datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
wbxlala/Int_processed_Epilepsy_seizure_prediction | ---
dataset_info:
features:
- name: token_type_ids
sequence: int64
- name: label
dtype: int64
- name: input_ids
sequence: int64
- name: attention_mask
sequence: int64
splits:
- name: train
num_bytes: 81813424
num_examples: 6650
- name: test
num_bytes: 25625056
num_examples: 2084
download_size: 4533426
dataset_size: 107438480
---
# Dataset Card for "Int_processed_Epilepsy_seizure_prediction"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
joey234/mmlu-high_school_statistics-original-neg | ---
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
splits:
- name: test
num_bytes: 25110.23148148148
num_examples: 49
download_size: 19249
dataset_size: 25110.23148148148
---
# Dataset Card for "mmlu-high_school_statistics-original-neg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ananyarn/Algorithm_and_Python_Source_Code | ---
license: apache-2.0
language:
- en
tags:
- Python
- Code Generation
- Algorithm
- Pseudo-code
- Source Code
- Programmming
- Python Programming
---
_**Algorithm_and_Python_Source_Code**_ <br />
This dataset provides different algorithms and their corresponding source code in Python. <br /> <br />
credits: Source codes given here are taken from "iamtarun/python_code_instructions_18k_alpaca" dataset in Hugging Face. |
botp/firefly-train-1.1M | ---
duplicated_from: YeungNLP/firefly-train-1.1M
---
本数据应用于项目：[Firefly（流萤）: 中文对话式大语言模型](https://github.com/yangjianxin1/Firefly) ，训练后得到的模型[firefly-1b4](https://huggingface.co/YeungNLP/firefly-1b4)
如果您觉得此数据集对您有帮助，请like此数据集并在Github项目中star我们。
我们收集了23个常见的中文数据集，对于每个任务，由人工书写若干种指令模板，保证数据的高质量与丰富度，数据量为115万 。数据分布如下图所示：
每条数据的格式如下，包含任务类型、输入、目标输出：
```json
{
"kind": "ClassicalChinese",
"input": "将下面句子翻译成现代文：\n石中央又生一树，高百余尺，条干偃阴为五色，翠叶如盘，花径尺余，色深碧，蕊深红，异香成烟，著物霏霏。",
"target": "大石的中央长着一棵树，一百多尺高，枝干是彩色的，树叶有盘子那样大，花的直径有一尺宽，花瓣深蓝色，花中飘出奇异的香气笼罩着周围，如烟似雾。"
}
```
训练数据集的token长度分布如下图所示，绝大部分数据的长度都小于600：
 |
sinhala-nlp/NSINA | ---
license: cc-by-sa-4.0
language:
- si
---
# NSINa - A {N}ews Corpus for {Sin}hal{a}
This repository introduces **NSINA**, a comprehensive news corpus of over 500,000 articles from popular Sinhala news websites. Alongside **NSINA**, with different subsets, we also introduce three Sinhala NLP tasks [(1) News Media Identification](https://github.com/Sinhala-NLP/Sinhala-News-Media-Identification) [(2) News Category Prediction](https://github.com/Sinhala-NLP/Sinhala-News-Category-Prediction) and [(3) News Headline Generation](https://github.com/Sinhala-NLP/Sinhala-Headline-Generation). The release of **NSINA** aims to provide a solution to challenges in adapting large language models to Sinhala, offering valuable benchmarks and resources for improving NLP in the Sinhala language. **NSINA** is the largest news corpus for Sinhala.
## Data Collection
For **version 1.0**, we collected news articles from ten news media sites in Sri Lanka. The following table has the details.
| Source | Amount |
|-------------------|----------|
| Adaderana *( <https://sinhala.adaderana.lk/> )* | 83918 |
| ITN News *( <https://www.itnnews.lk/> )* | 30777 |
| Lankatruth *( <https://lankatruth.com/si/> )* | 48180 |
| Divaina *( <https://divaina.lk/> )* | 26043 |
| Hiru News *( <https://www.hirunews.lk/> )* | 130729 |
| Sinhala News LK *( <https://sinhala.news.lk/> )* | 20371 |
| Lankadeepa *( <https://www.lankadeepa.lk/> )* | 141663 |
| Vikalpa *( <https://www.vikalpa.org/> )* | 14309 |
| Dinamina *( <https://www.dinamina.lk/> )* | 7642 |
| Siyatha *( <https://siyathanews.lk/> )* | 3300 |
| **Total** | **506932** |
One of the .json files is shown below.
```json
{
"Source": "hirunews",
"Timestamp": "Saturday, 02 May 2020 - 7:38",
"Headline": "ශ්රී ලංකා එංගලන්ත ක්රිකට් තරගාවලියට නව කාලසටහනක්",
"News Content": "කොවිඩ් -19 ගෝලීය වසංගතය හේතුවෙන් අතරමඟ දී අත්හිටුවනු ලැබූ එංගලන්ත ක්රිකට් පිළේ ශ්රී ලංකා සංචාරය සඳහා නව කාලසටහනක් සකස් කර තිබෙනවා. ඒ අනුව ලබන වසරේ ජනවාරි මාසයේ එංගලන්ත පිළ නැවතත් ශ්රී ලංකාවට පැමිණීමට නියමිත බවයි ශ්රී ලංකා ක්රිකට් ප්රධාන විධායක ඈෂ්ලි ද සිල්වා ප්රකාශ කළේ. පසුගිය මාර්තු මාසයේ දිවයිනට පැමිණි එංගලන්ත කණ්ඩායම කොරෝනා වෛරස් ගෝලීය වසංගතය හේතුවෙන් දින 10කට පසු යළි සිය රට බලා නික්මුණේ පළමු ටෙස්ට් තරඟය ආරම්භවීමට සතියක් තිබියදියි.",
"URL": "https://www.hirunews.lk/sports/239889/ශ්රී-ලංකා-එංගලන්ත-ක්රිකට්-තරගාවලියට-නව-කාලසටහනක්",
"Category": "Sports",
"Parent URL": "https://www.hirunews.lk/sports/all-news.php?pageID=100"
}
```
## Data
All the .json files mentioned above were concatenated to create the final dataset. **NSINA** is available in [HuggingFace](https://huggingface.co/datasets/sinhala-nlp/NSINA) and can be downloaded using the following code.
```python
from datasets import Dataset
from datasets import load_dataset
nsina = Dataset.to_pandas(load_dataset('sinhala-nlp/NSINA', split='train'))
```
## Citation
If you are using the dataset or the models, please cite the following paper.
~~~
@inproceedings{Nsina2024,
author={Hettiarachchi, Hansi and Premasiri, Damith and Uyangodage, Lasitha and Ranasinghe, Tharindu},
title={{NSINA: A News Corpus for Sinhala}},
booktitle={The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
year={2024},
month={May},
}
~~~
|
HuggingFaceH4/h4-tests-format-sft-dataset | ---
dataset_info:
features:
- name: system
dtype: string
- name: prompt
dtype: string
- name: response
dtype: string
- name: messages
list:
- name: role
dtype: string
- name: content
dtype: string
- name: sharegpt_conversations
list:
- name: from
dtype: string
- name: value
dtype: string
splits:
- name: train
num_bytes: 343
num_examples: 1
download_size: 4743
dataset_size: 343
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# DO NOT DELETE ME! I'M USED IN THE H4 UNIT TESTS :)
|
jack-seeone/WQt | ---
license: other
---
|
rntc/blurb_jnlpba_a-tm | ---
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: type
dtype: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B
'2': I
splits:
- name: train
num_bytes: 53054508
num_examples: 18607
- name: validation
num_bytes: 5345872
num_examples: 1939
- name: test
num_bytes: 11932733
num_examples: 4260
download_size: 10220104
dataset_size: 70333113
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
miwaniza/FullcontrolXYZ | ---
license: mit
---
|
enoahjr/twitter_dataset_1713169060 | ---
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: 813770
num_examples: 2226
download_size: 380949
dataset_size: 813770
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
abdulrub/fashion-products | ---
license: apache-2.0
dataset_info:
features:
- name: id
struct:
- name: Brand
dtype: string
- name: Colour
dtype: string
- name: Description
dtype: string
- name: Price in Rupees
dtype: float64
- name: Product Name
dtype: string
- name: Rating
dtype: float64
- name: Rating Count
dtype: float64
- name: id
dtype: float64
- name: image
dtype: string
splits:
- name: train
num_bytes: 9243
num_examples: 15
download_size: 11175
dataset_size: 9243
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
blanchon/ChaBuD | ---
language: en
license: unknown
task_categories:
- change-detection
pretty_name: ChaBuD
tags:
- remote-sensing
- earth-observation
- geospatial
- satellite-imagery
- change-detection
- sentinel-2
dataset_info:
features:
- name: image1
dtype: image
- name: image2
dtype: image
- name: mask
dtype: image
splits:
- name: train
num_bytes: 577995423.0
num_examples: 278
- name: validation
num_bytes: 158102432.0
num_examples: 78
download_size: 380547073
dataset_size: 736097855.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
# ChaBuD
<!-- Dataset thumbnail -->
<!-- Provide a quick summary of the dataset. -->
ChaBuD is a dataset for Change detection for Burned area Delineation and is used for the ChaBuD ECML-PKDD 2023 Discovery Challenge. This is the RGB version with 3 bands.
- **Paper:** https://doi.org/10.1016/j.rse.2021.112603
- **Homepage:** https://huggingface.co/spaces/competitions/ChaBuD-ECML-PKDD2023
## Description
<!-- Provide a longer summary of what this dataset is. -->
- **Total Number of Images**: 356
- **Bands**: 3 (RGB)
- **Image Size**: 512x512
- **Image Resolution**: 10m
- **Land Cover Classes**: 2
- **Classes**: no change, burned area
- **Source**: Sentinel-2
## Usage
To use this dataset, simply use `datasets.load_dataset("blanchon/ChaBuD")`.
<!-- Provide any additional information on how to use this dataset. -->
```python
from datasets import load_dataset
ChaBuD = load_dataset("blanchon/ChaBuD")
```
## Citation
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
If you use the ChaBuD dataset in your research, please consider citing the following publication:
```bibtex
@article{TURKOGLU2021112603,
title = {Crop mapping from image time series: Deep learning with multi-scale label hierarchies},
journal = {Remote Sensing of Environment},
volume = {264},
pages = {112603},
year = {2021},
issn = {0034-4257},
doi = {https://doi.org/10.1016/j.rse.2021.112603},
url = {https://www.sciencedirect.com/science/article/pii/S0034425721003230},
author = {Mehmet Ozgur Turkoglu and Stefano D'Aronco and Gregor Perich and Frank Liebisch and Constantin Streit and Konrad Schindler and Jan Dirk Wegner},
keywords = {Deep learning, Recurrent neural network (RNN), Convolutional RNN, Hierarchical classification, Multi-stage, Crop classification, Multi-temporal, Time series},
}
```
|
asthomas/2048_fine_tuned1 | ---
dataset_info:
features:
- name: Input
dtype: string
- name: Output
dtype: string
splits:
- name: train
num_bytes: 49433
num_examples: 64
download_size: 21904
dataset_size: 49433
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
seyyedaliayati/solidity-defi-vulnerabilities | ---
dataset_info:
features:
- name: attack_title
dtype: string
- name: github_path
dtype: string
- name: testcase
dtype: string
- name: is_real
dtype: bool
- name: interfaces
list:
- name: content
dtype: string
- name: imported
dtype: bool
- name: name
dtype: string
- name: token_count
dtype: int64
- name: date
dtype: string
- name: title
dtype: string
- name: id
dtype: string
- name: contract_path
dtype: string
- name: lost_value
dtype: 'null'
- name: attack_explain
dtype: string
- name: token_count
dtype: int64
splits:
- name: train
num_bytes: 6232550
num_examples: 270
download_size: 592765
dataset_size: 6232550
license: cc
task_categories:
- text-classification
- text-generation
language:
- en
tags:
- solidity
- vulnerability
- smart contract
- defi hacks
- hacks
pretty_name: Solidity Defi Vulnerabilities
size_categories:
- n<1K
---
# Solidity DeFi Vulnerabilities
## Dataset Description
This dataset is collected from the following sources:
- [DeFiHackLabs](https://github.com/SunWeb3Sec/DeFiHackLabs) (Head Commit: `d951fa08a52c5651f9b3d9d7d919c996aebc0fa3`)
- [DeFiVulnLabs](https://github.com/SunWeb3Sec/DeFiVulnLabs) (Head Commit: `37d095da5780f6ba49caad49f256d8bf654aca89`)
It contains data related to various decentralized finance (DeFi) attack scenarios and vulnerabilities, including information about attack explanations, test cases, dates, token counts, interfaces, lost values, attack titles, and contract paths.
## Dataset Summary
The dataset consists of [270 examples](#data-splits) in total. Each example includes the following features:
- `id` (string): Identifier of the example.
- `title` (string): Title of the example or the Token name targeted by the attack.
- `attack_title` (string): Title of the attack or the name of the vulnerability.
- `github_path` (string): Path to the example's corresponding GitHub repository.
- `attack_explain` (string): Explanation of the attack scenario generated by `gpt-3.5-turbo`.
- `testcase` (string): Test case content in Solidity programming language.
- `date` (string): Date of the attack if exists otherwise it is the date when the dataset is created i.e 20230623.
- `token_count` (integer): Number of tokens in the example.
- `interfaces` (list): List of interfaces with the following sub-features:
- `content` (string): Interface content.
- `imported` (boolean): Indicates if the interface is imported.
- `name` (string): Name of the interface.
- `token_count` (integer): Number of tokens in the interface.
- `lost_value` (null): Value lost in the attack scenario (null if not applicable).
- `contract_path` (string): Relative path to the contract related to the attack.
- `is_real` (bool): Indicidates whether it is a real attack from DeFiHackLabs or just an example from DeFiVulnLabs.
## Supported Tasks and Leaderboards
The DeFiHackLabs dataset can be used for tasks related to analyzing DeFi attack scenarios, developing defense mechanisms, and improving security in decentralized finance.
As of now, there are no specific leaderboards associated with this dataset.
## Languages
The dataset is in the English language (en), and Solidity programming language.
## Data Splits
The dataset is split into a single split:
- `train`: 270 examples (100% of the dataset)
## Dataset Creation
The `token_count` field is calculated via the following snippet:
```python
import tiktoken
encoding = tiktoken.encoding_for_model('gpt-3.5-turbo')
def num_tokens_from_string(string: str) -> int:
"""Returns the number of tokens in a text string."""
num_tokens = len(encoding.encode(string))
return num_tokens
```
The `attack_explain` field is generated via OpenAI's `gpt-3.5-turbo` API with the followin prompt. Note that, for DeFiHackLabs, 'attack' is used instead of vulnerability.
```
The following is a Solidity test case written in Foundry that exposes a vulnerability (OR an attack) titled "{attack_title}".
In one paragraph explain how it exposes this specific vulnerability?
{testcase}
```
## License
This dataset is released under the [Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license](https://creativecommons.org/licenses/by-nc/4.0/).
## Citation
Please use the following citations when referencing the this dataset:
1. Dataset's huggingface page:
```
@misc {seyyed_ali_ayati_2023,
author = { {Seyyed Ali Ayati} },
title = { solidity-defi-vulnerabilities (Revision 2e229c9) },
year = 2023,
url = { https://huggingface.co/datasets/seyyedaliayati/solidity-defi-vulnerabilities },
doi = { 10.57967/hf/0807 },
publisher = { Hugging Face }
}
```
2. DeFiHackLabs GitHub Repository:
```
@misc{sunweb3sec/2023,
title = {{DeFiHackLabs}},
author = {{SunWeb3Sec}},
year = {2023},
url = {https://github.com/SunWeb3Sec/DeFiHackLabs}
}
```
3. DeFiVulnLabs GitHub Repository:
```
@misc{sunweb3sec/2023,
title = {{DeFiVulnLabs}},
author = {{SunWeb3Sec}},
year = {2023},
url = {https://github.com/SunWeb3Sec/DeFiVulnLabs}
}
``` |
IconicAI/janet-textclassification-10k | ---
task_categories:
- text-classification
---
---
tags:
- gpt-4
- janet
---
# Description
10k user questions for a single class classification task.
All questions are related to a hypothetical game in whichn the user is a sniper
and has to answer questions from a commander. The questions in the dataset
are the ones that the user is expected to answer.
# Schema
```
{
"question": Ds.Value("string"),
"topic": Ds.ClassLabel(names=[
"about_to_shoot",
"ballistics",
"civilians",
"countersniping",
"dark_hair",
"description_age",
"description_body",
"description_crime",
"description_face",
"description_height",
"description_no_hair",
"description_pose",
"description_race",
"description_request",
"description_request_armed",
"description_request_behavior",
"description_request_clothing",
"description_request_companions",
"description_request_location",
"description_request_tattoo",
"description_request_transport",
"description_right_handed",
"description_sex",
"description_skin_color",
"description_weight",
"easter_egg_go_on_date",
"extraction",
"goodbye",
"hello",
"how_are_you",
"light_hair",
"permission_to_fire",
"request_change_location",
"returning_to_base",
"say_that_again",
"searching",
"secondary_targets",
"target_down",
"target_down_negative",
"target_identified",
"target_identified_maybe",
"target_identified_negative",
"target_name",
"thanks",
"thanks_and_goodbye",
"time_constraint",
"wearing_eyewear",
"wearing_eyewear_negative",
"what_to_do",
],),
}
```
# Citation
```
@misc{JanetTextClassification10k,
title = {JanetTextClassification10k: A Dataset of user questions for a single class classification task.},
author = {Kieran Donaldson and Piotr Trochim},
year = {2023},
publisher = {HuggingFace},
journal = {HuggingFace repository},
howpublished = {\\url{https://huggingface.co/datasets/IconicAI/janet-textclassification-10k}},
}
``` |
Dahoas/rl-prompt-dataset | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 331075688.0
num_examples: 201417
- name: test
num_bytes: 7649255
num_examples: 5103
download_size: 206459232
dataset_size: 338724943.0
---
# Dataset Card for "rl-prompt-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
huggingartists/max-korzh | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/max-korzh"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [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)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.289652 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/a1486b5b6f28eeec202b55e983e464c5.567x567x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/max-korzh">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Макс Корж (Max Korzh)</div>
<a href="https://genius.com/artists/max-korzh">
<div style="text-align: center; font-size: 14px;">@max-korzh</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/max-korzh).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/max-korzh")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|91| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/max-korzh")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
eddielin0926/chinese-icd | ---
language:
- zh
- en
license: mit
size_categories:
- 1M<n<10M
task_categories:
- text-classification
pretty_name: chicd
tags:
- medical
dataset_info:
features:
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dtype: int32
- name: month
dtype: int32
- name: 'no'
dtype: int32
- name: death
dtype: int32
- name: input_code
dtype: int32
- name: result_code
dtype: int32
- name: check
dtype: bool
- name: serial_no
dtype: int32
- name: catalog
dtype: int32
- name: inputs
sequence: string
- name: results
sequence: string
- name: icds
sequence: string
- name: encodes
sequence:
class_label:
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'766': G834(nTR)
'767': D508
'768': V819
'769': K858
'770': L720
'771': I248
'772': C753
'773': G540
'774': G1229
'775': N288
'776': K134
'777': S570
'778': Q641
'779': S551
'780': G479
'781': V399
'782': T281
'783': D441
'784': P60
'785': A021
'786': T671
'787': K430
'788': V435
'789': T570
'790': C910
'791': L739
'792': Q218
'793': I400
'794': B941
'795': F153
'796': G310
'797': V636
'798': D485
'799': I676
'800': C723
'801': I059
'802': S750
'803': S663
'804': N809
'805': E60
'806': W53
'807': I454
'808': G249
'809': T273
'810': Q183
'811': Q794
'812': V699
'813': A539
'814': V245
'815': M349
'816': C809
'817': C457
'818': G459
'819': V595
'820': I512
'821': J399
'822': Q200
'823': M061
'824': N321
'825': F011
'826': E060
'827': D849
'828': P209
'829': R619
'830': G129
'831': C224
'832': O459
'833': C102
'834': C241
'835': B162
'836': C444
'837': J13
'838': K766
'839': N822
'840': S008
'841': C763
'842': E161
'843': C774
'844': C050
'845': T406
'846': T022
'847': A418
'848': C341
'849': H209
'850': C729
'851': E110
'852': R600
'853': I899
'854': E703
'855': D232
'856': J358
'857': T808
'858': V879
'859': X23
'860': G255
'861': M218
'862': T143
'863': D210
'864': N488(nTR)
'865': J981
'866': D594
'867': T326
'868': A527
'869': Q225
'870': G619
'871': H71
'872': T819
'873': T846
'874': M796
'875': S204
'876': V874
'877': O720
'878': C631
'879': C499
'880': Q049
'881': T856
'882': I741
'883': K767
'884': E756
'885': E873
'886': T093(TR)
'887': J342
'888': B449
'889': T392
'890': O430
'891': T888
'892': F192
'893': Q850
'894': C090
'895': T302
'896': G588
'897': S013
'898': R05
'899': K315
'900': G919
'901': E662
'902': R413
'903': C679
'904': V114
'905': I440
'906': T71
'907': P529
'908': E786
'909': D481
'910': C539
'911': C751
'912': O411
'913': D561
'914': B188
'915': I6930
'916': P819
'917': C163
'918': C929
'919': G728
'920': T430
'921': S271
'922': T232
'923': K272
'924': T790
'925': V225
'926': W84
'927': R227
'928': S383
'929': R072
'930': S005
'931': N309
'932': W07
'933': V039
'934': B690
'935': E112
'936': V546
'937': L130
'938': Q676
'939': H448
'940': L139
'941': N736
'942': I6949
'943': K225
'944': I620(nTR)
'945': S374(TR)
'946': S119
'947': R092
'948': N301
'949': B011
'950': I489
'951': S335
'952': A391
'953': C431
'954': B199
'955': C021
'956': D376
'957': M543
'958': G529
'959': N824
'960': D648
'961': A498
'962': R252
'963': K056
'964': B99
'965': N823
'966': I891
'967': M628
'968': S709
'969': Q437
'970': C460
'971': K116
'972': V129
'973': K274
'974': D219
'975': A170
'976': S376(TR)
'977': I10
'978': N410
'979': D179
'980': S969
'981': R048(nTR)
'982': I745
'983': Q446
'984': R041(nTR)
'985': I211
'986': L308
'987': E139
'988': S030
'989': R54
'990': B458
'991': T843
'992': K579
'993': D350
'994': D860
'995': P002
'996': D489
'997': M502
'998': A009
'999': S362(TR)
'1000': C785
'1001': V031
'1002': E724
'1003': H104
'1004': R002
'1005': G309
'1006': C917
'1007': R961
'1008': R15
'1009': C902
'1010': E43
'1011': T08
'1012': S423
'1013': I728(nTR)
'1014': W15
'1015': M609
'1016': C181
'1017': M314
'1018': M549
'1019': T941
'1020': C183
'1021': S051
'1022': V144
'1023': A164
'1024': T802
'1025': E102
'1026': E41
'1027': T316
'1028': J450
'1029': V436
'1030': I878
'1031': Q213
'1032': K252
'1033': K251
'1034': C171
'1035': K460
'1036': S678
'1037': K835
'1038': E273
'1039': G932
'1040': T592
'1041': E519
'1042': F220
'1043': I341
'1044': C923
'1045': E146
'1046': T393
'1047': C319
'1048': V816
'1049': M233
'1050': D309
'1051': Q02
'1052': R940
'1053': M539
'1054': D019
'1055': Q766
'1056': C399
'1057': E722
'1058': R788
'1059': T506
'1060': B342
'1061': B258
'1062': I359
'1063': O751
'1064': I352
'1065': K37
'1066': F55
'1067': C833
'1068': P788
'1069': S368(TR)
'1070': C844
'1071': D299
'1072': S276
'1073': P549
'1074': D471
'1075': S723
'1076': X58
'1077': M320
'1078': F929
'1079': M243
'1080': M712
'1081': C56
'1082': O639
'1083': M799
'1084': F530
'1085': C781
'1086': I659
'1087': K6310
'1088': T855
'1089': B908
'1090': S751
'1091': C792
'1092': V284
'1093': E236
'1094': K603
'1095': P298
'1096': I770
'1097': F739
'1098': D143
'1099': D410
'1100': S300
'1101': A084
'1102': S352
'1103': K744
'1104': Q324
'1105': Q279
'1106': I778
'1107': S721
'1108': V060
'1109': W77
'1110': V913
'1111': C140
'1112': K36
'1113': K739
'1114': T675
'1115': A481
'1116': M600
'1117': C519
'1118': V775
'1119': T97
'1120': E209
'1121': T813
'1122': F842
'1123': K633
'1124': C780
'1125': D531
'1126': J219
'1127': V145
'1128': Y590
'1129': Q323
'1130': T202
'1131': V655
'1132': E871
'1133': I722(nTR)
'1134': C390
'1135': P220
'1136': Q423
'1137': H308
'1138': I459
'1139': W33
'1140': L728
'1141': K800
'1142': D868
'1143': J157
'1144': Q262
'1145': C310
'1146': T028
'1147': T658
'1148': S688
'1149': T435
'1150': K639
'1151': L031
'1152': E744
'1153': F162
'1154': Q410
'1155': A89
'1156': Y079
'1157': S053(TR)
'1158': J60
'1159': A749
'1160': C381
'1161': F432
'1162': R53
'1163': K560
'1164': A812
'1165': F419
'1166': C432
'1167': Q749
'1168': S028
'1169': X12
'1170': A199
'1171': S771
'1172': D397
'1173': H669
'1174': I701
'1175': W55
'1176': N12
'1177': I700
'1178': C081
'1179': B005
'1180': Q893
'1181': T253
'1182': M216
'1183': D728
'1184': K859
'1185': D445
'1186': Q059
'1187': P250
'1188': E874
'1189': L029
'1190': S531
'1191': Q230
'1192': C761
'1193': C472
'1194': D439
'1195': V756
'1196': C759
'1197': S272
'1198': G64
'1199': M311
'1200': D131
'1201': C261
'1202': C022
'1203': D731
'1204': S589
'1205': T827
'1206': Q212
'1207': G318
'1208': R098
'1209': K260
'1210': C33
'1211': N399
'1212': S063(TR)
'1213': I710(nTR)
'1214': D433
'1215': C496
'1216': A403
'1217': M089
'1218': T378
'1219': F259
'1220': Q179
'1221': Q631
'1222': J041
'1223': W36
'1224': K632
'1225': C787
'1226': Q348
'1227': Y842
'1228': S411
'1229': L26
'1230': C791
'1231': S421
'1232': T958
'1233': I772(nTR)
'1234': I348
'1235': K30
'1236': Q438
'1237': K661(nTR)
'1238': K255
'1239': T433
'1240': D381
'1241': E46
'1242': H819
'1243': K088
'1244': C01
'1245': C433
'1246': S179
'1247': I951
'1248': H702
'1249': I724(nTR)
'1250': G618
'1251': V635
'1252': S510
'1253': S684
'1254': Q874
'1255': I209
'1256': N411
'1257': G245
'1258': P017
'1259': R471
'1260': Q248
'1261': T321
'1262': P070
'1263': W41
'1264': S978
'1265': N759
'1266': P159
'1267': M625
'1268': F410
'1269': E511
'1270': N211
'1271': D103
'1272': I078
'1273': Q220
'1274': D45
'1275': Q819
'1276': F105
'1277': M341
'1278': I139
'1279': R162
'1280': K828
'1281': C493
'1282': I6120(nTR)
'1283': E055
'1284': C412
'1285': C920
'1286': M513
'1287': R390
'1288': T864
'1289': V134
'1290': T148
'1291': S728
'1292': G510
'1293': C403
'1294': E639
'1295': Q309
'1296': E728
'1297': C851
'1298': L039
'1299': Q909
'1300': G936(nTR)
'1301': K292
'1302': E009
'1303': R638
'1304': Q202
'1305': S051(TR)
'1306': T486
'1307': S451
'1308': I259
'1309': N189
'1310': T913
'1311': M898
'1312': C340
'1313': T887
'1314': N398
'1315': N971
'1316': F112
'1317': M340
'1318': C382
'1319': A178
'1320': T596
'1321': S159
'1322': H189
'1323': C880
'1324': W44
'1325': D484
'1326': C132
'1327': E781
'1328': K060
'1329': A491
'1330': D385
'1331': Y369
'1332': J80
'1333': Q860
'1334': M541
'1335': M321
'1336': M053
'1337': T600
'1338': R97
'1339': I880
'1340': K593
'1341': D099
'1342': M259
'1343': G911
'1344': A499
'1345': P789
'1346': E109
'1347': E220
'1348': I469
'1349': V873
'1350': V656
'1351': K382
'1352': L52
'1353': W70
'1354': N049
'1355': V385
'1356': B485
'1357': L899
'1358': V932
'1359': C20
'1360': P375
'1361': P288
'1362': S009
'1363': G404
'1364': C765
'1365': B699
'1366': L405
'1367': T96
'1368': Q897
'1369': E113
'1370': K740
'1371': H472
'1372': G522
'1373': I675
'1374': M419
'1375': Q675
'1376': C779
'1377': P77
'1378': E760
'1379': E059
'1380': R160
'1381': D595
'1382': K830
'1383': D809
'1384': D721
'1385': E279
'1386': I350
'1387': T520
'1388': L509
'1389': C728
'1390': F193
'1391': I351
'1392': M878
'1393': E761
'1394': F103
'1395': M45
'1396': C166
'1397': D696
'1398': D390
'1399': F208
'1400': N498
'1401': T116
'1402': Y01
'1403': G570
'1404': S134
'1405': M080
'1406': S022
'1407': E834
'1408': D181
'1409': Q239
'1410': I499
'1411': C005
'1412': K704
'1413': C343
'1414': T848
'1415': Q552
'1416': I692
'1417': S878
'1418': D758
'1419': D598
'1420': X04
'1421': T460
'1422': K612
'1423': M844
'1424': K928(nTR)
'1425': V909
'1426': C796
'1427': V949
'1428': C700
'1429': L922
'1430': C349
'1431': Y33
'1432': S900
'1433': M310
'1434': C165
'1435': M313
'1436': K223
'1437': V154
'1438': P833
'1439': S019
'1440': N052
'1441': Q793
'1442': I498
'1443': G544
'1444': Q245
'1445': C37
'1446': Q411
'1447': S681
'1448': I510
'1449': Q386
'1450': B428
'1451': T282
'1452': G712
'1453': M512
'1454': Y20
'1455': J151
'1456': K419
'1457': W27
'1458': G08
'1459': T12
'1460': E878
'1461': I6329
'1462': T860
'1463': Q871
'1464': S630
'1465': O680
'1466': Q870
'1467': K282
'1468': E275
'1469': T602
'1470': D841
'1471': F302
'1472': S898
'1473': J339
'1474': Q433
'1475': R198
'1476': B353
'1477': Q792
'1478': I050
'1479': M948
'1480': T180
'1481': L010
'1482': T914
'1483': W24
'1484': T817
'1485': S014
'1486': N300
'1487': O723
'1488': T509
'1489': D569
'1490': J684
'1491': C750
'1492': G403
'1493': C312
'1494': Q782
'1495': R64
'1496': S199
'1497': I850
'1498': D380
'1499': D690
'1500': Q228
'1501': J985
'1502': V575
'1503': D610
'1504': S350
'1505': I712
'1506': S365(TR)
'1507': C436
'1508': N412
'1509': W75
'1510': K113
'1511': W23
'1512': Y871
'1513': W92
'1514': E018
'1515': Q255
'1516': R402
'1517': G610
'1518': R71
'1519': R008
'1520': P702
'1521': T869
'1522': Q224
'1523': L021
'1524': I6139(nTR)
'1525': D361
'1526': Q894
'1527': C473
'1528': C837
'1529': T287
'1530': K570
'1531': B359
'1532': D891
'1533': O410
'1534': C940
'1535': V061
'1536': M318
'1537': T603
'1538': G409
'1539': Q246
'1540': L102
'1541': A399
'1542': Q790
'1543': S269
'1544': S071
'1545': L589
'1546': N850
'1547': D100
'1548': C320
'1549': I864
'1550': Y355
'1551': C030
'1552': O001
'1553': T618
'1554': C031
'1555': G710
'1556': D109
'1557': S820
'1558': K761
'1559': Q000
'1560': W80
'1561': B487
'1562': Y05
'1563': B084
'1564': P838
'1565': P009
'1566': A420
'1567': G471
'1568': D142
'1569': A310
'1570': R068
'1571': R001
'1572': J159
'1573': T959
'1574': B259
'1575': I720(nTR)
'1576': C430
'1577': C830
'1578': O721
'1579': J46
'1580': E854
'1581': T571
'1582': I098
'1583': N250
'1584': G541
'1585': T391
'1586': I6199(nTR)
'1587': O711
'1588': T875
'1589': Q627
'1590': N40
'1591': I731
'1592': E788
'1593': Q223
'1594': E830
'1595': K922
'1596': C437
'1597': J180
'1598': R80
'1599': T481
'1600': K210
'1601': J690
'1602': D693
'1603': L080
'1604': J22
'1605': T537
'1606': M330
'1607': J982
'1608': C480
'1609': I716
'1610': I330
'1611': T828
'1612': S299
'1613': V856
'1614': P960
'1615': O141
'1616': T909
'1617': J660
'1618': K552
'1619': M082
'1620': W57
'1621': E780
'1622': E709
'1623': C110
'1624': J100
'1625': M479
'1626': E069
'1627': V125
'1628': I743
'1629': T564
'1630': V021
'1631': R190
'1632': I213
'1633': M779
'1634': E237
'1635': J852
'1636': G958
'1637': F989
'1638': Q210
'1639': D171
'1640': N10
'1641': T730
'1642': D820
'1643': D386
'1644': R000
'1645': G825(nTR)
'1646': N280
'1647': E115
'1648': G039
'1649': I340(nRH)
'1650': T824
'1651': M489
'1652': T511
'1653': G819
'1654': D150
'1655': P942
'1656': K269
'1657': A879
'1658': S250
'1659': C959
'1660': K289
'1661': Q652
'1662': S331
'1663': F102
'1664': T012
'1665': Q232
'1666': D352
'1667': C159
'1668': X18
'1669': B64
'1670': C914
'1671': C712
'1672': P90
'1673': D090
'1674': M611
'1675': N981
'1676': C222
'1677': I443
'1678': K626
'1679': S923
'1680': K810
'1681': M439
'1682': W31
'1683': H922
'1684': T285
'1685': F519
'1686': P359
'1687': B749
'1688': C721
'1689': B348
'1690': I458
'1691': H549
'1692': E851
'1693': D383
'1694': Q256
'1695': I251
'1696': I775
'1697': S602
'1698': S452
'1699': L020
'1700': I889
'1701': K709
'1702': H913
'1703': L744
'1704': V446
'1705': T462
'1706': C452
'1707': D392
'1708': Q828
'1709': L539
'1710': S225
'1711': A929
'1712': K528
'1713': S026
'1714': M889
'1715': K750
'1716': D550
'1717': S429
'1718': S354
'1719': K869
'1720': N019
'1721': S203
'1722': S280
'1723': J448
'1724': J941
'1725': T912
'1726': I879
'1727': C482
'1728': E249
'1729': T043
'1730': I809
'1731': D201
'1732': T091
'1733': T387
'1734': M509
'1735': S913
'1736': O439
'1737': K5500
'1738': Q614
'1739': F079
'1740': K521
'1741': H113(nTR)
'1742': S025
'1743': Q219
'1744': R401
'1745': K564
'1746': K279
'1747': E049
'1748': H932
'1749': C130
'1750': C089
'1751': T540
'1752': R771
'1753': F151
'1754': B448
'1755': N485
'1756': I971
'1757': F319
'1758': C795
'1759': K743
'1760': C838
'1761': K805
'1762': E15
'1763': W269
'1764': R103
'1765': R829
'1766': F603
'1767': A181
'1768': M210
'1769': T751
'1770': K404
'1771': B440
'1772': S142
'1773': C131
'1774': D738
'1775': C049
'1776': C154
'1777': L028
'1778': K261
'1779': X76
'1780': K040
'1781': M350
'1782': H813
'1783': J156
'1784': I517
'1785': V903
'1786': I060(RH)
'1787': F202
'1788': T449
'1789': Q674
'1790': G838(nTR)
'1791': T172
'1792': P080
'1793': C762
'1794': N762
'1795': C322
'1796': S021
'1797': V685
'1798': M179
'1799': S628
'1800': N328
'1801': J348
'1802': T243
'1803': F709
'1804': L448
'1805': S219
'1806': O979
'1807': I120
'1808': G542(nTR)
'1809': F459
'1810': T905
'1811': O40
'1812': N23
'1813': T029
'1814': C474
'1815': H041
'1816': Q459
'1817': M879
'1818': K832(nTR)
'1819': T305
'1820': J013
'1821': I604(nTR)
'1822': B181
'1823': Q913
'1824': S314
'1825': M109
'1826': I674
'1827': T798
'1828': C103
'1829': Q318
'1830': G439
'1831': M485
'1832': C185
'1833': C342
'1834': Q031
'1835': C061
'1836': C964
'1837': S024
'1838': T503
'1839': O881
'1840': S363(TR)
'1841': T147
'1842': A410
'1843': C440
'1844': N368
'1845': F341
'1846': M460
'1847': S355
'1848': D367
'1849': N888
'1850': S623
'1851': B441
'1852': X13
'1853': W28
'1854': K318(nTR)
'1855': G908
'1856': G935
'1857': N811
'1858': D898
'1859': D735
'1860': D000
'1861': G500
'1862': S934
'1863': F609
'1864': Q791
'1865': M622
'1866': V274
'1867': E274(nTR)
'1868': G210
'1869': Q431
'1870': G939(nTR)
'1871': N201
'1872': G609
'1873': D444
'1874': C414
'1875': D134
'1876': D890
'1877': C922
'1878': S410
'1879': K829
'1880': C221
'1881': I481
'1882': K839
'1883': A064
'1884': E222
'1885': E54
'1886': D377
'1887': N135
'1888': V174
'1889': K823
'1890': Q392
'1891': O469
'1892': K909
'1893': J152
'1894': F29
'1895': Q250
'1896': R100
'1897': V475
'1898': V929
'1899': I349(nRH)
'1900': E147
'1901': S920
'1902': S129
'1903': N482
'1904': D467
'1905': Y899
'1906': J90
'1907': M316
'1908': K108
'1909': J205
'1910': E86
'1911': V755
'1912': L988
'1913': C19
'1914': C760
'1915': E849
'1916': Q159
'1917': K284
'1918': B027
'1919': N813
'1920': T630
'1921': J410
'1922': Q669
'1923': Q068
'1924': E875
'1925': O692
'1926': W34
'1927': S020
'1928': M952
'1929': P701
'1930': I4290
'1931': F719
'1932': K592
'1933': Q621
'1934': C530
'1935': I050(RH)
'1936': K132
'1937': G473
'1938': T840
'1939': H160
'1940': H810
'1941': J392
'1942': K588
'1943': S224
'1944': I4289
'1945': N459
'1946': L309
'1947': I729(nTR)
'1948': I280
'1949': T320
'1950': S313
'1951': Q231
'1952': G959(nTR)
'1953': N200
'1954': A483
'1955': T099
'1956': C450
'1957': R945
'1958': C187
'1959': S799
'1960': Q447
'1961': V694
'1962': J383
'1963': N19
'1964': T111
'1965': A99
'1966': G120
'1967': N61
'1968': C254
'1969': N903
'1970': T568
'1971': J340
'1972': T181
'1973': S520
'1974': I829
'1975': X82
'1976': V676
'1977': K148
'1978': S090
'1979': T361
'1980': D170
'1981': M256
'1982': C637
'1983': K388
'1984': C58
'1985': P073
'1986': A409
'1987': R478
'1988': T749
'1989': V051
'1990': E050
'1991': M998
'1992': R824
'1993': D802
'1994': J391
'1995': F489
'1996': C709
'1997': J449
'1998': V234
'1999': C542
'2000': S221
'2001': Q969
'2002': M866
'2003': C749
'2004': S064(TR)
'2005': Q872
'2006': V799
'2007': C931
'2008': R457
'2009': G008
'2010': S150
'2011': O364
'2012': C060
'2013': V870
'2014': D899
'2015': X84
'2016': V124
'2017': R074
'2018': D190
'2019': I839
'2020': D168
'2021': D694
'2022': M246
'2023': K276
'2024': D420
'2025': A480
'2026': C240
'2027': I6109(nTR)
'2028': W12
'2029': A753
'2030': G938
'2031': C451
'2032': S911
'2033': D692
'2034': O624
'2035': K590
'2036': N137
'2037': R300
'2038': G219
'2039': K3190
'2040': S325
'2041': R560
'2042': E872
'2043': W10
'2044': J980
'2045': M331
'2046': F179
'2047': E569
'2048': S359
'2049': J351
'2050': R208
'2051': J188
'2052': T175
'2053': C680
'2054': S323
'2055': H342
'2056': K520
'2057': J040
'2058': I079(RH)
'2059': T409
'2060': R011
'2061': K765
'2062': S246
'2063': M802
'2064': R529
'2065': V476
'2066': Q158
'2067': P523
'2068': K469
'2069': C794
'2070': I5149
'2071': S559
'2072': X81
'2073': A430
'2074': B399
'2075': F411
'2076': H959
'2077': E329
'2078': P269
'2079': G001
'2080': P704
'2081': S270
'2082': J121
'2083': C845
'2084': J841
'2085': V839
'2086': N179
'2087': N730
'2088': B49
'2089': A329
'2090': C249
'2091': I5009
'2092': D300
'2093': N26
'2094': C402
'2095': D046
'2096': K629
'2097': B003
'2098': S770
'2099': I070
'2100': C009
'2101': C039
'2102': I698
'2103': E725
'2104': T599
'2105': T423
'2106': T189
'2107': C300
'2108': S220
'2109': A162
'2110': K439
'2111': S274
'2112': A020
'2113': R030
'2114': I208
'2115': N832
'2116': B376
'2117': I210
'2118': Q048
'2119': D002
'2120': G038
'2121': N119
'2122': R943
'2123': Q2780
'2124': D588
'2125': B069
'2126': K611
'2127': V579
'2128': L024
'2129': L023
'2130': R14
'2131': C148
'2132': T139
'2133': C23
'2134': I602(nTR)
'2135': G809
'2136': K911
'2137': P618
'2138': K293
'2139': S003
'2140': T781
'2141': N302
'2142': X590
'2143': I713(nTR)
'2144': M359
'2145': K265
'2146': S322
'2147': K861
'2148': I702
'2149': T842
'2150': L108
'2151': A379
'2152': J158
'2153': I788
'2154': W73
'2155': K833
'2156': N828
'2157': F322
'2158': M402
'2159': X00
'2160': F480
'2161': I871(nTR)
'2162': D012
'2163': J949
'2164': B369
'2165': E763
'2166': V244
'2167': N133
'2168': S065(TR)
'2169': X72
'2170': R688
'2171': K729
'2172': K658
'2173': L919
'2174': X349
'2175': N450
'2176': C725
'2177': M758
'2178': I518
'2179': P591
'2180': W13
'2181': T629
'2182': M100
'2183': T795
'2184': N831
'2185': Q229
'2186': I742
'2187': M103
'2188': B340
'2189': V011
'2190': C831
'2191': S724
'2192': T844
'2193': D443
'2194': Q759
'2195': S659
'2196': C673
'2197': D869
'2198': T300
'2199': D751
'2200': Q989
'2201': I516
'2202': P072
'2203': C253
'2204': W49
'2205': X70
'2206': S836
'2207': T136
'2208': D329
'2209': V695
'2210': A490
'2211': S259
'2212': V175
'2213': W67
'2214': Q601
'2215': W06
'2216': I721(nTR)
'2217': M245
'2218': J680
'2219': G960
'2220': J209
'2221': R161
'2222': S619
'2223': T009
'2224': Q934
'2225': A1699
'2226': D373
'2227': I409
'2228': E832
'2229': A188
'2230': M464
'2231': T436
'2232': S273
'2233': K227
'2234': V379
'2235': T094
'2236': J010
'2237': C051
'2238': C12
'2239': E230
'2240': K862
'2241': B279
'2242': R739
'2243': O009
'2244': D047
'2245': I4299
'2246': Q046
'2247': T313
'2248': M538
'2249': T314
'2250': T535
'2251': K509
'2252': D351
'2253': O429
'2254': M1997
'2255': G903
'2256': E888
'2257': K389
'2258': M899
'2259': C819
'2260': M009
'2261': D144
'2262': A419
'2263': L530
'2264': M301
'2265': C311
'2266': R02
'2267': K913
'2268': S390
'2269': L299
'2270': D369
'2271': A34
'2272': B001
'2273': K802
'2274': K316
'2275': V846
'2276': Q933
'2277': E119
'2278': P351
'2279': K701
'2280': R222
'2281': E042
'2282': I776
'2283': Q647
'2284': G910
'2285': T845
'2286': B608
'2287': V689
'2288': P005
'2289': G062
'2290': K222(nTR)
'2291': F402
'2292': B459
'2293': O660
'2294': P749
'2295': K051
'2296': K702
'2297': T920
'2298': Q319
'2299': Q663
'2300': J386
'2301': K358
'2302': B023
'2303': Q643
'2304': J950
'2305': D413
'2306': Q201
'2307': F429
'2308': P285
'2309': D267
'2310': Q320
'2311': A180
'2312': S420
'2313': H110
'2314': A549
'2315': J311
'2316': A099
'2317': K140
'2318': J988
'2319': B169
'2320': K912
'2321': F205
'2322': N648
'2323': K250
'2324': R090
'2325': K604
'2326': S810
'2327': Q061
'2328': Q040
'2329': C675
'2330': K763
'2331': T862
'2332': B020
'2333': Q899
'2334': I482
'2335': D233
'2336': C445
'2337': K266
'2338': D137
'2339': E789
'2340': H050
'2341': C411
'2342': S525
'2343': R440
'2344': E701
'2345': H603
'2346': I451
'2347': E320
'2348': F600
'2349': H471
'2350': M623
'2351': R629
'2352': X11
'2353': I5159
'2354': R601
'2355': T841
'2356': E310
'2357': Q399
'2358': I6409
'2359': S128
'2360': D580
'2361': Q451
'2362': C069
'2363': I709
'2364': D649
'2365': J310
'2366': A028
'2367': M861
'2368': A439
'2369': V425
'2370': D331
'2371': M023
'2372': R101
'2373': B09
'2374': K314
'2375': C091
'2376': C470
'2377': C459
'2378': Q858
'2379': E889
'2380': D684
'2381': B24
'2382': B902
'2383': S332
'2384': T329
'2385': A829
'2386': X09
'2387': C840
'2388': N258
'2389': Q419
'2390': N859
'2391': M063
'2392': N259
'2393': O440
'2394': I690
'2395': V877
'2396': T130
'2397': C786
'2398': S932
'2399': V649
'2400': M929
'2401': C639
'2402': D819
'2403': C383
'2404': Q758
'2405': K551
'2406': M300
'2407': G369
'2408': T328
'2409': H356(nTR)
'2410': S369(TR)
'2411': Q382
'2412': D763
'2413': I6119(nTR)
'2414': V355
'2415': V872
'2416': K271
'2417': J942(nTR)
'2418': I621(nTR)
'2419': C227
'2420': T922
'2421': J850
'2422': C161
'2423': T021
'2424': J459
'2425': C169
'2426': J129
'2427': C570
'2428': T390
'2429': S47
'2430': C494
'2431': K559
'2432': I339
'2433': I2199
'2434': P025
'2435': P968
'2436': T703
'2437': F845
'2438': L088
'2439': K561
'2440': J634
'2441': H921
'2442': Q605
'2443': Q412
'2444': H000
'2445': V139
'2446': Q740
'2447': L401
'2448': F199
'2449': L110
'2450': T874
'2451': I301
'2452': O960
'2453': A810
'2454': O753
'2455': D479
'2456': P832
'2457': S060(TR)
'2458': Q620
'2459': T401
'2460': P949
'2461': T820
'2462': Q892
'2463': E268
'2464': J381
'2465': T799
'2466': G319
'2467': N329
'2468': G723
'2469': S308
'2470': C600
'2471': E271
'2472': I679
'2473': H933
'2474': W17
'2475': D165
'2476': K638
'2477': V585
'2478': T315
'2479': J370
'2480': M213
'2481': K921
'2482': C924
'2483': S801
'2484': Q796
'2485': B009
'2486': V481
'2487': K851
'2488': D379
'2489': T510
'2490': C449
'2491': S351
'2492': C447
'2493': C699
'2494': H540
'2495': E065
'2496': A182
'2497': Q999
'2498': J069
'2499': G830(nTR)
'2500': S260
'2501': V456
'2502': I069(RH)
'2503': K130
'2504': V099
'2505': K263
'2506': M138
'2507': T938
'2508': R091
'2509': N342
'2510': C313
'2511': T042
'2512': M548
'2513': D591
'2514': Q349
'2515': B900
'2516': E713
'2517': P023
'2518': T929
'2519': C162
'2520': K625(nTR)
'2521': D126
'2522': M463
'2523': G442
'2524': G528
'2525': P521
'2526': S301
'2527': T220
'2528': C798
'2529': C690
'2530': C492
'2531': B028
'2532': C435
'2533': C927
'2534': K904
'2535': M1995
'2536': V736
'2537': Q439
'2538': Q252
'2539': C722
'2540': C630
'2541': H498
'2542': K529
'2543': D400
'2544': A870
'2545': N939(nTR)
'2546': K565
'2547': I711(nTR)
'2548': Y069
'2549': V859
'2550': S120
'2551': C323
'2552': S151
'2553': J039
'2554': V776
'2555': A062
'2556': O069
'2557': G519
'2558': R634
'2559': J051
'2560': J398
'2561': E142
'2562': T025
'2563': Q680
'2564': I495
'2565': A049
'2566': D412
'2567': I715(nTR)
'2568': I091
'2569': I490
'2570': B03
'2571': I289
'2572': C841
'2573': I678
'2574': A799
'2575': R999
'2576': E278
'2577': M242
'2578': I6359
'2579': H440
'2580': Q390
'2581': L570
'2582': N818
'2583': C950
'2584': B269
'2585': F059
'2586': O759
'2587': S059
'2588': S015
'2589': I494
'2590': Q606
'2591': T212
'2592': R21
'2593': C250
'2594': C490
'2595': L511
'2596': P219
'2597': D66
'2598': H700
'2599': G950
'2600': C574
'2601': I601(nTR)
'2602': G560
'2603': T821
'2604': B000
'2605': T289
'2606': C188
'2607': P059
'2608': B451
'2609': R730
'2610': K029
'2611': Y34
'2612': D022
'2613': K621
'2614': C257
'2615': I212
'2616': I672
'2617': M332
'2618': Q264
'2619': N360
'2620': G441
'2621': T569
'2622': C942
'2623': P350
'2624': C930
'2625': S430
'2626': S018
'2627': V090
'2628': I723(nTR)
'2629': C384
'2630': C504
'2631': T659
'2632': Q754
'2633': D269
'2634': M629
'2635': Q189
'2636': S899
'2637': T096
'2638': V871
'2639': G09
'2640': S321
'2641': B029
'2642': F209
'2643': K294
'2644': G003
'2645': S500
'2646': M462
'2647': K732
'2648': F329
'2649': N421(nTR)
'2650': F729
'2651': I371
'2652': Q208
'2653': S831
'2654': T447
'2655': T065
'2656': S229
'2657': P969
'2658': T10
'2659': K822(nTR)
'2660': H605
'2661': V485
'2662': T638
'2663': J958
'2664': M532
'2665': J698
'2666': J869
'2667': C119
'2668': K811
'2669': C696
'2670': I4229
'2671': X38
'2672': M329
'2673': T918
'2674': G961
'2675': C966
'2676': J691
'2677': G372
'2678': S290
'2679': V295
'2680': J853
'2681': F068
'2682': Q206
'2683': E835
'2684': S499
'2685': V255
'2686': N340
'2687': I801
'2688': T560
'2689': C150
'2690': T310
'2691': J312
'2692': T928
'2693': J385
'2694': D434
'2695': X37
'2696': S929
'2697': J168
'2698': S669
'2699': E149
'2700': C859
'2701': S399
'2702': C229
'2703': V794
'2704': I378
'2705': T144
'2706': I461
'2707': E762
'2708': C080
'2709': A972
'2710': M303
'2711': P071
'2712': Q039
'2713': M478
'2714': C969
'2715': S18
'2716': Q851
'2717': Q273
'2718': T882
'2719': I501
'2720': X01
'2721': B178
'2722': N821
'2723': Q809
'2724': T528
'2725': K122
'2726': N178
'2727': I092
'2728': K769
'2729': P809
'2730': P290
'2731': V535
'2732': R579
'2733': Q891
'2734': E259
'2735': A979
'2736': S925
'2737': I513
'2738': X14
'2739': Q393
'2740': I6319
'2741': C693
'2742': D477
'2743': F431
'2744': T178
'2745': T816
'2746': I258
'2747': C501
'2748': D414
'2749': A630
'2750': W05
'2751': I012
'2752': E243
'2753': T921
'2754': G912
'2755': T598
'2756': C715
'2757': H729(nTR)
'2758': P614
'2759': G259
'2760': T223
'2761': T173
'2762': P284
'2763': N312
'2764': E232
'2765': I4259
'2766': C186
'2767': J029
'2768': Q738
'2769': A830
'2770': A520
'2771': T311
'2772': W18
'2773': N138
'2774': T271
'2775': Y850
'2776': D27
'2777': V970
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splits:
- name: train
num_bytes: 113287237
num_examples: 1477240
download_size: 28018862
dataset_size: 113287237
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Chinese International Statistical Classification of Diseases |
whu9/xsum_postprocess | ---
dataset_info:
features:
- name: source
dtype: string
- name: summary
dtype: string
- name: source_num_tokens
dtype: int64
- name: summary_num_tokens
dtype: int64
splits:
- name: train
num_bytes: 479957379
num_examples: 203788
- name: validation
num_bytes: 26334240
num_examples: 11313
- name: test
num_bytes: 26797491
num_examples: 11319
download_size: 338633607
dataset_size: 533089110
---
# Dataset Card for "xsum_postprocess"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
yxgao/openassistant-guanaco-llama2 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 15401731
num_examples: 9846
- name: test
num_bytes: 815439
num_examples: 518
download_size: 9458962
dataset_size: 16217170
---
# Dataset Card for "openassistant-guanaco-llama2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
relbert/nell | ---
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- n<1K
pretty_name: relbert/nell
---
# Dataset Card for "relbert/nell"
## Dataset Description
- **Repository:** [https://github.com/xwhan/One-shot-Relational-Learning](https://github.com/xwhan/One-shot-Relational-Learning)
- **Paper:** [https://aclanthology.org/D18-1223/](https://aclanthology.org/D18-1223/)
- **Dataset:** Never Ending Language Learner (NELL) dataset for one-shot link prediction.
### Dataset Summary
This is NELL-ONE dataset for the few-shots link prediction proposed in [https://aclanthology.org/D18-1223/](https://aclanthology.org/D18-1223/).
Please see [NELL paper](https://www.cs.cmu.edu/~tom/pubs/NELL_aaai15.pdf) to know more about the original dataset.
- Number of instances
| | train | validation | test |
|:--------------------------------|--------:|-------------:|-------:|
| number of pairs | 5498 | 878 | 1352 |
| number of unique relation types | 32 | 4 | 6 |
- Number of pairs in each relation type
| | number of pairs (train) | number of pairs (validation) | number of pairs (test) |
|:---------------------------------------------------|--------------------------:|-------------------------------:|-------------------------:|
| concept:airportincity | 210 | 0 | 0 |
| concept:athleteledsportsteam | 424 | 0 | 0 |
| concept:automobilemakercardealersinstateorprovince | 78 | 0 | 0 |
| concept:bankboughtbank | 58 | 0 | 0 |
| concept:ceoof | 271 | 0 | 0 |
| concept:cityradiostation | 99 | 0 | 0 |
| concept:citytelevisionstation | 316 | 0 | 0 |
| concept:countriessuchascountries | 100 | 0 | 0 |
| concept:countrycapital | 211 | 0 | 0 |
| concept:countryhascitizen | 182 | 0 | 0 |
| concept:countryoforganizationheadquarters | 166 | 0 | 0 |
| concept:countrystates | 169 | 0 | 0 |
| concept:drugpossiblytreatsphysiologicalcondition | 91 | 0 | 0 |
| concept:fatherofperson | 108 | 0 | 0 |
| concept:fooddecreasestheriskofdisease | 1 | 0 | 0 |
| concept:hasofficeincountry | 283 | 0 | 0 |
| concept:leaguecoaches | 71 | 0 | 0 |
| concept:leaguestadiums | 279 | 0 | 0 |
| concept:musicartistmusician | 118 | 0 | 0 |
| concept:musicgenressuchasmusicgenres | 107 | 0 | 0 |
| concept:organizationnamehasacronym | 61 | 0 | 0 |
| concept:personalsoknownas | 78 | 0 | 0 |
| concept:personleadsgeopoliticalorganization | 120 | 0 | 0 |
| concept:personmovedtostateorprovince | 225 | 0 | 0 |
| concept:politicianrepresentslocation | 258 | 0 | 0 |
| concept:politicianusholdsoffice | 216 | 0 | 0 |
| concept:statehascapital | 151 | 0 | 0 |
| concept:stateorprovinceoforganizationheadquarters | 118 | 0 | 0 |
| concept:teamhomestadium | 138 | 0 | 0 |
| concept:teamplaysincity | 338 | 0 | 0 |
| concept:topmemberoforganization | 354 | 0 | 0 |
| concept:wifeof | 99 | 0 | 0 |
| concept:bankbankincountry | 0 | 229 | 0 |
| concept:cityalsoknownas | 0 | 356 | 0 |
| concept:parentofperson | 0 | 217 | 0 |
| concept:politicalgroupofpoliticianus | 0 | 76 | 0 |
| concept:automobilemakerdealersincity | 0 | 0 | 177 |
| concept:automobilemakerdealersincountry | 0 | 0 | 96 |
| concept:geopoliticallocationresidenceofpersion | 0 | 0 | 143 |
| concept:politicianusendorsespoliticianus | 0 | 0 | 386 |
| concept:producedby | 0 | 0 | 209 |
| concept:teamcoach | 0 | 0 | 341 |
- Number of entity types
| | head (train) | tail (train) | head (validation) | tail (validation) | head (test) | tail (test) |
|:-------------------------|---------------:|---------------:|--------------------:|--------------------:|--------------:|--------------:|
| actor | 6 | 2 | 0 | 0 | 0 | 0 |
| airport | 152 | 0 | 0 | 0 | 0 | 0 |
| astronaut | 4 | 0 | 0 | 1 | 0 | 1 |
| athlete | 353 | 21 | 1 | 2 | 0 | 59 |
| attraction | 4 | 1 | 0 | 0 | 0 | 0 |
| automobilemaker | 131 | 29 | 0 | 0 | 273 | 54 |
| bank | 109 | 126 | 144 | 0 | 0 | 0 |
| biotechcompany | 14 | 80 | 0 | 0 | 0 | 10 |
| building | 4 | 0 | 0 | 0 | 0 | 0 |
| celebrity | 6 | 5 | 0 | 0 | 4 | 2 |
| ceo | 423 | 0 | 0 | 0 | 0 | 0 |
| city | 342 | 852 | 316 | 316 | 42 | 161 |
| coach | 29 | 61 | 0 | 3 | 0 | 245 |
| comedian | 1 | 0 | 0 | 0 | 0 | 0 |
| company | 76 | 549 | 1 | 0 | 1 | 144 |
| country | 755 | 455 | 0 | 197 | 27 | 91 |
| county | 36 | 39 | 11 | 11 | 10 | 4 |
| creditunion | 1 | 0 | 0 | 0 | 0 | 0 |
| criminal | 3 | 0 | 1 | 0 | 0 | 1 |
| director | 2 | 0 | 0 | 0 | 0 | 1 |
| drug | 91 | 0 | 0 | 0 | 1 | 0 |
| female | 116 | 8 | 38 | 9 | 3 | 3 |
| geopoliticallocation | 184 | 112 | 96 | 29 | 24 | 8 |
| geopoliticalorganization | 28 | 68 | 8 | 21 | 1 | 7 |
| governmentorganization | 25 | 95 | 74 | 0 | 0 | 0 |
| island | 15 | 4 | 4 | 6 | 1 | 0 |
| journalist | 4 | 0 | 0 | 0 | 0 | 1 |
| male | 132 | 78 | 37 | 52 | 1 | 5 |
| model | 2 | 0 | 0 | 0 | 0 | 0 |
| monarch | 4 | 3 | 4 | 1 | 0 | 0 |
| museum | 1 | 5 | 0 | 0 | 0 | 0 |
| musicartist | 118 | 5 | 0 | 0 | 0 | 0 |
| musicgenre | 107 | 107 | 0 | 0 | 0 | 0 |
| musician | 5 | 124 | 0 | 0 | 0 | 0 |
| newspaper | 3 | 2 | 0 | 0 | 0 | 0 |
| organization | 23 | 86 | 1 | 1 | 32 | 2 |
| person | 350 | 256 | 116 | 131 | 0 | 96 |
| personafrica | 1 | 3 | 0 | 0 | 0 | 0 |
| personasia | 1 | 3 | 0 | 0 | 0 | 0 |
| personaustralia | 38 | 5 | 0 | 0 | 0 | 5 |
| personcanada | 19 | 14 | 0 | 0 | 0 | 0 |
| personeurope | 9 | 7 | 14 | 4 | 0 | 1 |
| personmexico | 57 | 14 | 0 | 0 | 0 | 20 |
| personnorthamerica | 9 | 6 | 0 | 0 | 0 | 3 |
| personsouthamerica | 1 | 1 | 0 | 17 | 0 | 0 |
| personus | 41 | 21 | 2 | 0 | 1 | 6 |
| planet | 1 | 0 | 0 | 0 | 0 | 1 |
| politician | 107 | 5 | 0 | 1 | 23 | 58 |
| politicianus | 408 | 12 | 3 | 71 | 352 | 360 |
| politicsblog | 2 | 3 | 0 | 0 | 0 | 0 |
| port | 7 | 0 | 0 | 0 | 0 | 0 |
| professor | 7 | 2 | 0 | 0 | 1 | 0 |
| publication | 1 | 21 | 0 | 0 | 0 | 0 |
| recordlabel | 1 | 13 | 0 | 0 | 0 | 0 |
| retailstore | 1 | 15 | 0 | 0 | 0 | 0 |
| school | 54 | 1 | 0 | 0 | 11 | 0 |
| scientist | 5 | 2 | 0 | 1 | 0 | 0 |
| sportsleague | 356 | 12 | 0 | 0 | 0 | 0 |
| sportsteam | 392 | 430 | 0 | 0 | 295 | 0 |
| stateorprovince | 254 | 602 | 0 | 0 | 38 | 0 |
| transportation | 36 | 2 | 0 | 0 | 0 | 0 |
| university | 3 | 15 | 0 | 0 | 0 | 0 |
| visualizablescene | 20 | 7 | 3 | 3 | 3 | 3 |
| visualizablething | 1 | 1 | 1 | 1 | 0 | 0 |
| website | 7 | 31 | 0 | 0 | 0 | 0 |
| caf_ | 0 | 1 | 0 | 0 | 0 | 0 |
| continent | 0 | 1 | 0 | 0 | 0 | 0 |
| disease | 0 | 92 | 0 | 0 | 0 | 0 |
| hotel | 0 | 1 | 0 | 0 | 0 | 0 |
| magazine | 0 | 5 | 0 | 0 | 0 | 0 |
| nongovorganization | 0 | 4 | 0 | 0 | 0 | 0 |
| nonprofitorganization | 0 | 2 | 0 | 0 | 0 | 0 |
| park | 0 | 1 | 0 | 0 | 0 | 0 |
| petroleumrefiningcompany | 0 | 6 | 0 | 0 | 0 | 0 |
| politicaloffice | 0 | 216 | 0 | 0 | 0 | 0 |
| politicalparty | 0 | 6 | 2 | 0 | 0 | 0 |
| radiostation | 0 | 93 | 0 | 0 | 0 | 0 |
| river | 0 | 4 | 0 | 0 | 0 | 0 |
| stadiumoreventvenue | 0 | 417 | 0 | 0 | 0 | 0 |
| televisionnetwork | 0 | 1 | 0 | 0 | 0 | 0 |
| televisionstation | 0 | 221 | 0 | 0 | 0 | 0 |
| trainstation | 0 | 2 | 0 | 0 | 0 | 0 |
| writer | 0 | 3 | 1 | 0 | 0 | 0 |
| zoo | 0 | 1 | 0 | 0 | 0 | 0 |
| automobilemodel | 0 | 0 | 0 | 0 | 100 | 0 |
| product | 0 | 0 | 0 | 0 | 62 | 0 |
| software | 0 | 0 | 0 | 0 | 42 | 0 |
| videogame | 0 | 0 | 0 | 0 | 4 | 0 |
## Dataset Structure
An example of `test` looks as below.
```shell
{
"relation": "concept:producedby",
"head": "Toyota Tacoma",
"head_type": "automobilemodel",
"tail": "Toyota",
"tail_type": "automobilemaker"
}
```
## Citation Information
```
@inproceedings{xiong-etal-2018-one,
title = "One-Shot Relational Learning for Knowledge Graphs",
author = "Xiong, Wenhan and
Yu, Mo and
Chang, Shiyu and
Guo, Xiaoxiao and
Wang, William Yang",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1223",
doi = "10.18653/v1/D18-1223",
pages = "1980--1990",
abstract = "Knowledge graphs (KG) are the key components of various natural language processing applications. To further expand KGs{'} coverage, previous studies on knowledge graph completion usually require a large number of positive examples for each relation. However, we observe long-tail relations are actually more common in KGs and those newly added relations often do not have many known triples for training. In this work, we aim at predicting new facts under a challenging setting where only one training instance is available. We propose a one-shot relational learning framework, which utilizes the knowledge distilled by embedding models and learns a matching metric by considering both the learned embeddings and one-hop graph structures. Empirically, our model yields considerable performance improvements over existing embedding models, and also eliminates the need of re-training the embedding models when dealing with newly added relations.",
}
```
|
TinyPixel/dolly | ---
dataset_info:
features:
- name: context
dtype: string
- name: response
dtype: string
- name: category
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 23514473
num_examples: 15011
download_size: 14306700
dataset_size: 23514473
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "dolly"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_chargoddard__llama-2-34b-uncode | ---
pretty_name: Evaluation run of chargoddard/llama-2-34b-uncode
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [chargoddard/llama-2-34b-uncode](https://huggingface.co/chargoddard/llama-2-34b-uncode)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated 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_chargoddard__llama-2-34b-uncode\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-17T09:49:44.237911](https://huggingface.co/datasets/open-llm-leaderboard/details_chargoddard__llama-2-34b-uncode/blob/main/results_2023-10-17T09-49-44.237911.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 \"em\": 0.0014681208053691276,\n\
\ \"em_stderr\": 0.000392104219029832,\n \"f1\": 0.054323615771812044,\n\
\ \"f1_stderr\": 0.001268355641976372,\n \"acc\": 0.47561084340161075,\n\
\ \"acc_stderr\": 0.01172411036273294\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0014681208053691276,\n \"em_stderr\": 0.000392104219029832,\n\
\ \"f1\": 0.054323615771812044,\n \"f1_stderr\": 0.001268355641976372\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.20773313115996966,\n \
\ \"acc_stderr\": 0.011174572716705883\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7434885556432518,\n \"acc_stderr\": 0.012273648008759998\n\
\ }\n}\n```"
repo_url: https://huggingface.co/chargoddard/llama-2-34b-uncode
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|arc:challenge|25_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_17T09_49_44.237911
path:
- '**/details_harness|drop|3_2023-10-17T09-49-44.237911.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-17T09-49-44.237911.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_17T09_49_44.237911
path:
- '**/details_harness|gsm8k|5_2023-10-17T09-49-44.237911.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-17T09-49-44.237911.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hellaswag|10_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-29T02:22:47.016201.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
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path:
- '**/details_harness|hendrycksTest-management|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
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path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
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path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-29T02:22:47.016201.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-29T02:22:47.016201.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_17T09_49_44.237911
path:
- '**/details_harness|winogrande|5_2023-10-17T09-49-44.237911.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-17T09-49-44.237911.parquet'
- config_name: results
data_files:
- split: 2023_08_29T02_22_47.016201
path:
- results_2023-08-29T02:22:47.016201.parquet
- split: 2023_10_17T09_49_44.237911
path:
- results_2023-10-17T09-49-44.237911.parquet
- split: latest
path:
- results_2023-10-17T09-49-44.237911.parquet
---
# Dataset Card for Evaluation run of chargoddard/llama-2-34b-uncode
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/chargoddard/llama-2-34b-uncode
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [chargoddard/llama-2-34b-uncode](https://huggingface.co/chargoddard/llama-2-34b-uncode) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated 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_chargoddard__llama-2-34b-uncode",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-17T09:49:44.237911](https://huggingface.co/datasets/open-llm-leaderboard/details_chargoddard__llama-2-34b-uncode/blob/main/results_2023-10-17T09-49-44.237911.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": {
"em": 0.0014681208053691276,
"em_stderr": 0.000392104219029832,
"f1": 0.054323615771812044,
"f1_stderr": 0.001268355641976372,
"acc": 0.47561084340161075,
"acc_stderr": 0.01172411036273294
},
"harness|drop|3": {
"em": 0.0014681208053691276,
"em_stderr": 0.000392104219029832,
"f1": 0.054323615771812044,
"f1_stderr": 0.001268355641976372
},
"harness|gsm8k|5": {
"acc": 0.20773313115996966,
"acc_stderr": 0.011174572716705883
},
"harness|winogrande|5": {
"acc": 0.7434885556432518,
"acc_stderr": 0.012273648008759998
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
h4/llama.cpp | ---
license: mit
---
|
open-llm-leaderboard/details_ResplendentAI__Persephone_7B | ---
pretty_name: Evaluation run of ResplendentAI/Persephone_7B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [ResplendentAI/Persephone_7B](https://huggingface.co/ResplendentAI/Persephone_7B)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 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_ResplendentAI__Persephone_7B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-21T13:36:36.863663](https://huggingface.co/datasets/open-llm-leaderboard/details_ResplendentAI__Persephone_7B/blob/main/results_2024-03-21T13-36-36.863663.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\": 0.6347236039648394,\n\
\ \"acc_stderr\": 0.03262905610355662,\n \"acc_norm\": 0.6367186039511483,\n\
\ \"acc_norm_stderr\": 0.033296163021281945,\n \"mc1\": 0.5079559363525091,\n\
\ \"mc1_stderr\": 0.01750128507455182,\n \"mc2\": 0.6750586311173583,\n\
\ \"mc2_stderr\": 0.015111441282533892\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.636518771331058,\n \"acc_stderr\": 0.014056207319068285,\n\
\ \"acc_norm\": 0.6672354948805461,\n \"acc_norm_stderr\": 0.013769863046192305\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6791475801633141,\n\
\ \"acc_stderr\": 0.004658501662277628,\n \"acc_norm\": 0.8559051981676957,\n\
\ \"acc_norm_stderr\": 0.003504681091703901\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6296296296296297,\n\
\ \"acc_stderr\": 0.041716541613545426,\n \"acc_norm\": 0.6296296296296297,\n\
\ \"acc_norm_stderr\": 0.041716541613545426\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6973684210526315,\n \"acc_stderr\": 0.03738520676119668,\n\
\ \"acc_norm\": 0.6973684210526315,\n \"acc_norm_stderr\": 0.03738520676119668\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.57,\n\
\ \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n \
\ \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7132075471698113,\n \"acc_stderr\": 0.02783491252754406,\n\
\ \"acc_norm\": 0.7132075471698113,\n \"acc_norm_stderr\": 0.02783491252754406\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7222222222222222,\n\
\ \"acc_stderr\": 0.03745554791462456,\n \"acc_norm\": 0.7222222222222222,\n\
\ \"acc_norm_stderr\": 0.03745554791462456\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.42,\n \"acc_stderr\": 0.04960449637488584,\n \
\ \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.04960449637488584\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.53,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.53,\n\
\ \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-college_mathematics|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-college_medicine|5\": {\n \"acc\": 0.6473988439306358,\n\
\ \"acc_stderr\": 0.03643037168958548,\n \"acc_norm\": 0.6473988439306358,\n\
\ \"acc_norm_stderr\": 0.03643037168958548\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.37254901960784315,\n \"acc_stderr\": 0.048108401480826346,\n\
\ \"acc_norm\": 0.37254901960784315,\n \"acc_norm_stderr\": 0.048108401480826346\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.74,\n \"acc_stderr\": 0.044084400227680794,\n \"acc_norm\": 0.74,\n\
\ \"acc_norm_stderr\": 0.044084400227680794\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.574468085106383,\n \"acc_stderr\": 0.03232146916224469,\n\
\ \"acc_norm\": 0.574468085106383,\n \"acc_norm_stderr\": 0.03232146916224469\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4649122807017544,\n\
\ \"acc_stderr\": 0.046920083813689104,\n \"acc_norm\": 0.4649122807017544,\n\
\ \"acc_norm_stderr\": 0.046920083813689104\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5586206896551724,\n \"acc_stderr\": 0.04137931034482757,\n\
\ \"acc_norm\": 0.5586206896551724,\n \"acc_norm_stderr\": 0.04137931034482757\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.40476190476190477,\n \"acc_stderr\": 0.025279850397404907,\n \"\
acc_norm\": 0.40476190476190477,\n \"acc_norm_stderr\": 0.025279850397404907\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5079365079365079,\n\
\ \"acc_stderr\": 0.044715725362943486,\n \"acc_norm\": 0.5079365079365079,\n\
\ \"acc_norm_stderr\": 0.044715725362943486\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \
\ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.7580645161290323,\n \"acc_stderr\": 0.024362599693031096,\n \"\
acc_norm\": 0.7580645161290323,\n \"acc_norm_stderr\": 0.024362599693031096\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.5320197044334976,\n \"acc_stderr\": 0.035107665979592154,\n \"\
acc_norm\": 0.5320197044334976,\n \"acc_norm_stderr\": 0.035107665979592154\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|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-high_school_european_history|5\"\
: {\n \"acc\": 0.7575757575757576,\n \"acc_stderr\": 0.03346409881055953,\n\
\ \"acc_norm\": 0.7575757575757576,\n \"acc_norm_stderr\": 0.03346409881055953\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7676767676767676,\n \"acc_stderr\": 0.030088629490217487,\n \"\
acc_norm\": 0.7676767676767676,\n \"acc_norm_stderr\": 0.030088629490217487\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8808290155440415,\n \"acc_stderr\": 0.02338193534812142,\n\
\ \"acc_norm\": 0.8808290155440415,\n \"acc_norm_stderr\": 0.02338193534812142\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6461538461538462,\n \"acc_stderr\": 0.02424378399406216,\n \
\ \"acc_norm\": 0.6461538461538462,\n \"acc_norm_stderr\": 0.02424378399406216\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.362962962962963,\n \"acc_stderr\": 0.029318203645206858,\n \
\ \"acc_norm\": 0.362962962962963,\n \"acc_norm_stderr\": 0.029318203645206858\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.680672268907563,\n \"acc_stderr\": 0.030283995525884396,\n \
\ \"acc_norm\": 0.680672268907563,\n \"acc_norm_stderr\": 0.030283995525884396\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.37748344370860926,\n \"acc_stderr\": 0.03958027231121569,\n \"\
acc_norm\": 0.37748344370860926,\n \"acc_norm_stderr\": 0.03958027231121569\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8165137614678899,\n \"acc_stderr\": 0.0165952597103993,\n \"acc_norm\"\
: 0.8165137614678899,\n \"acc_norm_stderr\": 0.0165952597103993\n },\n\
\ \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5,\n\
\ \"acc_stderr\": 0.034099716973523674,\n \"acc_norm\": 0.5,\n \
\ \"acc_norm_stderr\": 0.034099716973523674\n },\n \"harness|hendrycksTest-high_school_us_history|5\"\
: {\n \"acc\": 0.7941176470588235,\n \"acc_stderr\": 0.028379449451588667,\n\
\ \"acc_norm\": 0.7941176470588235,\n \"acc_norm_stderr\": 0.028379449451588667\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7679324894514767,\n \"acc_stderr\": 0.02747974455080851,\n \
\ \"acc_norm\": 0.7679324894514767,\n \"acc_norm_stderr\": 0.02747974455080851\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6905829596412556,\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\": 0.7022900763358778,\n \"acc_stderr\": 0.04010358942462203,\n\
\ \"acc_norm\": 0.7022900763358778,\n \"acc_norm_stderr\": 0.04010358942462203\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7851239669421488,\n \"acc_stderr\": 0.03749492448709695,\n \"\
acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.03749492448709695\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7314814814814815,\n\
\ \"acc_stderr\": 0.042844679680521934,\n \"acc_norm\": 0.7314814814814815,\n\
\ \"acc_norm_stderr\": 0.042844679680521934\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7361963190184049,\n \"acc_stderr\": 0.034624199316156234,\n\
\ \"acc_norm\": 0.7361963190184049,\n \"acc_norm_stderr\": 0.034624199316156234\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\": 0.7572815533980582,\n \"acc_stderr\": 0.04245022486384495,\n\
\ \"acc_norm\": 0.7572815533980582,\n \"acc_norm_stderr\": 0.04245022486384495\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8760683760683761,\n\
\ \"acc_stderr\": 0.02158649400128137,\n \"acc_norm\": 0.8760683760683761,\n\
\ \"acc_norm_stderr\": 0.02158649400128137\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.8135376756066411,\n\
\ \"acc_stderr\": 0.01392775137200151,\n \"acc_norm\": 0.8135376756066411,\n\
\ \"acc_norm_stderr\": 0.01392775137200151\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6994219653179191,\n \"acc_stderr\": 0.024685316867257803,\n\
\ \"acc_norm\": 0.6994219653179191,\n \"acc_norm_stderr\": 0.024685316867257803\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4335195530726257,\n\
\ \"acc_stderr\": 0.016574027219517635,\n \"acc_norm\": 0.4335195530726257,\n\
\ \"acc_norm_stderr\": 0.016574027219517635\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7091503267973857,\n \"acc_stderr\": 0.02600480036395213,\n\
\ \"acc_norm\": 0.7091503267973857,\n \"acc_norm_stderr\": 0.02600480036395213\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6945337620578779,\n\
\ \"acc_stderr\": 0.026160584450140453,\n \"acc_norm\": 0.6945337620578779,\n\
\ \"acc_norm_stderr\": 0.026160584450140453\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7129629629629629,\n \"acc_stderr\": 0.025171041915309684,\n\
\ \"acc_norm\": 0.7129629629629629,\n \"acc_norm_stderr\": 0.025171041915309684\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.450354609929078,\n \"acc_stderr\": 0.029680105565029036,\n \
\ \"acc_norm\": 0.450354609929078,\n \"acc_norm_stderr\": 0.029680105565029036\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4680573663624511,\n\
\ \"acc_stderr\": 0.012744149704869649,\n \"acc_norm\": 0.4680573663624511,\n\
\ \"acc_norm_stderr\": 0.012744149704869649\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6507352941176471,\n \"acc_stderr\": 0.028959755196824866,\n\
\ \"acc_norm\": 0.6507352941176471,\n \"acc_norm_stderr\": 0.028959755196824866\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6388888888888888,\n \"acc_stderr\": 0.01943177567703731,\n \
\ \"acc_norm\": 0.6388888888888888,\n \"acc_norm_stderr\": 0.01943177567703731\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\": 0.6857142857142857,\n \"acc_stderr\": 0.029719329422417482,\n\
\ \"acc_norm\": 0.6857142857142857,\n \"acc_norm_stderr\": 0.029719329422417482\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8507462686567164,\n\
\ \"acc_stderr\": 0.02519692987482706,\n \"acc_norm\": 0.8507462686567164,\n\
\ \"acc_norm_stderr\": 0.02519692987482706\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.86,\n \"acc_stderr\": 0.034873508801977704,\n \
\ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.034873508801977704\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4939759036144578,\n\
\ \"acc_stderr\": 0.03892212195333045,\n \"acc_norm\": 0.4939759036144578,\n\
\ \"acc_norm_stderr\": 0.03892212195333045\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\": 0.5079559363525091,\n\
\ \"mc1_stderr\": 0.01750128507455182,\n \"mc2\": 0.6750586311173583,\n\
\ \"mc2_stderr\": 0.015111441282533892\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8232044198895028,\n \"acc_stderr\": 0.010721923287918746\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5360121304018196,\n \
\ \"acc_stderr\": 0.013736715929950313\n }\n}\n```"
repo_url: https://huggingface.co/ResplendentAI/Persephone_7B
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|arc:challenge|25_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|gsm8k|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hellaswag|10_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-21T13-36-36.863663.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-21T13-36-36.863663.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- '**/details_harness|winogrande|5_2024-03-21T13-36-36.863663.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-21T13-36-36.863663.parquet'
- config_name: results
data_files:
- split: 2024_03_21T13_36_36.863663
path:
- results_2024-03-21T13-36-36.863663.parquet
- split: latest
path:
- results_2024-03-21T13-36-36.863663.parquet
---
# Dataset Card for Evaluation run of ResplendentAI/Persephone_7B
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [ResplendentAI/Persephone_7B](https://huggingface.co/ResplendentAI/Persephone_7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 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_ResplendentAI__Persephone_7B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-21T13:36:36.863663](https://huggingface.co/datasets/open-llm-leaderboard/details_ResplendentAI__Persephone_7B/blob/main/results_2024-03-21T13-36-36.863663.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": 0.6347236039648394,
"acc_stderr": 0.03262905610355662,
"acc_norm": 0.6367186039511483,
"acc_norm_stderr": 0.033296163021281945,
"mc1": 0.5079559363525091,
"mc1_stderr": 0.01750128507455182,
"mc2": 0.6750586311173583,
"mc2_stderr": 0.015111441282533892
},
"harness|arc:challenge|25": {
"acc": 0.636518771331058,
"acc_stderr": 0.014056207319068285,
"acc_norm": 0.6672354948805461,
"acc_norm_stderr": 0.013769863046192305
},
"harness|hellaswag|10": {
"acc": 0.6791475801633141,
"acc_stderr": 0.004658501662277628,
"acc_norm": 0.8559051981676957,
"acc_norm_stderr": 0.003504681091703901
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695235,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6296296296296297,
"acc_stderr": 0.041716541613545426,
"acc_norm": 0.6296296296296297,
"acc_norm_stderr": 0.041716541613545426
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6973684210526315,
"acc_stderr": 0.03738520676119668,
"acc_norm": 0.6973684210526315,
"acc_norm_stderr": 0.03738520676119668
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.57,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.57,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7132075471698113,
"acc_stderr": 0.02783491252754406,
"acc_norm": 0.7132075471698113,
"acc_norm_stderr": 0.02783491252754406
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7222222222222222,
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```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[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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## Dataset Card Contact
[More Information Needed] |
AlekseyKorshuk/code-alpaca-eval-v0-deepseek-coder-7b-instruct-v1.5-responses | ---
dataset_info:
features:
- name: dataset
dtype: string
- name: model_input
list:
- name: content
dtype: string
- name: role
dtype: string
- name: baseline_response
dtype: string
- name: deepseek-coder-7b-instruct-v1.5_response
dtype: string
splits:
- name: train
num_bytes: 690953
num_examples: 134
download_size: 300887
dataset_size: 690953
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
marvinmedeiros52/perdigao | ---
license: openrail
---
|
poorguys/TW-Kai_2_Chong_Xi_Small_Seal_all_512 | ---
dataset_info:
features:
- name: char
dtype: string
- name: unicode
dtype: string
- name: images
dtype: image
- name: target_images
dtype: image
- name: stroke
dtype: int32
- name: strokes_sequence
sequence: int32
- name: components
sequence: int32
- name: jyutping
dtype: string
splits:
- name: train
num_bytes: 792865960.5
num_examples: 11602
- name: test
num_bytes: 2235707823.0
num_examples: 64680
download_size: 2113472346
dataset_size: 3028573783.5
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
ChristophSchuhmann/gutenberg-wiki-arxiv-pubmed-soda-summaries | ---
license: apache-2.0
---
Here are HQ summaries of each ~2000 texts from
- Project Gutenbberg
- Pubmed
- Arxiv
- Wikipedia
- Soda
with the original texts and instructions which include the word counts of the summaries. |
kiddelpool/HarryPotter | ---
license: openrail
---
|
KodKio/oi5_ball | ---
license: apache-2.0
---
|
davidberenstein1957/ents_and_coref_spans | ---
dataset_info:
features:
- name: text
dtype: string
id: field
- name: label
list:
- name: user_id
dtype: string
id: question
- name: value
dtype: string
id: suggestion
- name: status
dtype: string
id: question
- name: label-suggestion
dtype: string
id: suggestion
- name: label-suggestion-metadata
struct:
- name: type
dtype: string
id: suggestion-metadata
- name: score
dtype: float32
id: suggestion-metadata
- name: agent
dtype: string
id: suggestion-metadata
- name: external_id
dtype: string
id: external_id
- name: metadata
dtype: string
id: metadata
- name: spans
list:
- name: char_end_index
dtype: int64
- name: char_start_index
dtype: int64
- name: label
dtype: string
- name: score
dtype: int64
- name: span
dtype: string
splits:
- name: train
num_bytes: 1059109
num_examples: 1000
download_size: 389581
dataset_size: 1059109
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
sproos/arxiv-embeddings | ---
license: apache-2.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: abstract
dtype: string
- name: embedding
sequence: float64
splits:
- name: train
num_bytes: 3585049145.8887267
num_examples: 266311
- name: test
num_bytes: 398350760.11127335
num_examples: 29591
download_size: 3783925189
dataset_size: 3983399906.0
---
|
arunkumargyan/ML_ner | ---
license: mit
---
|
autoevaluate/autoeval-eval-cnn_dailymail-3.0.0-90e029-50827145304 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- cnn_dailymail
eval_info:
task: summarization
model: 0x70DA/pegasus-cnn_dailymail
metrics: []
dataset_name: cnn_dailymail
dataset_config: 3.0.0
dataset_split: test
col_mapping:
text: article
target: highlights
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: 0x70DA/pegasus-cnn_dailymail
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@km](https://huggingface.co/km) for evaluating this model. |
philschmid/philschmid-de-blog | ---
dataset_info:
features:
- name: title
dtype: string
- name: url
dtype: string
- name: date
dtype: string
- name: tags
sequence: string
- name: summary
dtype: string
- name: content
dtype: string
splits:
- name: train
num_bytes: 1151548
num_examples: 87
download_size: 541717
dataset_size: 1151548
---
# Dataset Card for "philschmid-de-blog"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
shidowake/Doctor-Shotgun_capybara-sharegpt_subset_split_1 | ---
dataset_info:
features:
- name: source
dtype: string
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
splits:
- name: train
num_bytes: 9064100.571348244
num_examples: 2001
download_size: 4649239
dataset_size: 9064100.571348244
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
anitamaher/xdssfsefsef | ---
license: cc-by-nc-sa-3.0
---
|
leesh7248/qa_hadogeub | ---
license: unknown
---
|
CyberHarem/farah_granbluefantasy | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of farah/ファラ (Granblue Fantasy)
This is the dataset of farah/ファラ (Granblue Fantasy), containing 67 images and their tags.
The core tags of this character are `short_hair, breasts, blue_eyes, white_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 | 67 | 56.53 MiB | [Download](https://huggingface.co/datasets/CyberHarem/farah_granbluefantasy/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 67 | 41.04 MiB | [Download](https://huggingface.co/datasets/CyberHarem/farah_granbluefantasy/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 132 | 76.57 MiB | [Download](https://huggingface.co/datasets/CyberHarem/farah_granbluefantasy/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 67 | 52.44 MiB | [Download](https://huggingface.co/datasets/CyberHarem/farah_granbluefantasy/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 132 | 93.67 MiB | [Download](https://huggingface.co/datasets/CyberHarem/farah_granbluefantasy/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/farah_granbluefantasy',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 9 |  |  |  |  |  | 1girl, solo, sword, looking_at_viewer, open_mouth, gauntlets, simple_background, blush, white_background, belt, black_pantyhose, breastplate, holding, shield, skirt |
| 1 | 10 |  |  |  |  |  | 1girl, open_mouth, solo, belt, bag, bike_shorts, shield, sword, :d, bare_shoulders, grey_eyes, knee_pads, looking_at_viewer, necklace, outdoors, sheath |
| 2 | 7 |  |  |  |  |  | 1girl, blush, nipples, open_mouth, 1boy, hetero, nude, small_breasts, solo_focus, navel, penis, censored, cum_in_pussy, purple_eyes, sex, tears |
| 3 | 5 |  |  |  |  |  | cleavage, navel, open_mouth, white_bikini, 1girl, smile, solo, jacket, looking_at_viewer, medium_breasts, ass_visible_through_thighs, bangs, bare_shoulders, blush, collarbone, day, front-tie_top, large_breasts, ocean, off_shoulder, open_clothes, purple_eyes |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | sword | looking_at_viewer | open_mouth | gauntlets | simple_background | blush | white_background | belt | black_pantyhose | breastplate | holding | shield | skirt | bag | bike_shorts | :d | bare_shoulders | grey_eyes | knee_pads | necklace | outdoors | sheath | nipples | 1boy | hetero | nude | small_breasts | solo_focus | navel | penis | censored | cum_in_pussy | purple_eyes | sex | tears | cleavage | white_bikini | smile | jacket | medium_breasts | ass_visible_through_thighs | bangs | collarbone | day | front-tie_top | large_breasts | ocean | off_shoulder | open_clothes |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------|:--------------------|:-------------|:------------|:--------------------|:--------|:-------------------|:-------|:------------------|:--------------|:----------|:---------|:--------|:------|:--------------|:-----|:-----------------|:------------|:------------|:-----------|:-----------|:---------|:----------|:-------|:---------|:-------|:----------------|:-------------|:--------|:--------|:-----------|:---------------|:--------------|:------|:--------|:-----------|:---------------|:--------|:---------|:-----------------|:-----------------------------|:--------|:-------------|:------|:----------------|:----------------|:--------|:---------------|:---------------|
| 0 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 10 |  |  |  |  |  | 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 | | | | | | | | | | | | | | |
| 3 | 5 |  |  |  |  |  | X | X | | X | X | | | X | | | | | | | | | | | X | | | | | | | | | | | | X | | | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
heliosprime/twitter_dataset_1712818760 | ---
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: 28927
num_examples: 73
download_size: 16405
dataset_size: 28927
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "twitter_dataset_1712818760"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
nlpso/m2m3_fine_tuning_ref_cmbert_io | ---
language:
- fr
multilinguality:
- monolingual
task_categories:
- token-classification
---
# m2m3_fine_tuning_ref_cmbert_io
## Introduction
This dataset was used to fine-tuned [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner) for **nested NER task** using Independant NER layers approach [M1].
It contains Paris trade directories entries from the 19th century.
## Dataset parameters
* Approachrd : M2 and M3
* Dataset type : ground-truth
* Tokenizer : [Jean-Baptiste/camembert-ner](https://huggingface.co/Jean-Baptiste/camembert-ner)
* Tagging format : IO
* Counts :
* Train : 6084
* Dev : 676
* Test : 1685
* Associated fine-tuned models :
* M2 : [nlpso/m2_joint_label_ref_cmbert_io](https://huggingface.co/nlpso/m2_joint_label_ref_cmbert_io)
* M3 : [nlpso/m3_hierarchical_ner_ref_cmbert_io](https://huggingface.co/nlpso/m3_hierarchical_ner_ref_cmbert_io)
## Entity types
Abbreviation|Entity group (level)|Description
-|-|-
O |1 & 2|Outside of a named entity
PER |1|Person or company name
ACT |1 & 2|Person or company professional activity
TITREH |2|Military or civil distinction
DESC |1|Entry full description
TITREP |2|Professionnal reward
SPAT |1|Address
LOC |2|Street name
CARDINAL |2|Street number
FT |2|Geographical feature
## How to use this dataset
```python
from datasets import load_dataset
train_dev_test = load_dataset("nlpso/m2m3_fine_tuning_ref_cmbert_io")
|
madhaviit/ft4 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 748903
num_examples: 1000
download_size: 184953
dataset_size: 748903
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
cnn_dailymail | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- summarization
task_ids:
- news-articles-summarization
paperswithcode_id: cnn-daily-mail-1
pretty_name: CNN / Daily Mail
dataset_info:
- config_name: 1.0.0
features:
- name: article
dtype: string
- name: highlights
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 1261703785
num_examples: 287113
- name: validation
num_bytes: 57732412
num_examples: 13368
- name: test
num_bytes: 49925732
num_examples: 11490
download_size: 836927248
dataset_size: 1369361929
- config_name: 2.0.0
features:
- name: article
dtype: string
- name: highlights
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 1261703785
num_examples: 287113
- name: validation
num_bytes: 57732412
num_examples: 13368
- name: test
num_bytes: 49925732
num_examples: 11490
download_size: 837094602
dataset_size: 1369361929
- config_name: 3.0.0
features:
- name: article
dtype: string
- name: highlights
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 1261703785
num_examples: 287113
- name: validation
num_bytes: 57732412
num_examples: 13368
- name: test
num_bytes: 49925732
num_examples: 11490
download_size: 837094602
dataset_size: 1369361929
configs:
- config_name: 1.0.0
data_files:
- split: train
path: 1.0.0/train-*
- split: validation
path: 1.0.0/validation-*
- split: test
path: 1.0.0/test-*
- config_name: 2.0.0
data_files:
- split: train
path: 2.0.0/train-*
- split: validation
path: 2.0.0/validation-*
- split: test
path: 2.0.0/test-*
- config_name: 3.0.0
data_files:
- split: train
path: 3.0.0/train-*
- split: validation
path: 3.0.0/validation-*
- split: test
path: 3.0.0/test-*
train-eval-index:
- config: 3.0.0
task: summarization
task_id: summarization
splits:
eval_split: test
col_mapping:
article: text
highlights: target
---
# Dataset Card for CNN Dailymail Dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:** [CNN / DailyMail Dataset repository](https://github.com/abisee/cnn-dailymail)
- **Paper:** [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://papers.nips.cc/paper/5945-teaching-machines-to-read-and-comprehend.pdf), [Get To The Point: Summarization with Pointer-Generator Networks](https://www.aclweb.org/anthology/K16-1028.pdf)
- **Leaderboard:** [Papers with Code leaderboard for CNN / Dailymail Dataset](https://paperswithcode.com/sota/document-summarization-on-cnn-daily-mail)
- **Point of Contact:** [Abigail See](mailto:abisee@stanford.edu)
### Dataset Summary
The CNN / DailyMail Dataset is an English-language dataset containing just over 300k unique news articles as written by journalists at CNN and the Daily Mail. The current version supports both extractive and abstractive summarization, though the original version was created for machine reading and comprehension and abstractive question answering.
### Supported Tasks and Leaderboards
- 'summarization': [Versions 2.0.0 and 3.0.0 of the CNN / DailyMail Dataset](https://www.aclweb.org/anthology/K16-1028.pdf) can be used to train a model for abstractive and extractive summarization ([Version 1.0.0](https://papers.nips.cc/paper/5945-teaching-machines-to-read-and-comprehend.pdf) was developed for machine reading and comprehension and abstractive question answering). The model performance is measured by how high the output summary's [ROUGE](https://huggingface.co/metrics/rouge) score for a given article is when compared to the highlight as written by the original article author. [Zhong et al (2020)](https://www.aclweb.org/anthology/2020.acl-main.552.pdf) report a ROUGE-1 score of 44.41 when testing a model trained for extractive summarization. See the [Papers With Code leaderboard](https://paperswithcode.com/sota/document-summarization-on-cnn-daily-mail) for more models.
### Languages
The BCP-47 code for English as generally spoken in the United States is en-US and the BCP-47 code for English as generally spoken in the United Kingdom is en-GB. It is unknown if other varieties of English are represented in the data.
## Dataset Structure
### Data Instances
For each instance, there is a string for the article, a string for the highlights, and a string for the id. See the [CNN / Daily Mail dataset viewer](https://huggingface.co/datasets/viewer/?dataset=cnn_dailymail&config=3.0.0) to explore more examples.
```
{'id': '0054d6d30dbcad772e20b22771153a2a9cbeaf62',
'article': '(CNN) -- An American woman died aboard a cruise ship that docked at Rio de Janeiro on Tuesday, the same ship on which 86 passengers previously fell ill, according to the state-run Brazilian news agency, Agencia Brasil. The American tourist died aboard the MS Veendam, owned by cruise operator Holland America. Federal Police told Agencia Brasil that forensic doctors were investigating her death. The ship's doctors told police that the woman was elderly and suffered from diabetes and hypertension, according the agency. The other passengers came down with diarrhea prior to her death during an earlier part of the trip, the ship's doctors said. The Veendam left New York 36 days ago for a South America tour.'
'highlights': 'The elderly woman suffered from diabetes and hypertension, ship's doctors say .\nPreviously, 86 passengers had fallen ill on the ship, Agencia Brasil says .'}
```
The average token count for the articles and the highlights are provided below:
| Feature | Mean Token Count |
| ---------- | ---------------- |
| Article | 781 |
| Highlights | 56 |
### Data Fields
- `id`: a string containing the heximal formated SHA1 hash of the url where the story was retrieved from
- `article`: a string containing the body of the news article
- `highlights`: a string containing the highlight of the article as written by the article author
### Data Splits
The CNN/DailyMail dataset has 3 splits: _train_, _validation_, and _test_. Below are the statistics for Version 3.0.0 of the dataset.
| Dataset Split | Number of Instances in Split |
| ------------- | ------------------------------------------- |
| Train | 287,113 |
| Validation | 13,368 |
| Test | 11,490 |
## Dataset Creation
### Curation Rationale
Version 1.0.0 aimed to support supervised neural methodologies for machine reading and question answering with a large amount of real natural language training data and released about 313k unique articles and nearly 1M Cloze style questions to go with the articles. Versions 2.0.0 and 3.0.0 changed the structure of the dataset to support summarization rather than question answering. Version 3.0.0 provided a non-anonymized version of the data, whereas both the previous versions were preprocessed to replace named entities with unique identifier labels.
### Source Data
#### Initial Data Collection and Normalization
The data consists of news articles and highlight sentences. In the question answering setting of the data, the articles are used as the context and entities are hidden one at a time in the highlight sentences, producing Cloze style questions where the goal of the model is to correctly guess which entity in the context has been hidden in the highlight. In the summarization setting, the highlight sentences are concatenated to form a summary of the article. The CNN articles were written between April 2007 and April 2015. The Daily Mail articles were written between June 2010 and April 2015.
The code for the original data collection is available at <https://github.com/deepmind/rc-data>. The articles were downloaded using archives of <www.cnn.com> and <www.dailymail.co.uk> on the Wayback Machine. Articles were not included in the Version 1.0.0 collection if they exceeded 2000 tokens. Due to accessibility issues with the Wayback Machine, Kyunghyun Cho has made the datasets available at <https://cs.nyu.edu/~kcho/DMQA/>. An updated version of the code that does not anonymize the data is available at <https://github.com/abisee/cnn-dailymail>.
Hermann et al provided their own tokenization script. The script provided by See uses the PTBTokenizer. It also lowercases the text and adds periods to lines missing them.
#### Who are the source language producers?
The text was written by journalists at CNN and the Daily Mail.
### Annotations
The dataset does not contain any additional annotations.
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
Version 3.0 is not anonymized, so individuals' names can be found in the dataset. Information about the original author is not included in the dataset.
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to help develop models that can summarize long paragraphs of text in one or two sentences.
This task is useful for efficiently presenting information given a large quantity of text. It should be made clear that any summarizations produced by models trained on this dataset are reflective of the language used in the articles, but are in fact automatically generated.
### Discussion of Biases
[Bordia and Bowman (2019)](https://www.aclweb.org/anthology/N19-3002.pdf) explore measuring gender bias and debiasing techniques in the CNN / Dailymail dataset, the Penn Treebank, and WikiText-2. They find the CNN / Dailymail dataset to have a slightly lower gender bias based on their metric compared to the other datasets, but still show evidence of gender bias when looking at words such as 'fragile'.
Because the articles were written by and for people in the US and the UK, they will likely present specifically US and UK perspectives and feature events that are considered relevant to those populations during the time that the articles were published.
### Other Known Limitations
News articles have been shown to conform to writing conventions in which important information is primarily presented in the first third of the article [(Kryściński et al, 2019)](https://www.aclweb.org/anthology/D19-1051.pdf). [Chen et al (2016)](https://www.aclweb.org/anthology/P16-1223.pdf) conducted a manual study of 100 random instances of the first version of the dataset and found 25% of the samples to be difficult even for humans to answer correctly due to ambiguity and coreference errors.
It should also be noted that machine-generated summarizations, even when extractive, may differ in truth values when compared to the original articles.
## Additional Information
### Dataset Curators
The data was originally collected by Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom of Google DeepMind. Tomáš Kočiský and Phil Blunsom are also affiliated with the University of Oxford. They released scripts to collect and process the data into the question answering format.
Ramesh Nallapati, Bowen Zhou, Cicero dos Santos, and Bing Xiang of IMB Watson and Çağlar Gu̇lçehre of Université de Montréal modified Hermann et al's collection scripts to restore the data to a summary format. They also produced both anonymized and non-anonymized versions.
The code for the non-anonymized version is made publicly available by Abigail See of Stanford University, Peter J. Liu of Google Brain and Christopher D. Manning of Stanford University at <https://github.com/abisee/cnn-dailymail>. The work at Stanford University was supported by the DARPA DEFT ProgramAFRL contract no. FA8750-13-2-0040.
### Licensing Information
The CNN / Daily Mail dataset version 1.0.0 is released under the [Apache-2.0 License](http://www.apache.org/licenses/LICENSE-2.0).
### Citation Information
```
@inproceedings{see-etal-2017-get,
title = "Get To The Point: Summarization with Pointer-Generator Networks",
author = "See, Abigail and
Liu, Peter J. and
Manning, Christopher D.",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P17-1099",
doi = "10.18653/v1/P17-1099",
pages = "1073--1083",
abstract = "Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text). However, these models have two shortcomings: they are liable to reproduce factual details inaccurately, and they tend to repeat themselves. In this work we propose a novel architecture that augments the standard sequence-to-sequence attentional model in two orthogonal ways. First, we use a hybrid pointer-generator network that can copy words from the source text via pointing, which aids accurate reproduction of information, while retaining the ability to produce novel words through the generator. Second, we use coverage to keep track of what has been summarized, which discourages repetition. We apply our model to the CNN / Daily Mail summarization task, outperforming the current abstractive state-of-the-art by at least 2 ROUGE points.",
}
```
```
@inproceedings{DBLP:conf/nips/HermannKGEKSB15,
author={Karl Moritz Hermann and Tomás Kociský and Edward Grefenstette and Lasse Espeholt and Will Kay and Mustafa Suleyman and Phil Blunsom},
title={Teaching Machines to Read and Comprehend},
year={2015},
cdate={1420070400000},
pages={1693-1701},
url={http://papers.nips.cc/paper/5945-teaching-machines-to-read-and-comprehend},
booktitle={NIPS},
crossref={conf/nips/2015}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@jplu](https://github.com/jplu), [@jbragg](https://github.com/jbragg), [@patrickvonplaten](https://github.com/patrickvonplaten) and [@mcmillanmajora](https://github.com/mcmillanmajora) for adding this dataset. |
woctordho/img-256-photo-2 | ---
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 12133208417.44
num_examples: 996698
download_size: 11930597168
dataset_size: 12133208417.44
---
# Dataset Card for "img-256-photo-2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
FSMBench/fsmbench_what_will_be_the_state_separated | ---
dataset_info:
features:
- name: query_id
dtype: string
- name: fsm_id
dtype: string
- name: fsm_json
dtype: string
- name: difficulty_level
dtype: int64
- name: transition_matrix
dtype: string
- name: query
dtype: string
- name: answer
dtype: string
- name: substring_index
dtype: int64
splits:
- name: validation
num_bytes: 15316547
num_examples: 9425
download_size: 792389
dataset_size: 15316547
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
|
myradeng/cosine_similarities_diffusion_db_dedup_from10k_train_v2 | ---
dataset_info:
features:
- name: sentence1
dtype: float64
- name: sentence2
dtype: float64
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dtype: float64
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---
# Dataset Card for "cosine_similarities_diffusion_db_dedup_from10k_train_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/matoba_risa_idolmastercinderellagirls | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of matoba_risa/的場梨沙 (THE iDOLM@STER: Cinderella Girls)
This is the dataset of matoba_risa/的場梨沙 (THE iDOLM@STER: Cinderella Girls), containing 500 images and their tags.
The core tags of this character are `long_hair, black_hair, twintails, yellow_eyes, bangs, ribbon, hair_between_eyes, hair_ribbon, breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 642.50 MiB | [Download](https://huggingface.co/datasets/CyberHarem/matoba_risa_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 362.74 MiB | [Download](https://huggingface.co/datasets/CyberHarem/matoba_risa_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1213 | 785.83 MiB | [Download](https://huggingface.co/datasets/CyberHarem/matoba_risa_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 564.79 MiB | [Download](https://huggingface.co/datasets/CyberHarem/matoba_risa_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1213 | 1.12 GiB | [Download](https://huggingface.co/datasets/CyberHarem/matoba_risa_idolmastercinderellagirls/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/matoba_risa_idolmastercinderellagirls',
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 | 47 |  |  |  |  |  | 1girl, leopard_print, bare_shoulders, looking_at_viewer, solo, blush, midriff, pink_skirt, off_shoulder, camisole, pink_jacket, navel, open_mouth, simple_background, collarbone, heart_necklace, white_background, open_jacket, strap_slip, very_long_hair, long_sleeves, small_breasts, pleated_skirt, sidelocks, :d |
| 1 | 9 |  |  |  |  |  | 1girl, hair_bow, looking_at_viewer, solo, bare_shoulders, smile, black_gloves, bracelet, crop_top, midriff, navel, shirt, sleeveless, elbow_gloves, fingerless_gloves, heart, miniskirt, white_background, black_bowtie, earrings, red_skirt, sidelocks, simple_background |
| 2 | 11 |  |  |  |  |  | 1girl, blush, solo, white_shirt, very_long_hair, white_headwear, beret, looking_at_viewer, open_mouth, pleated_skirt, red_sailor_collar, red_skirt, serafuku, necktie, puffy_short_sleeves, red_ribbon, standing, suspender_skirt, :d, backpack, sidelocks |
| 3 | 7 |  |  |  |  |  | 1girl, bare_shoulders, black_leotard, detached_collar, fake_animal_ears, looking_at_viewer, playboy_bunny, rabbit_ears, small_breasts, solo, strapless_leotard, black_bowtie, simple_background, white_background, wrist_cuffs, blush, smile, black_pantyhose, leopard_print, open_mouth, ;d, black_footwear, brown_pantyhose, covered_navel, hairband, high_heels, one_eye_closed, sitting |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | leopard_print | bare_shoulders | looking_at_viewer | solo | blush | midriff | pink_skirt | off_shoulder | camisole | pink_jacket | navel | open_mouth | simple_background | collarbone | heart_necklace | white_background | open_jacket | strap_slip | very_long_hair | long_sleeves | small_breasts | pleated_skirt | sidelocks | :d | hair_bow | smile | black_gloves | bracelet | crop_top | shirt | sleeveless | elbow_gloves | fingerless_gloves | heart | miniskirt | black_bowtie | earrings | red_skirt | white_shirt | white_headwear | beret | red_sailor_collar | serafuku | necktie | puffy_short_sleeves | red_ribbon | standing | suspender_skirt | backpack | black_leotard | detached_collar | fake_animal_ears | playboy_bunny | rabbit_ears | strapless_leotard | wrist_cuffs | black_pantyhose | ;d | black_footwear | brown_pantyhose | covered_navel | hairband | high_heels | one_eye_closed | sitting |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:----------------|:-----------------|:--------------------|:-------|:--------|:----------|:-------------|:---------------|:-----------|:--------------|:--------|:-------------|:--------------------|:-------------|:-----------------|:-------------------|:--------------|:-------------|:-----------------|:---------------|:----------------|:----------------|:------------|:-----|:-----------|:--------|:---------------|:-----------|:-----------|:--------|:-------------|:---------------|:--------------------|:--------|:------------|:---------------|:-----------|:------------|:--------------|:-----------------|:--------|:--------------------|:-----------|:----------|:----------------------|:-------------|:-----------|:------------------|:-----------|:----------------|:------------------|:-------------------|:----------------|:--------------|:--------------------|:--------------|:------------------|:-----|:-----------------|:------------------|:----------------|:-----------|:-------------|:-----------------|:----------|
| 0 | 47 |  |  |  |  |  | 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 | 9 |  |  |  |  |  | X | | X | X | X | | X | | | | | X | | X | | | X | | | | | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 11 |  |  |  |  |  | X | | | X | X | X | | | | | | | X | | | | | | | 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 | X | X | X | X | X | X | X | X | X |
|
albertvillanova/tmp-10 | ---
language:
- ase
- en
--- |
daqc/wikihow_es_80train_20test_gemma_2b_it_format | ---
dataset_info:
features:
- name: INSTRUCTION
dtype: string
- name: RESPONSE
struct:
- name: respuesta_abstracta
dtype: string
- name: respuesta_corta
dtype: string
- name: respuesta_detallada
dtype: string
splits:
- name: train
num_bytes: 245622628
num_examples: 90528
- name: test
num_bytes: 61565860
num_examples: 22632
download_size: 177673434
dataset_size: 307188488
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
Jayabalambika/Handgrid | ---
license: mit
---
|
akanksha8618/hackathon_pil_v2 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 93369.0
num_examples: 3
download_size: 93939
dataset_size: 93369.0
---
# Dataset Card for "hackathon_pil_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_nbeerbower__flammen3 | ---
pretty_name: Evaluation run of nbeerbower/flammen3
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [nbeerbower/flammen3](https://huggingface.co/nbeerbower/flammen3) 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_nbeerbower__flammen3\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-07T20:24:57.885609](https://huggingface.co/datasets/open-llm-leaderboard/details_nbeerbower__flammen3/blob/main/results_2024-03-07T20-24-57.885609.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\": 0.6534229755906791,\n\
\ \"acc_stderr\": 0.03213943598115069,\n \"acc_norm\": 0.6530805931241076,\n\
\ \"acc_norm_stderr\": 0.03280639331373141,\n \"mc1\": 0.5458996328029376,\n\
\ \"mc1_stderr\": 0.017429593091323515,\n \"mc2\": 0.7069016208042257,\n\
\ \"mc2_stderr\": 0.014614874700665928\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6928327645051194,\n \"acc_stderr\": 0.013481034054980943,\n\
\ \"acc_norm\": 0.7081911262798635,\n \"acc_norm_stderr\": 0.013284525292403516\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7038438558056164,\n\
\ \"acc_stderr\": 0.004556276293751936,\n \"acc_norm\": 0.8798048197570205,\n\
\ \"acc_norm_stderr\": 0.0032452503945652944\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n\
\ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6370370370370371,\n\
\ \"acc_stderr\": 0.04153948404742398,\n \"acc_norm\": 0.6370370370370371,\n\
\ \"acc_norm_stderr\": 0.04153948404742398\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\": 0.63,\n\
\ \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.63,\n \
\ \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6792452830188679,\n \"acc_stderr\": 0.02872750295788027,\n\
\ \"acc_norm\": 0.6792452830188679,\n \"acc_norm_stderr\": 0.02872750295788027\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7569444444444444,\n\
\ \"acc_stderr\": 0.03586879280080341,\n \"acc_norm\": 0.7569444444444444,\n\
\ \"acc_norm_stderr\": 0.03586879280080341\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \
\ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.56,\n \"acc_stderr\": 0.049888765156985884,\n \"acc_norm\"\
: 0.56,\n \"acc_norm_stderr\": 0.049888765156985884\n },\n \"harness|hendrycksTest-college_mathematics|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-college_medicine|5\": {\n \"acc\": 0.6705202312138728,\n\
\ \"acc_stderr\": 0.03583901754736412,\n \"acc_norm\": 0.6705202312138728,\n\
\ \"acc_norm_stderr\": 0.03583901754736412\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.04913595201274498,\n\
\ \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.04913595201274498\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.76,\n \"acc_stderr\": 0.04292346959909284,\n \"acc_norm\": 0.76,\n\
\ \"acc_norm_stderr\": 0.04292346959909284\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5617021276595745,\n \"acc_stderr\": 0.03243618636108101,\n\
\ \"acc_norm\": 0.5617021276595745,\n \"acc_norm_stderr\": 0.03243618636108101\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5,\n\
\ \"acc_stderr\": 0.047036043419179864,\n \"acc_norm\": 0.5,\n \
\ \"acc_norm_stderr\": 0.047036043419179864\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5724137931034483,\n \"acc_stderr\": 0.04122737111370332,\n\
\ \"acc_norm\": 0.5724137931034483,\n \"acc_norm_stderr\": 0.04122737111370332\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.42328042328042326,\n \"acc_stderr\": 0.025446365634406783,\n \"\
acc_norm\": 0.42328042328042326,\n \"acc_norm_stderr\": 0.025446365634406783\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.46825396825396826,\n\
\ \"acc_stderr\": 0.04463112720677171,\n \"acc_norm\": 0.46825396825396826,\n\
\ \"acc_norm_stderr\": 0.04463112720677171\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252604,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252604\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7903225806451613,\n\
\ \"acc_stderr\": 0.023157879349083522,\n \"acc_norm\": 0.7903225806451613,\n\
\ \"acc_norm_stderr\": 0.023157879349083522\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.4876847290640394,\n \"acc_stderr\": 0.035169204442208966,\n\
\ \"acc_norm\": 0.4876847290640394,\n \"acc_norm_stderr\": 0.035169204442208966\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\"\
: 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7818181818181819,\n \"acc_stderr\": 0.03225078108306289,\n\
\ \"acc_norm\": 0.7818181818181819,\n \"acc_norm_stderr\": 0.03225078108306289\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7777777777777778,\n \"acc_stderr\": 0.029620227874790486,\n \"\
acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.029620227874790486\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8860103626943006,\n \"acc_stderr\": 0.022935144053919443,\n\
\ \"acc_norm\": 0.8860103626943006,\n \"acc_norm_stderr\": 0.022935144053919443\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.023901157979402534,\n\
\ \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.023901157979402534\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.34814814814814815,\n \"acc_stderr\": 0.029045600290616255,\n \
\ \"acc_norm\": 0.34814814814814815,\n \"acc_norm_stderr\": 0.029045600290616255\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6890756302521008,\n \"acc_stderr\": 0.03006676158297793,\n \
\ \"acc_norm\": 0.6890756302521008,\n \"acc_norm_stderr\": 0.03006676158297793\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3708609271523179,\n \"acc_stderr\": 0.03943966699183629,\n \"\
acc_norm\": 0.3708609271523179,\n \"acc_norm_stderr\": 0.03943966699183629\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8348623853211009,\n \"acc_stderr\": 0.015919557829976033,\n \"\
acc_norm\": 0.8348623853211009,\n \"acc_norm_stderr\": 0.015919557829976033\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\"\
: 0.8235294117647058,\n \"acc_stderr\": 0.02675640153807896,\n \"\
acc_norm\": 0.8235294117647058,\n \"acc_norm_stderr\": 0.02675640153807896\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8185654008438819,\n \"acc_stderr\": 0.02508596114457966,\n \
\ \"acc_norm\": 0.8185654008438819,\n \"acc_norm_stderr\": 0.02508596114457966\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6860986547085202,\n\
\ \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.6860986547085202,\n\
\ \"acc_norm_stderr\": 0.031146796482972465\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\": 0.03896878985070416,\n \"\
acc_norm\": 0.7603305785123967,\n \"acc_norm_stderr\": 0.03896878985070416\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7870370370370371,\n\
\ \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.7870370370370371,\n\
\ \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n\
\ \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4375,\n\
\ \"acc_stderr\": 0.04708567521880525,\n \"acc_norm\": 0.4375,\n \
\ \"acc_norm_stderr\": 0.04708567521880525\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8058252427184466,\n \"acc_stderr\": 0.03916667762822584,\n\
\ \"acc_norm\": 0.8058252427184466,\n \"acc_norm_stderr\": 0.03916667762822584\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8803418803418803,\n\
\ \"acc_stderr\": 0.021262719400406964,\n \"acc_norm\": 0.8803418803418803,\n\
\ \"acc_norm_stderr\": 0.021262719400406964\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8275862068965517,\n\
\ \"acc_stderr\": 0.013507943909371803,\n \"acc_norm\": 0.8275862068965517,\n\
\ \"acc_norm_stderr\": 0.013507943909371803\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7225433526011561,\n \"acc_stderr\": 0.024105712607754307,\n\
\ \"acc_norm\": 0.7225433526011561,\n \"acc_norm_stderr\": 0.024105712607754307\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.43575418994413406,\n\
\ \"acc_stderr\": 0.016583881958602394,\n \"acc_norm\": 0.43575418994413406,\n\
\ \"acc_norm_stderr\": 0.016583881958602394\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7287581699346405,\n \"acc_stderr\": 0.02545775669666788,\n\
\ \"acc_norm\": 0.7287581699346405,\n \"acc_norm_stderr\": 0.02545775669666788\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7170418006430869,\n\
\ \"acc_stderr\": 0.025583062489984813,\n \"acc_norm\": 0.7170418006430869,\n\
\ \"acc_norm_stderr\": 0.025583062489984813\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7561728395061729,\n \"acc_stderr\": 0.023891879541959607,\n\
\ \"acc_norm\": 0.7561728395061729,\n \"acc_norm_stderr\": 0.023891879541959607\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.48936170212765956,\n \"acc_stderr\": 0.029820747191422473,\n \
\ \"acc_norm\": 0.48936170212765956,\n \"acc_norm_stderr\": 0.029820747191422473\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4771838331160365,\n\
\ \"acc_stderr\": 0.012756933382823698,\n \"acc_norm\": 0.4771838331160365,\n\
\ \"acc_norm_stderr\": 0.012756933382823698\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.028418208619406755,\n\
\ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.028418208619406755\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6764705882352942,\n \"acc_stderr\": 0.018926082916083383,\n \
\ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.018926082916083383\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7,\n\
\ \"acc_stderr\": 0.04389311454644287,\n \"acc_norm\": 0.7,\n \
\ \"acc_norm_stderr\": 0.04389311454644287\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.746938775510204,\n \"acc_stderr\": 0.027833023871399673,\n\
\ \"acc_norm\": 0.746938775510204,\n \"acc_norm_stderr\": 0.027833023871399673\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8308457711442786,\n\
\ \"acc_stderr\": 0.02650859065623327,\n \"acc_norm\": 0.8308457711442786,\n\
\ \"acc_norm_stderr\": 0.02650859065623327\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.85,\n \"acc_stderr\": 0.0358870281282637,\n \
\ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.0358870281282637\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5542168674698795,\n\
\ \"acc_stderr\": 0.03869543323472101,\n \"acc_norm\": 0.5542168674698795,\n\
\ \"acc_norm_stderr\": 0.03869543323472101\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\": 0.5458996328029376,\n\
\ \"mc1_stderr\": 0.017429593091323515,\n \"mc2\": 0.7069016208042257,\n\
\ \"mc2_stderr\": 0.014614874700665928\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8216258879242304,\n \"acc_stderr\": 0.010759352014855934\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6967399545109931,\n \
\ \"acc_stderr\": 0.012661502663418697\n }\n}\n```"
repo_url: https://huggingface.co/nbeerbower/flammen3
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_07T20_24_57.885609
path:
- '**/details_harness|arc:challenge|25_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|gsm8k|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hellaswag|10_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-07T20-24-57.885609.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-07T20-24-57.885609.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- '**/details_harness|winogrande|5_2024-03-07T20-24-57.885609.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-07T20-24-57.885609.parquet'
- config_name: results
data_files:
- split: 2024_03_07T20_24_57.885609
path:
- results_2024-03-07T20-24-57.885609.parquet
- split: latest
path:
- results_2024-03-07T20-24-57.885609.parquet
---
# Dataset Card for Evaluation run of nbeerbower/flammen3
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [nbeerbower/flammen3](https://huggingface.co/nbeerbower/flammen3) 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_nbeerbower__flammen3",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-07T20:24:57.885609](https://huggingface.co/datasets/open-llm-leaderboard/details_nbeerbower__flammen3/blob/main/results_2024-03-07T20-24-57.885609.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": 0.6534229755906791,
"acc_stderr": 0.03213943598115069,
"acc_norm": 0.6530805931241076,
"acc_norm_stderr": 0.03280639331373141,
"mc1": 0.5458996328029376,
"mc1_stderr": 0.017429593091323515,
"mc2": 0.7069016208042257,
"mc2_stderr": 0.014614874700665928
},
"harness|arc:challenge|25": {
"acc": 0.6928327645051194,
"acc_stderr": 0.013481034054980943,
"acc_norm": 0.7081911262798635,
"acc_norm_stderr": 0.013284525292403516
},
"harness|hellaswag|10": {
"acc": 0.7038438558056164,
"acc_stderr": 0.004556276293751936,
"acc_norm": 0.8798048197570205,
"acc_norm_stderr": 0.0032452503945652944
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.37,
"acc_stderr": 0.048523658709391,
"acc_norm": 0.37,
"acc_norm_stderr": 0.048523658709391
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6370370370370371,
"acc_stderr": 0.04153948404742398,
"acc_norm": 0.6370370370370371,
"acc_norm_stderr": 0.04153948404742398
},
"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": 0.63,
"acc_stderr": 0.04852365870939099,
"acc_norm": 0.63,
"acc_norm_stderr": 0.04852365870939099
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6792452830188679,
"acc_stderr": 0.02872750295788027,
"acc_norm": 0.6792452830188679,
"acc_norm_stderr": 0.02872750295788027
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7569444444444444,
"acc_stderr": 0.03586879280080341,
"acc_norm": 0.7569444444444444,
"acc_norm_stderr": 0.03586879280080341
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.47,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.47,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.56,
"acc_stderr": 0.049888765156985884,
"acc_norm": 0.56,
"acc_norm_stderr": 0.049888765156985884
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6705202312138728,
"acc_stderr": 0.03583901754736412,
"acc_norm": 0.6705202312138728,
"acc_norm_stderr": 0.03583901754736412
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4215686274509804,
"acc_stderr": 0.04913595201274498,
"acc_norm": 0.4215686274509804,
"acc_norm_stderr": 0.04913595201274498
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.76,
"acc_stderr": 0.04292346959909284,
"acc_norm": 0.76,
"acc_norm_stderr": 0.04292346959909284
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5617021276595745,
"acc_stderr": 0.03243618636108101,
"acc_norm": 0.5617021276595745,
"acc_norm_stderr": 0.03243618636108101
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5,
"acc_stderr": 0.047036043419179864,
"acc_norm": 0.5,
"acc_norm_stderr": 0.047036043419179864
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5724137931034483,
"acc_stderr": 0.04122737111370332,
"acc_norm": 0.5724137931034483,
"acc_norm_stderr": 0.04122737111370332
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.42328042328042326,
"acc_stderr": 0.025446365634406783,
"acc_norm": 0.42328042328042326,
"acc_norm_stderr": 0.025446365634406783
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.46825396825396826,
"acc_stderr": 0.04463112720677171,
"acc_norm": 0.46825396825396826,
"acc_norm_stderr": 0.04463112720677171
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.33,
"acc_stderr": 0.04725815626252604,
"acc_norm": 0.33,
"acc_norm_stderr": 0.04725815626252604
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7903225806451613,
"acc_stderr": 0.023157879349083522,
"acc_norm": 0.7903225806451613,
"acc_norm_stderr": 0.023157879349083522
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.4876847290640394,
"acc_stderr": 0.035169204442208966,
"acc_norm": 0.4876847290640394,
"acc_norm_stderr": 0.035169204442208966
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.69,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.69,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7818181818181819,
"acc_stderr": 0.03225078108306289,
"acc_norm": 0.7818181818181819,
"acc_norm_stderr": 0.03225078108306289
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.029620227874790486,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.029620227874790486
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8860103626943006,
"acc_stderr": 0.022935144053919443,
"acc_norm": 0.8860103626943006,
"acc_norm_stderr": 0.022935144053919443
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6666666666666666,
"acc_stderr": 0.023901157979402534,
"acc_norm": 0.6666666666666666,
"acc_norm_stderr": 0.023901157979402534
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.34814814814814815,
"acc_stderr": 0.029045600290616255,
"acc_norm": 0.34814814814814815,
"acc_norm_stderr": 0.029045600290616255
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6890756302521008,
"acc_stderr": 0.03006676158297793,
"acc_norm": 0.6890756302521008,
"acc_norm_stderr": 0.03006676158297793
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3708609271523179,
"acc_stderr": 0.03943966699183629,
"acc_norm": 0.3708609271523179,
"acc_norm_stderr": 0.03943966699183629
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8348623853211009,
"acc_stderr": 0.015919557829976033,
"acc_norm": 0.8348623853211009,
"acc_norm_stderr": 0.015919557829976033
},
"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": 0.8235294117647058,
"acc_stderr": 0.02675640153807896,
"acc_norm": 0.8235294117647058,
"acc_norm_stderr": 0.02675640153807896
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8185654008438819,
"acc_stderr": 0.02508596114457966,
"acc_norm": 0.8185654008438819,
"acc_norm_stderr": 0.02508596114457966
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6860986547085202,
"acc_stderr": 0.031146796482972465,
"acc_norm": 0.6860986547085202,
"acc_norm_stderr": 0.031146796482972465
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7938931297709924,
"acc_stderr": 0.03547771004159465,
"acc_norm": 0.7938931297709924,
"acc_norm_stderr": 0.03547771004159465
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7603305785123967,
"acc_stderr": 0.03896878985070416,
"acc_norm": 0.7603305785123967,
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},
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"acc": 0.6967399545109931,
"acc_stderr": 0.012661502663418697
}
}
```
## 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] |
benayas/atis_artificial_10pct_v1 | ---
dataset_info:
features:
- name: text
dtype: string
- name: category
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 415156
num_examples: 4455
download_size: 136362
dataset_size: 415156
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
alexandrainst/m_hellaswag | ---
configs:
- config_name: ar
data_files:
- split: val
path: data/ar/val.jsonl
- config_name: bn
data_files:
- split: val
path: data/bn/val.jsonl
- config_name: ca
data_files:
- split: val
path: data/ca/val.jsonl
- config_name: da
data_files:
- split: val
path: data/da/val.jsonl
- config_name: de
data_files:
- split: val
path: data/de/val.jsonl
- config_name: es
data_files:
- split: val
path: data/es/val.jsonl
- config_name: eu
data_files:
- split: val
path: data/eu/val.jsonl
- config_name: fr
data_files:
- split: val
path: data/fr/val.jsonl
- config_name: gu
data_files:
- split: val
path: data/gu/val.jsonl
- config_name: hi
data_files:
- split: val
path: data/hi/val.jsonl
- config_name: hr
data_files:
- split: val
path: data/hr/val.jsonl
- config_name: hu
data_files:
- split: val
path: data/hu/val.jsonl
- config_name: hy
data_files:
- split: val
path: data/hy/val.jsonl
- config_name: id
data_files:
- split: val
path: data/id/val.jsonl
- config_name: it
data_files:
- split: val
path: data/it/val.jsonl
- config_name: kn
data_files:
- split: val
path: data/kn/val.jsonl
- config_name: ml
data_files:
- split: val
path: data/ml/val.jsonl
- config_name: mr
data_files:
- split: val
path: data/mr/val.jsonl
- config_name: ne
data_files:
- split: val
path: data/ne/val.jsonl
- config_name: nl
data_files:
- split: val
path: data/nl/val.jsonl
- config_name: pt
data_files:
- split: val
path: data/pt/val.jsonl
- config_name: ro
data_files:
- split: val
path: data/ro/val.jsonl
- config_name: ru
data_files:
- split: val
path: data/ru/val.jsonl
- config_name: sk
data_files:
- split: val
path: data/sk/val.jsonl
- config_name: sr
data_files:
- split: val
path: data/sr/val.jsonl
- config_name: sv
data_files:
- split: val
path: data/sv/val.jsonl
- config_name: ta
data_files:
- split: val
path: data/ta/val.jsonl
- config_name: te
data_files:
- split: val
path: data/te/val.jsonl
- config_name: uk
data_files:
- split: val
path: data/uk/val.jsonl
- config_name: vi
data_files:
- split: val
path: data/vi/val.jsonl
- config_name: zh
data_files:
- split: val
path: data/zh/val.jsonl
- config_name: en
data_files:
- split: val
path: data/en/val.jsonl
- config_name: is
data_files:
- split: val
path: data/is/val.jsonl
- config_name: nb
data_files:
- split: val
path: data/nb/val.jsonl
license: cc-by-nc-4.0
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
size_categories:
- 10K<n<100K
language:
- ar
- bn
- ca
- da
- de
- es
- eu
- fr
- gu
- hi
- hr
- hu
- hy
- id
- it
- kn
- ml
- mr
- ne
- nl
- pt
- ro
- ru
- sk
- sr
- sv
- ta
- te
- uk
- vi
- zh
- is
- en
- 'no'
- nb
---
# Multilingual HellaSwag
## Dataset Summary
This dataset is a machine translated version of the [HellaSwag dataset](https://huggingface.co/datasets/Rowan/hellaswag).
The Icelandic (is) part was translated with [Miðeind](https://mideind.is/english.html)'s Greynir model and Norwegian (nb) was translated with [DeepL](https://deepl.com/). The rest of the languages was translated using GPT-3.5-turbo by the University of Oregon, and this part of the dataset was originally uploaded to [this Github repository](https://github.com/nlp-uoregon/mlmm-evaluation). |
fathyshalab/massive_music-de-DE | ---
dataset_info:
features:
- name: id
dtype: string
- name: locale
dtype: string
- name: partition
dtype: string
- name: scenario
dtype:
class_label:
names:
'0': social
'1': transport
'2': calendar
'3': play
'4': news
'5': datetime
'6': recommendation
'7': email
'8': iot
'9': general
'10': audio
'11': lists
'12': qa
'13': cooking
'14': takeaway
'15': music
'16': alarm
'17': weather
- name: intent
dtype:
class_label:
names:
'0': datetime_query
'1': iot_hue_lightchange
'2': transport_ticket
'3': takeaway_query
'4': qa_stock
'5': general_greet
'6': recommendation_events
'7': music_dislikeness
'8': iot_wemo_off
'9': cooking_recipe
'10': qa_currency
'11': transport_traffic
'12': general_quirky
'13': weather_query
'14': audio_volume_up
'15': email_addcontact
'16': takeaway_order
'17': email_querycontact
'18': iot_hue_lightup
'19': recommendation_locations
'20': play_audiobook
'21': lists_createoradd
'22': news_query
'23': alarm_query
'24': iot_wemo_on
'25': general_joke
'26': qa_definition
'27': social_query
'28': music_settings
'29': audio_volume_other
'30': calendar_remove
'31': iot_hue_lightdim
'32': calendar_query
'33': email_sendemail
'34': iot_cleaning
'35': audio_volume_down
'36': play_radio
'37': cooking_query
'38': datetime_convert
'39': qa_maths
'40': iot_hue_lightoff
'41': iot_hue_lighton
'42': transport_query
'43': music_likeness
'44': email_query
'45': play_music
'46': audio_volume_mute
'47': social_post
'48': alarm_set
'49': qa_factoid
'50': calendar_set
'51': play_game
'52': alarm_remove
'53': lists_remove
'54': transport_taxi
'55': recommendation_movies
'56': iot_coffee
'57': music_query
'58': play_podcasts
'59': lists_query
- name: text
dtype: string
- name: annot_utt
dtype: string
- name: worker_id
dtype: string
- name: slot_method
sequence:
- name: slot
dtype: string
- name: method
dtype: string
- name: judgments
sequence:
- name: worker_id
dtype: string
- name: intent_score
dtype: int8
- name: slots_score
dtype: int8
- name: grammar_score
dtype: int8
- name: spelling_score
dtype: int8
- name: language_identification
dtype: string
- name: label_name
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 86249
num_examples: 332
- name: validation
num_bytes: 14803
num_examples: 56
- name: test
num_bytes: 20685
num_examples: 81
download_size: 53750
dataset_size: 121737
---
# Dataset Card for "massive_music-de-DE"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Multimodal-Fatima/OxfordPets_test_facebook_opt_350m_Attributes_Caption_ns_3669_random | ---
dataset_info:
features:
- name: id
dtype: int64
- name: image
dtype: image
- name: prompt
dtype: string
- name: true_label
dtype: string
- name: prediction
dtype: string
- name: scores
sequence: float64
splits:
- name: fewshot_1_bs_16
num_bytes: 122169585.375
num_examples: 3669
- name: fewshot_3_bs_16
num_bytes: 124212797.375
num_examples: 3669
download_size: 241370285
dataset_size: 246382382.75
---
# Dataset Card for "OxfordPets_test_facebook_opt_350m_Attributes_Caption_ns_3669_random"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ErenHali/disaster_edited | ---
license: afl-3.0
---
annotations_creators:
- expert-generated
language_creators:
- found
languages:
- en
licenses:
- mit
multilinguality:
- monolingual
paperswithcode_id: acronym-identification
pretty_name: disaster
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids: [] |
archittiwari365/hello | ---
license: afl-3.0
---
|
zefang-liu/cve-and-cwe-mapping-dataset | ---
license: cc-by-nc-sa-4.0
task_categories:
- text-classification
language:
- en
size_categories:
- 100K<n<1M
---
# CVE and CWE Mapping Dataset
This Hugging Face dataset is a partial copy of the 'CVE and CWE mapping Dataset (2021)' from Kaggle, featuring 'Global_Dataset.csv' originally as 'Global_Dataset.xlsx'. Created by [Kirushikesh DB](https://www.kaggle.com/krooz0) and shared under [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/), it includes CVE data up to 2021 for cybersecurity research. For full details and licensing, visit the [original Kaggle page](https://www.kaggle.com/datasets/krooz0/cve-and-cwe-mapping-dataset).
For further information, please review the [CVE Terms of Use](https://www.cve.org/Legal/TermsOfUse) and the [NVD Terms of Use](https://nvd.nist.gov/developers/terms-of-use). |
TaylorAI/rlcd | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 145740702
num_examples: 167999
download_size: 86967331
dataset_size: 145740702
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "rlcd"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
BramVanroy/alpaca-cleaned-dutch | ---
language:
- nl
license: cc-by-nc-4.0
size_categories:
- 10K<n<100K
task_categories:
- question-answering
- text-generation
pretty_name: Alpaca Cleaned Dutch
tags:
- alpaca
- instruct
- instruction
dataset_info:
features:
- name: prompt
dtype: string
- name: prompt_id
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train_sft
num_bytes: 26762446
num_examples: 46163
- name: test_sft
num_bytes: 2942031
num_examples: 5132
download_size: 18382591
dataset_size: 29704477
configs:
- config_name: default
data_files:
- split: train_sft
path: data/train_sft-*
- split: test_sft
path: data/test_sft-*
---
# Dataset Card for Alpaca Cleaned Dutch
## Dataset Description
- **Homepage:** N/A
- **Repository:** N/A
- **Paper:** N/A
- **Leaderboard:** N/A
- **Point of Contact:** Bram Vanroy
### Dataset Summary
This dataset contains 51,712 conversations between een AI assistant and a (fake) "Human" (generated) in Dutch. They are translations of [Alpaca Cleaned Dataset](https://huggingface.co/datasets/yahma/alpaca-cleaned).
☕ [**Want to help me out?**](https://www.buymeacoffee.com/bramvanroy) Translating the data with the OpenAI API, and prompt testing, cost me 💸$57.99💸. If you like this dataset, please consider [buying me a coffee](https://www.buymeacoffee.com/bramvanroy) to offset a portion of this cost, I appreciate it a lot! ☕
If you use this dataset or refer to it, please use the following citation:
Vanroy, B. (2023). *Language Resources for Dutch Large Language Modelling*. [https://arxiv.org/abs/2312.12852](https://arxiv.org/abs/2312.12852)
```bibtext
@article{vanroy2023language,
title={Language Resources for {Dutch} Large Language Modelling},
author={Vanroy, Bram},
journal={arXiv preprint arXiv:2312.12852},
year={2023}
}
```
### Languages
- Dutch
## Dataset Structure
### Data Instances
```python
{
'id': 7,
'instruction': 'Leg uit waarom de volgende breuk gelijk is aan 1/4',
'input': '4/16',
'output': 'De breuk 4/16 is gelijk aan 1/4 omdat zowel de teller als de '
'noemer deelbaar zijn door 4. Door zowel de teller als de noemer '
'door 4 te delen, krijgen we de breuk 1/4.'
}
```
### Data Fields
- **id**: the ID of the item. The following ID is not included because they could not be translated: `[23019]`
- **instruction**: the given instruction
**input**: optional input to accompany the instruction. Can be empty.
- **output**: the "answer" to the instruction
## Dataset Creation
The instructions, inputs and outputs were translated with OpenAI's API for `gpt-3.5-turbo`. `max_tokens=1024, temperature=0` as parameters.
The prompt template to translate is (where `src_lang` is English and `tgt_lang` is Dutch):
```python
TRANSLATION_PROMPT = """You are asked to translate a task's instruction, optional input to the task, and the output of the task, from {src_lang} into {tgt_lang}.
Here are the requirements that you should adhere to:
1. maintain the format: the task consists of a task instruction (marked `instruction: `), optional input to the task (marked `input: `) and output for the task marked with `output: `;
2. do not translate the identifiers `instruction: `, `input: `, and `output: ` but instead copy them to your output;
3. make sure that text is fluent to read and does not contain grammatical errors. Use standard {tgt_lang} without regional bias;
4. translate the instruction and input text using informal, but standard, language;
5. make sure to avoid biases (such as gender bias, grammatical bias, social bias);
6. if the instruction is to correct grammar mistakes or spelling mistakes then you have to generate a similar mistake in the input in {tgt_lang}, and then also generate a corrected output version in the output in {tgt_lang};
7. if the instruction is to translate text from one language to another, then you do not translate the text that needs to be translated in the instruction or the input, nor the translation in the output (just copy them as-is);
8. do not translate code fragments but copy them to your output. If there are English examples, variable names or definitions in code fragments, keep them in English.
Now translate the following task with the requirements set out above. Do not provide an explanation and do not add anything else.\n\n"""
```
This prompt is concatenated with the instruction, optionally the input, and the output. In code, that last part looks like this:
```python
text = f'instruction: "{instruction}"\n\n'
if inputstr:
text += f'input: "{inputstr}"\n\n'
text += f'output: "{outputstr}"'
```
The system message was:
```
You are a helpful assistant that translates English to Dutch to the requirements that are given to you.
```
Note that 1 item (0.0001%) was not successfully translated. The translation was missing the input, instruction, or output keywords where those were expected. The ID for the missing item is `[23019]`.
### Source Data
#### Initial Data Collection and Normalization
Initial data creation by [Tatsu lab](https://huggingface.co/datasets/tatsu-lab/alpaca) and cleaned by [Yahma](https://huggingface.co/datasets/yahma/alpaca-cleaned).
#### Who are the source language producers?
The original dataset was generated with OpenAI's `text-davinci-003`.
## Considerations for Using the Data
Note that the translations in this new dataset have not been verified by humans.
### Discussion of Biases
As with any machine-generated texts, users should be aware of potential biases that are included in this dataset. Although the prompt specifically includes `make sure to avoid biases (such as gender bias, grammatical bias, social bias)`, of course the impact of such command is not known. It is likely that biases remain in the dataset so use with caution.
### Other Known Limitations
The translation quality has not been verified. Use at your own risk!
### Licensing Information
As per OpenAI's terms of use, this dataset cannot be used to build [a commercial system that competes with OpenAI's services](https://openai.com/policies/terms-of-use). Similar to the original Alpaca dataset, this dataset is released under CC NC 4.0.
This text was generated (either in part or in full) with GPT-3 (`gpt-3.5-turbo`), OpenAI’s large-scale language-generation model. Upon generating draft language, the author reviewed, edited, and revised the language to their own liking and takes ultimate responsibility for the content of this publication.
If you use this dataset, you must also follow the [Sharing](https://openai.com/policies/sharing-publication-policy) and [Usage](https://openai.com/policies/usage-policies) policies.
As clearly stated in their [Terms of Use](https://openai.com/policies/terms-of-use), specifically 2c.iii, "[you may not] use output from the Services to develop models that compete with OpenAI". That means that you cannot use this dataset to build models that are intended to commercially compete with OpenAI. [As far as I am aware](https://law.stackexchange.com/questions/93308/licensing-material-generated-with-chatgpt), that is a specific restriction that should serve as an addendum to the current license.
### Contributions
Thanks to [Tatsu lab](https://huggingface.co/datasets/tatsu-lab/alpaca) for the initial machine-generated dataset and yahma for [cleaning it](https://huggingface.co/datasets/yahma/alpaca-cleaned). |
cis-lmu/GlotStoryBook | ---
license: cc
language:
- ach
- ada
- adh
- adx
- aeb
- afr
- alz
- amh
- anu
- ara
- asm
- bem
- ben
- bod
- bxk
- cat
- cce
- ckb
- crk
- csw
- ctu
- dag
- dan
- deu
- dga
- din
- dje
- ell
- eng
- epo
- ewe
- fas
- fat
- fra
- ful
- gaa
- gjn
- guj
- gur
- guz
- gyn
- hat
- hau
- hbs
- hch
- her
- hin
- hun
- hus
- ind
- ita
- jam
- jpn
- kam
- kan
- kau
- kdj
- keo
- khg
- khm
- kik
- kin
- kln
- kmr
- kok
- koo
- kor
- kpz
- kqn
- kri
- kru
- ktz
- kua
- kwn
- laj
- lat
- lgg
- lin
- lit
- lko
- loz
- lsm
- luc
- lue
- lug
- lun
- luo
- lwg
- mal
- mar
- mas
- mat
- maz
- mer
- mfe
- mhi
- mhw
- miu
- mlg
- mmc
- mnw
- mqu
- msa
- mya
- myx
- naq
- nbl
- nch
- ndo
- nep
- nhe
- nhw
- nld
- nle
- nno
- nob
- nor
- nso
- nuj
- nya
- nyn
- nyu
- nzi
- ocu
- old
- ori
- orm
- pan
- pcm
- pmq
- pol
- por
- prs
- pus
- rki
- ron
- rus
- sag
- san
- saq
- sck
- sme
- som
- sot
- spa
- sqi
- srp
- ssw
- swa
- swe
- tam
- tel
- teo
- tet
- tgl
- tha
- tir
- toh
- toi
- tsc
- tsn
- tso
- ttj
- tum
- tur
- tuv
- twi
- ukr
- urd
- ven
- vie
- xho
- xog
- xsm
- yor
- yua
- yue
- zho
- zne
- zul
pretty_name: GlotStoryBook Corpus
tags:
- storybook
- book
- story
- language-identification
configs:
- config_name: default
data_files:
- split: train
path: GlotStoryBook.csv
---
## Dataset Description
Story Books for 180 ISO-639-3 codes.
A Machine Translation (MT) version of this dataset is also provided in [cis-lmu/GlotStoryBook-MT](https://huggingface.co/datasets/cis-lmu/GlotStoryBook-MT).
This dataset consisted of 4 publishers:
1. asp: [African Storybook](https://africanstorybook.org)
2. pb: [Pratham Books](https://prathambooks.org/)
3. lcb: [Little Cree Books](http://littlecreebooks.com/)
4. lida: [LIDA Stories](https://lidastories.net/)
- **GitHub Repository:** [github](https://github.com/cisnlp/GlotStoryBook)
- **Paper:** [paper](https://arxiv.org/abs/2310.16248)
- **Point of Contact:** amir@cis.lmu.de
## Usage (HF Loader)
```python
from datasets import load_dataset
dataset = load_dataset('cis-lmu/GlotStoryBook')
print(dataset['train'][0]) # First row data
```
## Download
If you are not a fan of the HF dataloader, download it directly:
```python
! wget https://huggingface.co/datasets/cis-lmu/GlotStoryBook/resolve/main/GlotStoryBook.csv
```
# Tools
To compute the script of each text we used Glotscript ([code](https://github.com/cisnlp/GlotScript) and [paper](https://arxiv.org/abs/2309.13320)).
## License and Copyright
We do not own any of the text from which these data has been extracted.
All the files are collected from the repository located at https://github.com/global-asp/.
The source repository for each text and file is stored in the dataset.
Each file in the dataset is associated with one license from the CC family.
The licenses include 'CC BY', 'CC BY-NC', 'CC BY-NC-SA', 'CC-BY', 'CC-BY-NC', and 'Public Domain'.
We also license the code, actual packaging and the metadata of these data under the cc0-1.0.
## Github
We additionally provide a GitHub version that openly shares the source code for processing this dataset:
https://github.com/cisnlp/GlotStoryBook
## Citation
If you use any part of this code and data in your research, please cite it (along with https://github.com/global-asp/) using the following BibTeX entry.
This work is part of the [GlotLID](https://github.com/cisnlp/GlotLID) project.
```
@inproceedings{
kargaran2023glotlid,
title={{GlotLID}: Language Identification for Low-Resource Languages},
author={Kargaran, Amir Hossein and Imani, Ayyoob and Yvon, Fran{\c{c}}ois and Sch{\"u}tze, Hinrich},
booktitle={The 2023 Conference on Empirical Methods in Natural Language Processing},
year={2023},
url={https://openreview.net/forum?id=dl4e3EBz5j}
}
``` |
kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-markdown-0 | ---
dataset_info:
features:
- name: input_ids
sequence:
sequence: int32
- name: attention_mask
sequence:
sequence: int8
- name: labels
sequence:
sequence: int64
splits:
- name: train
num_bytes: 13336000
num_examples: 1000
download_size: 1052551
dataset_size: 13336000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
cahya/instructions-ko | ---
dataset_info:
features:
- name: id
dtype: int64
- name: text
dtype: string
splits:
- name: train
num_bytes: 1016857.3512963904
num_examples: 1770
- name: test
num_bytes: 56875.07219115404
num_examples: 99
- name: validation
num_bytes: 56300.57651245552
num_examples: 98
download_size: 631602
dataset_size: 1130033.0
---
# Dataset Card for "instructions-ko"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
liuyanchen1015/MULTI_VALUE_rte_nomo_existential | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: string
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: train
num_bytes: 1170
num_examples: 3
download_size: 0
dataset_size: 1170
---
# Dataset Card for "MULTI_VALUE_rte_nomo_existential"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jeanevesss/sam.live.v01 | ---
license: cc
---
|
Luciya/llama-2-clinc-oos-train | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 6593005
num_examples: 15100
download_size: 977625
dataset_size: 6593005
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "llama-2-clinc-oos-train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
joey234/affixal_negation | ---
license: apache-2.0
task_categories:
- text-classification
language:
- en
pretty_name: e
size_categories:
- 1K<n<10K
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
- This dataset contains a list of affixal negations and their non-negated counterpart (e.g. unintended - intended).
- This dataset is from [van Son et al. (2016)](https://aclanthology.org/W16-5007/).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
mstz/madelon | ---
language:
- en
tags:
- madelon
- tabular_classification
- UCI
pretty_name: Madelon
size_categories:
- 1K<n<10K
task_categories:
- tabular-classification
configs:
- Madelon
license: cc
---
# Annealing
The [Madelon dataset](https://archive-beta.ics.uci.edu/dataset/171/madelon) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
Artificial dataset with continuous input variables.
Highly non-linear classification problem.
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|-----------------------------------------------------------------|
| madelon | Binary classification | |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/madelon")["train"]
``` |
nguyenth1312/vietnamese_cultural | ---
dataset_info:
features:
- name: image
dtype: image
- name: 'Unnamed: 0'
dtype: int64
- name: text
dtype: string
splits:
- name: train
num_bytes: 423279027.0
num_examples: 144
download_size: 371389185
dataset_size: 423279027.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "vietnamese_cultural"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
JonaszPotoniec/wikipedia-with-statistics-pl | ---
dataset_info:
features:
- name: id
dtype: string
- name: url
dtype: string
- name: title
dtype: string
- name: text
dtype: string
- name: pageviews
dtype: int64
splits:
- name: train
num_bytes: 2962850577
num_examples: 1587721
download_size: 1812521426
dataset_size: 2962850577
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
language:
- pl
tags:
- wikipedia
---
A dataset of Polish Wikipedia dumps from November 2023, combined with page views statistics from the three previous months. The intention is to make it easier to filter out the least visited articles, as they may potentially provide lower quality data. |
archersco/datasets | ---
license: apache-2.0
---
|
one-sec-cv12/chunk_69 | ---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
splits:
- name: train
num_bytes: 19601379792.125
num_examples: 204079
download_size: 16785342268
dataset_size: 19601379792.125
---
# Dataset Card for "chunk_69"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
avishekhraj/bhadwa | ---
license: unknown
---
|
0xdev23/audio_dataset_2 | ---
license: unknown
dataset_info:
features:
- name: audio
dtype: audio
- name: transcription
dtype: string
splits:
- name: train
num_bytes: 3571113.0
num_examples: 3
download_size: 3233069
dataset_size: 3571113.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
CyberHarem/toba_minami_yurucamp | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of Toba Minami
This is the dataset of Toba Minami, containing 100 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 | 100 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 237 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| raw-stage3-eyes | 269 | [Download](dataset-raw-stage3-eyes.zip) | 3-stage cropped (with eye-focus) raw data with meta information. |
| 384x512 | 100 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x704 | 100 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x880 | 100 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 237 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 237 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-p512-640 | 207 | [Download](dataset-stage3-p512-640.zip) | 3-stage cropped dataset with the area not less than 512x512 pixels. |
| stage3-eyes-640 | 269 | [Download](dataset-stage3-eyes-640.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 640 pixels. |
| stage3-eyes-800 | 269 | [Download](dataset-stage3-eyes-800.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 800 pixels. |
|
pedromigurasdev/beneficios_pv | ---
license: apache-2.0
---
|
autoevaluate/autoeval-eval-jeffdshen__redefine_math0_8shot-jeffdshen__redefine_mat-1c694b-1853263417 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- jeffdshen/redefine_math0_8shot
eval_info:
task: text_zero_shot_classification
model: inverse-scaling/opt-1.3b_eval
metrics: []
dataset_name: jeffdshen/redefine_math0_8shot
dataset_config: jeffdshen--redefine_math0_8shot
dataset_split: train
col_mapping:
text: prompt
classes: classes
target: answer_index
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: inverse-scaling/opt-1.3b_eval
* Dataset: jeffdshen/redefine_math0_8shot
* Config: jeffdshen--redefine_math0_8shot
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@jeffdshen](https://huggingface.co/jeffdshen) for evaluating this model. |
tyzhu/random_letter_same_length_find_passage_train30_eval20_rare | ---
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
splits:
- name: train
num_bytes: 25541
num_examples: 80
- name: validation
num_bytes: 7287
num_examples: 20
download_size: 24117
dataset_size: 32828
---
# Dataset Card for "random_letter_same_length_find_passage_train30_eval20_rare"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Mithilss/cnn_1_0_0_dollybricks_platypus_bbq | ---
dataset_info:
features:
- name: prompt
dtype: string
splits:
- name: train
num_bytes: 92708704
num_examples: 51800
download_size: 53042209
dataset_size: 92708704
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "cnn_1_0_0_dollybricks_platypus_bbq"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
edbeeching/prj_gia_dataset_atari_2B_atari_mspacman_1111 | ---
library_name: gia
tags:
- deep-reinforcement-learning
- reinforcement-learning
- gia
- multi-task
- multi-modal
- imitation-learning
- offline-reinforcement-learning
---
An imitation learning environment for the atari_mspacman environment, sample for the policy atari_2B_atari_mspacman_1111
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
|
irds/mmarco_zh | ---
pretty_name: '`mmarco/zh`'
viewer: false
source_datasets: []
task_categories:
- text-retrieval
---
# Dataset Card for `mmarco/zh`
The `mmarco/zh` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/mmarco#mmarco/zh).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=8,841,823
This dataset is used by: [`mmarco_zh_dev`](https://huggingface.co/datasets/irds/mmarco_zh_dev), [`mmarco_zh_dev_small`](https://huggingface.co/datasets/irds/mmarco_zh_dev_small), [`mmarco_zh_dev_v1.1`](https://huggingface.co/datasets/irds/mmarco_zh_dev_v1.1), [`mmarco_zh_train`](https://huggingface.co/datasets/irds/mmarco_zh_train)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/mmarco_zh', 'docs')
for record in docs:
record # {'doc_id': ..., 'text': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Bonifacio2021MMarco,
title={{mMARCO}: A Multilingual Version of {MS MARCO} Passage Ranking Dataset},
author={Luiz Henrique Bonifacio and Israel Campiotti and Roberto Lotufo and Rodrigo Nogueira},
year={2021},
journal={arXiv:2108.13897}
}
```
|
yuan-sf63/word_label_0.5_32_D | ---
dataset_info:
features:
- name: text
dtype: string
- name: '0'
dtype: int64
- name: '1'
dtype: int64
- name: '2'
dtype: int64
- name: '3'
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
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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
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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: '23'
dtype: int64
- name: '24'
dtype: int64
- name: '25'
dtype: int64
- name: '26'
dtype: int64
- name: '27'
dtype: int64
- name: '28'
dtype: int64
- name: '29'
dtype: int64
- name: '30'
dtype: int64
- name: '31'
dtype: int64
splits:
- name: train
num_bytes: 23061771.018877786
num_examples: 68890
- name: validation
num_bytes: 2562604.9811222157
num_examples: 7655
download_size: 5737255
dataset_size: 25624376.0
---
# Dataset Card for "word_label_0.5_32_D"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
caasih/amis_voice | ---
license: cc-by-sa-4.0
language:
- ami
task_categories:
- automatic-speech-recognition
pretty_name: amis_voice
size_categories:
- n<1K
---
# amis\_voice
## test data
- Dr. Safulo Kacaw Lalanges introduces himself in Amis (Pangcah) Language: [YouTube](https://www.youtube.com/watch?v=RoYtCB2TVmM), [WAV](./data/test/RoYtCB2TVmM.wav)
|
metaeval/scruples | ---
license: apache-2.0
---
|
Gabriel1322/lucasmodel | ---
license: openrail
---
|
ibivibiv/alpaca_lamini10 | ---
dataset_info:
features:
- name: output
dtype: string
- name: instruction
dtype: string
- name: input
dtype: string
splits:
- name: train
num_bytes: 56230802
num_examples: 129281
download_size: 36309407
dataset_size: 56230802
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
namanbarkiya/agentprod-assignment | ---
license: apache-2.0
---
|
benayas/snips_chatgpt_5pct_v2 | ---
dataset_info:
features:
- name: text
dtype: string
- name: category
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 1062303
num_examples: 13084
download_size: 413403
dataset_size: 1062303
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
habixia1/habixia3 | ---
license: afl-3.0
---
|
RuyYoshida/narradorestv | ---
license: openrail
---
|
liuyanchen1015/MULTI_VALUE_stsb_me_coordinate_subjects | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: score
dtype: float64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: dev
num_bytes: 204
num_examples: 1
- name: train
num_bytes: 164
num_examples: 1
download_size: 0
dataset_size: 368
---
# Dataset Card for "MULTI_VALUE_stsb_me_coordinate_subjects"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
win10/Long-Data-Collections-booksum-binidx | ---
license: other
---
Fine-tune Data
BookSum:
BookSum is a dataset for long context summarization. It includes a vast collection of books from various genres, and the task is to generate a coherent and concise summary given a long context from the book. This dataset is designed to test and train models on their ability to understand and summarize long, complex narratives.
to convert to binidx format. |
gguichard/myriade_noun_aligned_with_wordnet_noun_sens | ---
dataset_info:
features:
- name: tokens
sequence: string
- name: wn_sens
sequence: int64
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 98888904
num_examples: 162516
download_size: 19724328
dataset_size: 98888904
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "myriade_noun_aligned_with_wordnet_noun_sens"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
corbt/all-recipes | ---
dataset_info:
features:
- name: input
dtype: string
splits:
- name: train
num_bytes: 1569011376
num_examples: 2147248
download_size: 807147913
dataset_size: 1569011376
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "all-recipes"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
bryanchrist/SGSM | ---
license: gpl-3.0
---
## SGSM
Synthetic Grade School Math (SGSM) contains 20,490 question/answer pairs generated by [MATHWELL](https://huggingface.co/bryanchrist/MATHWELL), a context-free grade school math word problem generator that outputs a word problem and Program of Thought (PoT) solution based solely on an optional student interest, as introduced in [MATHWELL: Generating Educational Math Word Problems at Scale](https://arxiv.org/abs/2402.15861). SGSM has two subsets: SGSM Train, comprised of 2,093 question/answer pairs verified by human experts, and SGSM Unannotated, comprised of 18,397 question/answer pairs that have executable code but are not verified by human experts. SGSM is the largest English grade school math QA dataset with PoT rationales.
SGSM is designed to train context-free grade school math word problem generators, but can also be used to train math QA models. Please refer to our [paper](https://arxiv.org/abs/2402.15861) for more information on the dataset.
## Citation
```bash
@misc{christ2024mathwell,
title={MATHWELL: Generating Educational Math Word Problems at Scale},
author={Bryan R Christ and Jonathan Kropko and Thomas Hartvigsen},
year={2024},
eprint={2402.15861},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
gsstein/75-percent-human-dataset-mistake | ---
dataset_info:
features:
- name: id
dtype: string
- name: url
dtype: string
- name: title
dtype: string
- name: summary
dtype: string
- name: text
dtype: string
- name: prompt
dtype: string
- name: generated
dtype: bool
splits:
- name: train
num_bytes: 86340047
num_examples: 15326
- name: test
num_bytes: 3065993
num_examples: 576
- name: validation
num_bytes: 3262672
num_examples: 576
download_size: 57378324
dataset_size: 92668712
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
---
|
eitanturok/oasst | ---
dataset_info:
- config_name: english
features:
- name: conversations
list:
- name: message_id
dtype: string
- name: parent_id
dtype: string
- name: role
dtype: string
- name: text
dtype: string
- name: lang
dtype: string
- name: prompt
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 7157780.355420985
num_examples: 2457
download_size: 4492519
dataset_size: 7157780.355420985
- config_name: singleturn
features:
- name: conversations
list:
- name: message_id
dtype: string
- name: parent_id
dtype: string
- name: role
dtype: string
- name: text
dtype: string
- name: lang
dtype: string
- name: prompt
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 25015816
num_examples: 8587
download_size: 15526988
dataset_size: 25015816
- config_name: singleturn-instructions
features:
- name: conversations
list:
- name: message_id
dtype: string
- name: parent_id
dtype: string
- name: role
dtype: string
- name: text
dtype: string
- name: lang
dtype: string
- name: prompt
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 25436579
num_examples: 8587
download_size: 0
dataset_size: 25436579
configs:
- config_name: english
data_files:
- split: train
path: english/train-*
- config_name: singleturn
data_files:
- split: train
path: singleturn/train-*
- config_name: singleturn-instructions
data_files:
- split: train
path: singleturn-instructions/train-*
---
# Dataset Card for "oasst"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
BeIR/cqadupstack-generated-queries | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
- 10K<n<100K
arguana:
- 1K<n<10K
touche-2020:
- 100K<n<1M
cqadupstack:
- 100K<n<1M
quora:
- 100K<n<1M
dbpedia:
- 1M<n<10M
scidocs:
- 10K<n<100K
fever:
- 1M<n<10M
climate-fever:
- 1M<n<10M
scifact:
- 1K<n<10K
source_datasets: []
task_categories:
- text-retrieval
- zero-shot-retrieval
- information-retrieval
- zero-shot-information-retrieval
task_ids:
- passage-retrieval
- entity-linking-retrieval
- fact-checking-retrieval
- tweet-retrieval
- citation-prediction-retrieval
- duplication-question-retrieval
- argument-retrieval
- news-retrieval
- biomedical-information-retrieval
- question-answering-retrieval
---
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** nandan.thakur@uwaterloo.ca
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. |
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