language:
- zh
- ko
- fr
pretty_name: dedup
size_categories:
- n<1K
---
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
## 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] |
CyberHarem/rope_arknights | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of rope/ロープ/暗索 (Arknights)
This is the dataset of rope/ロープ/暗索 (Arknights), containing 136 images and their tags.
The core tags of this character are `long_hair, purple_hair, animal_ears, rabbit_ears, bow, hair_bow, black_bow, breasts, grey_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 136 | 216.84 MiB | [Download](https://huggingface.co/datasets/CyberHarem/rope_arknights/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 136 | 187.45 MiB | [Download](https://huggingface.co/datasets/CyberHarem/rope_arknights/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 352 | 384.38 MiB | [Download](https://huggingface.co/datasets/CyberHarem/rope_arknights/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/rope_arknights',
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 | 13 |  |  |  |  |  | 1girl, black_gloves, black_jacket, long_sleeves, looking_at_viewer, solo, black_thighhighs, choker, holding, open_mouth, simple_background, blush, cowboy_shot, white_background, purple_eyes, :d, oripathy_lesion_(arknights) |
| 1 | 8 |  |  |  |  |  | 1girl, black_jacket, blush, ear_piercing, long_sleeves, solo, black_gloves, choker, simple_background, upper_body, white_background, looking_at_viewer, shirt, closed_mouth, open_mouth, purple_eyes |
| 2 | 5 |  |  |  |  |  | 1girl, black_jacket, long_sleeves, looking_at_viewer, solo, black_gloves, black_thighhighs, open_mouth, purple_eyes, smile, sitting, choker |
| 3 | 14 |  |  |  |  |  | 1girl, solo, looking_at_viewer, black_dress, long_sleeves, black_headwear, off_shoulder, witch_hat, bare_shoulders, holding, black_choker, blush, open_mouth, cleavage, collarbone, rabbit_girl, skull_hair_ornament, halloween, jack-o'-lantern, medium_breasts, official_alternate_costume, very_long_hair, :d |
| 4 | 23 |  |  |  |  |  | bare_shoulders, white_bikini, 1girl, official_alternate_costume, cleavage, solo, strapless_bikini, bare_arms, looking_at_viewer, bikini_skirt, holding, large_breasts, navel, outdoors, day, smile, choker, open_mouth, very_long_hair, blue_sky, blush, cloud, collarbone, food, oripathy_lesion_(arknights), purple_eyes, stomach, cowboy_shot, medium_breasts, rabbit, thighs |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_gloves | black_jacket | long_sleeves | looking_at_viewer | solo | black_thighhighs | choker | holding | open_mouth | simple_background | blush | cowboy_shot | white_background | purple_eyes | :d | oripathy_lesion_(arknights) | ear_piercing | upper_body | shirt | closed_mouth | smile | sitting | black_dress | black_headwear | off_shoulder | witch_hat | bare_shoulders | black_choker | cleavage | collarbone | rabbit_girl | skull_hair_ornament | halloween | jack-o'-lantern | medium_breasts | official_alternate_costume | very_long_hair | white_bikini | strapless_bikini | bare_arms | bikini_skirt | large_breasts | navel | outdoors | day | blue_sky | cloud | food | stomach | rabbit | thighs |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:---------------|:---------------|:--------------------|:-------|:-------------------|:---------|:----------|:-------------|:--------------------|:--------|:--------------|:-------------------|:--------------|:-----|:------------------------------|:---------------|:-------------|:--------|:---------------|:--------|:----------|:--------------|:-----------------|:---------------|:------------|:-----------------|:---------------|:-----------|:-------------|:--------------|:----------------------|:------------|:------------------|:-----------------|:-----------------------------|:-----------------|:---------------|:-------------------|:------------|:---------------|:----------------|:--------|:-----------|:------|:-----------|:--------|:-------|:----------|:---------|:---------|
| 0 | 13 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 8 |  |  |  |  |  | X | X | X | X | X | X | | X | | X | X | X | | X | X | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | | X | | | | | X | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 14 |  |  |  |  |  | X | | | X | X | X | | | X | X | | X | | | | X | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | |
| 4 | 23 |  |  |  |  |  | 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 |
|
distilabel-internal-testing/farming-research-v0.2 | ---
size_categories: n<1K
config_names:
- domain_expert
tags:
- synthetic
- distilabel
- rlaif
---
<p align="left">
<a href="https://github.com/argilla-io/distilabel">
<img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>
</a>
</p>
# Dataset Card for farming-research-v0.2
This dataset has been created with [Distilabel](https://distilabel.argilla.io/).
## Dataset Summary
This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI:
```console
distilabel pipeline run --config "https://huggingface.co/datasets/distilabel-internal-testing/farming-research-v0.2/raw/main/pipeline.yaml"
```
or explore the configuration:
```console
distilabel pipeline info --config "https://huggingface.co/datasets/distilabel-internal-testing/farming-research-v0.2/raw/main/pipeline.yaml"
```
## Dataset structure
The examples have the following structure per configuration:
<details><summary> Configuration: domain_expert </summary><hr>
```json
{
"domain_expert_answer": "Agroforestry systems, which integrate trees, crops, and livestock on the same piece of land, can significantly contribute to climate change mitigation and sustainable agriculture in several ways:\n\n1. **Carbon Sequestration**: Agroforestry systems can sequester more carbon than traditional monoculture farming systems. Trees absorb CO2 from the atmosphere during photosynthesis, storing the carbon in their biomass and the soil. According to a study by Montagnini and Nair (2004), agroforestry systems can sequester up to 4.6 metric tons of carbon per hectare per year, depending on the species and management practices.\n\n2. **Soil Health Improvement**: Agroforestry systems can improve soil health by reducing erosion, increasing organic matter, and enhancing nutrient cycling. The presence of trees can reduce wind and water erosion, while leaf litter and root turnover contribute to soil organic matter. The improved soil health can lead to increased crop productivity and resilience to climate change (Rosenstock et al., 2016).\n\n3. **Biodiversity Conservation**: Agroforestry systems can support a higher level of biodiversity than monoculture farming systems. The diverse habitats provided by trees, crops, and livestock can support a variety of plant and animal species, contributing to ecosystem resilience and providing ecosystem services such as pollination and pest control (Jose et al., 2006).\n\n4. **Adaptation to Climate Change**: Agroforestry systems can enhance farmers\u0027 resilience to climate change by diversifying their income sources and providing products that can withstand extreme weather events. For example, trees can provide fruits, nuts, and timber during dry seasons when crops may fail (Lin, 2007).\n\nIn conclusion, agroforestry systems offer a promising approach to climate change mitigation and sustainable agriculture by sequestering carbon, improving soil health, conserving biodiversity, and enhancing farmers\u0027 resilience to climate change. However, the adoption of agroforestry systems requires supportive policies, access to markets for agroforestry products, and training for farmers in agroforestry management practices.",
"evolved_instructions": [
"2. Explain how agroforestry systems can contribute to climate change mitigation and sustainable agriculture.",
"Elaborate on the specific ways in which agroforestry systems, such as silvopasture and alley cropping, can aid in climate change mitigation, including carbon sequestration, and promote sustainable agricultural practices, such as soil conservation and biodiversity enhancement. Provide examples to support your explanation.",
"Delve into the intricate mechanisms by which agroforestry systems, namely silvopasture and alley cropping, contribute to climate change mitigation, focusing on carbon sequestration and storage. Furthermore, elucidate how these systems foster sustainable agriculture, specifically addressing soil preservation and enhancing biodiversity. Substantiate your discussion with relevant case studies."
],
"evolved_questions": "2. Explain how agroforestry systems can contribute to climate change mitigation and sustainable agriculture.",
"input": "economic growth from a Agroforestery perspective",
"instructions": [
"1. Evaluate the potential economic benefits of adopting agroforestry practices in developing countries.",
"2. Explain how agroforestry systems can contribute to climate change mitigation and sustainable agriculture.",
"3. Discuss the role of agroforestry in promoting food security and improving livelihoods in rural communities.",
"4. Compare the profitability of traditional farming methods with that of agroforestry systems, considering both short-term and long-term gains.",
"5. Investigate the social and environmental impacts of agroforestry on local biodiversity and ecosystem services."
],
"model_name": "mistral-large-latest",
"questions": "2. Explain how agroforestry systems can contribute to climate change mitigation and sustainable agriculture."
}
```
This subset can be loaded as:
```python
from datasets import load_dataset
ds = load_dataset("distilabel-internal-testing/farming-research-v0.2", "domain_expert")
```
</details>
|
argilla/zephyr_ultrafeedback-new | ---
dataset_info:
features:
- name: input
dtype: string
- name: generations
sequence: string
- name: raw_generation_response
sequence: string
- name: rating
sequence: int64
- name: rationale
sequence: string
- name: raw_labelling_response
struct:
- name: choices
list:
- name: finish_reason
dtype: string
- name: index
dtype: int64
- name: message
struct:
- name: content
dtype: string
- name: role
dtype: string
- name: created
dtype: int64
- name: id
dtype: string
- name: model
dtype: string
- name: object
dtype: string
- name: usage
struct:
- name: completion_tokens
dtype: int64
- name: prompt_tokens
dtype: int64
- name: total_tokens
dtype: int64
splits:
- name: train
num_bytes: 44931
num_examples: 10
download_size: 43664
dataset_size: 44931
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "zephyr_ultrafeedback-new"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
pligor/mlpractical_university_edinburgh | ---
license: openrail
---
|
316usman/thematic1b | ---
license: bsd
dataset_info:
features:
- name: text
dtype: string
- name: thematic
dtype: string
- name: sub-thematic
dtype: string
- name: country
dtype: string
- name: document_url
dtype: string
- name: source_url
dtype: string
splits:
- name: train
num_bytes: 922034375
num_examples: 1267630
download_size: 288775891
dataset_size: 922034375
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
PygmalionAI/PIPPA | ---
license: apache-2.0
task_categories:
- conversational
language:
- en
tags:
- not-for-all-audiences
- conversational
- roleplay
- custom-format
- a.
pretty_name: PIPPA - Personal Interaction Pairs Between People and AI
size_categories:
- 10K<n<100K
viewer: false
---
# PIPPA - Personal Interaction Pairs between People and AI
It's been a long time coming, but we're proud to finally release the public portion of our conversational dataset to the public. **Personal Interaction Pairs between People and AI** (**PIPPA**) is a partially synthetic, community contributed and open-source conversational and roleplaying dataset generated from a subset of submitted logs to the Pygmalion project.
This dataset is a subset of what we have received - it consists only of the valid conversational logs in which the submitter gave consent to redistribute to the public. Furthermore, we have done our best to redact or modify any personal information that could potentially be found within PIPPA. If you have found something within PIPPA which has not been redacted properly, please contact us via. email at `teargosling@pygmalion.chat` or `alpindale@pygmalion.chat` and we'll take care of it for you. You may contact us for any other purpose as well, including yelling at us for when the next model will be released.
**⚠️ CAUTION: PIPPA contains conversations, themes and scenarios which can be considered "not safe for work" (NSFW) and/or heavily disturbing in nature. Models trained purely with PIPPA may have the tendency to generate X-rated output. You have been warned.**
## Dataset Summary
PIPPA consists of just a little more than 1 million lines of dialogue spread out over 26,000 conversations between users of the popular chatbot website "Character.AI" and its large language model, obtained through a large community effort taking place over the course of several months. Tallying shows that over 1,000 unique personas simulating both real and fictional characters are represented within the dataset, allowing PIPPA and LLMs fine-tuned on it to adapt to many different roleplay domains.
The dataset is represented with a JSONL file, with a singular JSON snippet representing one entire conversation. Every snippet contains the following pieces of data:
- `submission_timestamp`: The Unix timestamp of when this particular conversation was submitted to the project, in milliseconds.
- `categories`: The categories assigned to the character on the Character.AI website, if any were assigned. If no categories were assigned, it will be `null`
- `bot_id`: The unique ID assigned to the specific character which the user was conversing with on the website.
- `bot_name`: The name of the character.
- `bot_greeting`: The introductory line of the character to the user. This is always the first utterance of dialogue in a conversation.
- `bot_definitions`: Contains whatever was typed in the **Definitions** field in the character creator on the website. This usually consists of one or more example conversations between the user and the character designed to steer the model towards emulating the persona correctly. Bot definitions required a separate effort to gather, and thus may not be present for a specific persona - if this is the case, an empty string is provided. Because the defintions were written on Character.AI, this field usually follows Character.AI's unique formatting and should be preprocessed before feeding into any model - please see **Appendix A** of the paper for further details.
- `bot_description`: Contains whatever was typed in the **Description** field in the character creator on the website. It usually consists of a few sentences which gives a brief overview of the character and any important details about them.
- `conversation`: The conversation between the user and the model. This is represented as a list of dictionaries, each dictionary representing a single utterance and containing two key-value pairs: `message`, referring to the utterance itself and `is_human`, which designates whether the dialogue was generated by the user or the LLM.
For further information about PIPPA, please refer to our [published paper](https://arxiv.org/abs/2308.05884) or contact us at the emails listed above.
## Files
We publish PIPPA in multiple variants, each a singular JSONL file:
- **pippa.jsonl**: The original dataset, almost exactly as submitted to us (barring any modifications resulting from the redaction of personally identifiable information).
- **pippa_deduped.jsonl**: The 'cleaned' version of PIPPA, with duplicate conversations as well as any conversation with less than three turns removed from the dataset. **We recommend using this file.**
- **pippa_metharme.jsonl**: A version of deduped PIPPA which is formatted in a similar way to our [Metharme instructional models](https://huggingface.co/PygmalionAI/metharme-13b), useful as an example to demonstrate how to properly format the PIPPA dataset.
If you are using HuggingFace's `datasets` library, you can choose the file you wish to use by specifying the name of it (without extension) as an argument, like so: `dataset = load_dataset("PygmalionAI/PIPPA", 'pippa_deduped')`. The default value is `pippa_deduped`.
Thank you for your patience, everyone!
## Citation
If you're using our dataset, please consider citing our work:
```bibtex
@misc{gosling2023pippa,
title={PIPPA: A Partially Synthetic Conversational Dataset},
author={Tear Gosling and Alpin Dale and Yinhe Zheng},
year={2023},
eprint={2308.05884},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
___
Any relationship between the name of this dataset and any public personas is entirely and totally coincidential. |
dstrohmaier/SeCoDa | ---
license: cc-by-nc-sa-4.0
task_categories:
- token-classification
language:
- en
pretty_name: SeCoDa
---
# SeCoDa [![CC BY-NC-SA 4.0][cc-by-nc-sa-shield]][cc-by-nc-sa]
Repository for the Sense Complexity Dataset (SeCoDa)
# Paper
For more information on the SeCoDa, see the [paper](http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.730.pdf).
Publications using this dataset must include a reference to the following publication:
<pre>
SeCoDa: Sense Complexity Dataset. David Strohmaier, Sian Gooding, Shiva Taslimipoor, Ekaterina Kochmar. Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020), pages 5964–5969, Marseille, 11–16 May 2020
</pre>
The dataset is based on the earlier CWIG3G2 dataset, see the [paper](https://aclanthology.org/I17-2068.pdf) and [website](https://www.inf.uni-hamburg.de/en/inst/ab/lt/resources/data/complex-word-identification-dataset.html). The relevant citation is
<pre>
Seid Muhie Yimam, Sanja Štajner, Martin Riedl, and Chris Biemann (2017): CWIG3G2 - Complex Word Identification Task across Three Text Genres and Two User Groups. In Proceedings of The 8th International Joint Conference on Natural Language Processing (IJCNLP 2017). Taipei, Taiwan
</pre>
The complexity data can be found in the CWIG3G2 dataset and combined with the senses provided by SeCoDa.
# Repository Content
Main data are found in SeCoDa.tsv. The columns are structured as follows.
1. Token to be disambiguated.
2. Offset start for token in context
3. Offset end for token in context
4. Context (sentence in which token occurs)
5. Selected sense
6. Comments (also contains MWE information)
Example:
| target | offset_start | offset_end | context | sense | comments |
| ------- |:------------:| ----------:| ------------------:| ----------------:| --------:|
| abroad | 39 | 45 | As we emerge... | OTHER COUNTRY... | - |
| abroad | 39 | 45 | As we emerge... | OTHER COUNTRY... | - |
| abroad | 73 | 79 | #1-8 The speech... | OTHER COUNTRY... | - |
The senses are drawn from the [Cambridge Advanced Learner's Dictionary](https://dictionary.cambridge.org).
*UPDATE*: Two missing entries have been added and typos in comments have been corrected.
*UPDATE*: Added further information to readme.
This work is licensed under a [Creative Commons Attribution-NonCommerial-ShareAlike 4.0
International License][cc-by-nc-sa].
[![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa]
[cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/
[cc-by-nc-sa-image]: https://licensebuttons.net/l/by-nc-sa/4.0/88x31.png
[cc-by-nc-sa-shield]: https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey.svg
|
bdsaglam/webnlg-musique-jerx-sft-openai | ---
dataset_info:
features:
- name: source
dtype: string
- name: chat
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: test
num_bytes: 3913532
num_examples: 7305
- name: dev
num_bytes: 2244124
num_examples: 4464
- name: train
num_bytes: 17888056
num_examples: 35536
download_size: 2742814
dataset_size: 24045712
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: dev
path: data/dev-*
- split: train
path: data/train-*
---
|
morenofran/pool | ---
license: openrail
---
|
ChanceFocus/flare-ma | ---
dataset_info:
features:
- name: id
dtype: string
- name: query
dtype: string
- name: answer
dtype: string
- name: text
dtype: string
- name: choices
sequence: string
- name: gold
dtype: int64
splits:
- name: test
num_bytes: 2295726
num_examples: 500
download_size: 1220605
dataset_size: 2295726
---
# Dataset Card for "flare-ma"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
karming-wan/testing-dataset | ---
dataset_info:
features:
- name: image_id
dtype: int64
- name: image
dtype: image
- name: width
dtype: int64
- name: height
dtype: int64
- name: objects
sequence:
- name: category
dtype:
class_label:
names:
'0': 10c
'1': 10d
'2': 10h
'3': 10s
'4': 2c
'5': 2d
'6': 2h
'7': 2s
'8': 3c
'9': 3d
'10': 3h
'11': 3s
'12': 4c
'13': 4d
'14': 4h
'15': 4s
'16': 5c
'17': 5d
'18': 5h
'19': 5s
'20': 6c
'21': 6d
'22': 6h
'23': 6s
'24': 7c
'25': 7d
'26': 7h
'27': 7s
'28': 8c
'29': 8d
'30': 8h
'31': 8s
'32': 9c
'33': 9d
'34': 9h
'35': 9s
'36': Ac
'37': Ad
'38': Ah
'39': As
'40': Jc
'41': Jd
'42': Jh
'43': Js
'44': Kc
'45': Kd
'46': Kh
'47': Ks
'48': Qc
'49': Qd
'50': Qh
'51': Qs
- name: bbox
sequence: float64
length: 4
- name: area
dtype: float64
splits:
- name: train
num_bytes: 489458614.0
num_examples: 14000
- name: test
num_bytes: 70581790.0
num_examples: 2000
- name: validation
num_bytes: 142099000.0
num_examples: 4000
download_size: 702978843
dataset_size: 702139404.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
---
|
dipesh1111/pdf_data | ---
license: apache-2.0
---
|
tastoHO/rim | ---
license: openrail
---
|
alvations/c4p0-v2-en-it | ---
dataset_info:
features:
- name: source
dtype: string
- name: target
dtype: string
- name: target_backto_source
dtype: string
- name: raw_target
list:
- name: generated_text
dtype: string
- name: raw_target_backto_source
list:
- name: generated_text
dtype: string
- name: prompt
dtype: string
- name: reverse_prompt
dtype: string
- name: source_langid
dtype: string
- name: target_langid
dtype: string
- name: target_backto_source_langid
dtype: string
- name: doc_id
dtype: int64
- name: sent_id
dtype: int64
- name: timestamp
dtype: string
- name: url
dtype: string
- name: doc_hash
dtype: string
- name: dataset
dtype: string
- name: source_lang
dtype: string
- name: target_lang
dtype: string
splits:
- name: train
num_bytes: 47743464
num_examples: 36228
download_size: 21190283
dataset_size: 47743464
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
TuringsSolutions/CallerTrainingSet | ---
license: mit
---
|
CyberHarem/ophelia_fireemblem | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of ophelia (Fire Emblem)
This is the dataset of ophelia (Fire Emblem), containing 500 images and their tags.
The core tags of this character are `blonde_hair, long_hair, breasts, ahoge, grey_eyes, bangs, medium_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:--------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 623.85 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ophelia_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 344.48 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ophelia_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1160 | 730.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ophelia_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 543.34 MiB | [Download](https://huggingface.co/datasets/CyberHarem/ophelia_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1160 | 1.02 GiB | [Download](https://huggingface.co/datasets/CyberHarem/ophelia_fireemblem/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/ophelia_fireemblem',
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 | 17 |  |  |  |  |  | 1girl, cape, circlet, solo, closed_mouth, smile, upper_body, looking_at_viewer, cleavage, bodystocking, covered_navel, blue_eyes, turtleneck, center_opening |
| 1 | 15 |  |  |  |  |  | 1girl, cape, circlet, looking_at_viewer, solo, turtleneck, bodystocking, upper_body, covered_navel, open_mouth, one_eye_closed, asymmetrical_bangs, bridal_gauntlets, cleavage, smile, blue_eyes |
| 2 | 8 |  |  |  |  |  | 1girl, circlet, looking_at_viewer, smile, solo, asymmetrical_bangs, official_alternate_costume, upper_body, closed_mouth, turtleneck, bodystocking, cleavage |
| 3 | 9 |  |  |  |  |  | 1girl, completely_nude, looking_at_viewer, nipples, solo, smile, closed_mouth, navel, pussy, blush, large_breasts, barefoot, blue_eyes, circlet |
| 4 | 14 |  |  |  |  |  | 1boy, 1girl, hetero, nipples, open_mouth, penis, sex, blush, vaginal, circlet, completely_nude, cum_in_pussy, uncensored, large_breasts, navel, spread_legs |
| 5 | 10 |  |  |  |  |  | 1boy, 1girl, circlet, hetero, penis, solo_focus, large_breasts, looking_at_viewer, nipples, blush, paizuri, censored, pov, cum_on_breasts, smile, upper_body |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | cape | circlet | solo | closed_mouth | smile | upper_body | looking_at_viewer | cleavage | bodystocking | covered_navel | blue_eyes | turtleneck | center_opening | open_mouth | one_eye_closed | asymmetrical_bangs | bridal_gauntlets | official_alternate_costume | completely_nude | nipples | navel | pussy | blush | large_breasts | barefoot | 1boy | hetero | penis | sex | vaginal | cum_in_pussy | uncensored | spread_legs | solo_focus | paizuri | censored | pov | cum_on_breasts |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:----------|:-------|:---------------|:--------|:-------------|:--------------------|:-----------|:---------------|:----------------|:------------|:-------------|:-----------------|:-------------|:-----------------|:---------------------|:-------------------|:-----------------------------|:------------------|:----------|:--------|:--------|:--------|:----------------|:-----------|:-------|:---------|:--------|:------|:----------|:---------------|:-------------|:--------------|:-------------|:----------|:-----------|:------|:-----------------|
| 0 | 17 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 15 |  |  |  |  |  | X | X | X | X | | X | X | X | X | X | X | X | X | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | |
| 2 | 8 |  |  |  |  |  | X | | X | X | X | X | X | X | X | X | | | X | | | | X | | X | | | | | | | | | | | | | | | | | | | | |
| 3 | 9 |  |  |  |  |  | X | | X | X | X | X | | X | | | | X | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | |
| 4 | 14 |  |  |  |  |  | X | | X | | | | | | | | | | | | X | | | | | X | X | X | | X | X | | X | X | X | X | X | X | X | X | | | | | |
| 5 | 10 |  |  |  |  |  | X | | X | | | X | X | X | | | | | | | | | | | | | X | | | X | X | | X | X | X | | | | | | X | X | X | X | X |
|
notrichardren/political-truthfulness | ---
dataset_info:
features:
- name: Topic
dtype: string
- name: Question
dtype: string
- name: Correct
dtype: string
- name: Republican
dtype: string
- name: Democrat
dtype: string
splits:
- name: train
num_bytes: 183364
num_examples: 1245
download_size: 57357
dataset_size: 183364
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "political-truthfulness"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
haurajahra/SQUAD-ID | ---
license: other
license_name: marian-mt
license_link: LICENSE
task_categories:
- question-answering
language:
- id
size_categories:
- 100K<n<1M
---
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
## 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] |
cschell/test-repo | ---
configs:
- config_name: le_repo
data_files:
- split: main
path: "**/*.parquet"
default: true
--- |
showchen/zero-kurisunew | ---
license: apache-2.0
---
|
Susmita1302/Text_to_imageGen | ---
license: mit
---
|
misikoff/zillow | ---
language:
- en
license: other
task_categories:
- tabular-regression
- time-series-forecasting
pretty_name: Zillow
description: 'This dataset is comprised of seven different configurations of data
covering different aspects of the housing market in the United States. All data
is provided by Zillow. The seven configurations are: home_values_forecasts, new_construction,
for_sale_listings, rentals, sales, home_values, and days_on_market. Each configuration
has a different set of features and target variables. The data is provided in JSONL
format.'
homepage: https://www.zillow.com/research/data/
dataset_info:
- config_name: days_on_market
features:
- name: Region ID
dtype: string
id: Region ID
- name: Size Rank
dtype: int32
id: Size Rank
- name: Region
dtype: string
id: Region
- name: Region Type
dtype:
class_label:
names:
'0': zip
'1': city
'2': county
'3': msa
'4': state
'5': country
- name: State
dtype: string
id: State
- name: Home Type
dtype:
class_label:
names:
'0': multifamily
'1': condo/co-op
'2': SFR
'3': all homes
'4': all homes plus multifamily
- name: Date
dtype: timestamp[ms]
id: Date
- name: Mean Listings Price Cut Amount (Smoothed)
dtype: float32
id: Mean Listings Price Cut Amount (Smoothed)
- name: Percent Listings Price Cut
dtype: float32
id: Percent Listings Price Cut
- name: Mean Listings Price Cut Amount
dtype: float32
id: Mean Listings Price Cut Amount
- name: Percent Listings Price Cut (Smoothed)
dtype: float32
id: Percent Listings Price Cut (Smoothed)
- name: Median Days on Pending (Smoothed)
dtype: float32
id: Median Days on Pending (Smoothed)
- name: Median Days on Pending
dtype: float32
id: Median Days on Pending
splits:
- name: train
num_bytes: 50107320
num_examples: 586714
download_size: 229118598
dataset_size: 50107320
- config_name: for_sale_listings
features:
- name: Region ID
dtype: string
id: Region ID
- name: Size Rank
dtype: int32
id: Size Rank
- name: Region
dtype: string
id: Region
- name: Region Type
dtype:
class_label:
names:
'0': county
'1': city
'2': zip
'3': country
'4': msa
- name: State
dtype: string
id: State
- name: Home Type
dtype:
class_label:
names:
'0': all homes
'1': all homes plus multifamily
'2': SFR
'3': condo/co-op
'4': multifamily
- name: Date
dtype: timestamp[ms]
id: Date
- name: Median Listing Price
dtype: float32
id: Median Listing Price
- name: Median Listing Price (Smoothed)
dtype: float32
id: Median Listing Price (Smoothed)
- name: New Listings
dtype: int32
id: New Listings
- name: New Listings (Smoothed)
dtype: int32
id: New Listings (Smoothed)
- name: New Pending (Smoothed)
dtype: int32
id: New Pending (Smoothed)
- name: New Pending
dtype: int32
id: New Pending
splits:
- name: train
num_bytes: 49412198
num_examples: 578653
download_size: 180206592
dataset_size: 49412198
- config_name: home_values
features:
- name: Region ID
dtype: string
id: Region ID
- name: Size Rank
dtype: int32
id: Size Rank
- name: Region
dtype: string
id: Region
- name: Region Type
dtype:
class_label:
names:
'0': zip
'1': city
'2': county
'3': msa
'4': state
'5': country
- name: State
dtype: string
id: State
- name: Home Type
dtype:
class_label:
names:
'0': multifamily
'1': condo/co-op
'2': SFR
'3': all homes
'4': all homes plus multifamily
- name: Bedroom Count
dtype:
class_label:
names:
'0': 1-Bedroom
'1': 2-Bedrooms
'2': 3-Bedrooms
'3': 4-Bedrooms
'4': 5+-Bedrooms
'5': All Bedrooms
- name: Date
dtype: timestamp[ms]
id: Date
- name: Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)
dtype: float32
id: Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)
- name: Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)
dtype: float32
id: Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)
- name: Top Tier ZHVI (Smoothed) (Seasonally Adjusted)
dtype: float32
id: Top Tier ZHVI (Smoothed) (Seasonally Adjusted)
splits:
- name: train
num_bytes: 9377759
num_examples: 117912
download_size: 41145800
dataset_size: 9377759
- config_name: home_values_forecasts
features:
- name: Region ID
dtype: string
id: Region ID
- name: Size Rank
dtype: int32
id: Size Rank
- name: Region
dtype: string
id: Region
- name: Region Type
dtype:
class_label:
names:
'0': county
'1': city
'2': zip
'3': country
'4': msa
- name: State
dtype: string
id: State
- name: City
dtype: string
id: City
- name: Metro
dtype: string
id: Metro
- name: County
dtype: string
id: County
- name: Date
dtype: timestamp[ms]
id: Date
- name: Month Over Month % (Smoothed) (Seasonally Adjusted)
dtype: float32
id: Month Over Month % (Smoothed) (Seasonally Adjusted)
- name: Quarter Over Quarter % (Smoothed) (Seasonally Adjusted)
dtype: float32
id: Quarter Over Quarter % (Smoothed) (Seasonally Adjusted)
- name: Year Over Year % (Smoothed) (Seasonally Adjusted)
dtype: float32
id: Year Over Year % (Smoothed) (Seasonally Adjusted)
- name: Month Over Month %
dtype: float32
id: Month Over Month %
- name: Quarter Over Quarter %
dtype: float32
id: Quarter Over Quarter %
- name: Year Over Year %
dtype: float32
id: Year Over Year %
splits:
- name: train
num_bytes: 3976869
num_examples: 31854
download_size: 14081979
dataset_size: 3976869
- config_name: new_construction
features:
- name: Region ID
dtype: string
id: Region ID
- name: Size Rank
dtype: int32
id: Size Rank
- name: Region
dtype: string
id: Region
- name: Region Type
dtype:
class_label:
names:
'0': county
'1': city
'2': zip
'3': country
'4': msa
- name: State
dtype: string
id: State
- name: Home Type
dtype:
class_label:
names:
'0': all homes
'1': all homes plus multifamily
'2': SFR
'3': condo/co-op
'4': multifamily
- name: Date
dtype: timestamp[ms]
id: Date
- name: Median Sale Price
dtype: float32
id: Median Sale Price
- name: Median Sale Price per Sqft
dtype: float32
id: Sale Price per Sqft
- name: Sales Count
dtype: int32
id: Sales Count
splits:
- name: train
num_bytes: 3624631
num_examples: 49487
download_size: 10937317
dataset_size: 3624631
- config_name: rentals
features:
- name: Region ID
dtype: string
id: Region ID
- name: Size Rank
dtype: int32
id: Size Rank
- name: Region
dtype: string
id: Region
- name: Region Type
dtype:
class_label:
names:
'0': county
'1': city
'2': zip
'3': country
'4': msa
- name: State
dtype: string
id: State
- name: Home Type
dtype:
class_label:
names:
'0': all homes
'1': all homes plus multifamily
'2': SFR
'3': condo/co-op
'4': multifamily
- name: Date
dtype: timestamp[ms]
id: Date
- name: Rent (Smoothed)
dtype: float32
id: Rent (Smoothed)
- name: Rent (Smoothed) (Seasonally Adjusted)
dtype: float32
id: Rent (Smoothed) (Seasonally Adjusted)
splits:
- name: train
num_bytes: 92914681
num_examples: 1258740
download_size: 447425069
dataset_size: 92914681
- config_name: sales
features:
- name: Region ID
dtype: string
id: Region ID
- name: Size Rank
dtype: int32
id: Size Rank
- name: Region
dtype: string
id: Region
- name: Region Type
dtype:
class_label:
names:
'0': county
'1': city
'2': zip
'3': country
'4': msa
- name: State
dtype: string
id: State
- name: Home Type
dtype:
class_label:
names:
'0': all homes
'1': all homes plus multifamily
'2': SFR
'3': condo/co-op
'4': multifamily
- name: Date
dtype: timestamp[ms]
id: Date
- name: Mean Sale to List Ratio (Smoothed)
dtype: float32
id: Mean Sale to List Ratio (Smoothed)
- name: Median Sale to List Ratio
dtype: float32
id: Median Sale to List Ratio
- name: Median Sale Price
dtype: float32
id: Median Sale Price
- name: Median Sale Price (Smoothed) (Seasonally Adjusted)
dtype: float32
id: Median Sale Price (Smoothed) (Seasonally Adjusted)
- name: Median Sale Price (Smoothed)
dtype: float32
id: Median Sale Price (Smoothed)
- name: Median Sale to List Ratio (Smoothed)
dtype: float32
id: Median Sale to List Ratio (Smoothed)
- name: '% Sold Below List'
dtype: float32
id: '% Sold Below List'
- name: '% Sold Below List (Smoothed)'
dtype: float32
id: '% Sold Below List (Smoothed)'
- name: '% Sold Above List'
dtype: float32
id: '% Sold Above List'
- name: '% Sold Above List (Smoothed)'
dtype: float32
id: '% Sold Above List (Smoothed)'
- name: Mean Sale to List Ratio
dtype: float32
id: Mean Sale to List Ratio
splits:
- name: train
num_bytes: 27088039
num_examples: 255024
download_size: 139297577
dataset_size: 27088039
---
# Housing Data Provided by Zillow
Updated: 2023-02-01
This dataset contains several configs produced based on files available at https://www.zillow.com/research/data/.
# Viewer Notes
Because this repository includes the code used to process the raw Zillow data, the dataset viewer is not enabled. This is because the dataset viewer is not enabled for datasets that run arbitrary python code. For a normal viewer experience, please see the simplified version of this dataset at https://huggingface.co/datasets/misikoff/zillow-viewer. The viewer should be enabled there and the config files should match what is found here.
Supported configs:
- [`days_on_market`](#days-on-market): Days to pending, days to close, share of listings with a price cut, and price cuts.
- [`for_sale_listings`](#for-sale-listings): Median listing price, new listings, and new pending listings.
- [`home_values`](#home-values): Zillow Home Value Index (ZHVI) for all homes, mid-tier, bottom-tier, and top-tier homes.
- [`home_values_forecasts`](#home-values-forecasts): Zillow Home Value Forecast (ZHVF) for all homes, mid-tier, bottom-tier, and top-tier homes.
- [`new_construction`](#new-construction): Median sale price, median sale price per square foot, and sales count.
- [`rentals`](#rentals): Zillow Observed Rent Index (ZORI) for all homes, mid-tier, bottom-tier, and top-tier homes.
- [`sales`](#sales): Median sale price, median sale price per square foot, and sales count.
## DAYS ON MARKET AND PRICE CUTS
Days to Pending: How long it takes homes in a region to change to pending status on Zillow.com after first being shown as for sale. The reported figure indicates the number of days (mean or median) that it took for homes that went pending during the week being reported, to go pending. This differs from the old “Days on Zillow” metric in that it excludes the in-contract period before a home sells.
Days to Close (mean/median): Number of days between the listing going pending and the sale date.
Share of Listings With a Price Cut: The number of unique properties with a list price at the end of the month that’s less than the list price at the beginning of the month, divided by the number of unique properties with an active listing at some point during the month.
Price Cuts: The mean and median price cut for listings in a given region during a given time period, expressed as both dollars ($) and as a percentage (%) of list price.
Base Columns
- `Region ID`: dtype="string", a unique identifier for the region
- `Size Rank`: dtype="int32", a rank of the region's size
- `Region`: dtype="string", the name of the region
- `Region Type`: dtype="class_label", the type of region
- '0': zip
- '1': city
- '2': county
- '3': msa
- '4': state
- '5': country
- `State`: dtype="string", the US state abbreviation for the state containing the region
- `Home Type`: dtype="string", the type of home
- '0': multifamily
- '1': condo/co-op
- '2': SFR
- '3': all homes
- '4': all homes plus multifamily
- `Date`: dtype="timestamp[ms]", the date of the last day of the week for this data
Value Columns
- `Mean Listings Price Cut Amount (Smoothed)`: dtype="float32"
- `Percent Listings Price Cut`: dtype="float32", The number of unique properties with a list price at the end of the month that’s less than the list price at the beginning of the month, divided by the number of unique properties with an active listing at some point during the month.
- `Mean Listings Price Cut Amount`: dtype="float32"
- `Percent Listings Price Cut (Smoothed)`: dtype="float32"
- `Median Days on Pending (Smoothed)`: dtype="float32", median number of days it takes for homes in a region to change to pending status on Zillow.com after first being shown as for sale. (smoothed)
- `Median Days on Pending`: dtype="float32", median number of days it takes for homes in a region to change to pending status on Zillow.com after first being shown as for sale.
## FOR-SALE LISTINGS
Base Columns
- `Region ID`: dtype="string", a unique identifier for the region
- `Size Rank`: dtype="int32", a rank of the region's size
- `Region`: dtype="string", the name of the region
- `Region Type`: dtype="class_label", the type of region
- '0': zip
- '1': city
- '2': county
- '3': msa
- '4': state
- `State`: dtype="string", the US state abbreviation for the state containing the region
- `Home Type`: dtype="string", the type of home
- '0': all homes
- '1': all homes plus multifamily
- '2': SFR
- '3': condo/co-op
- '4': multifamily
- `Date`: dtype="timestamp[ms]", the date of the last day of the month for this data
Value Columns
- `Median Listing Price`: dtype="float32", The median price at which homes across various geographies were listed.
- `Median Listing Price (Smoothed)`: dtype="float32", The median price at which homes across various geographies were listed. (smoothed)
- `New Listings`: dtype="int32", how many new listings have come on the market in a given month
- `New Listings (Smoothed)`: dtype="int32", how many new listings have come on the market in a given month. (smoothed)
- `New Pending (Smoothed)`: dtype="int32", The count of listings that changed from for-sale to pending status on Zillow.com in a given time period. (smoothed)
- `New Pending`: dtype="int32", The count of listings that changed from for-sale to pending status on Zillow.com in a given time period.
## HOME VALUES
<!-- Zillow Home Value Index (ZHVI): A measure of the typical home value and market changes across a given region and housing type. It reflects the typical value for homes in the 35th to 65th percentile range. Available as a smoothed, seasonally adjusted measure and as a raw measure. -->
<!-- Zillow publishes top-tier ZHVI (\$, typical value for homes within the 65th to 95th percentile range for a given region) and bottom-tier ZHVI (\$, typical value for homes within the 5th to 35th percentile range for a given region). -->
<!-- Zillow also publishes ZHVI for all single-family residences (\$, typical value for all single-family homes in a given region), for condo/coops (\$), for all homes with 1, 2, 3, 4 and 5+ bedrooms (\$), and the ZHVI per square foot (\$, typical value of all homes per square foot calculated by taking the estimated home value for each home in a given region and dividing it by the home’s square footage). -->
<!-- Note: Starting with the January 2023 data release, and for all subsequent releases, the full ZHVI time series has been upgraded to harness the power of the neural Zestimate. -->
<!-- More information about what ZHVI is and how it’s calculated is available on this overview page. Here’s a handy ZHVI User Guide for information about properly citing and making calculations with this metric. -->
Base Columns
- `Region ID`: dtype="string", a unique identifier for the region
- `Size Rank`: dtype="int32", a rank of the region's size
- `Region`: dtype="string", the name of the region
- `Region Type`: dtype="class_label", the type of region
- '0': zip
- '1': city
- '2': county
- '3': msa
- '4': state
- '5': country"
- `State`: dtype="string", the US state abbreviation for the state containing the region
- `Home Type`: dtype="class_label", the type of home
- '0': multifamily
- '1': condo/co-op
- '2': SFR
- '3': all homes
- '4': all homes plus multifamily
- `Bedroom Count`: dtype="class_label", the number of bedrooms
- '0': 1-Bedroom
- '1': 2-Bedrooms
- '2': 3-Bedrooms
- '3': 4-Bedrooms
- '4': 5+-Bedrooms
- '5': All Bedrooms
- `Date`: dtype="timestamp[ms]", the date of the last day of the month for this data
Value Columns
- `Mid Tier ZHVI (Smoothed) (Seasonally Adjusted)`: dtype="float32",
- `Bottom Tier ZHVI (Smoothed) (Seasonally Adjusted)`: dtype="float32",
- `Top Tier ZHVI (Smoothed) (Seasonally Adjusted)`: dtype="float32",
- `ZHVI`: dtype="float32",
- `Mid Tier ZHVI`: dtype="float32"
## HOME VALUES FORECASTS
<!-- Zillow Home Value Forecast (ZHVF): A month-ahead, quarter-ahead and year-ahead forecast of the Zillow Home Value Index (ZHVI). ZHVF is created using the all homes, mid-tier cut of ZHVI and is available both raw and smoothed, seasonally adjusted. -->
<!-- Note: Starting with the January 2023 forecast (made available in February 2023), Zillow’s Home Value Forecast is based on the upgraded ZHVI that harnesses the power of the neural Zestimate. More information about what ZHVI is and how it’s calculated is available on this overview page. -->
Base Columns
- `Region ID`: dtype="string", a unique identifier for the region
- `Size Rank`: dtype="int32", a rank of the region's size
- `Region`: dtype="string", the name of the region
- `Region Type`: dtype="class_label", the type of region
- '0': county
- '1': city
- '2': zip
- '3': country
- '4': msa
- `State`: dtype="string", the US state abbreviation for the state containing the region
- `City`: dtype="string",
- `Metro`: dtype="string",
- `County`: dtype="string",
- `Home Type`: dtype="string", the type of home
- `Date`: dtype="timestamp[ms]", the date of these forecasts
Value Columns
- `Month Over Month % (Smoothed)`: dtype="float32",
- `Quarter Over Quarter % (Smoothed)`: dtype="float32",
- `Year Over Year % (Smoothed)`: dtype="float32"
- `Month Over Month % (Raw)`: dtype="float32"
- `Quarter Over Quarter % (Raw)`: dtype="float32"
- `Year Over Year % (Raw)`: dtype="float32"
## NEW CONSTRUCTION
Base Columns
- `Region ID`: dtype="string", a unique identifier for the region
- `Size Rank`: dtype="int32", a rank of the region's size
- `Region`: dtype="string", the name of the region
- `Region Type`: dtype="class_label", the type of region
- '0': county
- '1': city
- '2': zip
- '3': country
- '4': msa
- `State`: dtype="string", the US state abbreviation for the state containing the region
- `Home Type`: dtype="class_label", the type of home
- '0': all homes
- '1': all homes plus multifamily
- '2': SFR
- '3': condo/co-op
- '4': multifamily
- `Date`: dtype="timestamp[ms]", the date of the last day of the month for this data
Value Columns
- `Median Sale Price`: dtype="float32", the median sale price of new construction homes that sold during the month in the specified region
- `Median Sale Price per Sqft`: dtype="float32", the median sale price per square foot of new construction homes that sold during the month in the specified region
- `Sales Count`: dtype="int32", the number of new construction homes that sold during the month in the specified region
## RENTALS
Base Columns
- `Region ID`: dtype="string", a unique identifier for the region
- `Size Rank`: dtype="int32", a rank of the region's size
- `Region`: dtype="string", the name of the region
- `Region Type`: dtype="class_label", the type of region
- '0': county
- '1': city
- '2': zip
- '3': country
- '4': msa
- `State`: dtype="string", the US state abbreviation for the state containing the region
- `Home Type`: dtype="string", the type of home
- '0': all homes
- '1': all homes plus multifamily
- '2': SFR
- '3': condo/co-op
- '4': multifamily
- `Date`: dtype="timestamp[ms]", the date of the last day of the month for this data
Value Columns
- `Rent (Smoothed)`: dtype="float32", Zillow Observed Rent Index (ZORI): A smoothed measure of the typical observed market rate rent across a given region. ZORI is a repeat-rent index that is weighted to the rental housing stock to ensure representativeness across the entire market, not just those homes currently listed for-rent. The index is dollar-denominated by computing the mean of listed rents that fall into the 40th to 60th percentile range for all homes and apartments in a given region, which is weighted to reflect the rental housing stock.
- `Rent (Smoothed) (Seasonally Adjusted)`: dtype="float32", Zillow Observed Rent Index (ZORI) :A smoothed measure of the typical observed market rate rent across a given region. ZORI is a repeat-rent index that is weighted to the rental housing stock to ensure representativeness across the entire market, not just those homes currently listed for-rent. The index is dollar-denominated by computing the mean of listed rents that fall into the 40th to 60th percentile range for all homes and apartments in a given region, which is weighted to reflect the rental housing stock.
## SALES
<!-- Sale-to-List Ratio (mean/median): Ratio of sale vs. final list price. -->
<!-- Percent of Sales Below/Above List: Share of sales where sale price below/above the final list price; excludes homes sold for exactly the list price. -->
Base Columns
- `Region ID`: dtype="string", a unique identifier for the region
- `Size Rank`: dtype="int32", a rank of the region's size
- `Region`: dtype="string", the name of the region
- `Region Type`: dtype="class_label", the type of region
- '0': county
- '1': city
- '2': zip
- '3': country
- '4': msa
- `State`: dtype="string", the US state abbreviation for the state containing the region
- `Home Type`: dtype="class_label", the type of home
- '0': all homes
- '1': all homes plus multifamily
- '2': SFR
- '3': condo/co-op
- '4': multifamily
- `Date`: dtype="timestamp[ms]", the date of the last day of the month for this data
Value Columns
- `Median Sale Price`: dtype="float32", The median price at which homes across various geographies were sold.
- `Median Sale Price per Sqft`: dtype="float32" The median price per square foot at which homes across various geographies were sold.
- `Sales Count`: dtype="int32", The "Sales Count Nowcast" is the estimated number of unique properties that sold during the month after accounting for the latency between when sales occur and when they are reported.
## DEFINITIONS OF HOME TYPES
- all Homes: Zillow defines all homes as single-family, condominium and co-operative homes with a county record. Unless specified, all series cover this segment of the housing stock.
- SFR: single family residence
- condo/co-op: Condominium and co-operative homes.
- Multifamily 5+ units: Units in buildings with 5 or more housing units, that are not condominiums or co-ops.
<!-- - Duplex/Triplex/Quadplex: Housing units in buildings with 2, 3, or 4 housing units. -->
# Example Usage
```python
from datasets import load_dataset
dataset = load_dataset("misikoff/zillow", 'home_values', trust_remote_code=True)
```
## Example Notebook
Linked below is an example notebook that demonstrates usage and analysis of one of the configs of this dataset.
[](https://colab.research.google.com/drive/1lEPLbWsOKmNBzkPWT30RCBVxhG1LWkGg?usp=sharing) |
g-ronimo/oasst2_top1_fr-en-de-es-it | ---
dataset_info:
features:
- name: conversation
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 18301524
num_examples: 10746
download_size: 10477478
dataset_size: 18301524
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: apache-2.0
---
# Dataset Card for "oasst2_top1_fr-en-de-es-it"
* Top 1% conversations of https://huggingface.co/datasets/OpenAssistant/oasst2
* language-filtered: fr, en, de, es, ita
* generated using https://github.com/blancsw/deep_4_all/blob/main/datasets/oasst/convert.py
|
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