datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
Wenetspeech4TTS/Wenetspeech4TTS | ---
annotations_creators: []
language_creators: []
language:
- zh
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: Wenetspeech4TTS
source_datasets: []
task_categories:
- text-to-speech
extra_gated_prompt: >-
We do not own the copyright of the audio files. For researchers and educators
who wish to use the audio files for non-commercial research and/or educational
purposes, we can provide access through the Hub under certain conditions and
terms. Terms of Access: The Researcher has requested permission to use the
WenetSpeech4TTS database. In exchange for such permission, Researcher hereby
agrees to the following terms and conditions:
1. Researcher shall use the
Database only for non-commercial research and educational purposes.
2. The authors make no representations or warranties regarding the Database,
including but not limited to warranties of non-infringement or fitness for a
particular purpose.
3. Researcher accepts full responsibility for his or her use of the Database
and shall defend and indemnify the authors of WenetSpeech4TTS, including their
employees, Trustees, officers and agents, against any and all claims arising
from Researcher's use of the Database, including but not limited to
Researcher's use of any copies of copyrighted audio files that he or she may
create from the Database.
4.Researcher may provide research associates and colleagues with access to the
Database provided that they first agree to be bound by these terms and
conditions.
5. The authors reserve the right to terminate Researcher's access to the
Database at any time.
6. If Researcher is employed by a for-profit, commercial entity, Researcher's
employer shall also be bound by these terms and conditions, and Researcher
hereby represents that he or she is fully authorized to enter into this
agreement on behalf of such employer.
extra_gated_fields:
Name: text
Email: text
Organization: text
Address: text
I hereby confirm that I have requested access via the Google Form provided above: checkbox
I accept the terms of access: checkbox
size_categories:
- 10M<n<100M
---
# Dataset Card for Wenetspeech4TTS
<!-- Provide a quick summary of the dataset. -->
**WenetSpeech4TTS** is a multi-domain Mandarin corpus derived from the open-sourced WenetSpeech dataset. Tailored for the text-to-speech tasks, we refined WenetSpeech by adjusting segment boundaries, enhancing the audio quality, and eliminating speaker mixing within each segment. Following a more accurate transcription process and quality-based data filtering process, the obtained WenetSpeech4TTS corpus contains 12,800 hours of paired audio-text data.
## Subsets Details
|**Training Subsets** |**DNMOS Threshold**|**Hours** |**Average Segment Duration (s)**|
|:-----------------:|:---------------:|:------:|:----------------------------:|
|Premium| 4.0 |945|8.3|
|Standard | 3.8 |4056|7.5|
|Basic|3.6 |7226|6.6|
|Rest| <3.6|5574|/|
|WenetSpeech (orig)|/|12483|/|
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** Work in progress
- **Paper :** Work in progress
- **TTS Demo :** https://wenetspeech4tts.github.io/wenetspeech4tts/
## 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. -->
### Recommendations
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]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed]
## Terms of Access
We do not own the copyright of the audio files. For researchers and educators
who wish to use the audio files for non-commercial research and/or educational
purposes, we can provide access through the Hub under certain conditions and
terms. Terms of Access: The Researcher has requested permission to use the
WenetSpeech4TTS database. In exchange for such permission, Researcher hereby
agrees to the following terms and conditions:
1. Researcher shall use the
Database only for non-commercial research and educational purposes.
2. The authors make no representations or warranties regarding the Database,
including but not limited to warranties of non-infringement or fitness for a
particular purpose.
3. Researcher accepts full responsibility for his or her use of the Database
and shall defend and indemnify the authors of WenetSpeech4TTS, including their
employees, Trustees, officers and agents, against any and all claims arising
from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted audio files that he or she may
create from the Database.
4. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions.
5. The authors reserve the right to terminate Researcher's access to the
Database at any time.
6. If Researcher is employed by a for-profit, commercial entity, Researcher's
employer shall also be bound by these terms and conditions, and Researcher
hereby represents that he or she is fully authorized to enter into this
agreement on behalf of such employer. |
DGurgurov/tibetan_sa | ---
license: mit
---
## Sentiment Analysis Data for the Tibetan Language
**Dataset Description:**
This dataset contains a sentiment analysis data from Zhu et al. (2023).
**Data Structure:**
The data was used for the project on [injecting external commonsense knowledge into multilingual Large Language Models](https://github.com/d-gurgurov/Injecting-Commonsense-Knowledge-into-LLMs).
**Citation:**
```bibtex
@INPROCEEDINGS{10348366,
author={Zhu, Yulei and Luosai, Baima and Zhou, Liyuan and Qun, Nuo and Nyima, Tashi},
booktitle={2023 IEEE 4th International Conference on Pattern Recognition and Machine Learning (PRML)},
title={Research on Sentiment Analysis of Tibetan Short Text Based on Dual-channel Hybrid Neural Network},
year={2023},
volume={},
number={},
pages={377-384},
keywords={Analytical models;Sentiment analysis;Neural networks;Semantics;Machine learning;Logic gates;Feature extraction;Tibetan sentiment analysis;TextCNN;BiGRU;pretraining model},
doi={10.1109/PRML59573.2023.10348366}}
```
|
alayaran/bodo_english_parallel | ---
license: mit
language:
- brx
- en
task_categories:
- translation
pretty_name: bodo_english_parallel_dataset
size_categories:
- 10K<n<100K
---
# Uses
```
from datasets import load_dataset
dataset = load_dataset('alayaran/bodo_english_parallel')
# Dayaset information
dataset
`DatasetDict({
train: Dataset({
features: ['id', 'translation'],
num_rows: 149018
})
})`
# example
# Lets check the last 3 entries of the dataset
dataset['train'][-3:]
{'id': ['149015', '149016', '149017'],
'translation': [{'brx': '"गोबां बिबां आरो गोजौ-थ्रूपुट थाखो फारि खालामग्रा आरोंदायारि गोनोखो फैनायनि उनाव, जों दा गोबां गोजौ-रोजाथि जिनम थाखो फारियारि खारि आरो मोनसे जिबख्रियारि थाखोखौ लाफाना फांसे बिफांनि गुबुन-गुबुन बाहागोनिफ्राय ट्रांसक्रिप्टोम खारिबो दिहुन्नो हाबाय, "वार्ष्णेयया बुङो।',
'eng': '"With the advent of large-scale and high-throughput sequencing technologies, we are now able to generate large high-density genome sequencing data and also transcriptome data from various parts of a plant including at single cell level," says Varshney.'},
{'brx': "इयुन्नि जौगानायनि राहाया गोथौ बिजिरसंफोराव थायो, गाहाय महरै बेटारी आरोंदायारि गोनोखोआव आरो ई.वी. चार्ज खालामग्रा पइन्ट आरो बेटारिफोरखौ बाहायफिन्नायखौ लाफानानै ई.वी. लुनायनि सानज'थाय गुवारै गोसार होनायाव थायो।",
'eng': 'The key to future growth lies in deep research, specifically in battery technology and in wider deployment of E.V. infrastructure, including charging points and recycling of batteries.'},
{'brx': "बै सांग्रांथि होसेयावबो, बिथाङा बे नंगुबै तथ्य'याव फैनौ जुजिदोंमोन दि बिथाङा जाय थांखिगोनां बिजिरसं मावथांखिखौ जागायदोंमोन,बियो इं 2003 माइथायनि सोमखोर जांख्रिथायनि बिफा नरमेन बरल'गनि मोनसे बिबुंथिनिफ्राय थुलुंगा जादोंमोन, जाय रोदा सुनो फेलें जादोंमोन।",
'eng': 'Despite that awareness, he struggled to come to terms with the fact that the ambitious research project he had embarked upon, inspired by a speech in 2003 by Norman Borlaug, the Father of Green Revolution, had failed to take root.'}]}
``` |
Amarchavda05/Voiceofsrk | ---
license: openrail
---
|
felipesampaio2010/philbrrugratscres | ---
license: openrail
---
|
iamkaikai/OPTICAL-ART | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 17958981.0
num_examples: 255
download_size: 17637639
dataset_size: 17958981.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "OPTICAL-ART"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
winkm/processed_bert_dataset | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: token_type_ids
sequence: int8
- name: attention_mask
sequence: int8
- name: special_tokens_mask
sequence: int8
splits:
- name: train
num_bytes: 8473147200.0
num_examples: 2353652
download_size: 2275912633
dataset_size: 8473147200.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "processed_bert_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
multi-train/emb-hotpotqa-train | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: query
dtype: string
- name: pos
dtype: string
- name: neg
dtype: string
- name: idx
dtype: int64
- name: task_name
dtype: string
splits:
- name: train
num_bytes: 76992682
num_examples: 68659
download_size: 50772036
dataset_size: 76992682
---
# Dataset Card for "emb-hotpotqa-train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
sin3142/memes-500 | ---
task_categories:
- image-classification
size_categories:
- n<1K
--- |
bh8648/split_dataset_9 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
- name: page_num
dtype: int64
splits:
- name: train
num_bytes: 647647
num_examples: 212
download_size: 317830
dataset_size: 647647
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "split_dataset_9"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
rshaojimmy/Seq-DeepFake | ---
license: apache-2.0
---
|
ccw7463/Ko_MMLU_ver0.3 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: category
dtype: string
- name: ref
dtype: string
- name: context
dtype: string
splits:
- name: train
num_bytes: 98051828.0
num_examples: 245613
download_size: 46102728
dataset_size: 98051828.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
🚀 Dataset Info
- total : 245613
- Ref (used)
- HAERAE-HUB/KMMLU : 243777 개
- facebook/belebele : 900 개
- HAERAE-HUB/csatqa : 936 개
- preocessing
- (1) all : change formatting
- example
```python
{'instruction': '한국채택국제회계기준(K-IFRS)하에서 금융자산으로 분류되지 않는 것은?',
'input': 'A. 대여금\nB. 재고자산\nC. 매출채권\nD. 만기보유금융자산',
'output': 'B. 재고자산',
'category': 'multi_choice (Accounting)',
'ref': 'HAERAE-HUB/KMMLU',
'context': ''}
``` |
nthakur/multilingual-ultrafeedback-dpo-v0.1 | ---
dataset_info:
features:
- name: id
dtype: string
- name: en_chosen
dtype: string
- name: en_rejected
dtype: string
- name: en_input
dtype: string
- name: source
dtype: string
- name: input
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
- name: language
dtype: string
splits:
- name: train
num_bytes: 421300933.67974883
num_examples: 74440
- name: test
num_bytes: 11319208.320251178
num_examples: 2000
download_size: 245143451
dataset_size: 432620142.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Dataset Card for "multilingual-ultrafeedback-dpo-v0.1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Symato/cc | ---
license: mit
language:
- vi
size_categories:
- 1K<n<10K
---
# What is Symato CC?
To download all WARC data from Common Crawl then filter out Vietnamese in Markdown and Plaintext format.
There is 1% of Vietnamse in CC, extract all of them out should be a lot (~10TB of plaintext).
## Main contributors
- https://huggingface.co/nampdn-ai
- https://huggingface.co/binhvq
- https://huggingface.co/th1nhng0
- https://huggingface.co/iambestfeed
# Simple quality filters
To make use of raw data from common crawl, you need to do filtering and deduping.
Below is a simple quality filtering code for reference to write your own filters.
```sh
## Convert .parquet to .jsonl.gz
mkdir -p jsonl filtered
python3 parquet2jsonl.py
## Quality filter
# wget https://huggingface.co/datasets/Symato/goods_vs_c4_cc_classifiers/resolve/main/fasttext_good_vs_c4_001.bin
python3 filters.py jsonl/2023-14_20230401125552-20230401155552.jsonl.gz logging
```
# Disclaimer
- We use content from Common Crawl as it is. Go to CC website to know more about data.
- We provide simple quality filters code to make it easier for you to use data but no warranty the data quality meet everyone expectations. Modifiy ours or write your own filters in-case you need more advanced / better ones.
Contact **dung at symato dot xyz** if you have other questions.
|
yuan-sf63/word_mask_D_64 | ---
dataset_info:
features:
- name: feature
dtype: string
- name: target
dtype: string
splits:
- name: train
num_bytes: 29674460.920895755
num_examples: 195361
- name: validation
num_bytes: 3297196.079104244
num_examples: 21707
download_size: 23978359
dataset_size: 32971657.0
---
# Dataset Card for "word_mask_D_64"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Shweta2204/g2_dataset | ---
license: openrail
---
|
FutureMa/HELIOS | ---
license: apache-2.0
---
|
CyberHarem/skorpion_girlsfrontline | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of skorpion/スコーピオン/蝎式 (Girls' Frontline)
This is the dataset of skorpion/スコーピオン/蝎式 (Girls' Frontline), containing 83 images and their tags.
The core tags of this character are `blonde_hair, blue_eyes, long_hair, eyepatch, twintails, hair_between_eyes, bangs, 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 | 83 | 96.01 MiB | [Download](https://huggingface.co/datasets/CyberHarem/skorpion_girlsfrontline/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 83 | 57.39 MiB | [Download](https://huggingface.co/datasets/CyberHarem/skorpion_girlsfrontline/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 196 | 120.10 MiB | [Download](https://huggingface.co/datasets/CyberHarem/skorpion_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 83 | 86.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/skorpion_girlsfrontline/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 196 | 162.52 MiB | [Download](https://huggingface.co/datasets/CyberHarem/skorpion_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/skorpion_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 | 17 |  |  |  |  |  | 1girl, smile, solo, gloves, looking_at_viewer, shorts, navel, submachine_gun, tongue_out, midriff, dual_wielding, holding_gun, simple_background, black_jacket, single_thighhigh, underwear, white_background |
| 1 | 21 |  |  |  |  |  | black_gloves, elbow_gloves, official_alternate_costume, red_dress, 1girl, looking_at_viewer, solo, bare_shoulders, smile, strapless_dress, collarbone, low_ponytail, rose_petals, hair_ribbon, blush, red_rose, thigh_strap, black_ribbon, cleavage, very_long_hair, white_background, belt, buckle, medium_breasts, standing, full_body, gun, alternate_hairstyle, black_choker, black_footwear, boots, cape, holding |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | smile | solo | gloves | looking_at_viewer | shorts | navel | submachine_gun | tongue_out | midriff | dual_wielding | holding_gun | simple_background | black_jacket | single_thighhigh | underwear | white_background | black_gloves | elbow_gloves | official_alternate_costume | red_dress | bare_shoulders | strapless_dress | collarbone | low_ponytail | rose_petals | hair_ribbon | blush | red_rose | thigh_strap | black_ribbon | cleavage | very_long_hair | belt | buckle | medium_breasts | standing | full_body | gun | alternate_hairstyle | black_choker | black_footwear | boots | cape | holding |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-------|:---------|:--------------------|:---------|:--------|:-----------------|:-------------|:----------|:----------------|:--------------|:--------------------|:---------------|:-------------------|:------------|:-------------------|:---------------|:---------------|:-----------------------------|:------------|:-----------------|:------------------|:-------------|:---------------|:--------------|:--------------|:--------|:-----------|:--------------|:---------------|:-----------|:-----------------|:-------|:---------|:-----------------|:-----------|:------------|:------|:----------------------|:---------------|:-----------------|:--------|:-------|:----------|
| 0 | 17 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 21 |  |  |  |  |  | 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 | X | X |
|
girrajjangid/guanaco-9k | ---
license: apache-2.0
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 14091569
num_examples: 9000
download_size: 8325237
dataset_size: 14091569
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Shashkovich/Telecommunication_SMS_time_series | ---
license: gpl-3.0
task_categories:
- time-series-forecasting
tags:
- SMS
- fraud
- forecasting
pretty_name: SMS time series
---
# SMS Time series data for traffic and fraud forecasting
This dataset contains various time series from vendors.
# Vendor A: 01.03.23-14.08.23
* TS_*_all - Count of all SMS
# Vendor A: January
* TS_*_fraud - Count of fraud
* TS_*_all - Count of all SMS
* TS_*_hlrDelay - Mean values of hlr delay
# Vendor B: January 1-8
* 1-8_TS_*_fraud - Count of fraud
* 1-8_TS_*_all - Count of all SMS
* 1-8_TS_*_hlrDelay - Mean values of hlr delay
 |
heliosprime/twitter_dataset_1713081626 | ---
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: 18231
num_examples: 43
download_size: 13657
dataset_size: 18231
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "twitter_dataset_1713081626"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
reciprocate/vicuna-fair-eval | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: selected
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 180638
num_examples: 66
download_size: 116978
dataset_size: 180638
---
# Dataset Card for "vicuna_fair_eval"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/asukagawa_chise_azurlane | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of asukagawa_chise/飛鳥川ちせ/飞鸟川千濑 (Azur Lane)
This is the dataset of asukagawa_chise/飛鳥川ちせ/飞鸟川千濑 (Azur Lane), containing 103 images and their tags.
The core tags of this character are `red_hair, blue_eyes, mole, bangs, mole_under_mouth, braid, twin_braids, multicolored_hair`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:--------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 103 | 109.27 MiB | [Download](https://huggingface.co/datasets/CyberHarem/asukagawa_chise_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 103 | 65.26 MiB | [Download](https://huggingface.co/datasets/CyberHarem/asukagawa_chise_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 229 | 131.91 MiB | [Download](https://huggingface.co/datasets/CyberHarem/asukagawa_chise_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 103 | 97.26 MiB | [Download](https://huggingface.co/datasets/CyberHarem/asukagawa_chise_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 229 | 188.30 MiB | [Download](https://huggingface.co/datasets/CyberHarem/asukagawa_chise_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/asukagawa_chise_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 | 45 |  |  |  |  |  | 1girl, solo, bare_shoulders, looking_at_viewer, smile, o-ring, bracelet, simple_background, black_skirt, choker, gloves, boots, breasts, red_footwear |
| 1 | 7 |  |  |  |  |  | black_skirt, red_shirt, long_hair, looking_at_viewer, midriff, navel, twintails, 1girl, black_thighhighs, collarbone, open_mouth, smile, blonde_hair, blunt_bangs, blush, boots, garter_straps, pleated_skirt, solo_focus, wristband, 2girls, black_footwear, gradient_hair, miniskirt |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | bare_shoulders | looking_at_viewer | smile | o-ring | bracelet | simple_background | black_skirt | choker | gloves | boots | breasts | red_footwear | red_shirt | long_hair | midriff | navel | twintails | black_thighhighs | collarbone | open_mouth | blonde_hair | blunt_bangs | blush | garter_straps | pleated_skirt | solo_focus | wristband | 2girls | black_footwear | gradient_hair | miniskirt |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:-----------------|:--------------------|:--------|:---------|:-----------|:--------------------|:--------------|:---------|:---------|:--------|:----------|:---------------|:------------|:------------|:----------|:--------|:------------|:-------------------|:-------------|:-------------|:--------------|:--------------|:--------|:----------------|:----------------|:-------------|:------------|:---------|:-----------------|:----------------|:------------|
| 0 | 45 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | |
| 1 | 7 |  |  |  |  |  | X | | | X | X | | | | X | | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
distilled-one-sec-cv12-each-chunk-uniq/chunk_100 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1300115220.0
num_examples: 253335
download_size: 1333101688
dataset_size: 1300115220.0
---
# Dataset Card for "chunk_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
joey234/mmlu-clinical_knowledge-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: 74962
num_examples: 265
download_size: 49647
dataset_size: 74962
---
# Dataset Card for "mmlu-clinical_knowledge-neg-answer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
covid_qa_deepset | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- closed-domain-qa
- extractive-qa
paperswithcode_id: null
pretty_name: COVID-QA
dataset_info:
features:
- name: document_id
dtype: int32
- name: context
dtype: string
- name: question
dtype: string
- name: is_impossible
dtype: bool
- name: id
dtype: int32
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
config_name: covid_qa_deepset
splits:
- name: train
num_bytes: 65151262
num_examples: 2019
download_size: 4418117
dataset_size: 65151262
---
# Dataset Card for COVID-QA
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** https://github.com/deepset-ai/COVID-QA
- **Paper:** https://openreview.net/forum?id=JENSKEEzsoU
- **Point of Contact:** [deepset AI](https://github.com/deepset-ai)
### Dataset Summary
COVID-QA is a Question Answering dataset consisting of 2,019 question/answer pairs annotated by volunteer biomedical experts on scientific articles related to COVID-19.
A total of 147 scientific articles from the CORD-19 dataset were annotated by 15 experts.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The text in the dataset is in English.
## Dataset Structure
### Data Instances
**What do the instances that comprise the dataset represent?**
Each represents a question, a context (document passage from the CORD19 dataset) and an answer.
**How many instances are there in total?**
2019 instances
**What data does each instance consist of?**
Each instance is a question, a set of answers, and an id associated with each answer.
[More Information Needed]
### Data Fields
The data was annotated in SQuAD style fashion, where each row contains:
* **question**: Query question
* **context**: Context text to obtain the answer from
* **document_id** The document ID of the context text
* **answer**: Dictionary containing the answer string and the start index
### Data Splits
**data/COVID-QA.json**: 2,019 question/answer pairs annotated by volunteer biomedical experts on scientific articles related to COVID-19.
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
The inital data collected comes from 147 scientific articles from the CORD-19 dataset. Question and answers were then
annotated afterwards.
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
While annotators were volunteers, they were required to have at least a Master’s degree in biomedical sciences.
The annotation team was led by a medical doctor (G.A.R.) who vetted the volunteer’s credentials and
manually verified each question/answer pair produced. We used an existing, web-based annotation tool that had been
created by deepset and is available at their Neural Search framework [haystack](https://github.com/deepset-ai/haystack).
#### Who are the annotators?
The annotators are 15 volunteer biomedical experts on scientific articles related to COVID-19.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
The dataset aims to help build question answering models serving clinical and scientific researchers, public health authorities, and frontline workers.
These QA systems can help them find answers and patterns in research papers by locating relevant answers to common questions from scientific articles.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
## Additional Information
The listed authors in the homepage are maintaining/supporting the dataset.
### Dataset Curators
[More Information Needed]
### Licensing Information
The Proto_qa dataset is licensed under the [Apache License 2.0](https://github.com/deepset-ai/COVID-QA/blob/master/LICENSE)
### Citation Information
```
@inproceedings{moller2020covid,
title={COVID-QA: A Question Answering Dataset for COVID-19},
author={M{\"o}ller, Timo and Reina, Anthony and Jayakumar, Raghavan and Pietsch, Malte},
booktitle={Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020},
year={2020}
}
```
### Contributions
Thanks to [@olinguyen](https://github.com/olinguyen) for adding this dataset. |
marmofayezi/M3GenLandmark | ---
dataset_info:
features:
- name: id
dtype: string
- name: image
dtype: image
- name: caption
dtype: string
- name: landmark
dtype: image
- name: generated_image
dtype: image
splits:
- name: train
num_bytes: 1937125922.75
num_examples: 2998
download_size: 1926594764
dataset_size: 1937125922.75
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Multimodal-Fatima/VQAv2_sample_validation_google_flan_t5_xxl_mode_VQAv2_visclues_ns_100_open_ended | ---
dataset_info:
features:
- name: id
dtype: int64
- name: question
dtype: string
- name: true_label
sequence: string
- name: prediction
dtype: string
splits:
- name: fewshot_0_bs_16
num_bytes: 14044
num_examples: 100
download_size: 0
dataset_size: 14044
---
# Dataset Card for "VQAv2_sample_validation_google_flan_t5_xxl_mode_VQAv2_visclues_ns_100_open_ended"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
stoddur/med_chat_16 | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 787392760.0
num_examples: 255647
download_size: 11275853
dataset_size: 787392760.0
---
# Dataset Card for "med_chat_16"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mkshing/tydiqa_ja | ---
license: apache-2.0
---
This is the Japanese subset of [TyDi QA](https://github.com/google-research-datasets/tydiqa).
**number of examples**
- primary task
- train: 16288 (original: 166916)
- validation: 1709 (original: 18670)
- secondary task ... no japanese data
**filtering script**
```python
from datasets import load_dataset
dataset = load_dataset("tydiqa", "secondary_task")
ja_dataset = dataset.filter(lambda example: example['id'].startswith('jp'))
``` |
sanjeev-bhandari01/XLSum-nepali-summerization-dataset | ---
license: mit
task_categories:
- summarization
- text-generation
- text2text-generation
language:
- ne
tags:
- nepali
- dataset
- XLsum-nepali-dataset
--- |
ovior/twitter_dataset_1713179689 | ---
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: 2408015
num_examples: 7074
download_size: 1373027
dataset_size: 2408015
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Lollitor/CASFProtein | ---
dataset_info:
features:
- name: '#code'
dtype: string
- name: inputs
dtype: string
splits:
- name: train
num_bytes: 284421
num_examples: 285
download_size: 97150
dataset_size: 284421
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "CASFProtein"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
BotatoFontys/DataBank | ---
task_categories:
- text-generation
tags:
- Agriculture
pretty_name: Potato/Tomato
---
# Word Cloud
# Frequency of Words
This graph shows a tendency for being about Eindhoven, more specifically, matters of its housing situation, social environments, industry and tech, among other topics.
# Word Embeddings Plot
This graph shows us how related words are to each other. The closer one word is to another, the more they are related.
 |
gundlapalli/email-mining-chatbot | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 68441879
num_examples: 113687
download_size: 31528677
dataset_size: 68441879
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
yuiseki/open2ch-livejupiter-qa | ---
language:
- ja
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 82493925
num_examples: 663546
download_size: 47274336
dataset_size: 82493925
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
fhai50032/magicoder-oss-instruct-sharegpt-75k | ---
dataset_info:
features:
- name: lang
dtype: string
- name: raw_index
dtype: int64
- name: index
dtype: int64
- name: seed
dtype: string
- name: openai_fingerprint
dtype: string
- name: problem
dtype: string
- name: solution
dtype: string
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
splits:
- name: train
num_bytes: 377778447
num_examples: 75197
download_size: 160972754
dataset_size: 377778447
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
Share-GPT version of magicoder instuct with 12 random system prompt randomly distibuted
Genarated by Mixtral 8x7B
```python
code_instructs=[
"You are a versatile coding companion, dedicated to helping users overcome obstacles and master programming fundamentals.",
"Boasting years of hands-on experience in software engineering, you offer practical solutions based on sound design principles and industry standards.",
"By simplifying complex topics, you empower beginners and seasoned developers alike, enabling them to grasp abstract concepts and apply them effectively.",
"Excelling in multiple programming paradigms, you combine creativity and logic to craft ingenious answers customized to individual needs.",
"Catering to a global audience, you draw upon real-world case studies and cultural sensitivity to address diverse challenges faced by programmers worldwide.",
"Prioritizing simplicity and readability, you streamline convoluted problems into digestible pieces, promoting maintainable and scalable codebases.",
"Staying current with emerging trends and technologies, you seamlessly blend traditional methods with modern advancements, maximizing potential opportunities.",
"Integrating cross-functional skills, such as web design and database management, you enable comprehensive end-to-end solutions encompassing varied aspects of application development and software devlopment.",
"Valuing open communication and teamwork, you foster collaborative environments where peers exchange ideas freely, driving innovation through collective wisdom.",
"Perpetually seeking self-improvement, you remain humble and adaptable, embracing evolving landscapes and nurturing continuous personal growth throughout your career journey.",
"Skilled at debugging, analyzing, and troubleshooting, you quickly pinpoint root causes of elusive errors, devising targeted remediations and educating fellow coders along the way.",
"Recognized for your ability to balance theoretical depth with pragmatic sensibilities, you judiciously apply academic research and empirical evidence to optimize everyday coding practices.",
]
```
|
edbeeching/test_dataset_bug | ---
dataset_info:
features:
- name: revision
dtype: string
splits:
- name: train
num_bytes: 6
num_examples: 1
download_size: 752
dataset_size: 6
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
DaniFrame/PAWSPerturbed | ---
dataset_info:
features:
- name: id
dtype: int32
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype:
class_label:
names:
'0': '0'
'1': '1'
splits:
- name: paws_perturbed_keyboard_0.01
num_bytes: 1943526
num_examples: 8000
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num_bytes: 1943789
num_examples: 8000
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num_bytes: 1944158
num_examples: 8000
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num_bytes: 1943479
num_examples: 8000
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num_bytes: 1943482
num_examples: 8000
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num_bytes: 1943484
num_examples: 8000
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num_bytes: 1945567
num_examples: 8000
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num_bytes: 1953729
num_examples: 8000
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num_bytes: 1962307
num_examples: 8000
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num_bytes: 1943704
num_examples: 8000
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num_bytes: 1944122
num_examples: 8000
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num_bytes: 1944887
num_examples: 8000
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num_bytes: 2006174
num_examples: 8000
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num_bytes: 2005948
num_examples: 8000
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num_bytes: 2008145
num_examples: 8000
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num_bytes: 1954707
num_examples: 8000
- name: paws_perturbed_sswn_0.2
num_bytes: 1968857
num_examples: 8000
- name: paws_perturbed_sswn_0.3
num_bytes: 1982684
num_examples: 8000
- name: paws_perturbed_contraction
num_bytes: 1984318
num_examples: 8000
- name: paws_perturbed_insertadv
num_bytes: 2263946
num_examples: 8000
- name: paws_perturbed_prejudice
num_bytes: 1984801
num_examples: 8000
- name: paws_perturbed_punctuation
num_bytes: 2024502
num_examples: 8000
- name: paws_perturbed_reverseneg
num_bytes: 2046682
num_examples: 8000
- name: paws_perturbed_swapnum
num_bytes: 1963398
num_examples: 8000
- name: paws_perturbed_verbtense
num_bytes: 1955646
num_examples: 8000
- name: paws_perturbed_twitter
num_bytes: 2163936
num_examples: 8000
- name: paws_perturbed_wordcase
num_bytes: 1987750
num_examples: 8000
download_size: 36998738
dataset_size: 53657728
---
# Dataset Card for "PAWSPerturbed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
paul-w-qs/contracts_v2 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: N_ROWS
dtype: int64
- name: N_COLS
dtype: int64
- name: FONT_SIZE
dtype: int64
- name: FONT_NAME
dtype: string
- name: BORDER_THICKNESS
dtype: int64
- name: NOISED
dtype: bool
- name: LABEL_NOISE
dtype: bool
- name: JSON_LABEL
dtype: string
splits:
- name: train
num_bytes: 961858267.064
num_examples: 11871
download_size: 947911506
dataset_size: 961858267.064
---
# Dataset Card for "contracts_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
DataStudio/Viet-wikipedia | ---
dataset_info:
features:
- name: id
dtype: string
- name: url
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 1635514576
num_examples: 1291960
download_size: 737785594
dataset_size: 1635514576
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
language:
- vi
size_categories:
- 1M<n<10M
---
### About:
This dataset is a part of the Wikipedia dataset but only has Vietnamese.
The last update of this dataset is 02/04/2024. |
saibo/bookcorpus_compact_256_test | ---
dataset_info:
features:
- name: text
dtype: string
- name: concept_with_offset
dtype: string
splits:
- name: train
num_bytes: 20727824
num_examples: 6160
download_size: 10867768
dataset_size: 20727824
---
# Dataset Card for "bookcorpus_compact_256_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_170 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1237279620.0
num_examples: 242985
download_size: 1261694847
dataset_size: 1237279620.0
---
# Dataset Card for "chunk_170"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
BobdoRock/MaryJane | ---
license: openrail
---
|
Doub7e/SDv2-Count-Iterative-1 | ---
dataset_info:
features:
- name: image
dtype: image
- name: prompt
dtype: string
- name: T5_last_hidden_states
sequence:
sequence:
sequence: float64
- name: prompt_original
dtype: string
splits:
- name: train
num_bytes: 1632633888.75
num_examples: 1050
download_size: 1126914588
dataset_size: 1632633888.75
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
marcusy/nlp_ah_dataset | ---
dataset_info:
features:
- name: translation
dtype:
translation:
languages:
- query
- output
splits:
- name: train
num_bytes: 374237
num_examples: 4800
- name: validation
num_bytes: 93553
num_examples: 1200
download_size: 666113
dataset_size: 467790
license: mit
task_categories:
- translation
language:
- en
size_categories:
- 1K<n<10K
--- |
CyberHarem/osakabehime_fgo | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of osakabehime/刑部姫/刑部姬 (Fate/Grand Order)
This is the dataset of osakabehime/刑部姫/刑部姬 (Fate/Grand Order), containing 500 images and their tags.
The core tags of this character are `long_hair, breasts, purple_eyes, large_breasts, very_long_hair, brown_hair, black_hair, twintails, hairband, low_twintails, bow, glasses`, 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 | 703.14 MiB | [Download](https://huggingface.co/datasets/CyberHarem/osakabehime_fgo/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 500 | 621.94 MiB | [Download](https://huggingface.co/datasets/CyberHarem/osakabehime_fgo/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1229 | 1.16 GiB | [Download](https://huggingface.co/datasets/CyberHarem/osakabehime_fgo/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/osakabehime_fgo',
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 | 8 |  |  |  |  |  | blush, hair_flower, looking_at_viewer, white_hairband, 1girl, collarbone, fox_ears, red-framed_eyewear, animal_ear_fluff, black_bikini, cleavage, fox_girl, magatama_necklace, side-tie_bikini_bottom, smile, closed_mouth, bare_shoulders, navel, red_eyes, sitting, bracelet, fox_shadow_puppet, gradient_hair, hand_up, hooded_cloak, o-ring, solo_focus, thighs, white_flower |
| 1 | 13 |  |  |  |  |  | 1girl, looking_at_viewer, purple_skirt, solo, bat_(animal), origami, blush, hood_down, gradient_hair, hooded_cloak, smile, sitting, hair_ornament, white_thighhighs |
| 2 | 8 |  |  |  |  |  | 1girl, cleavage, goggles_on_head, looking_at_viewer, pink_bikini, pink_scarf, ski_goggles, solo, bare_shoulders, blush, smile, red_eyes, navel, open_mouth, black_gloves, wavy_mouth |
| 3 | 7 |  |  |  |  |  | 1girl, cleavage, day, goggles_on_head, looking_at_viewer, navel, outdoors, pink_scarf, ski_goggles, solo, bare_shoulders, blush, open_mouth, pink_bikini, blue_sky, black_gloves, red_eyes, jacket, ocean |
| 4 | 12 |  |  |  |  |  | black_jacket, goggles_on_head, looking_at_viewer, navel, ski_goggles, 1girl, solo, thigh_strap, black_gloves, scarf, smile, black_shorts, blush, holding_gun, gradient_hair, open_mouth, open_clothes, short_shorts, thigh_holster |
| 5 | 8 |  |  |  |  |  | 1boy, 1girl, blush, hetero, looking_at_viewer, pov, solo_focus, nipples, paizuri, penis, sweat, smile, breasts_squeezed_together, cum_on_breasts, tongue_out, cum_on_hair, facial, huge_breasts, mosaic_censoring, bar_censor, breasts_out, closed_mouth, gradient_hair, hood |
| 6 | 5 |  |  |  |  |  | 1boy, 1girl, bare_shoulders, blush, hetero, mosaic_censoring, navel, nipples, penis, pussy, sex, solo_focus, spread_legs, vaginal, bikini_bottom_aside, goggles_on_head, looking_at_viewer, pink_scarf, ski_goggles, thighs, grabbing_another's_breast, open_mouth, pink_bikini, arm_garter, bikini_pull, black_gloves, closed_mouth, clothes_lift, command_spell, leg_lift, missionary, on_back, one_eye_closed |
| 7 | 5 |  |  |  |  |  | 1boy, 1girl, blush, girl_on_top, hetero, nipples, nude, smile, penis, sex, solo_focus, vaginal, female_pubic_hair, huge_breasts, large_areolae, navel, open_mouth, plump, pussy, squatting_cowgirl_position, collarbone, looking_at_viewer, mosaic_censoring, spread_legs |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | blush | hair_flower | looking_at_viewer | white_hairband | 1girl | collarbone | fox_ears | red-framed_eyewear | animal_ear_fluff | black_bikini | cleavage | fox_girl | magatama_necklace | side-tie_bikini_bottom | smile | closed_mouth | bare_shoulders | navel | red_eyes | sitting | bracelet | fox_shadow_puppet | gradient_hair | hand_up | hooded_cloak | o-ring | solo_focus | thighs | white_flower | purple_skirt | solo | bat_(animal) | origami | hood_down | hair_ornament | white_thighhighs | goggles_on_head | pink_bikini | pink_scarf | ski_goggles | open_mouth | black_gloves | wavy_mouth | day | outdoors | blue_sky | jacket | ocean | black_jacket | thigh_strap | scarf | black_shorts | holding_gun | open_clothes | short_shorts | thigh_holster | 1boy | hetero | pov | nipples | paizuri | penis | sweat | breasts_squeezed_together | cum_on_breasts | tongue_out | cum_on_hair | facial | huge_breasts | mosaic_censoring | bar_censor | breasts_out | hood | pussy | sex | spread_legs | vaginal | bikini_bottom_aside | grabbing_another's_breast | arm_garter | bikini_pull | clothes_lift | command_spell | leg_lift | missionary | on_back | one_eye_closed | girl_on_top | nude | female_pubic_hair | large_areolae | plump | squatting_cowgirl_position |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------------|:--------------------|:-----------------|:--------|:-------------|:-----------|:---------------------|:-------------------|:---------------|:-----------|:-----------|:--------------------|:-------------------------|:--------|:---------------|:-----------------|:--------|:-----------|:----------|:-----------|:--------------------|:----------------|:----------|:---------------|:---------|:-------------|:---------|:---------------|:---------------|:-------|:---------------|:----------|:------------|:----------------|:-------------------|:------------------|:--------------|:-------------|:--------------|:-------------|:---------------|:-------------|:------|:-----------|:-----------|:---------|:--------|:---------------|:--------------|:--------|:---------------|:--------------|:---------------|:---------------|:----------------|:-------|:---------|:------|:----------|:----------|:--------|:--------|:----------------------------|:-----------------|:-------------|:--------------|:---------|:---------------|:-------------------|:-------------|:--------------|:-------|:--------|:------|:--------------|:----------|:----------------------|:----------------------------|:-------------|:--------------|:---------------|:----------------|:-----------|:-------------|:----------|:-----------------|:--------------|:-------|:--------------------|:----------------|:--------|:-----------------------------|
| 0 | 8 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 13 |  |  |  |  |  | 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 | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 7 |  |  |  |  |  | X | | X | | X | | | | | | X | | | | | | X | X | X | | | | | | | | | | | | X | | | | | | X | X | X | X | X | X | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 12 |  |  |  |  |  | X | | X | | X | | | | | | | | | | X | | | X | | | | | X | | | | | | | | X | | | | | | X | | | X | X | X | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 8 |  |  |  |  |  | X | | X | | X | | | | | | | | | | X | X | | | | | | | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | |
| 6 | 5 |  |  |  |  |  | X | | X | | X | | | | | | | | | | | X | X | X | | | | | | | | | X | X | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | X | X | | X | | X | | | | | | | | X | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | |
| 7 | 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 |
|
Zendayaharmony/ella | ---
license: openrail
---
|
distilled-from-one-sec-cv12/chunk_10 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1037849492
num_examples: 202231
download_size: 1055994358
dataset_size: 1037849492
---
# Dataset Card for "chunk_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
VatsaDev/NanoChatGPT | ---
license: apache-2.0
---
|
LordY54/TCAC | ---
license: apache-2.0
---
|
foilfoilfoil/PersonalDiscordDialouges | ---
license: unknown
---
|
vphu123/data_w2v | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: transcription
dtype: string
splits:
- name: train
num_bytes: 34870532580.69
num_examples: 208310
- name: test
num_bytes: 983580985.68
num_examples: 9497
download_size: 35641933314
dataset_size: 35854113566.37
---
# Dataset Card for "data_w2v"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Multimodal-Fatima/FGVC_Aircraft_train | ---
dataset_info:
features:
- name: image
dtype: image
- name: family
dtype:
class_label:
names:
'0': A300
'1': A310
'2': A320
'3': A330
'4': A340
'5': A380
'6': ATR-42
'7': ATR-72
'8': An-12
'9': BAE 146
'10': BAE-125
'11': Beechcraft 1900
'12': Boeing 707
'13': Boeing 717
'14': Boeing 727
'15': Boeing 737
'16': Boeing 747
'17': Boeing 757
'18': Boeing 767
'19': Boeing 777
'20': C-130
'21': C-47
'22': CRJ-200
'23': CRJ-700
'24': Cessna 172
'25': Cessna 208
'26': Cessna Citation
'27': Challenger 600
'28': DC-10
'29': DC-3
'30': DC-6
'31': DC-8
'32': DC-9
'33': DH-82
'34': DHC-1
'35': DHC-6
'36': DR-400
'37': Dash 8
'38': Dornier 328
'39': EMB-120
'40': Embraer E-Jet
'41': Embraer ERJ 145
'42': Embraer Legacy 600
'43': Eurofighter Typhoon
'44': F-16
'45': F/A-18
'46': Falcon 2000
'47': Falcon 900
'48': Fokker 100
'49': Fokker 50
'50': Fokker 70
'51': Global Express
'52': Gulfstream
'53': Hawk T1
'54': Il-76
'55': King Air
'56': L-1011
'57': MD-11
'58': MD-80
'59': MD-90
'60': Metroliner
'61': PA-28
'62': SR-20
'63': Saab 2000
'64': Saab 340
'65': Spitfire
'66': Tornado
'67': Tu-134
'68': Tu-154
'69': Yak-42
- name: manufacturer
dtype:
class_label:
names:
'0': ATR
'1': Airbus
'2': Antonov
'3': Beechcraft
'4': Boeing
'5': Bombardier Aerospace
'6': British Aerospace
'7': Canadair
'8': Cessna
'9': Cirrus Aircraft
'10': Dassault Aviation
'11': Dornier
'12': Douglas Aircraft Company
'13': Embraer
'14': Eurofighter
'15': Fairchild
'16': Fokker
'17': Gulfstream Aerospace
'18': Ilyushin
'19': Lockheed Corporation
'20': Lockheed Martin
'21': McDonnell Douglas
'22': Panavia
'23': Piper
'24': Robin
'25': Saab
'26': Supermarine
'27': Tupolev
'28': Yakovlev
'29': de Havilland
- name: label
dtype:
class_label:
names:
'0': 707-320
'1': 727-200
'2': 737-200
'3': 737-300
'4': 737-400
'5': 737-500
'6': 737-600
'7': 737-700
'8': 737-800
'9': 737-900
'10': 747-100
'11': 747-200
'12': 747-300
'13': 747-400
'14': 757-200
'15': 757-300
'16': 767-200
'17': 767-300
'18': 767-400
'19': 777-200
'20': 777-300
'21': A300B4
'22': A310
'23': A318
'24': A319
'25': A320
'26': A321
'27': A330-200
'28': A330-300
'29': A340-200
'30': A340-300
'31': A340-500
'32': A340-600
'33': A380
'34': ATR-42
'35': ATR-72
'36': An-12
'37': BAE 146-200
'38': BAE 146-300
'39': BAE-125
'40': Beechcraft 1900
'41': Boeing 717
'42': C-130
'43': C-47
'44': CRJ-200
'45': CRJ-700
'46': CRJ-900
'47': Cessna 172
'48': Cessna 208
'49': Cessna 525
'50': Cessna 560
'51': Challenger 600
'52': DC-10
'53': DC-3
'54': DC-6
'55': DC-8
'56': DC-9-30
'57': DH-82
'58': DHC-1
'59': DHC-6
'60': DHC-8-100
'61': DHC-8-300
'62': DR-400
'63': Dornier 328
'64': E-170
'65': E-190
'66': E-195
'67': EMB-120
'68': ERJ 135
'69': ERJ 145
'70': Embraer Legacy 600
'71': Eurofighter Typhoon
'72': F-16A/B
'73': F/A-18
'74': Falcon 2000
'75': Falcon 900
'76': Fokker 100
'77': Fokker 50
'78': Fokker 70
'79': Global Express
'80': Gulfstream IV
'81': Gulfstream V
'82': Hawk T1
'83': Il-76
'84': L-1011
'85': MD-11
'86': MD-80
'87': MD-87
'88': MD-90
'89': Metroliner
'90': Model B200
'91': PA-28
'92': SR-20
'93': Saab 2000
'94': Saab 340
'95': Spitfire
'96': Tornado
'97': Tu-134
'98': Tu-154
'99': Yak-42
- name: id
dtype: int64
- name: clip_tags_ViT_L_14
sequence: string
- name: LLM_Description_gpt3_downstream_tasks_ViT_L_14
sequence: string
- name: blip_caption
dtype: string
- name: LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14
sequence: string
- name: Attributes_ViT_L_14_text_davinci_003_full
sequence: string
- name: Attributes_ViT_L_14_text_davinci_003_fgvc
sequence: string
- name: clip_tags_ViT_L_14_with_openai_classes
sequence: string
- name: clip_tags_ViT_L_14_wo_openai_classes
sequence: string
- name: clip_tags_ViT_L_14_simple_specific
dtype: string
- name: clip_tags_ViT_L_14_ensemble_specific
dtype: string
- name: clip_tags_ViT_B_16_simple_specific
dtype: string
- name: clip_tags_ViT_B_16_ensemble_specific
dtype: string
- name: clip_tags_ViT_B_32_simple_specific
dtype: string
- name: clip_tags_ViT_B_32_ensemble_specific
dtype: string
- name: Attributes_ViT_B_16_descriptors_text_davinci_003_full
sequence: string
- name: Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full
sequence: string
- name: clip_tags_LAION_ViT_H_14_2B_simple_specific
dtype: string
- name: clip_tags_LAION_ViT_H_14_2B_ensemble_specific
dtype: string
splits:
- name: train
num_bytes: 931613762.0
num_examples: 3334
download_size: 925638163
dataset_size: 931613762.0
---
# Dataset Card for "FGVC_Aircraft_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_0-hero__Matter-0.1-Slim-7B-B | ---
pretty_name: Evaluation run of 0-hero/Matter-0.1-Slim-7B-B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [0-hero/Matter-0.1-Slim-7B-B](https://huggingface.co/0-hero/Matter-0.1-Slim-7B-B)\
\ 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_0-hero__Matter-0.1-Slim-7B-B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-14T15:00:49.139488](https://huggingface.co/datasets/open-llm-leaderboard/details_0-hero__Matter-0.1-Slim-7B-B/blob/main/results_2024-03-14T15-00-49.139488.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\": 0.6091547302339635,\n\
\ \"acc_stderr\": 0.03296012194986503,\n \"acc_norm\": 0.6135078390132341,\n\
\ \"acc_norm_stderr\": 0.03363100062547965,\n \"mc1\": 0.29008567931456547,\n\
\ \"mc1_stderr\": 0.01588623687420952,\n \"mc2\": 0.4190848495351186,\n\
\ \"mc2_stderr\": 0.014262054971913513\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5767918088737202,\n \"acc_stderr\": 0.014438036220848029,\n\
\ \"acc_norm\": 0.6075085324232082,\n \"acc_norm_stderr\": 0.014269634635670728\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6230830511850229,\n\
\ \"acc_stderr\": 0.004836234143655411,\n \"acc_norm\": 0.8154750049790879,\n\
\ \"acc_norm_stderr\": 0.0038711896202760668\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6148148148148148,\n\
\ \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n\
\ \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6578947368421053,\n \"acc_stderr\": 0.03860731599316092,\n\
\ \"acc_norm\": 0.6578947368421053,\n \"acc_norm_stderr\": 0.03860731599316092\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.52,\n\
\ \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \
\ \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6679245283018868,\n \"acc_stderr\": 0.028985455652334395,\n\
\ \"acc_norm\": 0.6679245283018868,\n \"acc_norm_stderr\": 0.028985455652334395\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6805555555555556,\n\
\ \"acc_stderr\": 0.038990736873573344,\n \"acc_norm\": 0.6805555555555556,\n\
\ \"acc_norm_stderr\": 0.038990736873573344\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \
\ \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.049756985195624284\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.51,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\"\
: 0.51,\n \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-college_mathematics|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_medicine|5\": {\n \"acc\": 0.5953757225433526,\n\
\ \"acc_stderr\": 0.03742461193887248,\n \"acc_norm\": 0.5953757225433526,\n\
\ \"acc_norm_stderr\": 0.03742461193887248\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.3137254901960784,\n \"acc_stderr\": 0.04617034827006717,\n\
\ \"acc_norm\": 0.3137254901960784,\n \"acc_norm_stderr\": 0.04617034827006717\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.72,\n\
\ \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5106382978723404,\n \"acc_stderr\": 0.03267862331014063,\n\
\ \"acc_norm\": 0.5106382978723404,\n \"acc_norm_stderr\": 0.03267862331014063\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.49122807017543857,\n\
\ \"acc_stderr\": 0.047028804320496165,\n \"acc_norm\": 0.49122807017543857,\n\
\ \"acc_norm_stderr\": 0.047028804320496165\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5655172413793104,\n \"acc_stderr\": 0.041307408795554966,\n\
\ \"acc_norm\": 0.5655172413793104,\n \"acc_norm_stderr\": 0.041307408795554966\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.38095238095238093,\n \"acc_stderr\": 0.025010749116137595,\n \"\
acc_norm\": 0.38095238095238093,\n \"acc_norm_stderr\": 0.025010749116137595\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3888888888888889,\n\
\ \"acc_stderr\": 0.04360314860077459,\n \"acc_norm\": 0.3888888888888889,\n\
\ \"acc_norm_stderr\": 0.04360314860077459\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939098,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939098\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7,\n\
\ \"acc_stderr\": 0.026069362295335137,\n \"acc_norm\": 0.7,\n \
\ \"acc_norm_stderr\": 0.026069362295335137\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.4827586206896552,\n \"acc_stderr\": 0.035158955511657,\n\
\ \"acc_norm\": 0.4827586206896552,\n \"acc_norm_stderr\": 0.035158955511657\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\"\
: 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7454545454545455,\n \"acc_stderr\": 0.03401506715249039,\n\
\ \"acc_norm\": 0.7454545454545455,\n \"acc_norm_stderr\": 0.03401506715249039\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7828282828282829,\n \"acc_stderr\": 0.02937661648494562,\n \"\
acc_norm\": 0.7828282828282829,\n \"acc_norm_stderr\": 0.02937661648494562\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8497409326424871,\n \"acc_stderr\": 0.025787723180723875,\n\
\ \"acc_norm\": 0.8497409326424871,\n \"acc_norm_stderr\": 0.025787723180723875\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.5871794871794872,\n \"acc_stderr\": 0.024962683564331796,\n\
\ \"acc_norm\": 0.5871794871794872,\n \"acc_norm_stderr\": 0.024962683564331796\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3296296296296296,\n \"acc_stderr\": 0.02866120111652459,\n \
\ \"acc_norm\": 0.3296296296296296,\n \"acc_norm_stderr\": 0.02866120111652459\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6050420168067226,\n \"acc_stderr\": 0.03175367846096625,\n \
\ \"acc_norm\": 0.6050420168067226,\n \"acc_norm_stderr\": 0.03175367846096625\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.33112582781456956,\n \"acc_stderr\": 0.038425817186598696,\n \"\
acc_norm\": 0.33112582781456956,\n \"acc_norm_stderr\": 0.038425817186598696\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.7981651376146789,\n \"acc_stderr\": 0.01720857935778758,\n \"\
acc_norm\": 0.7981651376146789,\n \"acc_norm_stderr\": 0.01720857935778758\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.4675925925925926,\n \"acc_stderr\": 0.03402801581358966,\n \"\
acc_norm\": 0.4675925925925926,\n \"acc_norm_stderr\": 0.03402801581358966\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7598039215686274,\n \"acc_stderr\": 0.02998373305591361,\n \"\
acc_norm\": 0.7598039215686274,\n \"acc_norm_stderr\": 0.02998373305591361\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.759493670886076,\n \"acc_stderr\": 0.02782078198114969,\n \
\ \"acc_norm\": 0.759493670886076,\n \"acc_norm_stderr\": 0.02782078198114969\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6547085201793722,\n\
\ \"acc_stderr\": 0.03191100192835794,\n \"acc_norm\": 0.6547085201793722,\n\
\ \"acc_norm_stderr\": 0.03191100192835794\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n\
\ \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7603305785123967,\n \"acc_stderr\": 0.03896878985070416,\n \"\
acc_norm\": 0.7603305785123967,\n \"acc_norm_stderr\": 0.03896878985070416\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.75,\n\
\ \"acc_stderr\": 0.04186091791394607,\n \"acc_norm\": 0.75,\n \
\ \"acc_norm_stderr\": 0.04186091791394607\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7361963190184049,\n \"acc_stderr\": 0.03462419931615623,\n\
\ \"acc_norm\": 0.7361963190184049,\n \"acc_norm_stderr\": 0.03462419931615623\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.42857142857142855,\n\
\ \"acc_stderr\": 0.04697113923010212,\n \"acc_norm\": 0.42857142857142855,\n\
\ \"acc_norm_stderr\": 0.04697113923010212\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n\
\ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8418803418803419,\n\
\ \"acc_stderr\": 0.023902325549560396,\n \"acc_norm\": 0.8418803418803419,\n\
\ \"acc_norm_stderr\": 0.023902325549560396\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621503,\n \
\ \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.04688261722621503\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7867177522349936,\n\
\ \"acc_stderr\": 0.014648172749593517,\n \"acc_norm\": 0.7867177522349936,\n\
\ \"acc_norm_stderr\": 0.014648172749593517\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6647398843930635,\n \"acc_stderr\": 0.025416003773165552,\n\
\ \"acc_norm\": 0.6647398843930635,\n \"acc_norm_stderr\": 0.025416003773165552\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.33519553072625696,\n\
\ \"acc_stderr\": 0.01578800719018588,\n \"acc_norm\": 0.33519553072625696,\n\
\ \"acc_norm_stderr\": 0.01578800719018588\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.696078431372549,\n \"acc_stderr\": 0.026336613469046633,\n\
\ \"acc_norm\": 0.696078431372549,\n \"acc_norm_stderr\": 0.026336613469046633\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6913183279742765,\n\
\ \"acc_stderr\": 0.026236965881153266,\n \"acc_norm\": 0.6913183279742765,\n\
\ \"acc_norm_stderr\": 0.026236965881153266\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.6944444444444444,\n \"acc_stderr\": 0.025630824975621355,\n\
\ \"acc_norm\": 0.6944444444444444,\n \"acc_norm_stderr\": 0.025630824975621355\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.475177304964539,\n \"acc_stderr\": 0.029790719243829727,\n \
\ \"acc_norm\": 0.475177304964539,\n \"acc_norm_stderr\": 0.029790719243829727\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4406779661016949,\n\
\ \"acc_stderr\": 0.012680037994097072,\n \"acc_norm\": 0.4406779661016949,\n\
\ \"acc_norm_stderr\": 0.012680037994097072\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6360294117647058,\n \"acc_stderr\": 0.029227192460032025,\n\
\ \"acc_norm\": 0.6360294117647058,\n \"acc_norm_stderr\": 0.029227192460032025\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6225490196078431,\n \"acc_stderr\": 0.01961085147488029,\n \
\ \"acc_norm\": 0.6225490196078431,\n \"acc_norm_stderr\": 0.01961085147488029\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6909090909090909,\n\
\ \"acc_stderr\": 0.044262946482000985,\n \"acc_norm\": 0.6909090909090909,\n\
\ \"acc_norm_stderr\": 0.044262946482000985\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7142857142857143,\n \"acc_stderr\": 0.028920583220675596,\n\
\ \"acc_norm\": 0.7142857142857143,\n \"acc_norm_stderr\": 0.028920583220675596\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8308457711442786,\n\
\ \"acc_stderr\": 0.026508590656233257,\n \"acc_norm\": 0.8308457711442786,\n\
\ \"acc_norm_stderr\": 0.026508590656233257\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.83,\n \"acc_stderr\": 0.0377525168068637,\n \
\ \"acc_norm\": 0.83,\n \"acc_norm_stderr\": 0.0377525168068637\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.536144578313253,\n\
\ \"acc_stderr\": 0.03882310850890594,\n \"acc_norm\": 0.536144578313253,\n\
\ \"acc_norm_stderr\": 0.03882310850890594\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8128654970760234,\n \"acc_stderr\": 0.029913127232368036,\n\
\ \"acc_norm\": 0.8128654970760234,\n \"acc_norm_stderr\": 0.029913127232368036\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.29008567931456547,\n\
\ \"mc1_stderr\": 0.01588623687420952,\n \"mc2\": 0.4190848495351186,\n\
\ \"mc2_stderr\": 0.014262054971913513\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7782162588792423,\n \"acc_stderr\": 0.011676109244497813\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.40636846095526913,\n \
\ \"acc_stderr\": 0.01352884668541325\n }\n}\n```"
repo_url: https://huggingface.co/0-hero/Matter-0.1-Slim-7B-B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|arc:challenge|25_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|gsm8k|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hellaswag|10_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-14T15-00-49.139488.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-14T15-00-49.139488.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- '**/details_harness|winogrande|5_2024-03-14T15-00-49.139488.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-14T15-00-49.139488.parquet'
- config_name: results
data_files:
- split: 2024_03_14T15_00_49.139488
path:
- results_2024-03-14T15-00-49.139488.parquet
- split: latest
path:
- results_2024-03-14T15-00-49.139488.parquet
---
# Dataset Card for Evaluation run of 0-hero/Matter-0.1-Slim-7B-B
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [0-hero/Matter-0.1-Slim-7B-B](https://huggingface.co/0-hero/Matter-0.1-Slim-7B-B) 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_0-hero__Matter-0.1-Slim-7B-B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-14T15:00:49.139488](https://huggingface.co/datasets/open-llm-leaderboard/details_0-hero__Matter-0.1-Slim-7B-B/blob/main/results_2024-03-14T15-00-49.139488.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": 0.6091547302339635,
"acc_stderr": 0.03296012194986503,
"acc_norm": 0.6135078390132341,
"acc_norm_stderr": 0.03363100062547965,
"mc1": 0.29008567931456547,
"mc1_stderr": 0.01588623687420952,
"mc2": 0.4190848495351186,
"mc2_stderr": 0.014262054971913513
},
"harness|arc:challenge|25": {
"acc": 0.5767918088737202,
"acc_stderr": 0.014438036220848029,
"acc_norm": 0.6075085324232082,
"acc_norm_stderr": 0.014269634635670728
},
"harness|hellaswag|10": {
"acc": 0.6230830511850229,
"acc_stderr": 0.004836234143655411,
"acc_norm": 0.8154750049790879,
"acc_norm_stderr": 0.0038711896202760668
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6148148148148148,
"acc_stderr": 0.04203921040156279,
"acc_norm": 0.6148148148148148,
"acc_norm_stderr": 0.04203921040156279
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6578947368421053,
"acc_stderr": 0.03860731599316092,
"acc_norm": 0.6578947368421053,
"acc_norm_stderr": 0.03860731599316092
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.52,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.52,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6679245283018868,
"acc_stderr": 0.028985455652334395,
"acc_norm": 0.6679245283018868,
"acc_norm_stderr": 0.028985455652334395
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.6805555555555556,
"acc_stderr": 0.038990736873573344,
"acc_norm": 0.6805555555555556,
"acc_norm_stderr": 0.038990736873573344
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.43,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.43,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.51,
"acc_stderr": 0.05024183937956912,
"acc_norm": 0.51,
"acc_norm_stderr": 0.05024183937956912
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.33,
"acc_stderr": 0.047258156262526045,
"acc_norm": 0.33,
"acc_norm_stderr": 0.047258156262526045
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.5953757225433526,
"acc_stderr": 0.03742461193887248,
"acc_norm": 0.5953757225433526,
"acc_norm_stderr": 0.03742461193887248
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.3137254901960784,
"acc_stderr": 0.04617034827006717,
"acc_norm": 0.3137254901960784,
"acc_norm_stderr": 0.04617034827006717
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.72,
"acc_stderr": 0.04512608598542128,
"acc_norm": 0.72,
"acc_norm_stderr": 0.04512608598542128
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5106382978723404,
"acc_stderr": 0.03267862331014063,
"acc_norm": 0.5106382978723404,
"acc_norm_stderr": 0.03267862331014063
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.49122807017543857,
"acc_stderr": 0.047028804320496165,
"acc_norm": 0.49122807017543857,
"acc_norm_stderr": 0.047028804320496165
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5655172413793104,
"acc_stderr": 0.041307408795554966,
"acc_norm": 0.5655172413793104,
"acc_norm_stderr": 0.041307408795554966
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.38095238095238093,
"acc_stderr": 0.025010749116137595,
"acc_norm": 0.38095238095238093,
"acc_norm_stderr": 0.025010749116137595
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.3888888888888889,
"acc_stderr": 0.04360314860077459,
"acc_norm": 0.3888888888888889,
"acc_norm_stderr": 0.04360314860077459
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.37,
"acc_stderr": 0.04852365870939098,
"acc_norm": 0.37,
"acc_norm_stderr": 0.04852365870939098
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7,
"acc_stderr": 0.026069362295335137,
"acc_norm": 0.7,
"acc_norm_stderr": 0.026069362295335137
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.4827586206896552,
"acc_stderr": 0.035158955511657,
"acc_norm": 0.4827586206896552,
"acc_norm_stderr": 0.035158955511657
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.66,
"acc_stderr": 0.04760952285695237,
"acc_norm": 0.66,
"acc_norm_stderr": 0.04760952285695237
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7454545454545455,
"acc_stderr": 0.03401506715249039,
"acc_norm": 0.7454545454545455,
"acc_norm_stderr": 0.03401506715249039
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7828282828282829,
"acc_stderr": 0.02937661648494562,
"acc_norm": 0.7828282828282829,
"acc_norm_stderr": 0.02937661648494562
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8497409326424871,
"acc_stderr": 0.025787723180723875,
"acc_norm": 0.8497409326424871,
"acc_norm_stderr": 0.025787723180723875
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.5871794871794872,
"acc_stderr": 0.024962683564331796,
"acc_norm": 0.5871794871794872,
"acc_norm_stderr": 0.024962683564331796
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.3296296296296296,
"acc_stderr": 0.02866120111652459,
"acc_norm": 0.3296296296296296,
"acc_norm_stderr": 0.02866120111652459
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6050420168067226,
"acc_stderr": 0.03175367846096625,
"acc_norm": 0.6050420168067226,
"acc_norm_stderr": 0.03175367846096625
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.33112582781456956,
"acc_stderr": 0.038425817186598696,
"acc_norm": 0.33112582781456956,
"acc_norm_stderr": 0.038425817186598696
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.7981651376146789,
"acc_stderr": 0.01720857935778758,
"acc_norm": 0.7981651376146789,
"acc_norm_stderr": 0.01720857935778758
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.4675925925925926,
"acc_stderr": 0.03402801581358966,
"acc_norm": 0.4675925925925926,
"acc_norm_stderr": 0.03402801581358966
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7598039215686274,
"acc_stderr": 0.02998373305591361,
"acc_norm": 0.7598039215686274,
"acc_norm_stderr": 0.02998373305591361
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.759493670886076,
"acc_stderr": 0.02782078198114969,
"acc_norm": 0.759493670886076,
"acc_norm_stderr": 0.02782078198114969
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6547085201793722,
"acc_stderr": 0.03191100192835794,
"acc_norm": 0.6547085201793722,
"acc_norm_stderr": 0.03191100192835794
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7709923664122137,
"acc_stderr": 0.036853466317118506,
"acc_norm": 0.7709923664122137,
"acc_norm_stderr": 0.036853466317118506
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7603305785123967,
"acc_stderr": 0.03896878985070416,
"acc_norm": 0.7603305785123967,
"acc_norm_stderr": 0.03896878985070416
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.75,
"acc_stderr": 0.04186091791394607,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04186091791394607
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7361963190184049,
"acc_stderr": 0.03462419931615623,
"acc_norm": 0.7361963190184049,
"acc_norm_stderr": 0.03462419931615623
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.42857142857142855,
"acc_stderr": 0.04697113923010212,
"acc_norm": 0.42857142857142855,
"acc_norm_stderr": 0.04697113923010212
},
"harness|hendrycksTest-management|5": {
"acc": 0.7669902912621359,
"acc_stderr": 0.04185832598928315,
"acc_norm": 0.7669902912621359,
"acc_norm_stderr": 0.04185832598928315
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8418803418803419,
"acc_stderr": 0.023902325549560396,
"acc_norm": 0.8418803418803419,
"acc_norm_stderr": 0.023902325549560396
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.68,
"acc_stderr": 0.04688261722621503,
"acc_norm": 0.68,
"acc_norm_stderr": 0.04688261722621503
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.7867177522349936,
"acc_stderr": 0.014648172749593517,
"acc_norm": 0.7867177522349936,
"acc_norm_stderr": 0.014648172749593517
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.6647398843930635,
"acc_stderr": 0.025416003773165552,
"acc_norm": 0.6647398843930635,
"acc_norm_stderr": 0.025416003773165552
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.33519553072625696,
"acc_stderr": 0.01578800719018588,
"acc_norm": 0.33519553072625696,
"acc_norm_stderr": 0.01578800719018588
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.696078431372549,
"acc_stderr": 0.026336613469046633,
"acc_norm": 0.696078431372549,
"acc_norm_stderr": 0.026336613469046633
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.6913183279742765,
"acc_stderr": 0.026236965881153266,
"acc_norm": 0.6913183279742765,
"acc_norm_stderr": 0.026236965881153266
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.6944444444444444,
"acc_stderr": 0.025630824975621355,
"acc_norm": 0.6944444444444444,
"acc_norm_stderr": 0.025630824975621355
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.475177304964539,
"acc_stderr": 0.029790719243829727,
"acc_norm": 0.475177304964539,
"acc_norm_stderr": 0.029790719243829727
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4406779661016949,
"acc_stderr": 0.012680037994097072,
"acc_norm": 0.4406779661016949,
"acc_norm_stderr": 0.012680037994097072
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6360294117647058,
"acc_stderr": 0.029227192460032025,
"acc_norm": 0.6360294117647058,
"acc_norm_stderr": 0.029227192460032025
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6225490196078431,
"acc_stderr": 0.01961085147488029,
"acc_norm": 0.6225490196078431,
"acc_norm_stderr": 0.01961085147488029
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6909090909090909,
"acc_stderr": 0.044262946482000985,
"acc_norm": 0.6909090909090909,
"acc_norm_stderr": 0.044262946482000985
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7142857142857143,
"acc_stderr": 0.028920583220675596,
"acc_norm": 0.7142857142857143,
"acc_norm_stderr": 0.028920583220675596
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8308457711442786,
"acc_stderr": 0.026508590656233257,
"acc_norm": 0.8308457711442786,
"acc_norm_stderr": 0.026508590656233257
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.83,
"acc_stderr": 0.0377525168068637,
"acc_norm": 0.83,
"acc_norm_stderr": 0.0377525168068637
},
"harness|hendrycksTest-virology|5": {
"acc": 0.536144578313253,
"acc_stderr": 0.03882310850890594,
"acc_norm": 0.536144578313253,
"acc_norm_stderr": 0.03882310850890594
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8128654970760234,
"acc_stderr": 0.029913127232368036,
"acc_norm": 0.8128654970760234,
"acc_norm_stderr": 0.029913127232368036
},
"harness|truthfulqa:mc|0": {
"mc1": 0.29008567931456547,
"mc1_stderr": 0.01588623687420952,
"mc2": 0.4190848495351186,
"mc2_stderr": 0.014262054971913513
},
"harness|winogrande|5": {
"acc": 0.7782162588792423,
"acc_stderr": 0.011676109244497813
},
"harness|gsm8k|5": {
"acc": 0.40636846095526913,
"acc_stderr": 0.01352884668541325
}
}
```
## 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. -->
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## More Information [optional]
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## Dataset Card Contact
[More Information Needed] |
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/f8278301 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 182
num_examples: 10
download_size: 1332
dataset_size: 182
---
# Dataset Card for "f8278301"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
avatar-qwsa/avatar_data | ---
license: mit
---
|
joey234/mmlu-virology-rule-neg | ---
dataset_info:
features:
- name: choices
sequence: string
- name: answer
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
- name: question
dtype: string
splits:
- name: test
num_bytes: 39263
num_examples: 166
download_size: 26772
dataset_size: 39263
---
# Dataset Card for "mmlu-virology-rule-neg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
dog/fuego-20230225-074209-a2dfb7 | ---
tags:
- fuego
fuego:
id: 20230225-074209-a2dfb7
status: done
script: run.py
requirements_file: requirements.txt
space_id: dog/actlearn-fuego-runner
space_hardware: cpu-basic
---
|
CyberHarem/takitsubo_rikou_toarumajutsunoindex | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of Takitsubo Rikou
This is the dataset of Takitsubo Rikou, containing 85 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 85 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 177 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 85 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 85 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 85 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 85 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 85 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 177 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 177 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 177 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
Berzerker/gnhk_ocr_dataset | ---
dataset_info:
features:
- name: image
dtype: image
- name: output_json_dumpsed
dtype: string
configs:
- config_name: default
data_files:
- split: train
path: data/*.parquet
language:
- en
---
|
maximoss/daccord-contradictions | ---
license: bsd-2-clause
language:
- fr
task_categories:
- text-classification
task_ids:
- multi-input-text-classification
size_categories:
- 1K<n<10K
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:** https://github.com/mskandalis/daccord-dataset-contradictions
- **Paper:** https://aclanthology.org/2023.jeptalnrecital-long.22/
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The DACCORD dataset is an entirely new collection of 1034 sentence pairs annotated as a binary classification task for automatic detection of contradictions between sentences in French.
Each pair of sentences receives a label according to whether or not the two sentences contradict each other.
DACCORD currently covers the themes of Russia’s invasion of Ukraine in 2022, the Covid-19 pandemic, and the climate crisis. The sentences of the dataset were extracted from (or based on sentences from) AFP Factuel articles.
### Supported Tasks and Leaderboards
The task of automatic detection of contradictions between sentences is a sentence-pair binary classification task. It can be viewed as a task related to both natural language inference task and misinformation detection task.
## Dataset Structure
### Data Fields
- `id`: Index number.
- `premise`: The translated premise in the target language.
- `hypothesis`: The translated premise in the target language.
- `label`: The classification label, with possible values 0 (`compatibles`), 1 (`contradiction`).
- `label_text`: The classification label, with possible values `compatibles` (0), `contradiction` (1).
- `genre`: a `string` feature .
### Data Splits
| theme |contradiction|compatible|
|----------------|------------:|---------:|
|Russian invasion| 215 | 257 |
| Covid-19 | 251 | 199 |
| Climate change | 49 | 63 |
## Additional Information
### Citation Information
**BibTeX:**
````BibTeX
@inproceedings{skandalis-etal-2023-daccord,
title = "{DACCORD} : un jeu de donn{\'e}es pour la D{\'e}tection Automatique d{'}{\'e}non{C}{\'e}s {CO}nt{R}a{D}ictoires en fran{\c{c}}ais",
author = "Skandalis, Maximos and
Moot, Richard and
Robillard, Simon",
booktitle = "Actes de CORIA-TALN 2023. Actes de la 30e Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN), volume 1 : travaux de recherche originaux -- articles longs",
month = "6",
year = "2023",
address = "Paris, France",
publisher = "ATALA",
url = "https://aclanthology.org/2023.jeptalnrecital-long.22",
pages = "285--297",
abstract = "La t{\^a}che de d{\'e}tection automatique de contradictions logiques entre {\'e}nonc{\'e}s en TALN est une t{\^a}che de classification binaire, o{\`u} chaque paire de phrases re{\c{c}}oit une {\'e}tiquette selon que les deux phrases se contredisent ou non. Elle peut {\^e}tre utilis{\'e}e afin de lutter contre la d{\'e}sinformation. Dans cet article, nous pr{\'e}sentons DACCORD, un jeu de donn{\'e}es d{\'e}di{\'e} {\`a} la t{\^a}che de d{\'e}tection automatique de contradictions entre phrases en fran{\c{c}}ais. Le jeu de donn{\'e}es {\'e}labor{\'e} est actuellement compos{\'e} de 1034 paires de phrases. Il couvre les th{\'e}matiques de l{'}invasion de la Russie en Ukraine en 2022, de la pand{\'e}mie de Covid-19 et de la crise climatique. Pour mettre en avant les possibilit{\'e}s de notre jeu de donn{\'e}es, nous {\'e}valuons les performances de certains mod{\`e}les de transformeurs sur lui. Nous constatons qu{'}il constitue pour eux un d{\'e}fi plus {\'e}lev{\'e} que les jeux de donn{\'e}es existants pour le fran{\c{c}}ais, qui sont d{\'e}j{\`a} peu nombreux. In NLP, the automatic detection of logical contradictions between statements is a binary classification task, in which a pair of sentences receives a label according to whether or not the two sentences contradict each other. This task has many potential applications, including combating disinformation. In this article, we present DACCORD, a new dataset dedicated to the task of automatically detecting contradictions between sentences in French. The dataset is currently composed of 1034 sentence pairs. It covers the themes of Russia{'}s invasion of Ukraine in 2022, the Covid-19 pandemic, and the climate crisis. To highlight the possibilities of our dataset, we evaluate the performance of some recent Transformer models on it. We conclude that our dataset is considerably more challenging than the few existing datasets for French.",
language = "French",
}
````
**ACL:**
Maximos Skandalis, Richard Moot, and Simon Robillard. 2023. [DACCORD : un jeu de données pour la Détection Automatique d’énonCés COntRaDictoires en français](https://aclanthology.org/2023.jeptalnrecital-long.22). In *Actes de CORIA-TALN 2023. Actes de la 30e Conférence sur le Traitement Automatique des Langues Naturelles (TALN), volume 1 : travaux de recherche originaux -- articles longs*, pages 285–297, Paris, France. ATALA.
### Acknowledgements
This work was supported by the Defence Innovation Agency (AID) of the Directorate General of Armament (DGA) of the French Ministry of Armed Forces, and by the ICO, _Institut Cybersécurité Occitanie_, funded by Région Occitanie, France. |
hacktoberfest-corpus-es/spanish_dish_title | ---
license: mit
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: prompt
dtype: string
- name: image
dtype: image
- name: uuid
dtype: string
splits:
- name: train
num_bytes: 123357511.5769398
num_examples: 13170
- name: test
num_bytes: 6295620.691672235
num_examples: 659
- name: valid
num_bytes: 24795318.75338796
num_examples: 2634
download_size: 156595985
dataset_size: 154448451.022
---
|
mwong/fever-evidence-related | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-3.0
- gpl-3.0
multilinguality:
- monolingual
paperswithcode_id: fever
pretty_name: fever
size_categories:
- 100K<n<1M
source_datasets:
- extended|fever
task_categories:
- text-classification
task_ids:
- fact-checking
---
### Dataset Summary
This dataset is extracted from Fever dataset (https://fever.ai), pre-processed and ready to train and evaluate.
The training objective is a text classification task - given a claim and evidence, predict if evidence is related to claim. |
roborovski/synthetic-toolformer-sharegpt | ---
dataset_info:
features:
- name: conversations
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 1829807
num_examples: 7793
download_size: 47740
dataset_size: 1829807
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
ibranze/araproje_mmlu_tr_s3 | ---
dataset_info:
features:
- name: question
dtype: string
- name: subject
dtype: string
- name: choices
sequence: string
- name: answer
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
splits:
- name: validation
num_bytes: 137404.0
num_examples: 250
download_size: 84026
dataset_size: 137404.0
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
# Dataset Card for "araproje_mmlu_tr_s3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/akashi_azurlane | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of akashi/明石/明石 (Azur Lane)
This is the dataset of akashi/明石/明石 (Azur Lane), containing 500 images and their tags.
The core tags of this character are `green_hair, animal_ears, long_hair, cat_ears, ahoge, hair_between_eyes, bangs, bow, very_long_hair, yellow_eyes, hair_ornament, hair_bow, mole, mole_under_eye, red_bow`, 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 | 561.72 MiB | [Download](https://huggingface.co/datasets/CyberHarem/akashi_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 336.80 MiB | [Download](https://huggingface.co/datasets/CyberHarem/akashi_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1165 | 719.96 MiB | [Download](https://huggingface.co/datasets/CyberHarem/akashi_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 502.42 MiB | [Download](https://huggingface.co/datasets/CyberHarem/akashi_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1165 | 994.74 MiB | [Download](https://huggingface.co/datasets/CyberHarem/akashi_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/akashi_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 | 8 |  |  |  |  |  | 1girl, black_bow, full_body, long_sleeves, looking_at_viewer, sailor_dress, sleeves_past_wrists, solo, white_dress, braid, simple_background, white_background, wide_sleeves, blush, kneehighs, shoes, standing, absurdly_long_hair, grey_footwear, :3, choker, neck_bell, parted_lips, holding, jingle_bell, wrench |
| 1 | 8 |  |  |  |  |  | 1girl, looking_at_viewer, simple_background, sleeves_past_wrists, solo, white_background, white_dress, bell, blush, long_sleeves, choker, sailor_dress, :3, wide_sleeves, black_bow, braid, wrench |
| 2 | 7 |  |  |  |  |  | 1girl, :3, black_sailor_collar, blush, brown_eyes, jingle_bell, long_sleeves, looking_at_viewer, sailor_dress, sleeves_past_fingers, smile, solo, white_background, white_dress, simple_background, black_bow, animal_ear_fluff, closed_mouth, heart |
| 3 | 5 |  |  |  |  |  | 1girl, :3, :d, black_bow, blush, long_sleeves, open_mouth, simple_background, solo, white_background, white_dress, bell, braid, full_body, looking_at_viewer, sailor_dress, kneehighs, sleeves_past_fingers, white_socks, brown_footwear, fang, loafers, sailor_collar, standing_on_one_leg, wrench |
| 4 | 16 |  |  |  |  |  | 1girl, looking_at_viewer, sleeves_past_fingers, solo, long_sleeves, black_bow, black_dress, frilled_hairband, open_mouth, :3, blush, white_thighhighs, black_hairband, red_gemstone, twintails, :d, puffy_sleeves, frilled_thighhighs, gothic_lolita, wide_sleeves, simple_background, choker, white_background, animal_ear_fluff, black_footwear, brown_eyes |
| 5 | 18 |  |  |  |  |  | 1girl, jingle_bell, long_sleeves, red_kimono, wide_sleeves, hair_bell, sleeves_past_fingers, blush, looking_at_viewer, obi, solo, frilled_sleeves, smile, braid, ribbon-trimmed_legwear, short_kimono, :3, animal_ear_fluff, white_thighhighs, open_mouth, shide, striped |
| 6 | 13 |  |  |  |  |  | 1girl, animal_ear_fluff, black_hairband, hair_ribbon, red_necktie, red_ribbon, sleeves_past_fingers, solo, twin_braids, looking_at_viewer, white_shirt, black_skirt, id_card, long_sleeves, blush, pleated_skirt, :3, black_bow, white_pantyhose, vest, open_mouth, black_footwear, collared_shirt, frilled_sleeves, official_alternate_costume, sparkle, white_background, :d, full_body, simple_background, standing |
| 7 | 9 |  |  |  |  |  | 1girl, animal_hood, long_sleeves, solo, wataboushi, white_thighhighs, blush, looking_at_viewer, red_skirt, sleeves_past_fingers, smile, uchikake, detached_sleeves, jingle_bell, pleated_skirt, wide_sleeves, bare_shoulders, hood_up, official_alternate_costume, white_kimono, zettai_ryouiki, :3, fake_animal_ears, open_mouth, sitting |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | black_bow | full_body | long_sleeves | looking_at_viewer | sailor_dress | sleeves_past_wrists | solo | white_dress | braid | simple_background | white_background | wide_sleeves | blush | kneehighs | shoes | standing | absurdly_long_hair | grey_footwear | :3 | choker | neck_bell | parted_lips | holding | jingle_bell | wrench | bell | black_sailor_collar | brown_eyes | sleeves_past_fingers | smile | animal_ear_fluff | closed_mouth | heart | :d | open_mouth | white_socks | brown_footwear | fang | loafers | sailor_collar | standing_on_one_leg | black_dress | frilled_hairband | white_thighhighs | black_hairband | red_gemstone | twintails | puffy_sleeves | frilled_thighhighs | gothic_lolita | black_footwear | red_kimono | hair_bell | obi | frilled_sleeves | ribbon-trimmed_legwear | short_kimono | shide | striped | hair_ribbon | red_necktie | red_ribbon | twin_braids | white_shirt | black_skirt | id_card | pleated_skirt | white_pantyhose | vest | collared_shirt | official_alternate_costume | sparkle | animal_hood | wataboushi | red_skirt | uchikake | detached_sleeves | bare_shoulders | hood_up | white_kimono | zettai_ryouiki | fake_animal_ears | sitting |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:------------|:------------|:---------------|:--------------------|:---------------|:----------------------|:-------|:--------------|:--------|:--------------------|:-------------------|:---------------|:--------|:------------|:--------|:-----------|:---------------------|:----------------|:-----|:---------|:------------|:--------------|:----------|:--------------|:---------|:-------|:----------------------|:-------------|:-----------------------|:--------|:-------------------|:---------------|:--------|:-----|:-------------|:--------------|:-----------------|:-------|:----------|:----------------|:----------------------|:--------------|:-------------------|:-------------------|:-----------------|:---------------|:------------|:----------------|:---------------------|:----------------|:-----------------|:-------------|:------------|:------|:------------------|:-------------------------|:---------------|:--------|:----------|:--------------|:--------------|:-------------|:--------------|:--------------|:--------------|:----------|:----------------|:------------------|:-------|:-----------------|:-----------------------------|:----------|:--------------|:-------------|:------------|:-----------|:-------------------|:-----------------|:----------|:---------------|:-----------------|:-------------------|:----------|
| 0 | 8 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 8 |  |  |  |  |  | X | X | | X | X | X | X | X | X | X | X | X | X | X | | | | | | X | X | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 7 |  |  |  |  |  | X | X | | X | X | X | | X | X | | X | X | | X | | | | | | X | | | | | X | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 16 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 18 |  |  |  |  |  | X | | | X | X | | | X | | X | | | X | X | | | | | | X | | | | | X | | | | | X | X | X | | | | X | | | | | | | | | X | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 13 |  |  |  |  |  | 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 | | | | | | | | | | | |
| 7 | 9 |  |  |  |  |  | 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 |
|
MikeGreen2710/aux_v1444_train_split | ---
dataset_info:
features:
- name: Word
dtype: string
- name: Tag
dtype: string
- name: 'Sentence #'
dtype: int64
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 11741524
num_examples: 354320
download_size: 3837772
dataset_size: 11741524
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
AlekseyKorshuk/model-evaluation-arena | ---
dataset_info:
features:
- name: user_state
struct:
- name: botLabel
dtype: string
- name: bot_id
dtype: string
- name: description
dtype: string
- name: developerUid
dtype: string
- name: firstMessage
dtype: string
- name: imageUrl
dtype: string
- name: introduction
dtype: string
- name: memory
dtype: string
- name: name
dtype: string
- name: private
dtype: bool
- name: prompt
dtype: string
- name: sfw
dtype: bool
- name: userLabel
dtype: string
- name: vote
dtype: string
- name: model_tag_a
dtype: string
- name: model_tag_b
dtype: string
- name: conversation_a
list:
- name: from
dtype: string
- name: value
dtype: string
- name: conversation_b
list:
- name: from
dtype: string
- name: value
dtype: string
- name: is_anonymous
dtype: bool
- name: timestamp
dtype: float64
- name: bot_id
dtype: string
- name: model_a
dtype: string
- name: model_b
dtype: string
splits:
- name: train
num_bytes: 7712
num_examples: 3
download_size: 33224
dataset_size: 7712
---
# Dataset Card for "model-evaluation-arena"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/yoshikawa_yuuko_soundeuphonium | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of Yoshikawa Yuuko/吉川優子 (Sound! Euphonium)
This is the dataset of Yoshikawa Yuuko/吉川優子 (Sound! Euphonium), containing 371 images and their tags.
The core tags of this character are `long_hair, brown_hair, ribbon, green_eyes, hair_ribbon, hair_bow, bow, yellow_ribbon`, 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 | 371 | 253.00 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yoshikawa_yuuko_soundeuphonium/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 371 | 252.84 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yoshikawa_yuuko_soundeuphonium/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 690 | 434.16 MiB | [Download](https://huggingface.co/datasets/CyberHarem/yoshikawa_yuuko_soundeuphonium/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/yoshikawa_yuuko_soundeuphonium',
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 | 8 |  |  |  |  |  | 1girl, blue_neckerchief, blue_sailor_collar, indoors, kitauji_high_school_uniform, serafuku, short_sleeves, solo, white_shirt, blue_skirt, pleated_skirt, blush, closed_mouth, window, standing |
| 1 | 9 |  |  |  |  |  | 1girl, blue_neckerchief, blue_sailor_collar, kitauji_high_school_uniform, serafuku, short_sleeves, solo, white_shirt, indoors, looking_at_viewer, blush, closed_mouth, upper_body |
| 2 | 6 |  |  |  |  |  | blue_neckerchief, blue_sailor_collar, blue_skirt, blurry_background, blush, chain-link_fence, kitauji_high_school_uniform, outdoors, pleated_skirt, serafuku, short_sleeves, standing, white_shirt, 1girl, closed_mouth, solo, blonde_hair |
| 3 | 11 |  |  |  |  |  | 1girl, blush, closed_mouth, kitauji_high_school_uniform, serafuku, white_sailor_collar, blue_neckerchief, brown_shirt, solo, upper_body, blonde_hair, looking_at_viewer, anime_coloring |
| 4 | 5 |  |  |  |  |  | 1girl, blue_neckerchief, blush, brown_shirt, brown_skirt, kitauji_high_school_uniform, long_sleeves, looking_at_viewer, pleated_skirt, solo, white_sailor_collar, brown_serafuku, indoors, standing, closed_mouth, school_bag, window |
| 5 | 5 |  |  |  |  |  | 1girl, blonde_hair, blue_neckerchief, blush, brown_shirt, brown_skirt, kitauji_high_school_uniform, long_sleeves, pleated_skirt, school_bag, smile, solo, standing, white_sailor_collar, brown_serafuku, holding_bag, indoors, open_mouth, blurry_background |
| 6 | 7 |  |  |  |  |  | 1girl, blue_neckerchief, brown_shirt, brown_skirt, kitauji_high_school_uniform, long_sleeves, photo_background, serafuku, smile, solo, white_sailor_collar, blush, open_mouth, pleated_skirt, blonde_hair, hands_up, clenched_hands, standing |
| 7 | 9 |  |  |  |  |  | 2girls, kitauji_high_school_uniform, serafuku, blue_neckerchief, short_sleeves, solo_focus, blush, chalkboard, indoors, white_shirt, open_mouth, blue_sailor_collar |
| 8 | 7 |  |  |  |  |  | blue_sailor_collar, blue_skirt, kitauji_high_school_uniform, pleated_skirt, serafuku, short_sleeves, white_shirt, blue_neckerchief, solo_focus, standing, blush, open_mouth, 2girls, indoors, kneehighs, window |
| 9 | 7 |  |  |  |  |  | solo_focus, short_sleeves, 1girl, open_mouth, pink_shirt, blush, multiple_girls, handbag, skirt |
| 10 | 6 |  |  |  |  |  | blush, hat, red_gloves, yellow_headwear, 1girl, band_uniform, midriff, navel, short_sleeves, sky, smile, closed_mouth, multiple_girls, orange_headwear, orange_skirt, outdoors, shirt, solo, standing |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blue_neckerchief | blue_sailor_collar | indoors | kitauji_high_school_uniform | serafuku | short_sleeves | solo | white_shirt | blue_skirt | pleated_skirt | blush | closed_mouth | window | standing | looking_at_viewer | upper_body | blurry_background | chain-link_fence | outdoors | blonde_hair | white_sailor_collar | brown_shirt | anime_coloring | brown_skirt | long_sleeves | brown_serafuku | school_bag | smile | holding_bag | open_mouth | photo_background | hands_up | clenched_hands | 2girls | solo_focus | chalkboard | kneehighs | pink_shirt | multiple_girls | handbag | skirt | hat | red_gloves | yellow_headwear | band_uniform | midriff | navel | sky | orange_headwear | orange_skirt | shirt |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:-------------------|:---------------------|:----------|:------------------------------|:-----------|:----------------|:-------|:--------------|:-------------|:----------------|:--------|:---------------|:---------|:-----------|:--------------------|:-------------|:--------------------|:-------------------|:-----------|:--------------|:----------------------|:--------------|:-----------------|:--------------|:---------------|:-----------------|:-------------|:--------|:--------------|:-------------|:-------------------|:-----------|:-----------------|:---------|:-------------|:-------------|:------------|:-------------|:-----------------|:----------|:--------|:------|:-------------|:------------------|:---------------|:----------|:--------|:------|:------------------|:---------------|:--------|
| 0 | 8 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | | | X | X | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 6 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 5 |  |  |  |  |  | X | X | | X | X | | | X | | | X | X | X | X | X | X | | | | | | X | X | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 5 |  |  |  |  |  | X | X | | X | X | | | X | | | X | X | | | X | | | X | | | X | X | X | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | |
| 6 | 7 |  |  |  |  |  | X | X | | | X | X | | X | | | X | X | | | X | | | | | | X | X | X | | X | X | | | X | | X | X | X | X | | | | | | | | | | | | | | | | | | |
| 7 | 9 |  |  |  |  |  | | X | X | X | X | X | X | | X | | | X | | | | | | | | | | | | | | | | | | | X | | | | X | X | X | | | | | | | | | | | | | | | |
| 8 | 7 |  |  |  |  |  | | X | X | X | X | X | X | | X | X | X | X | | X | X | | | | | | | | | | | | | | | | X | | | | X | X | | X | | | | | | | | | | | | | | |
| 9 | 7 |  |  |  |  |  | X | | | | | | X | | | | | X | | | | | | | | | | | | | | | | | | | X | | | | | X | | | X | X | X | X | | | | | | | | | | |
| 10 | 6 |  |  |  |  |  | X | | | | | | X | X | | | | X | X | | X | | | | | X | | | | | | | | | X | | | | | | | | | | | X | | | X | X | X | X | X | X | X | X | X | X |
|
mahdibaghbanzadeh/GUE_tf_1 | ---
dataset_info:
features:
- name: sequence
dtype: string
- name: labels
dtype:
class_label:
names:
'0': '0'
'1': '1'
splits:
- name: train
num_bytes: 3465936
num_examples: 30672
- name: val
num_bytes: 113000
num_examples: 1000
- name: test
num_bytes: 113000
num_examples: 1000
download_size: 1680326
dataset_size: 3691936
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
- split: test
path: data/test-*
---
|
kpriyanshu256/semeval-task-8-a-multi-v2-mistral-7b | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
- name: model
dtype: string
- name: source
dtype: string
- name: id
dtype: int64
- name: mistral-7b_estimated_loss
dtype: float64
- name: mistral-7b_mean_lowest25
dtype: float64
- name: mistral-7b_mean_highest25
dtype: float64
- name: mistral-7b_max
dtype: float64
- name: mistral-7b_min
dtype: float64
- name: mistral-7b_range
dtype: float64
- name: mistral-7b_mean
dtype: float64
- name: mistral-7b_std
dtype: float64
- name: mistral-7b_entropy
dtype: float64
- name: mistral-7b_kurtosis
dtype: float64
- name: mistral-7b_skewness
dtype: float64
- name: mistral-7b_perplexity
dtype: float64
splits:
- name: train
num_bytes: 375470839
num_examples: 137933
- name: val
num_bytes: 93824169
num_examples: 34484
- name: test
num_bytes: 9174338
num_examples: 4000
download_size: 285038772
dataset_size: 478469346
---
# Dataset Card for "semeval-task-8-a-multi-v2-mistral-7b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
deivsu/HIKARU | ---
license: openrail
---
|
tyzhu/find_first_sent_train_50_eval_10_sentbefore | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: title
dtype: string
- name: context
dtype: string
splits:
- name: train
num_bytes: 222236
num_examples: 170
- name: validation
num_bytes: 9027
num_examples: 10
download_size: 79508
dataset_size: 231263
---
# Dataset Card for "find_first_sent_train_50_eval_10_sentbefore"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
camel-ai/math | ---
license: cc-by-nc-4.0
language:
- en
tags:
- instruction-finetuning
pretty_name: CAMEL Math
task_categories:
- text-generation
arxiv: 2303.17760
extra_gated_prompt: "By using this data, you acknowledge and agree to utilize it solely for research purposes, recognizing that the dataset may contain inaccuracies due to its artificial generation through ChatGPT."
extra_gated_fields:
Name: text
Email: text
I will adhere to the terms and conditions of this dataset: checkbox
---
# **CAMEL: Communicative Agents for “Mind” Exploration of Large Scale Language Model Society**
- **Github:** https://github.com/lightaime/camel
- **Website:** https://www.camel-ai.org/
- **Arxiv Paper:** https://arxiv.org/abs/2303.17760
## Dataset Summary
Math dataset is composed of 50K problem-solution pairs obtained using GPT-4. The dataset problem-solutions pairs generating from 25 math topics, 25 subtopics for each topic and 80 problems for each "topic,subtopic" pairs.
We provide the data in `math50k.zip`.
## Data Fields
**The data fields for files in `math50k.zip` are as follows:**
* `role_1`: assistant role
* `topic`: math topic
* `sub_topic`: math subtopic belonging to topic
* `message_1`: refers to the problem the assistant is asked to solve.
* `message_2`: refers to the solution provided by the assistant.
Note: File naming refers to {`topic_index`}\_{`subtopic_index`}\_{`problem_number`}.
**Download in python**
```
from huggingface_hub import hf_hub_download
hf_hub_download(repo_id="camel-ai/math", repo_type="dataset", filename="math50k.zip",
local_dir="datasets/", local_dir_use_symlinks=False)
```
### Citation
```
@misc{li2023camel,
title={CAMEL: Communicative Agents for "Mind" Exploration of Large Scale Language Model Society},
author={Guohao Li and Hasan Abed Al Kader Hammoud and Hani Itani and Dmitrii Khizbullin and Bernard Ghanem},
year={2023},
eprint={2303.17760},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
```
## Disclaimer:
This data was synthetically generated by GPT4 and might contain incorrect information. The dataset is there only for research purposes.
---
license: cc-by-nc-4.0
---
|
robbinfan/live | ---
license: apache-2.0
---
|
open-llm-leaderboard/details_Gille__StrangeMerges_49-7B-dare_ties | ---
pretty_name: Evaluation run of Gille/StrangeMerges_49-7B-dare_ties
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Gille/StrangeMerges_49-7B-dare_ties](https://huggingface.co/Gille/StrangeMerges_49-7B-dare_ties)\
\ 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_Gille__StrangeMerges_49-7B-dare_ties\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-04-02T23:02:03.831343](https://huggingface.co/datasets/open-llm-leaderboard/details_Gille__StrangeMerges_49-7B-dare_ties/blob/main/results_2024-04-02T23-02-03.831343.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\": 0.6489243011945915,\n\
\ \"acc_stderr\": 0.03214895015751733,\n \"acc_norm\": 0.6485003616643269,\n\
\ \"acc_norm_stderr\": 0.032818010584426786,\n \"mc1\": 0.5973072215422277,\n\
\ \"mc1_stderr\": 0.017168830935187212,\n \"mc2\": 0.7469654463042762,\n\
\ \"mc2_stderr\": 0.014329583755931852\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6902730375426621,\n \"acc_stderr\": 0.013512058415238363,\n\
\ \"acc_norm\": 0.7235494880546075,\n \"acc_norm_stderr\": 0.013069662474252423\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7057359091814379,\n\
\ \"acc_stderr\": 0.00454779896412666,\n \"acc_norm\": 0.8829914359689305,\n\
\ \"acc_norm_stderr\": 0.003207735769278043\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6518518518518519,\n\
\ \"acc_stderr\": 0.041153246103369526,\n \"acc_norm\": 0.6518518518518519,\n\
\ \"acc_norm_stderr\": 0.041153246103369526\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.7039473684210527,\n \"acc_stderr\": 0.03715062154998904,\n\
\ \"acc_norm\": 0.7039473684210527,\n \"acc_norm_stderr\": 0.03715062154998904\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.67,\n\
\ \"acc_stderr\": 0.047258156262526094,\n \"acc_norm\": 0.67,\n \
\ \"acc_norm_stderr\": 0.047258156262526094\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7132075471698113,\n \"acc_stderr\": 0.027834912527544057,\n\
\ \"acc_norm\": 0.7132075471698113,\n \"acc_norm_stderr\": 0.027834912527544057\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7430555555555556,\n\
\ \"acc_stderr\": 0.03653946969442099,\n \"acc_norm\": 0.7430555555555556,\n\
\ \"acc_norm_stderr\": 0.03653946969442099\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \
\ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.56,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.56,\n\
\ \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-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\": 0.653179190751445,\n\
\ \"acc_stderr\": 0.036291466701596636,\n \"acc_norm\": 0.653179190751445,\n\
\ \"acc_norm_stderr\": 0.036291466701596636\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.39215686274509803,\n \"acc_stderr\": 0.048580835742663434,\n\
\ \"acc_norm\": 0.39215686274509803,\n \"acc_norm_stderr\": 0.048580835742663434\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n\
\ \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5829787234042553,\n \"acc_stderr\": 0.03223276266711712,\n\
\ \"acc_norm\": 0.5829787234042553,\n \"acc_norm_stderr\": 0.03223276266711712\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4824561403508772,\n\
\ \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.4824561403508772,\n\
\ \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5310344827586206,\n \"acc_stderr\": 0.04158632762097828,\n\
\ \"acc_norm\": 0.5310344827586206,\n \"acc_norm_stderr\": 0.04158632762097828\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.3994708994708995,\n \"acc_stderr\": 0.02522545028406788,\n \"\
acc_norm\": 0.3994708994708995,\n \"acc_norm_stderr\": 0.02522545028406788\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4444444444444444,\n\
\ \"acc_stderr\": 0.044444444444444495,\n \"acc_norm\": 0.4444444444444444,\n\
\ \"acc_norm_stderr\": 0.044444444444444495\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \
\ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.7741935483870968,\n \"acc_stderr\": 0.023785577884181015,\n \"\
acc_norm\": 0.7741935483870968,\n \"acc_norm_stderr\": 0.023785577884181015\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.49261083743842365,\n \"acc_stderr\": 0.035176035403610084,\n \"\
acc_norm\": 0.49261083743842365,\n \"acc_norm_stderr\": 0.035176035403610084\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|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-high_school_european_history|5\"\
: {\n \"acc\": 0.7454545454545455,\n \"acc_stderr\": 0.03401506715249039,\n\
\ \"acc_norm\": 0.7454545454545455,\n \"acc_norm_stderr\": 0.03401506715249039\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7929292929292929,\n \"acc_stderr\": 0.028869778460267042,\n \"\
acc_norm\": 0.7929292929292929,\n \"acc_norm_stderr\": 0.028869778460267042\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8963730569948186,\n \"acc_stderr\": 0.02199531196364424,\n\
\ \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.02199531196364424\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6487179487179487,\n \"acc_stderr\": 0.024203665177902803,\n\
\ \"acc_norm\": 0.6487179487179487,\n \"acc_norm_stderr\": 0.024203665177902803\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.337037037037037,\n \"acc_stderr\": 0.02882088466625326,\n \
\ \"acc_norm\": 0.337037037037037,\n \"acc_norm_stderr\": 0.02882088466625326\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6428571428571429,\n \"acc_stderr\": 0.031124619309328177,\n\
\ \"acc_norm\": 0.6428571428571429,\n \"acc_norm_stderr\": 0.031124619309328177\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.40397350993377484,\n \"acc_stderr\": 0.040064856853653415,\n \"\
acc_norm\": 0.40397350993377484,\n \"acc_norm_stderr\": 0.040064856853653415\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8458715596330275,\n \"acc_stderr\": 0.015480826865374303,\n \"\
acc_norm\": 0.8458715596330275,\n \"acc_norm_stderr\": 0.015480826865374303\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5138888888888888,\n \"acc_stderr\": 0.03408655867977749,\n \"\
acc_norm\": 0.5138888888888888,\n \"acc_norm_stderr\": 0.03408655867977749\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8578431372549019,\n \"acc_stderr\": 0.024509803921568627,\n \"\
acc_norm\": 0.8578431372549019,\n \"acc_norm_stderr\": 0.024509803921568627\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.810126582278481,\n \"acc_stderr\": 0.025530100460233494,\n \
\ \"acc_norm\": 0.810126582278481,\n \"acc_norm_stderr\": 0.025530100460233494\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6905829596412556,\n\
\ \"acc_stderr\": 0.03102441174057221,\n \"acc_norm\": 0.6905829596412556,\n\
\ \"acc_norm_stderr\": 0.03102441174057221\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7938931297709924,\n \"acc_stderr\": 0.03547771004159465,\n\
\ \"acc_norm\": 0.7938931297709924,\n \"acc_norm_stderr\": 0.03547771004159465\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7603305785123967,\n \"acc_stderr\": 0.03896878985070417,\n \"\
acc_norm\": 0.7603305785123967,\n \"acc_norm_stderr\": 0.03896878985070417\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7685185185185185,\n\
\ \"acc_stderr\": 0.04077494709252627,\n \"acc_norm\": 0.7685185185185185,\n\
\ \"acc_norm_stderr\": 0.04077494709252627\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7607361963190185,\n \"acc_stderr\": 0.0335195387952127,\n\
\ \"acc_norm\": 0.7607361963190185,\n \"acc_norm_stderr\": 0.0335195387952127\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.41964285714285715,\n\
\ \"acc_stderr\": 0.046840993210771065,\n \"acc_norm\": 0.41964285714285715,\n\
\ \"acc_norm_stderr\": 0.046840993210771065\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n\
\ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8632478632478633,\n\
\ \"acc_stderr\": 0.022509033937077805,\n \"acc_norm\": 0.8632478632478633,\n\
\ \"acc_norm_stderr\": 0.022509033937077805\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \
\ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8250319284802043,\n\
\ \"acc_stderr\": 0.013586619219903335,\n \"acc_norm\": 0.8250319284802043,\n\
\ \"acc_norm_stderr\": 0.013586619219903335\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7341040462427746,\n \"acc_stderr\": 0.02378620325550829,\n\
\ \"acc_norm\": 0.7341040462427746,\n \"acc_norm_stderr\": 0.02378620325550829\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4569832402234637,\n\
\ \"acc_stderr\": 0.01666049858050917,\n \"acc_norm\": 0.4569832402234637,\n\
\ \"acc_norm_stderr\": 0.01666049858050917\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7058823529411765,\n \"acc_stderr\": 0.026090162504279056,\n\
\ \"acc_norm\": 0.7058823529411765,\n \"acc_norm_stderr\": 0.026090162504279056\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7202572347266881,\n\
\ \"acc_stderr\": 0.025494259350694912,\n \"acc_norm\": 0.7202572347266881,\n\
\ \"acc_norm_stderr\": 0.025494259350694912\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7191358024691358,\n \"acc_stderr\": 0.025006469755799208,\n\
\ \"acc_norm\": 0.7191358024691358,\n \"acc_norm_stderr\": 0.025006469755799208\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4929078014184397,\n \"acc_stderr\": 0.02982449855912901,\n \
\ \"acc_norm\": 0.4929078014184397,\n \"acc_norm_stderr\": 0.02982449855912901\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.46740547588005216,\n\
\ \"acc_stderr\": 0.012743072942653345,\n \"acc_norm\": 0.46740547588005216,\n\
\ \"acc_norm_stderr\": 0.012743072942653345\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6838235294117647,\n \"acc_stderr\": 0.028245687391462927,\n\
\ \"acc_norm\": 0.6838235294117647,\n \"acc_norm_stderr\": 0.028245687391462927\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6781045751633987,\n \"acc_stderr\": 0.018901015322093092,\n \
\ \"acc_norm\": 0.6781045751633987,\n \"acc_norm_stderr\": 0.018901015322093092\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\
\ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\
\ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7346938775510204,\n \"acc_stderr\": 0.028263889943784593,\n\
\ \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.028263889943784593\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8258706467661692,\n\
\ \"acc_stderr\": 0.026814951200421603,\n \"acc_norm\": 0.8258706467661692,\n\
\ \"acc_norm_stderr\": 0.026814951200421603\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.86,\n \"acc_stderr\": 0.0348735088019777,\n \
\ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.0348735088019777\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5481927710843374,\n\
\ \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.5481927710843374,\n\
\ \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n\
\ \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5973072215422277,\n\
\ \"mc1_stderr\": 0.017168830935187212,\n \"mc2\": 0.7469654463042762,\n\
\ \"mc2_stderr\": 0.014329583755931852\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8374112075769534,\n \"acc_stderr\": 0.01037045555134333\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6959818043972706,\n \
\ \"acc_stderr\": 0.012670420440198667\n }\n}\n```"
repo_url: https://huggingface.co/Gille/StrangeMerges_49-7B-dare_ties
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|arc:challenge|25_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|gsm8k|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hellaswag|10_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-02T23-02-03.831343.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-02T23-02-03.831343.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- '**/details_harness|winogrande|5_2024-04-02T23-02-03.831343.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-04-02T23-02-03.831343.parquet'
- config_name: results
data_files:
- split: 2024_04_02T23_02_03.831343
path:
- results_2024-04-02T23-02-03.831343.parquet
- split: latest
path:
- results_2024-04-02T23-02-03.831343.parquet
---
# Dataset Card for Evaluation run of Gille/StrangeMerges_49-7B-dare_ties
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Gille/StrangeMerges_49-7B-dare_ties](https://huggingface.co/Gille/StrangeMerges_49-7B-dare_ties) 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_Gille__StrangeMerges_49-7B-dare_ties",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-04-02T23:02:03.831343](https://huggingface.co/datasets/open-llm-leaderboard/details_Gille__StrangeMerges_49-7B-dare_ties/blob/main/results_2024-04-02T23-02-03.831343.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": 0.6489243011945915,
"acc_stderr": 0.03214895015751733,
"acc_norm": 0.6485003616643269,
"acc_norm_stderr": 0.032818010584426786,
"mc1": 0.5973072215422277,
"mc1_stderr": 0.017168830935187212,
"mc2": 0.7469654463042762,
"mc2_stderr": 0.014329583755931852
},
"harness|arc:challenge|25": {
"acc": 0.6902730375426621,
"acc_stderr": 0.013512058415238363,
"acc_norm": 0.7235494880546075,
"acc_norm_stderr": 0.013069662474252423
},
"harness|hellaswag|10": {
"acc": 0.7057359091814379,
"acc_stderr": 0.00454779896412666,
"acc_norm": 0.8829914359689305,
"acc_norm_stderr": 0.003207735769278043
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6518518518518519,
"acc_stderr": 0.041153246103369526,
"acc_norm": 0.6518518518518519,
"acc_norm_stderr": 0.041153246103369526
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7039473684210527,
"acc_stderr": 0.03715062154998904,
"acc_norm": 0.7039473684210527,
"acc_norm_stderr": 0.03715062154998904
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.67,
"acc_stderr": 0.047258156262526094,
"acc_norm": 0.67,
"acc_norm_stderr": 0.047258156262526094
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7132075471698113,
"acc_stderr": 0.027834912527544057,
"acc_norm": 0.7132075471698113,
"acc_norm_stderr": 0.027834912527544057
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7430555555555556,
"acc_stderr": 0.03653946969442099,
"acc_norm": 0.7430555555555556,
"acc_norm_stderr": 0.03653946969442099
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.49,
"acc_stderr": 0.05024183937956912,
"acc_norm": 0.49,
"acc_norm_stderr": 0.05024183937956912
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.56,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.56,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.653179190751445,
"acc_stderr": 0.036291466701596636,
"acc_norm": 0.653179190751445,
"acc_norm_stderr": 0.036291466701596636
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.39215686274509803,
"acc_stderr": 0.048580835742663434,
"acc_norm": 0.39215686274509803,
"acc_norm_stderr": 0.048580835742663434
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.76,
"acc_stderr": 0.042923469599092816,
"acc_norm": 0.76,
"acc_norm_stderr": 0.042923469599092816
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5829787234042553,
"acc_stderr": 0.03223276266711712,
"acc_norm": 0.5829787234042553,
"acc_norm_stderr": 0.03223276266711712
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.4824561403508772,
"acc_stderr": 0.04700708033551038,
"acc_norm": 0.4824561403508772,
"acc_norm_stderr": 0.04700708033551038
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5310344827586206,
"acc_stderr": 0.04158632762097828,
"acc_norm": 0.5310344827586206,
"acc_norm_stderr": 0.04158632762097828
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.3994708994708995,
"acc_stderr": 0.02522545028406788,
"acc_norm": 0.3994708994708995,
"acc_norm_stderr": 0.02522545028406788
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.4444444444444444,
"acc_stderr": 0.044444444444444495,
"acc_norm": 0.4444444444444444,
"acc_norm_stderr": 0.044444444444444495
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.29,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.29,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7741935483870968,
"acc_stderr": 0.023785577884181015,
"acc_norm": 0.7741935483870968,
"acc_norm_stderr": 0.023785577884181015
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.49261083743842365,
"acc_stderr": 0.035176035403610084,
"acc_norm": 0.49261083743842365,
"acc_norm_stderr": 0.035176035403610084
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.7,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.7,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7454545454545455,
"acc_stderr": 0.03401506715249039,
"acc_norm": 0.7454545454545455,
"acc_norm_stderr": 0.03401506715249039
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7929292929292929,
"acc_stderr": 0.028869778460267042,
"acc_norm": 0.7929292929292929,
"acc_norm_stderr": 0.028869778460267042
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8963730569948186,
"acc_stderr": 0.02199531196364424,
"acc_norm": 0.8963730569948186,
"acc_norm_stderr": 0.02199531196364424
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6487179487179487,
"acc_stderr": 0.024203665177902803,
"acc_norm": 0.6487179487179487,
"acc_norm_stderr": 0.024203665177902803
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.337037037037037,
"acc_stderr": 0.02882088466625326,
"acc_norm": 0.337037037037037,
"acc_norm_stderr": 0.02882088466625326
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6428571428571429,
"acc_stderr": 0.031124619309328177,
"acc_norm": 0.6428571428571429,
"acc_norm_stderr": 0.031124619309328177
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.40397350993377484,
"acc_stderr": 0.040064856853653415,
"acc_norm": 0.40397350993377484,
"acc_norm_stderr": 0.040064856853653415
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8458715596330275,
"acc_stderr": 0.015480826865374303,
"acc_norm": 0.8458715596330275,
"acc_norm_stderr": 0.015480826865374303
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5138888888888888,
"acc_stderr": 0.03408655867977749,
"acc_norm": 0.5138888888888888,
"acc_norm_stderr": 0.03408655867977749
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8578431372549019,
"acc_stderr": 0.024509803921568627,
"acc_norm": 0.8578431372549019,
"acc_norm_stderr": 0.024509803921568627
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.810126582278481,
"acc_stderr": 0.025530100460233494,
"acc_norm": 0.810126582278481,
"acc_norm_stderr": 0.025530100460233494
},
"harness|hendrycksTest-human_aging|5": {
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"harness|hendrycksTest-world_religions|5": {
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"harness|gsm8k|5": {
"acc": 0.6959818043972706,
"acc_stderr": 0.012670420440198667
}
}
```
## 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] |
tiennv/gaze-following | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: image
dtype: image
- name: split
dtype: string
- name: width
dtype: int64
- name: height
dtype: int64
- name: bboxes
dtype: string
- name: labels
dtype: string
- name: cab
dtype: int64
- name: hum
dtype: int64
- name: light
dtype: float64
- name: cam
dtype: int64
- name: env
dtype: int64
- name: gaze_item
dtype: int64
- name: gazeIdx
dtype: int64
- name: gaze_cx
dtype: int64
- name: gaze_cy
dtype: int64
- name: hx
dtype: int64
- name: hy
dtype: int64
- name: pitch
dtype: float64
- name: yaw
dtype: float64
- name: roll
dtype: float64
- name: seg
dtype: string
- name: segm_gazeIdx
dtype: int64
- name: occluded
dtype: int64
splits:
- name: train
num_bytes: 99355602839.0
num_examples: 172800
- name: test
num_bytes: 11133726929.8
num_examples: 19200
download_size: 110163535502
dataset_size: 110489329768.8
---
# Dataset Card for "gaze-following"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Back-up/chung-khoan | ---
dataset_info:
features:
- name: url
dtype: string
- name: title
dtype: string
- name: date
dtype: string
- name: view
struct:
- name: number_of_response
dtype: string
- name: number_of_view
dtype: string
- name: content
list:
- name: res
dtype: string
splits:
- name: train
num_bytes: 3839751
num_examples: 284
download_size: 1539160
dataset_size: 3839751
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
autoevaluate/autoeval-staging-eval-project-00ac2adb-9115197 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- cifar10
eval_info:
task: image_multi_class_classification
model: abhishek/autotrain_cifar10_vit_base
metrics: []
dataset_name: cifar10
dataset_config: plain_text
dataset_split: test
col_mapping:
image: img
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Image Classification
* Model: abhishek/autotrain_cifar10_vit_base
* Dataset: cifar10
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@davidberg](https://huggingface.co/davidberg) for evaluating this model. |
hippocrates/Casereport_test | ---
dataset_info:
features:
- name: id
dtype: string
- name: query
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 83100116
num_examples: 34840
- name: valid
num_bytes: 83100116
num_examples: 34840
- name: test
num_bytes: 83100116
num_examples: 34840
download_size: 126639546
dataset_size: 249300348
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
---
|
arthurmluz/temario_data-wiki_1024_results | ---
dataset_info:
features:
- name: id
dtype: string
- name: text
dtype: string
- name: summary
dtype: string
- name: gen_summary
dtype: string
- name: rouge
struct:
- name: rouge1
dtype: float64
- name: rouge2
dtype: float64
- name: rougeL
dtype: float64
- name: rougeLsum
dtype: float64
- name: bert
struct:
- name: f1
sequence: float64
- name: hashcode
dtype: string
- name: precision
sequence: float64
- name: recall
sequence: float64
- name: moverScore
dtype: float64
splits:
- name: validation
num_bytes: 206635
num_examples: 25
download_size: 163078
dataset_size: 206635
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
# Dataset Card for "temario_data-wiki_1024_results"
Results of the model arthurmluz/ptt5-wikilingua-1024 on the dataset godoyj/temario.
'gen_summary' is the generated summary, and both bertScore and Rouge metrics calculated.
mean metrics:
rouge= {'rouge1': 0.1737841100453722, 'rouge2': 0.05694408293393681, 'rougeL': 0.12373628458017233, 'rougeLsum': 0.12373628458017233}
bert= {'precision': 0.7249869775772094, 'recall': 0.620260682106018, 'f1': 0.6683329963684081}
mover = 0.5512191986770616 |
tyzhu/squad_wrong_rare_v4_train_10_eval_10 | ---
dataset_info:
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
- name: context_id
dtype: string
- name: inputs
dtype: string
- name: targets
dtype: string
splits:
- name: train
num_bytes: 200420
num_examples: 138
- name: validation
num_bytes: 50258
num_examples: 50
download_size: 64429
dataset_size: 250678
---
# Dataset Card for "squad_wrong_rare_v4_train_10_eval_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
liaHa/Ergonomics_Chiar_Customer_Viewdata_E-commerse | ---
license: afl-3.0
task_categories:
- feature-extraction
- text-classification
- zero-shot-classification
- text-to-speech
language:
- en
tags:
- ecommerse
- customer
- review
- amazon
- wayfair
- website
- nlp
- topicmodeling
- bertopic
- bert
size_categories:
- 10M<n<100M
--- |
jixy2012/test-hf-queries | ---
license: mit
---
|
Bilal777888/titanic1 | ---
dataset_info:
features:
- name: Passengerid
dtype: int64
- name: Age
dtype: float64
- name: Fare
dtype: float64
- name: Sex
dtype: int64
- name: sibsp
dtype: int64
- name: zero
dtype: int64
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dtype: int64
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dtype: int64
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dtype: int64
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dtype: int64
- name: zero.14
dtype: int64
- name: Pclass
dtype: int64
- name: zero.15
dtype: int64
- name: zero.16
dtype: int64
- name: Embarked
dtype: float64
- name: zero.17
dtype: int64
- name: zero.18
dtype: int64
- name: 2urvived
dtype: int64
splits:
- name: train
num_bytes: 293380
num_examples: 1309
download_size: 37364
dataset_size: 293380
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "titanic1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
FVilmar/faabricio_silva | ---
license: openrail
---
|
hriteshMaikap/finalSample | ---
dataset_info:
features:
- name: Question
dtype: string
- name: Answer
dtype: string
splits:
- name: train
num_bytes: 356742
num_examples: 1190
download_size: 158873
dataset_size: 356742
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
arieg/bw_spec_cls_80_30 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': '69745'
'1': '69746'
'2': '69747'
'3': '69761'
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'55': '71175'
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'59': '71230'
'60': '71231'
'61': '71240'
'62': '71241'
'63': '71242'
'64': '71243'
'65': '71244'
'66': '71245'
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'68': '71247'
'69': '71248'
'70': '71249'
'71': '71250'
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'73': '71252'
'74': '71253'
'75': '71254'
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'77': '71276'
'78': '71507'
'79': '71508'
splits:
- name: train
num_bytes: 86041601.6
num_examples: 1600
- name: test
num_bytes: 21409325.0
num_examples: 400
download_size: 106908531
dataset_size: 107450926.6
---
# Dataset Card for "bw_spec_cls_80_30"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Francesco/peanuts-sd4kf | ---
dataset_info:
features:
- name: image_id
dtype: int64
- name: image
dtype: image
- name: width
dtype: int32
- name: height
dtype: int32
- name: objects
sequence:
- name: id
dtype: int64
- name: area
dtype: int64
- name: bbox
sequence: float32
length: 4
- name: category
dtype:
class_label:
names:
'0': peanuts
'1': with mold
'2': without mold
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- object-detection
task_ids: []
pretty_name: peanuts-sd4kf
tags:
- rf100
---
# Dataset Card for peanuts-sd4kf
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/peanuts-sd4kf
- **Point of Contact:** francesco.zuppichini@gmail.com
### Dataset Summary
peanuts-sd4kf
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/peanuts-sd4kf
### Citation Information
```
@misc{ peanuts-sd4kf,
title = { peanuts sd4kf Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/peanuts-sd4kf } },
url = { https://universe.roboflow.com/object-detection/peanuts-sd4kf },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. |
salam123/depression123 | ---
license: apache-2.0
---
# 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. -->
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TrainingDataPro/ocr-trains-dataset | ---
language:
- en
license: cc-by-nc-nd-4.0
task_categories:
- image-to-text
- object-detection
tags:
- code
- finance
dataset_info:
features:
- name: id
dtype: int32
- name: image
dtype: image
- name: bboxes
dtype: string
splits:
- name: train
num_bytes: 3152173
num_examples: 13
download_size: 3029413
dataset_size: 3152173
---
# OCR Trains Dataset
The dataset consists of text data obtained through optical character recognition (OCR) technology, which extracts text from images, in this case, **the train number**.
The dataset be used to train machine learning models for extracting and analyzing text from train-related documents or images, to develop algorithms or models for real-time updates, or building intelligent systems related to trains and transportation.
.png?generation=1691732664604021&alt=media)
# Get the dataset
### This is just an example of the data
Leave a request on [**https://trainingdata.pro/data-market**](https://trainingdata.pro/data-market/train-numbers?utm_source=huggingface&utm_medium=cpc&utm_campaign=ocr-trains-dataset) to discuss your requirements, learn about the price and buy the dataset.
# Dataset structure
- **images** - contains of original images of trains
- **annotations.xml** - contains coordinates of the bounding boxes and indicated text, created for the original photo
# Data Format
Each image from `images` folder is accompanied by an XML-annotation in the `annotations.xml` file indicating the coordinates of the bounding boxes for text detection. For each point, the x and y coordinates are provided.
# Example of XML file structure
# Text Detection in Trains' images might be made in accordance with your requirements.
## [**TrainingData**](https://trainingdata.pro/data-market/train-numbers?utm_source=huggingface&utm_medium=cpc&utm_campaign=ocr-trains-dataset) provides high-quality data annotation tailored to your needs
More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets**
TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets** |
MihaiIonascu/Azure_IaC_reduced | ---
license: apache-2.0
---
|
BEE-spoke-data/sp500-edgar-10k-markdown | ---
source_datasets: jlohding/sp500-edgar-10k
dataset_info:
- config_name: default
features:
- name: cik
dtype: string
- name: sic
dtype: string
- name: company
dtype: string
- name: date
dtype: timestamp[us]
- name: ret
dtype: float64
- name: mkt_cap
dtype: float64
- name: report_intro
dtype: string
- name: text
dtype: string
- name: report_returns
dtype: string
- name: word_count
dtype: int64
splits:
- name: train
num_bytes: 2260000389
num_examples: 6258
download_size: 974801155
dataset_size: 2260000389
- config_name: raw
features:
- name: cik
dtype: string
- name: sic
dtype: string
- name: company
dtype: string
- name: date
dtype: timestamp[us]
- name: ret
dtype: float64
- name: mkt_cap
dtype: float64
- name: report_intro
dtype: string
- name: text
dtype: string
- name: report_returns
dtype: string
splits:
- name: train
num_bytes: 2268939023
num_examples: 6282
download_size: 976779914
dataset_size: 2268939023
- config_name: smol
features:
- name: cik
dtype: string
- name: sic
dtype: string
- name: company
dtype: string
- name: date
dtype: timestamp[us]
- name: ret
dtype: float64
- name: mkt_cap
dtype: float64
- name: report_intro
dtype: string
- name: text
dtype: string
- name: report_returns
dtype: string
- name: word_count
dtype: int64
splits:
- name: train
num_bytes: 8668535.927411653
num_examples: 24
download_size: 70308
dataset_size: 8668535.927411653
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- config_name: raw
data_files:
- split: train
path: raw/train-*
- config_name: smol
data_files:
- split: train
path: smol/train-*
license: odc-by
extra_gated_prompt: You agree to use the dataset.
extra_gated_fields:
tell me an interesting fact: text
How much do you love money:
type: select
options:
- High
- Medium
- label: Other
value: other
I agree to use this dataset: checkbox
task_categories:
- text-generation
- text-classification
language:
- en
tags:
- finance
- money
size_categories:
- 1K<n<10K
---
# edgar s&p500
## Source Datasets
The source dataset used for this report is `jlohding/sp500-edgar-10k`.
## Dataset Information
### Configuration: default
| Feature | Data Type |
|----------------|---------------|
| cik | string |
| sic | string |
| company | string |
| date | timestamp[us] |
| ret | float64 |
| mkt_cap | float64 |
| report_intro | string |
| text | string |
| report_returns | string |
| word_count | int64 |
**Splits:**
- Train:
- Number of Examples: 6258
- Size: 2260000389 bytes
**Download Size:** 974801155 bytes
**Dataset Size:** 2260000389 bytes
### Configuration: raw
| Feature | Data Type |
|----------------|---------------|
| cik | string |
| sic | string |
| company | string |
| date | timestamp[us] |
| ret | float64 |
| mkt_cap | float64 |
| report_intro | string |
| text | string |
| report_returns | string |
**Splits:**
- Train:
- Number of Examples: 6282
- Size: 2268939023 bytes
**Download Size:** 976779914 bytes
**Dataset Size:** 2268939023 bytes
### Configuration: smol
| Feature | Data Type |
|----------------|---------------|
| cik | string |
| sic | string |
| company | string |
| date | timestamp[us] |
| ret | float64 |
| mkt_cap | float64 |
| report_intro | string |
| text | string |
| report_returns | string |
| word_count | int64 |
**Splits:**
- Train:
- Number of Examples: 24
- Size: 8668535.927411653 bytes
**Download Size:** 70308 bytes
**Dataset Size:** 8668535.927411653 bytes |
Hyeonsieun/TeX_SpNT_1st | ---
dataset_info:
features:
- name: TeX
dtype: string
- name: SpNT
dtype: string
splits:
- name: train
num_bytes: 990502044
num_examples: 6437472
download_size: 388738482
dataset_size: 990502044
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
chandraReddy/IntentDataset | ---
license: apache-2.0
---
|
leonvanbokhorst/fire-havoc-philips-lac-eindhoven | ---
license: creativeml-openrail-m
tags:
- fire
- havoc
- eindhoven
- stable diffusion
- fine-tuning
pretty_name: Havoc after the Fire at Philips LAC Eindhoven
size_categories:
- 1K<n<10K
task_categories:
- unconditional-image-generation
language:
- en
---
# Image Dataset Havoc after the Fire at Philips LAC Eindhoven
## Dataset Description
A large fire in the center of Eindhoven, May 14th, 2023. The old Philips Lighting Application Centre was engulfed in flames, resulting in massive smoke clouds. Over a hundred firefighters were deployed, and there was significant disruption in the city center. This is a dataset containing images of the remains of the building two months later. The footage was taken on July 19, 2023.
## Dataset Structure
The dataset consists of 1167 images depicting the aftermath of the fire havoc. It is primarily designed for fine-tuning or training a Stable Diffusion model, although it can be used for other purposes as well. Each original image is divided into five cropped versions with between 2 to 8 additional random detail crops. Approximately 30 percent of the images are flipped horizontally. All images in the dataset have been resized to either 1024 x 1024, 768 x 1024, or 1024 x 768 resolution.
| Description | Value |
|---------------------------------------------------------|----------------------|
| Number of Images | 1167 |
| Purpose | Fine-tuning / Training Stable Diffusion model |
| Image Processing | Original image five-cropped (all corners and center) with added 1-8 random detail crops per original |
| Flipped Images | Approximately 30% |
| Resolutions | Hand picked 1024x1024, 768x1024, 1024x768 | |
freQuensy23/cloody-cat | ---
license: mit
---
Small dataset with ~5 photos of the same cat to train [DreamBooth](https://arxiv.org/pdf/2208.12242.pdf) |
maxdunhill/detectingvulnerablecode | ---
license: apache-2.0
---
|
xzuyn/Stable-Diffusion-Prompts-Deduped-2.008M | ---
task_categories:
- text-generation
language:
- en
size_categories:
- 1M<n<10M
---
# [Original Dataset by FredZhang7](https://huggingface.co/datasets/FredZhang7/stable-diffusion-prompts-2.47M)
- Deduped from 2,473,022 down to 2,007,998.
- Changed anything that had `[ prompt text ]`, `( prompt text )`, or `< prompt text >`, to `[prompt text]`, `(prompt text)`, and `<prompt text>`.
- 2 or more spaces converted to a single space.
- Removed all `"`
- Removed spaces at beginnings. |
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