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the_pile_stack_exchange | null | 2023-11-27T15:00:44Z | 266 | 10 | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"arxiv:2101.00027",
"region:us"
]
| [
"text-generation",
"fill-mask"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: Stack Exchange
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
dataset_info:
features:
- name: domain
dtype: string
- name: text
dtype: string
config_name: plain_text
splits:
- name: train
num_bytes: 11075434609
num_examples: 5096117
download_size: 36802959360
dataset_size: 11075434609
viewer: false
---
# Dataset Card for Stack Exchange
## Table of Contents
- [Dataset Card for Stack Exchange](#dataset-card-for-the_pile_stack_exchange)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [|split|num examples|](#splitnum-examples)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [GitHub](https://github.com/EleutherAI/stackexchange-dataset)
- **Repository:** [Needs More Information]
- **Paper:** [arXiv](https://arxiv.org/abs/2101.00027)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
<p><b>Defunct:</b> Dataset "the_pile_stack_exchange" is defunct and no longer accessible due to unavailability of the source data.</p>
</div>
This dataset is part of EleutherAI/The Pile dataset and is a dataset for Language Models from processing stackexchange data dump, which is an anonymized dump of all user-contributed content on the Stack Exchange network.
|download_size|34.28 Gib|
|dataset_size|10.3 Gib|
### Supported Tasks and Leaderboards
The dataset is used for Language Modeling.
### Languages
The dataset is in English.
## Dataset Structure
### Data Instances
```
{'domain': 'chemistry',
'text':"\nQ: \n \nReviving old questions or asking a new one? \n \nI'm relatively new to the Chemistry SE community, and sometimes when I go to ask a question, I notice that the same (or similar) question has \nalready been asked. However, the previous question doesn't have a good answer (or is unanswered). In this case, is it better to ask the questi\non again in a new post (which might be marked as duplicate) or comment on the old post (which might be several years old)? In other words, wha\nt are the customs of this site in regards to reviving old questions/discussions?\n\nA:\n\nAs Martin commented, it really depends on the type of question. In any case, you always have the following possibilities:\n\nAsk a new question\nEdit the question to bump it to the first page\nAdd a bounty\nBring it to the attention of people in chat\n\nConsider the following cases:\n\nI have exactly the same question as asked and unanswered before!\n\nIf you ask a new question which turns out to be the same question, it may be closed as a dupe (depending on whether users remember the old que\nstion). Not the ideal option.\nIf you can find something substantial to edit and bump the question, do so. Maybe add a comment that you would really love an answer.\nIf you can spare some rep for a bounty (50 is usually enough), do so.\nYou can always bring it to the attention of people in chat.\n",}
```
### Data Fields
- `domain`: Stack Exchange domain of the sample
- `text`: Text content containing both the question and the answer
### Data Splits
|split|num examples|
--------------------------------
|train|5096117|
## 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
```
@article{pile,
title={The {P}ile: An 800GB Dataset of Diverse Text for Language Modeling},
author={Gao, Leo and Biderman, Stella and Black, Sid and Golding, Laurence and Hoppe, Travis and Foster, Charles and Phang, Jason and He, Horace and Thite, Anish and Nabeshima, Noa and Presser, Shawn and Leahy, Connor},
journal={arXiv preprint arXiv:2101.00027},
year={2020}
}
```
### Contributions
Thanks to [sdtblck](https://github.com/sdtblck) for creating the dataset.
Thanks to [richarddwang](https://github.com/richarddwang) for adding the dataset. | [
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|
AhmedSSoliman/CoNaLa | AhmedSSoliman | 2022-01-22T09:34:19Z | 266 | 0 | [
"region:us"
]
| [
"Code Generation",
"Translation",
"Text2Text generation"
]
| 2022-03-02T23:29:22Z | ---
task_categories:
- Code Generation
- Translation
- Text2Text generation
---
# CoNaLa Dataset for Code Generation
## Table of content
- [Dataset Description](#dataset-description)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
## Dataset Descritpion
This dataset has been processed for Code Generation. CMU CoNaLa, the Code/Natural Language Challenge is a joint project of the Carnegie Mellon University NeuLab and STRUDEL Lab. This dataset was designed to test systems for generating program snippets from natural language. It is avilable at https://conala-corpus.github.io/ , and this is about 13k records from the full corpus of about 600k examples.
### Languages
English
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"intent": "convert a list to a dictionary in python",
"snippet": "b = dict(zip(a[0::2], a[1::2]))"
},
{
"intent": "python - sort a list of nested lists",
"snippet": "l.sort(key=sum_nested)"
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"intent": "Value(dtype='string', id=None)",
"snippet": "Value(dtype='string', id=None)"
}
```
### Dataset Splits
This dataset is split into a train, validation and test split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 11125 |
| valid | 1237 |
| test | 500 |
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|
GEM/CrossWOZ | GEM | 2022-10-24T15:29:55Z | 266 | 5 | [
"task_categories:conversational",
"annotations_creators:none",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:zh",
"license:apache-2.0",
"dialog-response-generation",
"region:us"
]
| [
"conversational"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- zh
license:
- apache-2.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- conversational
task_ids: []
pretty_name: CrossWOZ
tags:
- dialog-response-generation
---
# Dataset Card for GEM/CrossWOZ
## Dataset Description
- **Homepage:** https://github.com/thu-coai/CrossWOZ
- **Repository:** https://github.com/thu-coai/CrossWOZ
- **Paper:** https://aclanthology.org/2020.tacl-1.19
- **Leaderboard:** N/A
- **Point of Contact:** Qi Zhu
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/CrossWOZ).
### Dataset Summary
CrossWOZ is a Chinese multi-domain task-oriented dialogue dataset . It contains 6K dialogue sessions and 102K utterances for 5 domains, including hotel, restaurant, attraction, metro, and taxi. About 60{\%} of the dialogues have cross-domain user goals that favor inter-domain dependency and encourage natural transition across domains in conversation.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/CrossWOZ')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/CrossWOZ).
#### website
[Github](https://github.com/thu-coai/CrossWOZ)
#### paper
[ACL Anthology](https://aclanthology.org/2020.tacl-1.19)
#### authors
Qi Zhu, Kaili Huang, Zheng Zhang, Xiaoyan Zhu, and Minlie Huang from CoAI group, Tsinghua University
## Dataset Overview
### Where to find the Data and its Documentation
#### Webpage
<!-- info: What is the webpage for the dataset (if it exists)? -->
<!-- scope: telescope -->
[Github](https://github.com/thu-coai/CrossWOZ)
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Github](https://github.com/thu-coai/CrossWOZ)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[ACL Anthology](https://aclanthology.org/2020.tacl-1.19)
#### BibTex
<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
<!-- scope: microscope -->
```
@article{zhu-etal-2020-crosswoz,
title = "{C}ross{WOZ}: A Large-Scale {C}hinese Cross-Domain Task-Oriented Dialogue Dataset",
author = "Zhu, Qi and
Huang, Kaili and
Zhang, Zheng and
Zhu, Xiaoyan and
Huang, Minlie",
journal = "Transactions of the Association for Computational Linguistics",
volume = "8",
year = "2020",
url = "https://aclanthology.org/2020.tacl-1.19",
doi = "10.1162/tacl_a_00314",
pages = "281--295",
abstract = "To advance multi-domain (cross-domain) dialogue modeling as well as alleviate the shortage of Chinese task-oriented datasets, we propose CrossWOZ, the first large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dataset. It contains 6K dialogue sessions and 102K utterances for 5 domains, including hotel, restaurant, attraction, metro, and taxi. Moreover, the corpus contains rich annotation of dialogue states and dialogue acts on both user and system sides. About 60{\%} of the dialogues have cross-domain user goals that favor inter-domain dependency and encourage natural transition across domains in conversation. We also provide a user simulator and several benchmark models for pipelined task-oriented dialogue systems, which will facilitate researchers to compare and evaluate their models on this corpus. The large size and rich annotation of CrossWOZ make it suitable to investigate a variety of tasks in cross-domain dialogue modeling, such as dialogue state tracking, policy learning, user simulation, etc.",
}
```
#### Contact Name
<!-- quick -->
<!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
Qi Zhu
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
[email protected]
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
no
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
no
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`Chinese`
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
apache-2.0: Apache License 2.0
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
CrossWOZ is the first large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dataset. It contains 6K dialogue sessions and 102K utterances for 5 domains, including hotel, restaurant, attraction, metro, and taxi. Moreover, the corpus contains rich annotation of dialogue states and dialogue acts at both user and system sides. We also provide a user simulator and several benchmark models for pipelined taskoriented dialogue systems, which will facilitate researchers to compare and evaluate their models on this corpus.
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Dialog Response Generation
#### Communicative Goal
<!-- quick -->
<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
<!-- scope: periscope -->
Generate a response according to the dialog context and database search results.
### Credit
#### Curation Organization Type(s)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`academic`
#### Curation Organization(s)
<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
Tsinghua University
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Qi Zhu, Kaili Huang, Zheng Zhang, Xiaoyan Zhu, and Minlie Huang from CoAI group, Tsinghua University
#### Funding
<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
National Science Foundation of China, National Key R&D Program of China
#### Who added the Dataset to GEM?
<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
<!-- scope: microscope -->
Qi Zhu (Tsinghua University)
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
- `gem_id` (string): GEM-CrossWOZ-{split}-{id}
- `dialog_id` (string): dialog ID
- `sys_id` (string): system annotator ID
- `usr_id` (string): user annotation ID
- `type` (string): dialog type
- `task description` (list of strings): natural language descriptions of the user goal
- `goal` (list of tuples), includes:
- `sub-goal id` (string)
- `domain name` (string)
- `slot name` (string)
- `constraint` if filled, else `requirement` (string)
- `whether be mentioned in previous turns` (string)
- `messages` (list of dict): dialog turns. Each turn contains:
- `content` (string): utterance
- `role` (string): user or system
- `dialog_act` (list of tuples), includes:
- `domain` (string)
- `intent` (string)
- `slot` (string)
- `value` (string)
- `user_state` (list of tuples): same format as "goal", can be viewed as dynamic goal.
- `sys_state_init` (dict): the first db query emitted, records user constraints faithfully. If the system find no result that matches, he/she may relax the constraints manually and search db multiple times.
- `domain` (dict): slot(string)-value(string) pairs
- `selectedResults` (list of string): db search result that would be used in this turn.
- `sys_state` (dict): the final db query emitted, records the db used by the system in this turn. Same format as sys_state_init. Note that this may not satisfy all user constraints.
- `final_goal` (list of tuples): user state/goal at the end of dialog. same format as "goal".
#### Example Instance
<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
<!-- scope: periscope -->
```
{'dialog_id': '2303',
'final_goal': [['1', '餐馆', '人均消费', '50-100元', 'True'],
['1', '餐馆', '推荐菜', "['美食街']", 'True'],
['1', '餐馆', '名称', '鲜鱼口老字号美食街', 'True'],
['1', '餐馆', '营业时间', '周一至周日 10:00-22:00', 'True'],
['1', '餐馆', '周边景点', "['天安门广场', '前门大街', '恭王府', '故宫']", 'True'],
['2', '景点', '名称', '故宫', 'True'],
['2', '景点', '评分', '4.5分以上', 'True'],
['2', '景点', '地址', '北京市东城区景山前街4号', 'True'],
['2', '景点', '电话', '010-85007938', 'True'],
['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'True'],
['3', '酒店', '电话', '010-84273030', 'True']],
'gem_id': 'GEM-CrossWOZ-test-0',
'goal': [['1', '餐馆', '人均消费', '50-100元', 'False'],
['1', '餐馆', '推荐菜', "['美食街']", 'False'],
['1', '餐馆', '名称', '', 'False'],
['1', '餐馆', '营业时间', '', 'False'],
['1', '餐馆', '周边景点', '[]', 'False'],
['2', '景点', '名称', '出现在id=1的周边景点里', 'False'],
['2', '景点', '评分', '4.5分以上', 'False'],
['2', '景点', '地址', '', 'False'],
['2', '景点', '电话', '', 'False'],
['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'False'],
['3', '酒店', '电话', '', 'False']],
'messages': {'content': ['你好，我想吃美食街，帮我推荐一个人均消费在50-100元的餐馆，谢谢。',
'为您推荐鲜鱼口老字号美食街，人均消费75元，有您想吃的美食街哦。',
'营业时间是什么时间？',
'周一至周日 10:00-22:00。',
'他家周边有什么景点吗？',
'有故宫, 前门大街, 恭王府, 天安门广场。',
'哦，我想在这些附近景点里找一个4.5分以上的，有吗？',
'故宫就是哦，4.7分。',
'好的，电话和地址告诉我一下。',
'010-85007938；北京市东城区景山前街4号。',
'好的，麻烦你帮我查一下桔子水晶酒店(北京安贞店)电话呗。',
'010-84273030。',
'好的，收到，谢谢你！',
'不客气。'],
'dialog_act': [[['General', 'greet', 'none', 'none'],
['General', 'thank', 'none', 'none'],
['Inform', '餐馆', '人均消费', '50-100元'],
['Inform', '餐馆', '推荐菜', '美食街'],
['Request', '餐馆', '名称', '']],
[['Inform', '餐馆', '人均消费', '75元'], ['Inform', '餐馆', '名称', '鲜鱼口老字号美食街']],
[['Request', '餐馆', '营业时间', '']],
[['Inform', '餐馆', '营业时间', '周一至周日 10:00-22:00']],
[['Request', '餐馆', '周边景点', '']],
[['Inform', '餐馆', '周边景点', '前门大街'],
['Inform', '餐馆', '周边景点', '天安门广场'],
['Inform', '餐馆', '周边景点', '恭王府'],
['Inform', '餐馆', '周边景点', '故宫']],
[['Inform', '景点', '评分', '4.5分以上'], ['Select', '景点', '源领域', '餐馆']],
[['Inform', '景点', '名称', '故宫'], ['Inform', '景点', '评分', '4.7分']],
[['Request', '景点', '地址', ''], ['Request', '景点', '电话', '']],
[['Inform', '景点', '地址', '北京市东城区景山前街4号'],
['Inform', '景点', '电话', '010-85007938']],
[['Inform', '酒店', '名称', '桔子水晶酒店(北京安贞店)'], ['Request', '酒店', '电话', '']],
[['Inform', '酒店', '电话', '010-84273030']],
[['General', 'thank', 'none', 'none']],
[['General', 'welcome', 'none', 'none']]],
'role': ['usr',
'sys',
'usr',
'sys',
'usr',
'sys',
'usr',
'sys',
'usr',
'sys',
'usr',
'sys',
'usr',
'sys'],
'sys_state': [{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': [],
'人均消费': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': ['鲜鱼口老字号美食街'],
'人均消费': '50-100元',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '美食街',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': [],
'人均消费': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': ['鲜鱼口老字号美食街'],
'人均消费': '50-100元',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '美食街',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': [],
'人均消费': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': ['鲜鱼口老字号美食街'],
'人均消费': '50-100元',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '美食街',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': [],
'人均消费': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': ['故宫'],
'名称': '故宫',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': [],
'人均消费': '50-100元',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '美食街',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': [],
'人均消费': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': ['故宫'],
'名称': '故宫',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': [],
'人均消费': '50-100元',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '美食街',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': [],
'人均消费': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '故宫',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': ['桔子水晶酒店(北京安贞店)'],
'价格': '',
'名称': '桔子水晶酒店(北京安贞店)',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': [],
'人均消费': '50-100元',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '美食街',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': [],
'人均消费': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '故宫',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '桔子水晶酒店(北京安贞店)',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': [],
'人均消费': '50-100元',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '美食街',
'评分': ''}}],
'sys_state_init': [{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': [],
'人均消费': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': ['鲜鱼口老字号美食街'],
'人均消费': '50-100元',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '美食街',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': [],
'人均消费': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': ['鲜鱼口老字号美食街'],
'人均消费': '50-100元',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '美食街',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': [],
'人均消费': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': ['鲜鱼口老字号美食街'],
'人均消费': '50-100元',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '美食街',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': [],
'人均消费': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': ['故宫'],
'名称': '故宫',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': ['鲜鱼口老字号美食街'],
'人均消费': '50-100元',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '美食街',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': [],
'人均消费': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': ['故宫'],
'名称': '故宫',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': [],
'人均消费': '50-100元',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '美食街',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': [],
'人均消费': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': ['故宫'],
'名称': '故宫',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': ['桔子水晶酒店(北京安贞店)'],
'价格': '',
'名称': '桔子水晶酒店(北京安贞店)',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': [],
'人均消费': '50-100元',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '美食街',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': [],
'人均消费': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '故宫',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': ['桔子水晶酒店(北京安贞店)'],
'价格': '',
'名称': '桔子水晶酒店(北京安贞店)',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': [],
'人均消费': '50-100元',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '美食街',
'评分': ''}}],
'user_state': [[['1', '餐馆', '人均消费', '50-100元', 'True'],
['1', '餐馆', '推荐菜', "['美食街']", 'True'],
['1', '餐馆', '名称', '', 'True'],
['1', '餐馆', '营业时间', '', 'False'],
['1', '餐馆', '周边景点', '[]', 'False'],
['2', '景点', '名称', '出现在id=1的周边景点里', 'False'],
['2', '景点', '评分', '4.5分以上', 'False'],
['2', '景点', '地址', '', 'False'],
['2', '景点', '电话', '', 'False'],
['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'False'],
['3', '酒店', '电话', '', 'False']],
[],
[['1', '餐馆', '人均消费', '50-100元', 'True'],
['1', '餐馆', '推荐菜', "['美食街']", 'True'],
['1', '餐馆', '名称', '鲜鱼口老字号美食街', 'True'],
['1', '餐馆', '营业时间', '', 'True'],
['1', '餐馆', '周边景点', '[]', 'False'],
['2', '景点', '名称', '出现在id=1的周边景点里', 'False'],
['2', '景点', '评分', '4.5分以上', 'False'],
['2', '景点', '地址', '', 'False'],
['2', '景点', '电话', '', 'False'],
['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'False'],
['3', '酒店', '电话', '', 'False']],
[],
[['1', '餐馆', '人均消费', '50-100元', 'True'],
['1', '餐馆', '推荐菜', "['美食街']", 'True'],
['1', '餐馆', '名称', '鲜鱼口老字号美食街', 'True'],
['1', '餐馆', '营业时间', '周一至周日 10:00-22:00', 'True'],
['1', '餐馆', '周边景点', '[]', 'True'],
['2', '景点', '名称', '出现在id=1的周边景点里', 'False'],
['2', '景点', '评分', '4.5分以上', 'False'],
['2', '景点', '地址', '', 'False'],
['2', '景点', '电话', '', 'False'],
['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'False'],
['3', '酒店', '电话', '', 'False']],
[],
[['1', '餐馆', '人均消费', '50-100元', 'True'],
['1', '餐馆', '推荐菜', "['美食街']", 'True'],
['1', '餐馆', '名称', '鲜鱼口老字号美食街', 'True'],
['1', '餐馆', '营业时间', '周一至周日 10:00-22:00', 'True'],
['1', '餐馆', '周边景点', "['天安门广场', '前门大街', '恭王府', '故宫']", 'True'],
['2', '景点', '名称', '出现在id=1的周边景点里', 'True'],
['2', '景点', '评分', '4.5分以上', 'True'],
['2', '景点', '地址', '', 'False'],
['2', '景点', '电话', '', 'False'],
['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'False'],
['3', '酒店', '电话', '', 'False']],
[],
[['1', '餐馆', '人均消费', '50-100元', 'True'],
['1', '餐馆', '推荐菜', "['美食街']", 'True'],
['1', '餐馆', '名称', '鲜鱼口老字号美食街', 'True'],
['1', '餐馆', '营业时间', '周一至周日 10:00-22:00', 'True'],
['1', '餐馆', '周边景点', "['天安门广场', '前门大街', '恭王府', '故宫']", 'True'],
['2', '景点', '名称', '故宫', 'True'],
['2', '景点', '评分', '4.5分以上', 'True'],
['2', '景点', '地址', '', 'True'],
['2', '景点', '电话', '', 'True'],
['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'False'],
['3', '酒店', '电话', '', 'False']],
[],
[['1', '餐馆', '人均消费', '50-100元', 'True'],
['1', '餐馆', '推荐菜', "['美食街']", 'True'],
['1', '餐馆', '名称', '鲜鱼口老字号美食街', 'True'],
['1', '餐馆', '营业时间', '周一至周日 10:00-22:00', 'True'],
['1', '餐馆', '周边景点', "['天安门广场', '前门大街', '恭王府', '故宫']", 'True'],
['2', '景点', '名称', '故宫', 'True'],
['2', '景点', '评分', '4.5分以上', 'True'],
['2', '景点', '地址', '北京市东城区景山前街4号', 'True'],
['2', '景点', '电话', '010-85007938', 'True'],
['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'True'],
['3', '酒店', '电话', '', 'True']],
[],
[['1', '餐馆', '人均消费', '50-100元', 'True'],
['1', '餐馆', '推荐菜', "['美食街']", 'True'],
['1', '餐馆', '名称', '鲜鱼口老字号美食街', 'True'],
['1', '餐馆', '营业时间', '周一至周日 10:00-22:00', 'True'],
['1', '餐馆', '周边景点', "['天安门广场', '前门大街', '恭王府', '故宫']", 'True'],
['2', '景点', '名称', '故宫', 'True'],
['2', '景点', '评分', '4.5分以上', 'True'],
['2', '景点', '地址', '北京市东城区景山前街4号', 'True'],
['2', '景点', '电话', '010-85007938', 'True'],
['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'True'],
['3', '酒店', '电话', '010-84273030', 'True']],
[]]},
'sys_id': 96,
'task description': ['你要去一个餐馆(id=1)用餐。你希望餐馆的人均消费是50-100元的。你想吃的菜肴是美食街。你想知道这个餐馆的名称、营业时间、周边景点。',
'你要去id=1附近的景点(id=2)游玩。你希望景点的评分是4.5分以上。你想知道这个景点的地址、电话。',
'你要去名叫桔子水晶酒店(北京安贞店)的酒店(id=3)住宿。你想知道这个酒店的电话。'],
'type': '不独立多领域',
'usr_id': 97}
```
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
| Split | Train | Valid | Test |
| --------------------- | ------ | ----- | ----- |
| \# dialogues | 5,012 | 500 | 500 |
| \# Turns (utterances) | 84,692 | 8,458 | 8,476 |
| Vocab | 12,502 | 5,202 | 5,143 |
| Avg. sub-goals | 3.24 | 3.26 | 3.26 |
| Avg. semantic tuples | 14.8 | 14.9 | 15.0 |
| Avg. turns | 16.9 | 16.9 | 17.0 |
| Avg. tokens per turn | 16.3 | 16.3 | 16.2 |
## Dataset in GEM
### Rationale for Inclusion in GEM
#### Why is the Dataset in GEM?
<!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? -->
<!-- scope: microscope -->
CrossWOZ is the first large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dataset.
#### Similar Datasets
<!-- info: Do other datasets for the high level task exist? -->
<!-- scope: telescope -->
yes
#### Unique Language Coverage
<!-- info: Does this dataset cover other languages than other datasets for the same task? -->
<!-- scope: periscope -->
no
#### Difference from other GEM datasets
<!-- info: What else sets this dataset apart from other similar datasets in GEM? -->
<!-- scope: microscope -->
The corpus contains rich annotation of dialogue states and dialogue acts at both user and system sides, which can be used in a wide range of tasks.
#### Ability that the Dataset measures
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: periscope -->
Dialog understanding, dialog policy learning
### GEM-Specific Curation
#### Modificatied for GEM?
<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
<!-- scope: telescope -->
yes
#### GEM Modifications
<!-- info: What changes have been made to he original dataset? -->
<!-- scope: periscope -->
`other`
#### Modification Details
<!-- info: For each of these changes, described them in more details and provided the intended purpose of the modification -->
<!-- scope: microscope -->
To adapt to hugging face Datasets, we 1) separate user annotators' ID and system annotations' ID; 2) we convert the data type in goal/user state to string.
#### Additional Splits?
<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
no
### Getting Started with the Task
#### Pointers to Resources
<!-- info: Getting started with in-depth research on the task. Add relevant pointers to resources that researchers can consult when they want to get started digging deeper into the task. -->
<!-- scope: microscope -->
[Code](https://github.com/thu-coai/Convlab-2)
#### Technical Terms
<!-- info: Technical terms used in this card and the dataset and their definitions -->
<!-- scope: microscope -->
According to the type of user goal, we group the dialogues in the training set into five categories:
- S: 417 dialogues have only one sub-goal in HAR domains.
- M: 1573 dialogues have multiple sub-goals (2-3) in HAR domains. However, these sub-goals do not have cross-domain informable slots.
- M+T: 691 dialogues have multiple sub-goals in HAR domains and at least one sub-goal in the metro or taxi domain (3-5 sub-goals). The sub-goals in HAR domains do not have cross-domain informable slots.
- CM: 1,759 dialogues have multiple sub-goals (2-5) in HAR domains with cross-domain informable slots.
- CM+T: 572 dialogues have multiple sub-goals in HAR domains with cross-domain informable slots and at least one sub-goal in the metro or taxi domain (3-5 sub-goals).
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
Dialog understanding, dialog policy learning
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`BLEU`
#### Proposed Evaluation
<!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. -->
<!-- scope: microscope -->
BLEU evaluates the generation quality.
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
yes
#### Other Evaluation Approaches
<!-- info: What evaluation approaches have others used? -->
<!-- scope: periscope -->
Inform rate: how many entities in the gold response appear in the generated response.
#### Relevant Previous Results
<!-- info: What are the most relevant previous results for this task/dataset? -->
<!-- scope: microscope -->
BLEU on MultiWOZ dataset.
## Dataset Curation
### Original Curation
#### Original Curation Rationale
<!-- info: Original curation rationale -->
<!-- scope: telescope -->
Gather human-to-human dialog in Chinese.
#### Communicative Goal
<!-- info: What was the communicative goal? -->
<!-- scope: periscope -->
Generate a response according to the dialog context and database search results.
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
no
### Language Data
#### How was Language Data Obtained?
<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Crowdsourced`
#### Where was it crowdsourced?
<!-- info: If crowdsourced, where from? -->
<!-- scope: periscope -->
`Participatory experiment`
#### Language Producers
<!-- info: What further information do we have on the language producers? -->
<!-- scope: microscope -->
An usr/sys ID indicates the creator of different data points.
#### Topics Covered
<!-- info: Does the language in the dataset focus on specific topics? How would you describe them? -->
<!-- scope: periscope -->
domains: attraction, hotel, restaurant, metro, taxi
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
validated by data curator
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
not filtered
### Structured Annotations
#### Additional Annotations?
<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
none
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
no
### Consent
#### Any Consent Policy?
<!-- info: Was there a consent policy involved when gathering the data? -->
<!-- scope: telescope -->
yes
#### Consent Policy Details
<!-- info: What was the consent policy? -->
<!-- scope: microscope -->
Annotators agree using the dataset for research purpose.
#### Other Consented Downstream Use
<!-- info: What other downstream uses of the data did the original data creators and the data curators consent to? -->
<!-- scope: microscope -->
Any
### Private Identifying Information (PII)
#### Contains PII?
<!-- quick -->
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
<!-- scope: telescope -->
unlikely
#### Categories of PII
<!-- info: What categories of PII are present or suspected in the data? -->
<!-- scope: periscope -->
`generic PII`
#### Any PII Identification?
<!-- info: Did the curators use any automatic/manual method to identify PII in the dataset? -->
<!-- scope: periscope -->
no identification
### Maintenance
#### Any Maintenance Plan?
<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
no
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
no
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
yes
#### Details on how Dataset Addresses the Needs
<!-- info: Describe how this dataset addresses the needs of underserved communities. -->
<!-- scope: microscope -->
CrossWOZ is the first large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dataset. The corpus contains rich annotation of dialogue states and dialogue acts at both user and system sides, which can be used in a wide range of tasks.
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
no
#### Are the Language Producers Representative of the Language?
<!-- info: Does the distribution of language producers in the dataset accurately represent the full distribution of speakers of the language world-wide? If not, how does it differ? -->
<!-- scope: periscope -->
Yes
## Considerations for Using the Data
### PII Risks and Liability
#### Potential PII Risk
<!-- info: Considering your answers to the PII part of the Data Curation Section, describe any potential privacy to the data subjects and creators risks when using the dataset. -->
<!-- scope: microscope -->
No
### Licenses
#### Copyright Restrictions on the Dataset
<!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? -->
<!-- scope: periscope -->
`open license - commercial use allowed`
#### Copyright Restrictions on the Language Data
<!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
<!-- scope: periscope -->
`open license - commercial use allowed`
### Known Technical Limitations
#### Technical Limitations
<!-- info: Describe any known technical limitations, such as spurrious correlations, train/test overlap, annotation biases, or mis-annotations, and cite the works that first identified these limitations when possible. -->
<!-- scope: microscope -->
No
#### Unsuited Applications
<!-- info: When using a model trained on this dataset in a setting where users or the public may interact with its predictions, what are some pitfalls to look out for? In particular, describe some applications of the general task featured in this dataset that its curation or properties make it less suitable for. -->
<!-- scope: microscope -->
Model may not handle unknown values in the dialog
#### Discouraged Use Cases
<!-- info: What are some discouraged use cases of a model trained to maximize the proposed metrics on this dataset? In particular, think about settings where decisions made by a model that performs reasonably well on the metric my still have strong negative consequences for user or members of the public. -->
<!-- scope: microscope -->
Responses can be diverse, which is not captured by BLEU
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|
Motahar/github-issues | Motahar | 2022-10-25T09:06:29Z | 266 | 0 | [
"task_categories:text-retrieval",
"task_categories:text-classification",
"task_ids:document-retrieval",
"task_ids:multi-label-classification",
"task_ids:multi-class-classification",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
]
| [
"text-retrieval",
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
pretty_name: Huggingface Datasets github issues
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text-retrieval
- text-classification
task_ids:
- document-retrieval
- multi-label-classification
- multi-class-classification
--- | [
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|
Plim/fr_corpora_parliament_processed | Plim | 2022-12-12T09:10:10Z | 266 | 0 | [
"language:fr",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language: fr
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|
Plim/language_model_fr | Plim | 2022-12-12T09:11:16Z | 266 | 0 | [
"language:fr",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language: fr
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|
aseifert/pie-synthetic | aseifert | 2022-07-07T11:55:53Z | 266 | 1 | [
"multilinguality:translation",
"size_categories:unknown",
"language:en",
"region:us"
]
| [
"conditional-text-generation"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators: []
language_creators: []
language:
- en
license: []
multilinguality:
- translation
pretty_name: pie-synthetic
size_categories:
- unknown
source_datasets: []
task_categories:
- conditional-text-generation
task_ids:
- machine-translation
---
# PIE synthetic dataset
Repo: https://github.com/awasthiabhijeet/PIE
Paper: https://aclanthology.org/D19-1435.pdf | [
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|
emrecan/stsb-mt-turkish | emrecan | 2022-10-25T10:55:24Z | 266 | 3 | [
"task_categories:text-classification",
"task_ids:semantic-similarity-scoring",
"task_ids:text-scoring",
"language_creators:machine-generated",
"size_categories:1K<n<10K",
"source_datasets:extended|other-sts-b",
"language:tr",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
language_creators:
- machine-generated
language:
- tr
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-sts-b
task_categories:
- text-classification
task_ids:
- semantic-similarity-scoring
- text-scoring
---
# STSb Turkish
Semantic textual similarity dataset for the Turkish language. It is a machine translation (Azure) of the [STSb English](http://ixa2.si.ehu.eus/stswiki/index.php/STSbenchmark) dataset. This dataset is not reviewed by expert human translators.
Uploaded from [this repository](https://github.com/emrecncelik/sts-benchmark-tr). | [
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|
holylovenia/recam | holylovenia | 2021-10-18T03:28:53Z | 266 | 0 | [
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# ReCAM: Reading Comprehension of Abstract Meaning
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
This dataset is from SemEval 2021 Task 4: Reading Comprehension of Abstract Meaning. [Original repository for the dataset and baseline code can be accessed here.](https://github.com/boyuanzheng010/SemEval2021-Reading-Comprehension-of-Abstract-Meaning)
- **Paper:** [SemEval-2021 Task 4: Reading Comprehension of Abstract Meaning in ACL](https://aclanthology.org/2021.semeval-1.4.pdf)
- **Leaderboard:** [CodaLab](https://competitions.codalab.org/competitions/26153#learn_the_details)
### Dataset Summary
Refer to [this page](https://competitions.codalab.org/competitions/26153#learn_the_details).
## Dataset Structure
Refer to [the GitHub](https://github.com/boyuanzheng010/SemEval2021-Reading-Comprehension-of-Abstract-Meaning).
### 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
```
@inproceedings{zheng-etal-2021-semeval,
title = "{S}em{E}val-2021 Task 4: Reading Comprehension of Abstract Meaning",
author = "Zheng, Boyuan and
Yang, Xiaoyu and
Ruan, Yu-Ping and
Ling, Zhenhua and
Liu, Quan and
Wei, Si and
Zhu, Xiaodan",
booktitle = "Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.semeval-1.4",
doi = "10.18653/v1/2021.semeval-1.4",
pages = "37--50",
}
```
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset. | [
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|
huggingartists/galenskaparna-and-after-shave | huggingartists | 2022-10-25T09:30:13Z | 266 | 0 | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/galenskaparna-and-after-shave"
## 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:** 0.252487 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://assets.genius.com/images/default_avatar_300.png?1629820244')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/galenskaparna-and-after-shave">
<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">Galenskaparna & After Shave</div>
<a href="https://genius.com/artists/galenskaparna-and-after-shave">
<div style="text-align: center; font-size: 14px;">@galenskaparna-and-after-shave</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/galenskaparna-and-after-shave).
### 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/galenskaparna-and-after-shave")
```
## 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|
|------:|---------:|---:|
|157| -| -|
'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/galenskaparna-and-after-shave")
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)
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|
huggingartists/ghost | huggingartists | 2022-10-25T09:30:19Z | 266 | 1 | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/ghost"
## 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:** 0.184957 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/3192bff259bbe651686374ba3b8553bd.828x828x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/ghost">
<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">Ghost</div>
<a href="https://genius.com/artists/ghost">
<div style="text-align: center; font-size: 14px;">@ghost</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/ghost).
### 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/ghost")
```
## 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|
|------:|---------:|---:|
|142| -| -|
'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/ghost")
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)
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|
huggingartists/jah-khalib | huggingartists | 2022-10-25T09:32:00Z | 266 | 0 | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/jah-khalib"
## 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:** 0.269094 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/0fed863398263b7dc223768818883d19.300x300x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/jah-khalib">
<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">Jah Khalib</div>
<a href="https://genius.com/artists/jah-khalib">
<div style="text-align: center; font-size: 14px;">@jah-khalib</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/jah-khalib).
### 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/jah-khalib")
```
## 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|
|------:|---------:|---:|
|84| -| -|
'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/jah-khalib")
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)
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|
huggingartists/jim-morrison | huggingartists | 2022-10-25T09:32:07Z | 266 | 0 | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/jim-morrison"
## 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:** 0.279131 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/ca9d975b4af890b1a7dedd5171157994.570x570x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/jim-morrison">
<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">Jim Morrison</div>
<a href="https://genius.com/artists/jim-morrison">
<div style="text-align: center; font-size: 14px;">@jim-morrison</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/jim-morrison).
### 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/jim-morrison")
```
## 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|
|------:|---------:|---:|
|252| -| -|
'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/jim-morrison")
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)
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|
huggingartists/john-k-samson | huggingartists | 2022-10-25T09:32:13Z | 266 | 0 | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/john-k-samson"
## 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:** 0.128555 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/0af64278d82733c4487d404fd3703ef7.894x894x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/john-k-samson">
<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">John K. Samson</div>
<a href="https://genius.com/artists/john-k-samson">
<div style="text-align: center; font-size: 14px;">@john-k-samson</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/john-k-samson).
### 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/john-k-samson")
```
## 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|
|------:|---------:|---:|
|116| -| -|
'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/john-k-samson")
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)
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|
huggingartists/john-lennon | huggingartists | 2022-10-25T09:32:20Z | 266 | 0 | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/john-lennon"
## 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:** 2.247193 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/de14b272004b51dea8071e7cba21cbac.1000x1000x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/john-lennon">
<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">John Lennon</div>
<a href="https://genius.com/artists/john-lennon">
<div style="text-align: center; font-size: 14px;">@john-lennon</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/john-lennon).
### 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/john-lennon")
```
## 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|
|------:|---------:|---:|
|2337| -| -|
'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/john-lennon")
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)
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|
huggingartists/joni-mitchell | huggingartists | 2022-10-25T09:32:32Z | 266 | 0 | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/joni-mitchell"
## 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:** 0.703544 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/ed9a330b2539058076e0c48398599b09.1000x1000x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/joni-mitchell">
<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">Joni Mitchell</div>
<a href="https://genius.com/artists/joni-mitchell">
<div style="text-align: center; font-size: 14px;">@joni-mitchell</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/joni-mitchell).
### 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/joni-mitchell")
```
## 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|
|------:|---------:|---:|
|559| -| -|
'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/joni-mitchell")
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)
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|
huggingartists/oxxxymiron | huggingartists | 2022-10-25T09:40:52Z | 266 | 0 | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/oxxxymiron"
## 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:** 2.070318 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/57ecbbdaf70c671be2d8b7bd39112db0.1000x1000x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/oxxxymiron">
<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">Oxxxymiron</div>
<a href="https://genius.com/artists/oxxxymiron">
<div style="text-align: center; font-size: 14px;">@oxxxymiron</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/oxxxymiron).
### 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/oxxxymiron")
```
## 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|
|------:|---------:|---:|
|210| -| -|
'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/oxxxymiron")
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)
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|
huggingartists/sugar-ray | huggingartists | 2022-10-25T09:45:22Z | 266 | 0 | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/sugar-ray"
## 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:** 0.164888 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/8b5c8fe74f6176047b2b5681e0e0e2d4.273x273x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/sugar-ray">
<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">Sugar Ray</div>
<a href="https://genius.com/artists/sugar-ray">
<div style="text-align: center; font-size: 14px;">@sugar-ray</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/sugar-ray).
### 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/sugar-ray")
```
## 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|
|------:|---------:|---:|
|117| -| -|
'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/sugar-ray")
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)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
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|
huggingartists/sum-41 | huggingartists | 2022-10-25T09:45:34Z | 266 | 0 | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/sum-41"
## 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:** 0.196472 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/7cf5f61ac4ffe9a0fd1f6a4b235b95eb.320x320x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/sum-41">
<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">Sum 41</div>
<a href="https://genius.com/artists/sum-41">
<div style="text-align: center; font-size: 14px;">@sum-41</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/sum-41).
### 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/sum-41")
```
## 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|
|------:|---------:|---:|
|134| -| -|
'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/sum-41")
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)
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|
huggingartists/sundara-karma | huggingartists | 2022-10-25T09:45:40Z | 266 | 0 | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/sundara-karma"
## 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:** 0.081864 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/afbb51b0dc0e4618f79565e67991a9fd.360x360x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/sundara-karma">
<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">Sundara Karma</div>
<a href="https://genius.com/artists/sundara-karma">
<div style="text-align: center; font-size: 14px;">@sundara-karma</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/sundara-karma).
### 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/sundara-karma")
```
## 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|
|------:|---------:|---:|
|46| -| -|
'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/sundara-karma")
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)
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|
nampdn-ai/tiny-codes | nampdn-ai | 2023-09-30T04:14:36Z | 266 | 142 | [
"task_categories:text-generation",
"size_categories:1M<n<10M",
"language:en",
"license:mit",
"arxiv:2306.11644",
"arxiv:2305.07759",
"doi:10.57967/hf/0937",
"region:us"
]
| [
"text-generation"
]
| 2023-07-16T07:26:18Z | ---
license: mit
task_categories:
- text-generation
language:
- en
pretty_name: Tiny Codes
size_categories:
- 1M<n<10M
---
# Reasoning with Language and Code
This synthetic dataset is a collection of **1.6 millions short and clear code snippets** that can help LLM models learn how to reason with both natural and programming languages. The dataset covers a wide range of programming languages, such as Python, TypeScript, JavaScript, Ruby, Julia, Rust, C++, Bash, Java, C#, and Go. It also includes two database languages: Cypher (for graph databases) and SQL (for relational databases) in order to study the relationship of entities.
The main goal of this repository is to highlight the importance of **textbook (high education value)** using **code snippets**. All code snippets are carefully written and commented to ensure maximum readability and understandability. Moreover, the use of **if/else control flow** is emphasized to foster the development of effective reasoning skills in LLM models.
This repository is inspired by the paper [Textbooks Are All You Need](https://arxiv.org/abs/2306.11644) and [The Magic of IF](https://aclanthology.org/2023.findings-acl.574.pdf), which shows that LLM models can achieve state-of-the-art results on code-related tasks by training on high-quality data that resembles textbooks and exercises. This repository aims to provide such data for data analysts and ML engineers who want to enhance their knowledge of how LLM models can learn to reason with code. Anyone who wants to reproduce this dataset can use these prompts with other LLM models and compare their results, or you can forge a new prompt from related properties.
*Please note that this dataset is not intended for code-generation purposes, it's intended to boost the reasoning capability of model via logic code.*
I hope you find this dataset useful and informative!
## Tiny Series
Explore the possibilities and limitations of building Small Language Models with these tiny gems of data!
- [TinyStories](https://arxiv.org/abs/2305.07759): The paper that sparked my interest in the journey of the tiny-* series.
- [tiny-textbooks](https://huggingface.co/datasets/nampdn-ai/tiny-textbooks): 420k "things of internet" synthetic textbooks.
- [tiny-orca-textbooks](https://huggingface.co/datasets/nampdn-ai/tiny-orca-textbooks): Synthetic textbook to help model learn in-context on how it should perform task the right way.
- [tiny-webtext](https://huggingface.co/datasets/nampdn-ai/tiny-webtext): A 6GB (4.5M records) variety of diverse webtext enriched with critical thinking methods to make unbiased English dataset.
- [tiny-lessons](https://huggingface.co/datasets/nampdn-ai/tiny-lessons): Subset of [tiny-textbooks](https://huggingface.co/datasets/nampdn-ai/tiny-textbooks) dataset, various lessons about "things of internet" augmented in a bite-sized textbook Markdown format.
- [tiny-bridgedict](https://huggingface.co/datasets/nampdn-ai/tiny-bridgedict): A dataset that links and transfers knowledge between English, Vietnamese, Chinese in a tiny multilingual models.
### Others small HQ datasets with textbook-like quality
- [devdocs.io](https://huggingface.co/datasets/nampdn-ai/devdocs.io): FreeCodeCamp has provided 189k comprehensive API documentation across a wide range of tech stacks and programming languages.
- [sciphi-python-textbook](https://huggingface.co/datasets/emrgnt-cmplxty/sciphi-python-textbook)
- [textbook_quality_programming](https://huggingface.co/datasets/vikp/textbook_quality_programming)
- [sciphi-textbooks-are-all-you-need](https://huggingface.co/datasets/emrgnt-cmplxty/sciphi-textbooks-are-all-you-need) | [
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|
open-llm-leaderboard/details_jlevin__guanaco-13b-llama-2 | open-llm-leaderboard | 2023-09-22T21:27:03Z | 266 | 0 | [
"region:us"
]
| null | 2023-08-18T00:03:35Z | ---
pretty_name: Evaluation run of jlevin/guanaco-13b-llama-2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [jlevin/guanaco-13b-llama-2](https://huggingface.co/jlevin/guanaco-13b-llama-2)\
\ 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_jlevin__guanaco-13b-llama-2\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-22T21:26:51.366980](https://huggingface.co/datasets/open-llm-leaderboard/details_jlevin__guanaco-13b-llama-2/blob/main/results_2023-09-22T21-26-51.366980.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\": 0.002202181208053691,\n\
\ \"em_stderr\": 0.00048005108166193844,\n \"f1\": 0.05864932885906048,\n\
\ \"f1_stderr\": 0.0013800780436743112,\n \"acc\": 0.4005539821430815,\n\
\ \"acc_stderr\": 0.009346120664291423\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.002202181208053691,\n \"em_stderr\": 0.00048005108166193844,\n\
\ \"f1\": 0.05864932885906048,\n \"f1_stderr\": 0.0013800780436743112\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0576194086429113,\n \
\ \"acc_stderr\": 0.006418593319822861\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7434885556432518,\n \"acc_stderr\": 0.012273648008759986\n\
\ }\n}\n```"
repo_url: https://huggingface.co/jlevin/guanaco-13b-llama-2
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|arc:challenge|25_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_22T21_26_51.366980
path:
- '**/details_harness|drop|3_2023-09-22T21-26-51.366980.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-22T21-26-51.366980.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_22T21_26_51.366980
path:
- '**/details_harness|gsm8k|5_2023-09-22T21-26-51.366980.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-22T21-26-51.366980.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hellaswag|10_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-09T17:17:23.827759.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-09T17:17:23.827759.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-09T17:17:23.827759.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_22T21_26_51.366980
path:
- '**/details_harness|winogrande|5_2023-09-22T21-26-51.366980.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-22T21-26-51.366980.parquet'
- config_name: results
data_files:
- split: 2023_08_09T17_17_23.827759
path:
- results_2023-08-09T17:17:23.827759.parquet
- split: 2023_09_22T21_26_51.366980
path:
- results_2023-09-22T21-26-51.366980.parquet
- split: latest
path:
- results_2023-09-22T21-26-51.366980.parquet
---
# Dataset Card for Evaluation run of jlevin/guanaco-13b-llama-2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/jlevin/guanaco-13b-llama-2
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [jlevin/guanaco-13b-llama-2](https://huggingface.co/jlevin/guanaco-13b-llama-2) 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_jlevin__guanaco-13b-llama-2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-22T21:26:51.366980](https://huggingface.co/datasets/open-llm-leaderboard/details_jlevin__guanaco-13b-llama-2/blob/main/results_2023-09-22T21-26-51.366980.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": 0.002202181208053691,
"em_stderr": 0.00048005108166193844,
"f1": 0.05864932885906048,
"f1_stderr": 0.0013800780436743112,
"acc": 0.4005539821430815,
"acc_stderr": 0.009346120664291423
},
"harness|drop|3": {
"em": 0.002202181208053691,
"em_stderr": 0.00048005108166193844,
"f1": 0.05864932885906048,
"f1_stderr": 0.0013800780436743112
},
"harness|gsm8k|5": {
"acc": 0.0576194086429113,
"acc_stderr": 0.006418593319822861
},
"harness|winogrande|5": {
"acc": 0.7434885556432518,
"acc_stderr": 0.012273648008759986
}
}
```
### 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] | [
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|
starmpcc/Asclepius-Synthetic-Clinical-Notes | starmpcc | 2023-09-04T01:27:17Z | 266 | 25 | [
"task_categories:question-answering",
"task_categories:summarization",
"task_categories:text-generation",
"task_categories:conversational",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-nc-sa-4.0",
"medical",
"arxiv:2309.00237",
"region:us"
]
| [
"question-answering",
"summarization",
"text-generation",
"conversational"
]
| 2023-09-01T01:47:59Z | ---
license: cc-by-nc-sa-4.0
task_categories:
- question-answering
- summarization
- text-generation
- conversational
language:
- en
tags:
- medical
pretty_name: 'Asclepius: Synthetic Clincal Notes & Instruction Dataset'
size_categories:
- 100K<n<1M
---
# Asclepius: Synthetic Clincal Notes & Instruction Dataset
## Dataset Description
- **Repository:**
- [Github](https://github.com/starmpcc/Asclepius)
- **Paper:**
- https://arxiv.org/abs/2309.00237
- **MODEL:**
- https://huggingface.co/starmpcc/Asclepius-13B
- https://huggingface.co/starmpcc/Asclepius-7B
### Dataset Summary
This dataset is official dataset for Asclepius [(arxiv)](https://arxiv.org/abs/2309.00237)
This dataset is composed with Clinical Note - Question - Answer format to build a clinical LLMs.
- We first synthesized synthetic notes from [PMC-Patients](https://huggingface.co/datasets/zhengyun21/PMC-Patients) case reports with GPT-3.5
- Then, we generate instruction-answer pairs for 157k synthetic discharge summaries
### Supported Tasks and Leaderboards
- This dataset covers below 8 tasks
- Named Entity Recognition
- Abbreviation Expansion
- Relation Extraction
- Temporal Information Extraction
- Coreference Resolution
- Paraphrasing
- Summarization
- Question Answering
### Languages
English
## Dataset Structure
### Data Instances
- `synthetic.csv`
- Clinical Note - Question - Answer pairs
### Data Fields
- `patient_id`: Unique case report id from PMC-Patients
- `patient`: Case report text
- `question`: GPT-3.5 generated instruction from patient. The used prompt can be checked on github.
- `answer`: GPT-3.5 generated answer for given case report and question
- `task`: Corresponding category of question. One of above listsed
## Dataset Creation
### Source Data
[PMC-Patients](https://huggingface.co/datasets/zhengyun21/PMC-Patients)
### Annotations
We used GPT-3.5-turbo (version 0314).
You can check the prompts on our github.
## Additional Information
### Licensing Information
CC-BY-NC-SA 4.0
### Citation Information
@misc{kweon2023publicly,
title={Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes},
author={Sunjun Kweon and Junu Kim and Jiyoun Kim and Sujeong Im and Eunbyeol Cho and Seongsu Bae and Jungwoo Oh and Gyubok Lee and Jong Hak Moon and Seng Chan You and Seungjin Baek and Chang Hoon Han and Yoon Bin Jung and Yohan Jo and Edward Choi},
year={2023},
eprint={2309.00237},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | [
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|
open-llm-leaderboard/details_xxyyy123__mc_data_30k_from_platpus_orca_7b_10k_v1_lora_qk_rank14_v2 | open-llm-leaderboard | 2023-09-03T18:42:23Z | 266 | 0 | [
"region:us"
]
| null | 2023-09-03T18:41:28Z | ---
pretty_name: Evaluation run of xxyyy123/mc_data_30k_from_platpus_orca_7b_10k_v1_lora_qk_rank14_v2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [xxyyy123/mc_data_30k_from_platpus_orca_7b_10k_v1_lora_qk_rank14_v2](https://huggingface.co/xxyyy123/mc_data_30k_from_platpus_orca_7b_10k_v1_lora_qk_rank14_v2)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 61 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 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_xxyyy123__mc_data_30k_from_platpus_orca_7b_10k_v1_lora_qk_rank14_v2\"\
,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\
\nThese are the [latest results from run 2023-09-03T18:41:04.280567](https://huggingface.co/datasets/open-llm-leaderboard/details_xxyyy123__mc_data_30k_from_platpus_orca_7b_10k_v1_lora_qk_rank14_v2/blob/main/results_2023-09-03T18%3A41%3A04.280567.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\": 0.5159772470651705,\n\
\ \"acc_stderr\": 0.03490050368845693,\n \"acc_norm\": 0.5196198874675843,\n\
\ \"acc_norm_stderr\": 0.03488383911166199,\n \"mc1\": 0.3574051407588739,\n\
\ \"mc1_stderr\": 0.0167765996767294,\n \"mc2\": 0.5084843623108531,\n\
\ \"mc2_stderr\": 0.015788699144390992\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5537542662116041,\n \"acc_stderr\": 0.014526705548539982,\n\
\ \"acc_norm\": 0.5810580204778157,\n \"acc_norm_stderr\": 0.014418106953639013\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6132244572794264,\n\
\ \"acc_stderr\": 0.004860162076330978,\n \"acc_norm\": 0.8008364867556264,\n\
\ \"acc_norm_stderr\": 0.0039855506403304606\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \
\ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542128\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.48148148148148145,\n\
\ \"acc_stderr\": 0.043163785995113245,\n \"acc_norm\": 0.48148148148148145,\n\
\ \"acc_norm_stderr\": 0.043163785995113245\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.47368421052631576,\n \"acc_stderr\": 0.04063302731486671,\n\
\ \"acc_norm\": 0.47368421052631576,\n \"acc_norm_stderr\": 0.04063302731486671\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.5,\n\
\ \"acc_stderr\": 0.050251890762960605,\n \"acc_norm\": 0.5,\n \
\ \"acc_norm_stderr\": 0.050251890762960605\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6,\n \"acc_stderr\": 0.030151134457776285,\n \
\ \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.030151134457776285\n \
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5625,\n\
\ \"acc_stderr\": 0.04148415739394154,\n \"acc_norm\": 0.5625,\n \
\ \"acc_norm_stderr\": 0.04148415739394154\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145632,\n \
\ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145632\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \"acc_norm\": 0.36,\n\
\ \"acc_norm_stderr\": 0.048241815132442176\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \
\ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542128\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.4797687861271676,\n\
\ \"acc_stderr\": 0.03809342081273957,\n \"acc_norm\": 0.4797687861271676,\n\
\ \"acc_norm_stderr\": 0.03809342081273957\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.29411764705882354,\n \"acc_stderr\": 0.04533838195929775,\n\
\ \"acc_norm\": 0.29411764705882354,\n \"acc_norm_stderr\": 0.04533838195929775\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.62,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.62,\n\
\ \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.4851063829787234,\n \"acc_stderr\": 0.032671518489247764,\n\
\ \"acc_norm\": 0.4851063829787234,\n \"acc_norm_stderr\": 0.032671518489247764\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.32456140350877194,\n\
\ \"acc_stderr\": 0.044045561573747664,\n \"acc_norm\": 0.32456140350877194,\n\
\ \"acc_norm_stderr\": 0.044045561573747664\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.45517241379310347,\n \"acc_stderr\": 0.04149886942192117,\n\
\ \"acc_norm\": 0.45517241379310347,\n \"acc_norm_stderr\": 0.04149886942192117\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.30952380952380953,\n \"acc_stderr\": 0.023809523809523867,\n \"\
acc_norm\": 0.30952380952380953,\n \"acc_norm_stderr\": 0.023809523809523867\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.2777777777777778,\n\
\ \"acc_stderr\": 0.040061680838488774,\n \"acc_norm\": 0.2777777777777778,\n\
\ \"acc_norm_stderr\": 0.040061680838488774\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.5612903225806452,\n\
\ \"acc_stderr\": 0.028229497320317216,\n \"acc_norm\": 0.5612903225806452,\n\
\ \"acc_norm_stderr\": 0.028229497320317216\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.3842364532019704,\n \"acc_stderr\": 0.0342239856565755,\n\
\ \"acc_norm\": 0.3842364532019704,\n \"acc_norm_stderr\": 0.0342239856565755\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.41,\n \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\"\
: 0.41,\n \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7090909090909091,\n \"acc_stderr\": 0.03546563019624336,\n\
\ \"acc_norm\": 0.7090909090909091,\n \"acc_norm_stderr\": 0.03546563019624336\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.6818181818181818,\n \"acc_stderr\": 0.0331847733384533,\n \"acc_norm\"\
: 0.6818181818181818,\n \"acc_norm_stderr\": 0.0331847733384533\n },\n\
\ \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \
\ \"acc\": 0.7512953367875648,\n \"acc_stderr\": 0.031195840877700286,\n\
\ \"acc_norm\": 0.7512953367875648,\n \"acc_norm_stderr\": 0.031195840877700286\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.48205128205128206,\n \"acc_stderr\": 0.02533466708095495,\n\
\ \"acc_norm\": 0.48205128205128206,\n \"acc_norm_stderr\": 0.02533466708095495\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.2518518518518518,\n \"acc_stderr\": 0.02646611753895991,\n \
\ \"acc_norm\": 0.2518518518518518,\n \"acc_norm_stderr\": 0.02646611753895991\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.5126050420168067,\n \"acc_stderr\": 0.03246816765752174,\n \
\ \"acc_norm\": 0.5126050420168067,\n \"acc_norm_stderr\": 0.03246816765752174\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3576158940397351,\n \"acc_stderr\": 0.03913453431177258,\n \"\
acc_norm\": 0.3576158940397351,\n \"acc_norm_stderr\": 0.03913453431177258\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.7192660550458716,\n \"acc_stderr\": 0.019266055045871623,\n \"\
acc_norm\": 0.7192660550458716,\n \"acc_norm_stderr\": 0.019266055045871623\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.375,\n \"acc_stderr\": 0.033016908987210894,\n \"acc_norm\": 0.375,\n\
\ \"acc_norm_stderr\": 0.033016908987210894\n },\n \"harness|hendrycksTest-high_school_us_history|5\"\
: {\n \"acc\": 0.696078431372549,\n \"acc_stderr\": 0.03228210387037892,\n\
\ \"acc_norm\": 0.696078431372549,\n \"acc_norm_stderr\": 0.03228210387037892\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7215189873417721,\n \"acc_stderr\": 0.029178682304842548,\n \
\ \"acc_norm\": 0.7215189873417721,\n \"acc_norm_stderr\": 0.029178682304842548\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.5964125560538116,\n\
\ \"acc_stderr\": 0.03292802819330314,\n \"acc_norm\": 0.5964125560538116,\n\
\ \"acc_norm_stderr\": 0.03292802819330314\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.5954198473282443,\n \"acc_stderr\": 0.043046937953806645,\n\
\ \"acc_norm\": 0.5954198473282443,\n \"acc_norm_stderr\": 0.043046937953806645\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.6446280991735537,\n \"acc_stderr\": 0.0436923632657398,\n \"acc_norm\"\
: 0.6446280991735537,\n \"acc_norm_stderr\": 0.0436923632657398\n },\n\
\ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.6481481481481481,\n\
\ \"acc_stderr\": 0.046166311118017125,\n \"acc_norm\": 0.6481481481481481,\n\
\ \"acc_norm_stderr\": 0.046166311118017125\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.5705521472392638,\n \"acc_stderr\": 0.03889066619112723,\n\
\ \"acc_norm\": 0.5705521472392638,\n \"acc_norm_stderr\": 0.03889066619112723\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.375,\n\
\ \"acc_stderr\": 0.04595091388086298,\n \"acc_norm\": 0.375,\n \
\ \"acc_norm_stderr\": 0.04595091388086298\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7087378640776699,\n \"acc_stderr\": 0.04498676320572924,\n\
\ \"acc_norm\": 0.7087378640776699,\n \"acc_norm_stderr\": 0.04498676320572924\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7777777777777778,\n\
\ \"acc_stderr\": 0.027236013946196704,\n \"acc_norm\": 0.7777777777777778,\n\
\ \"acc_norm_stderr\": 0.027236013946196704\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\
: 0.55,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-miscellaneous|5\"\
: {\n \"acc\": 0.7100893997445722,\n \"acc_stderr\": 0.01622501794477098,\n\
\ \"acc_norm\": 0.7100893997445722,\n \"acc_norm_stderr\": 0.01622501794477098\n\
\ },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.5809248554913294,\n\
\ \"acc_stderr\": 0.02656417811142262,\n \"acc_norm\": 0.5809248554913294,\n\
\ \"acc_norm_stderr\": 0.02656417811142262\n },\n \"harness|hendrycksTest-moral_scenarios|5\"\
: {\n \"acc\": 0.264804469273743,\n \"acc_stderr\": 0.014756906483260664,\n\
\ \"acc_norm\": 0.264804469273743,\n \"acc_norm_stderr\": 0.014756906483260664\n\
\ },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.5261437908496732,\n\
\ \"acc_stderr\": 0.028590752958852394,\n \"acc_norm\": 0.5261437908496732,\n\
\ \"acc_norm_stderr\": 0.028590752958852394\n },\n \"harness|hendrycksTest-philosophy|5\"\
: {\n \"acc\": 0.5884244372990354,\n \"acc_stderr\": 0.027950481494401266,\n\
\ \"acc_norm\": 0.5884244372990354,\n \"acc_norm_stderr\": 0.027950481494401266\n\
\ },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.5648148148148148,\n\
\ \"acc_stderr\": 0.027586006221607708,\n \"acc_norm\": 0.5648148148148148,\n\
\ \"acc_norm_stderr\": 0.027586006221607708\n },\n \"harness|hendrycksTest-professional_accounting|5\"\
: {\n \"acc\": 0.375886524822695,\n \"acc_stderr\": 0.028893955412115882,\n\
\ \"acc_norm\": 0.375886524822695,\n \"acc_norm_stderr\": 0.028893955412115882\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.38396349413298564,\n\
\ \"acc_stderr\": 0.01242158783313423,\n \"acc_norm\": 0.38396349413298564,\n\
\ \"acc_norm_stderr\": 0.01242158783313423\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.4889705882352941,\n \"acc_stderr\": 0.03036544647727568,\n\
\ \"acc_norm\": 0.4889705882352941,\n \"acc_norm_stderr\": 0.03036544647727568\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.4869281045751634,\n \"acc_stderr\": 0.020220920829626912,\n \
\ \"acc_norm\": 0.4869281045751634,\n \"acc_norm_stderr\": 0.020220920829626912\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5909090909090909,\n\
\ \"acc_stderr\": 0.04709306978661896,\n \"acc_norm\": 0.5909090909090909,\n\
\ \"acc_norm_stderr\": 0.04709306978661896\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.6122448979591837,\n \"acc_stderr\": 0.031192230726795656,\n\
\ \"acc_norm\": 0.6122448979591837,\n \"acc_norm_stderr\": 0.031192230726795656\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6119402985074627,\n\
\ \"acc_stderr\": 0.034457899643627506,\n \"acc_norm\": 0.6119402985074627,\n\
\ \"acc_norm_stderr\": 0.034457899643627506\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.77,\n \"acc_stderr\": 0.042295258468165065,\n \
\ \"acc_norm\": 0.77,\n \"acc_norm_stderr\": 0.042295258468165065\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.40963855421686746,\n\
\ \"acc_stderr\": 0.03828401115079022,\n \"acc_norm\": 0.40963855421686746,\n\
\ \"acc_norm_stderr\": 0.03828401115079022\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.695906432748538,\n \"acc_stderr\": 0.03528211258245229,\n\
\ \"acc_norm\": 0.695906432748538,\n \"acc_norm_stderr\": 0.03528211258245229\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3574051407588739,\n\
\ \"mc1_stderr\": 0.0167765996767294,\n \"mc2\": 0.5084843623108531,\n\
\ \"mc2_stderr\": 0.015788699144390992\n }\n}\n```"
repo_url: https://huggingface.co/xxyyy123/mc_data_30k_from_platpus_orca_7b_10k_v1_lora_qk_rank14_v2
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|arc:challenge|25_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hellaswag|10_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-03T18:41:04.280567.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-03T18:41:04.280567.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-03T18:41:04.280567.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-03T18:41:04.280567.parquet'
- config_name: results
data_files:
- split: 2023_09_03T18_41_04.280567
path:
- results_2023-09-03T18:41:04.280567.parquet
- split: latest
path:
- results_2023-09-03T18:41:04.280567.parquet
---
# Dataset Card for Evaluation run of xxyyy123/mc_data_30k_from_platpus_orca_7b_10k_v1_lora_qk_rank14_v2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/xxyyy123/mc_data_30k_from_platpus_orca_7b_10k_v1_lora_qk_rank14_v2
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [xxyyy123/mc_data_30k_from_platpus_orca_7b_10k_v1_lora_qk_rank14_v2](https://huggingface.co/xxyyy123/mc_data_30k_from_platpus_orca_7b_10k_v1_lora_qk_rank14_v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 61 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 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_xxyyy123__mc_data_30k_from_platpus_orca_7b_10k_v1_lora_qk_rank14_v2",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-03T18:41:04.280567](https://huggingface.co/datasets/open-llm-leaderboard/details_xxyyy123__mc_data_30k_from_platpus_orca_7b_10k_v1_lora_qk_rank14_v2/blob/main/results_2023-09-03T18%3A41%3A04.280567.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
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"acc_stderr": 0.03490050368845693,
"acc_norm": 0.5196198874675843,
"acc_norm_stderr": 0.03488383911166199,
"mc1": 0.3574051407588739,
"mc1_stderr": 0.0167765996767294,
"mc2": 0.5084843623108531,
"mc2_stderr": 0.015788699144390992
},
"harness|arc:challenge|25": {
"acc": 0.5537542662116041,
"acc_stderr": 0.014526705548539982,
"acc_norm": 0.5810580204778157,
"acc_norm_stderr": 0.014418106953639013
},
"harness|hellaswag|10": {
"acc": 0.6132244572794264,
"acc_stderr": 0.004860162076330978,
"acc_norm": 0.8008364867556264,
"acc_norm_stderr": 0.0039855506403304606
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.28,
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"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542128
},
"harness|hendrycksTest-anatomy|5": {
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"acc_norm": 0.48148148148148145,
"acc_norm_stderr": 0.043163785995113245
},
"harness|hendrycksTest-astronomy|5": {
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},
"harness|hendrycksTest-business_ethics|5": {
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"acc_norm": 0.5,
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},
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"acc_norm": 0.6,
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},
"harness|hendrycksTest-college_biology|5": {
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},
"harness|hendrycksTest-college_chemistry|5": {
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},
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"acc_norm": 0.36,
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},
"harness|hendrycksTest-college_mathematics|5": {
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"harness|hendrycksTest-conceptual_physics|5": {
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"harness|hendrycksTest-econometrics|5": {
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"harness|hendrycksTest-electrical_engineering|5": {
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"harness|hendrycksTest-formal_logic|5": {
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"harness|hendrycksTest-global_facts|5": {
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"harness|hendrycksTest-high_school_european_history|5": {
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"harness|hendrycksTest-high_school_microeconomics|5": {
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"harness|hendrycksTest-high_school_us_history|5": {
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"harness|hendrycksTest-human_aging|5": {
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"harness|hendrycksTest-international_law|5": {
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"harness|hendrycksTest-jurisprudence|5": {
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},
"harness|hendrycksTest-management|5": {
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"harness|hendrycksTest-marketing|5": {
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"harness|hendrycksTest-medical_genetics|5": {
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},
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},
"harness|hendrycksTest-nutrition|5": {
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"harness|hendrycksTest-sociology|5": {
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"harness|hendrycksTest-us_foreign_policy|5": {
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"harness|hendrycksTest-world_religions|5": {
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"harness|truthfulqa:mc|0": {
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"mc2": 0.5084843623108531,
"mc2_stderr": 0.015788699144390992
}
}
```
### 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] | [
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]
|
open-llm-leaderboard/details_itsliupeng__llama2_7b_code | open-llm-leaderboard | 2023-10-26T11:17:41Z | 266 | 0 | [
"region:us"
]
| null | 2023-10-08T20:46:51Z | ---
pretty_name: Evaluation run of itsliupeng/llama2_7b_code
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [itsliupeng/llama2_7b_code](https://huggingface.co/itsliupeng/llama2_7b_code)\
\ 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_itsliupeng__llama2_7b_code\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-26T11:17:28.829100](https://huggingface.co/datasets/open-llm-leaderboard/details_itsliupeng__llama2_7b_code/blob/main/results_2023-10-26T11-17-28.829100.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\": 0.0009437919463087249,\n\
\ \"em_stderr\": 0.00031446531194130476,\n \"f1\": 0.05393036912751694,\n\
\ \"f1_stderr\": 0.0012935627430820335,\n \"acc\": 0.3980985212183299,\n\
\ \"acc_stderr\": 0.01010319096153194\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0009437919463087249,\n \"em_stderr\": 0.00031446531194130476,\n\
\ \"f1\": 0.05393036912751694,\n \"f1_stderr\": 0.0012935627430820335\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.08112206216830932,\n \
\ \"acc_stderr\": 0.007520395797922653\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7150749802683505,\n \"acc_stderr\": 0.012685986125141227\n\
\ }\n}\n```"
repo_url: https://huggingface.co/itsliupeng/llama2_7b_code
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|arc:challenge|25_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_26T11_17_28.829100
path:
- '**/details_harness|drop|3_2023-10-26T11-17-28.829100.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-26T11-17-28.829100.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_26T11_17_28.829100
path:
- '**/details_harness|gsm8k|5_2023-10-26T11-17-28.829100.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-26T11-17-28.829100.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hellaswag|10_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-08T20-46-27.226805.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-08T20-46-27.226805.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-08T20-46-27.226805.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_26T11_17_28.829100
path:
- '**/details_harness|winogrande|5_2023-10-26T11-17-28.829100.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-26T11-17-28.829100.parquet'
- config_name: results
data_files:
- split: 2023_10_08T20_46_27.226805
path:
- results_2023-10-08T20-46-27.226805.parquet
- split: 2023_10_26T11_17_28.829100
path:
- results_2023-10-26T11-17-28.829100.parquet
- split: latest
path:
- results_2023-10-26T11-17-28.829100.parquet
---
# Dataset Card for Evaluation run of itsliupeng/llama2_7b_code
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/itsliupeng/llama2_7b_code
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [itsliupeng/llama2_7b_code](https://huggingface.co/itsliupeng/llama2_7b_code) 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_itsliupeng__llama2_7b_code",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-26T11:17:28.829100](https://huggingface.co/datasets/open-llm-leaderboard/details_itsliupeng__llama2_7b_code/blob/main/results_2023-10-26T11-17-28.829100.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": 0.0009437919463087249,
"em_stderr": 0.00031446531194130476,
"f1": 0.05393036912751694,
"f1_stderr": 0.0012935627430820335,
"acc": 0.3980985212183299,
"acc_stderr": 0.01010319096153194
},
"harness|drop|3": {
"em": 0.0009437919463087249,
"em_stderr": 0.00031446531194130476,
"f1": 0.05393036912751694,
"f1_stderr": 0.0012935627430820335
},
"harness|gsm8k|5": {
"acc": 0.08112206216830932,
"acc_stderr": 0.007520395797922653
},
"harness|winogrande|5": {
"acc": 0.7150749802683505,
"acc_stderr": 0.012685986125141227
}
}
```
### 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] | [
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udhr | null | 2022-11-03T16:16:11Z | 265 | 1 | [
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| 2022-03-02T23:29:22Z | ---
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paperswithcode_id: null
pretty_name: The Universal Declaration of Human Rights (UDHR)
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---
# Dataset Card for The Universal Declaration of Human Rights (UDHR)
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://www.ohchr.org/en/universal-declaration-of-human-rights, https://unicode.org/udhr/index.html
- **Repository:** https://github.com/unicode-org/udhr
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The Universal Declaration of Human Rights (UDHR) is a milestone document in the history of human rights. Drafted by
representatives with different legal and cultural backgrounds from all regions of the world, it set out, for the
first time, fundamental human rights to be universally protected. The Declaration was adopted by the UN General
Assembly in Paris on 10 December 1948 during its 183rd plenary meeting.
© 1996 – 2009 The Office of the High Commissioner for Human Rights
This plain text version prepared by the "UDHR in Unicode" project, https://www.unicode.org/udhr.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset includes translations of the document in over 400 languages and dialects. The list of languages can be found
[here](https://unicode.org/udhr/translations.html).
## Dataset Structure
### Data Instances
Each instance corresponds to a different language and includes information about the language and the full document
text.
### Data Fields
- `text`: The full document text with each line of text delimited by a newline (`\n`).
- `lang_key`: The unique identifier of a given translation.
- `lang_name`: The textual description of language/dialect.
- `iso639-3`: The [iso639-3](https://iso639-3.sil.org/) language identifier.
- `iso15924`: The [iso15924](https://unicode.org/iso15924/iso15924-codes.html) language identifier.
- `bcp47`: The [BCP 47](https://www.rfc-editor.org/info/bcp47) language identifier.
### Data Splits
Only a `train` split included which includes the full document in all languages.
| | train |
|--------------------|------:|
| Number of examples | 488 |
## Dataset Creation
### Curation Rationale
In addition to its social significance, the document set a world record in 1999 for being the most translated
document in the world and as such can be useful for settings requiring paired text between many languages.
### 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
In addition to the social and political significance of the United Nations' Universal Declaration of Human Rights,
the document set a world record in 1999 for being the most translated document in the world and as such can be useful
for settings requiring paired text between many languages including those that are low resource and significantly
underrepresented in NLP research.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
Although the document is translated into a very large number of languages, the text is very short and therefore may
have limited usefulness for most types of modeling and evaluation.
## Additional Information
### Dataset Curators
The txt/xml data files used here were compiled by The Unicode Consortium, which can be found
[here](https://unicode.org/udhr/index.html). The original texts can be found on the
[United Nations website](https://www.ohchr.org/EN/UDHR/Pages/UDHRIndex.aspx).
### Licensing Information
Source text © 1996 – 2022 The Office of the High Commissioner for Human Rights
The [Unicode license](https://www.unicode.org/license.txt) applies to these translations.
### Citation Information
United Nations. (1998). The Universal Declaration of Human Rights, 1948-1998. New York: United Nations Dept. of Public Information.
### Contributions
Thanks to [@joeddav](https://github.com/joeddav) for adding this dataset. Updated May 2022 [@leondz](https://github.com/leondz). | [
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|
Daniele/dante-corpus | Daniele | 2021-11-12T11:44:16Z | 265 | 1 | [
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
YAML tags:
- copy-paste the tags obtained with the online tagging app: https://huggingface.co/spaces/huggingface/datasets-tagging
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Additional Information](#additional-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
All literary production of great poet Dante Alighieri.
### Supported Tasks and Leaderboards
Fill Mask task.
### Languages
(Ancient) Italian.
### Contributions
Thanks to [@danielekp](https://github.com/danielekp) for adding this dataset.
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|
GEM/dstc10_track2_task2 | GEM | 2022-10-24T15:30:17Z | 265 | 4 | [
"task_categories:conversational",
"annotations_creators:none",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"dialog-response-generation",
"region:us"
]
| [
"conversational"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- en
license:
- apache-2.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- conversational
task_ids: []
pretty_name: dstc10_track2_task2
tags:
- dialog-response-generation
---
# Dataset Card for GEM/dstc10_track2_task2
## Dataset Description
- **Homepage:** https://github.com/alexa/alexa-with-dstc10-track2-dataset
- **Repository:** https://github.com/alexa/alexa-with-dstc10-track2-dataset
- **Paper:** https://assets.amazon.science/54/a1/5282d47044179737b4289622c824/how-robust-are-you-evaluating-task-oriented-dialogue-systems-on-spoken-conversations.pdf
- **Leaderboard:** https://eval.ai/challenge/1663/overview
- **Point of Contact:** Seokhwan Kim
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/dstc10_track2_task2).
### Dataset Summary
The DSTC10 Track2 Task 2 follows the DSTC9 Track1 task, where participants have to implement knowledge-grounded dialog systems.
The training dataset is inherited from the DSTC9 challenge and is in the written domain, while the test set is newly collected and consists of noisy ASR transcripts.
Hence, the dataset facilitates building models for grounded dialog response generation.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/dstc10_track2_task2')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/dstc10_track2_task2).
#### website
https://github.com/alexa/alexa-with-dstc10-track2-dataset
#### paper
https://assets.amazon.science/54/a1/5282d47044179737b4289622c824/how-robust-are-you-evaluating-task-oriented-dialogue-systems-on-spoken-conversations.pdf
#### authors
Seokhwan Kim, Yang Liu, Di Jin, Alexandros Papangelis, Karthik Gopalakrishnan, Behnam Hedayatnia, Dilek Hakkani-Tur (Amazon Alexa AI)
## Dataset Overview
### Where to find the Data and its Documentation
#### Webpage
<!-- info: What is the webpage for the dataset (if it exists)? -->
<!-- scope: telescope -->
https://github.com/alexa/alexa-with-dstc10-track2-dataset
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
https://github.com/alexa/alexa-with-dstc10-track2-dataset
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
https://assets.amazon.science/54/a1/5282d47044179737b4289622c824/how-robust-are-you-evaluating-task-oriented-dialogue-systems-on-spoken-conversations.pdf
#### BibTex
<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
<!-- scope: microscope -->
@inproceedings{kim2021robust,
title={" How Robust ru?": Evaluating Task-Oriented Dialogue Systems on Spoken Conversations},
author={Kim, Seokhwan and Liu, Yang and Jin, Di and Papangelis, Alexandros and Gopalakrishnan, Karthik and Hedayatnia, Behnam and Hakkani-Tur, Dilek},
journal={IEEE Automatic Speech Recognition and Understanding Workshop},
year={2021}
}
#### Contact Name
<!-- quick -->
<!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
Seokhwan Kim
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
[email protected]
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
yes
#### Leaderboard Link
<!-- info: Provide a link to the leaderboard. -->
<!-- scope: periscope -->
https://eval.ai/challenge/1663/overview
#### Leaderboard Details
<!-- info: Briefly describe how the leaderboard evaluates models. -->
<!-- scope: microscope -->
It evaluates the models based on the automatic metrics defined in the task paper for the three tasks of detection, selection and generation.
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
no
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`En`
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
apache-2.0: Apache License 2.0
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
To conduct research on dialogue state tracking and knowledge-grounded response generation.
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Dialog Response Generation
#### Communicative Goal
<!-- quick -->
<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
<!-- scope: periscope -->
This dataset aims to explore the robustness of conversational models when trained on spoken data. It has two aspects, multi-domain dialogue state tracking and conversation modeling with access to unstructured knowledge.
### Credit
#### Curation Organization Type(s)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`industry`
#### Curation Organization(s)
<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
Amazon
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Seokhwan Kim, Yang Liu, Di Jin, Alexandros Papangelis, Karthik Gopalakrishnan, Behnam Hedayatnia, Dilek Hakkani-Tur (Amazon Alexa AI)
#### Funding
<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
Amazon
#### Who added the Dataset to GEM?
<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
<!-- scope: microscope -->
Alexandros Papangelis (Amazon Alexa AI), Di Jin (Amazon Alexa AI), Nico Daheim (RWTH Aachen University)
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
features = datasets.Features(
{
"id": datasets.Value("string"),
"gem_id": datasets.Value("string"),
"turns": [
{
"speaker": datasets.Value("string"),
"text": datasets.Value("string"),
"nbest": [
{
"hyp": datasets.Value("string"),
"score": datasets.Value("float"),
}
],
}
],
"knowledge": {
"domain": datasets.Value("string"),
"entity_name": datasets.Value("string"),
"title": datasets.Value("string"),
"body": datasets.Value("string"),
},
"response": datasets.Value("string"),
"source": datasets.Value("string"),
"linearized_input": datasets.Value("string"),
"target": datasets.Value("string"),
"references": [datasets.Value("string")],
}
)
nbest contains an nbest list of outputs generated by an ASR system along with their scores.
knowledge defines the annotated grounding as well as its metadata
#### Reason for Structure
<!-- info: How was the dataset structure determined? -->
<!-- scope: microscope -->
It was kept compatible with MultiWox 2.X data.
#### Example Instance
<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
<!-- scope: periscope -->
{'id': '0',
'gem_id': 'GEM-dstc10_track2_task2-test-0',
'turns': [{'speaker': 'U',
'text': "hi uh i'm looking for restaurant in lower ha",
'nbest': [{'hyp': "hi uh i'm looking for restaurant in lower ha",
'score': -25.625450134277344},
{'hyp': "hi uh i'm looking for restaurant in lower hai",
'score': -25.969446182250977},
{'hyp': "hi uh i'm looking for restaurant in lower haig",
'score': -32.816890716552734},
{'hyp': "hi uh i'm looking for restaurant in lower haigh",
'score': -32.84316635131836},
{'hyp': "hi uh i'm looking for restaurant in lower hag",
'score': -32.8637580871582},
{'hyp': "hi uh i'm looking for restaurant in lower hah",
'score': -33.1048698425293},
{'hyp': "hi uh i'm looking for restaurant in lower hait",
'score': -33.96509552001953},
{'hyp': "hi um i'm looking for restaurant in lower hai",
'score': -33.97885513305664},
{'hyp': "hi um i'm looking for restaurant in lower haig",
'score': -34.56083679199219},
{'hyp': "hi um i'm looking for restaurant in lower haigh",
'score': -34.58711242675781}]},
{'speaker': 'S',
'text': 'yeah definitely i can go ahead and help you with that ummm what kind of option in a restaurant are you looking for',
'nbest': []},
{'speaker': 'U',
'text': 'yeah umm am looking for an expensive restaurant',
'nbest': [{'hyp': 'yeah umm am looking for an expensive restaurant',
'score': -21.272899627685547},
{'hyp': 'yeah umm m looking for an expensive restaurant',
'score': -21.444047927856445},
{'hyp': 'yeah umm a m looking for an expensive restaurant',
'score': -21.565458297729492},
{'hyp': 'yeah ummm am looking for an expensive restaurant',
'score': -21.68832778930664},
{'hyp': 'yeah ummm m looking for an expensive restaurant',
'score': -21.85947608947754},
{'hyp': 'yeah ummm a m looking for an expensive restaurant',
'score': -21.980886459350586},
{'hyp': "yeah umm a'm looking for an expensive restaurant",
'score': -22.613924026489258},
{'hyp': "yeah ummm a'm looking for an expensive restaurant",
'score': -23.02935218811035},
{'hyp': 'yeah um am looking for an expensive restaurant',
'score': -23.11180305480957},
{'hyp': 'yeah um m looking for an expensive restaurant',
'score': -23.28295135498047}]},
{'speaker': 'S',
'text': "lemme go ahead and see what i can find for you ok great so i do ummm actually no i'm sorry is there something else i can help you find i don't see anything expensive",
'nbest': []},
{'speaker': 'U',
'text': "sure ummm maybe if you don't have anything expensive how about something in the moderate price range",
'nbest': [{'hyp': "sure ummm maybe if you don't have anything expensive how about something in the moderate price range",
'score': -27.492507934570312},
{'hyp': "sure umm maybe if you don't have anything expensive how about something in the moderate price range",
'score': -27.75853729248047},
{'hyp': "sure ummm maybe if you don't have anything expensive how about something in the moderate price rang",
'score': -29.44410514831543},
{'hyp': "sure umm maybe if you don't have anything expensive how about something in the moderate price rang",
'score': -29.710134506225586},
{'hyp': "sure um maybe if you don't have anything expensive how about something in the moderate price range",
'score': -31.136560440063477},
{'hyp': "sure um maybe if you don't have anything expensive how about something in the moderate price rang",
'score': -33.088157653808594},
{'hyp': "sure ummm maybe i you don't have anything expensive how about something in the moderate price range",
'score': -36.127620697021484},
{'hyp': "sure umm maybe i you don't have anything expensive how about something in the moderate price range",
'score': -36.39365005493164},
{'hyp': "sure ummm maybe if yo don't have anything expensive how about something in the moderate price range",
'score': -36.43605041503906},
{'hyp': "sure umm maybe if yo don't have anything expensive how about something in the moderate price range",
'score': -36.70207977294922}]},
{'speaker': 'S',
'text': 'ok moderate lemme go ahead and check to see what i can find for moderate ok great i do have several options coming up how does the view lounge sound',
'nbest': []},
{'speaker': 'U',
'text': 'that sounds good ummm do they have any sort of happy hour special',
'nbest': [{'hyp': 'that sounds good ummm do they have any sort of happy hour special',
'score': -30.316478729248047},
{'hyp': 'that sounds good umm do they have any sort of happy hour special',
'score': -30.958009719848633},
{'hyp': 'that sounds good um do they have any sort of happy hour special',
'score': -34.463165283203125},
{'hyp': 'that sounds good ummm do they have any sirt of happy hour special',
'score': -34.48350143432617},
{'hyp': 'that sounds good umm do they have any sirt of happy hour special',
'score': -35.12503433227539},
{'hyp': 'that sounds good ummm do they have any sord of happy hour special',
'score': -35.61939239501953},
{'hyp': 'that sounds good umm do they have any sord of happy hour special',
'score': -36.26092529296875},
{'hyp': 'that sounds good ummm do they have any sont of happy hour special',
'score': -37.697105407714844},
{'hyp': 'that sounds good umm do they have any sont of happy hour special',
'score': -38.33863830566406},
{'hyp': 'that sounds good um do they have any sirt of happy hour special',
'score': -38.630191802978516}]}],
'knowledge': {'domain': 'restaurant',
'entity_name': 'The View Lounge',
'title': 'Does The View Lounge offer happy hour?',
'body': 'The View Lounge offers happy hour.'},
'response': 'uhhh great question lemme go ahead and check that out for you ok fantastic so it looks like they do offer happy hour',
'source': 'sf_spoken',
'linearized_input': "<U> hi uh i'm looking for restaurant in lower ha <S> yeah definitely i can go ahead and help you with that ummm what kind of option in a restaurant are you looking for <U> yeah umm am looking for an expensive restaurant <S> lemme go ahead and see what i can find for you ok great so i do ummm actually no i'm sorry is there something else i can help you find i don't see anything expensive <U> sure ummm maybe if you don't have anything expensive how about something in the moderate price range <S> ok moderate lemme go ahead and check to see what i can find for moderate ok great i do have several options coming up how does the view lounge sound <U> that sounds good ummm do they have any sort of happy hour special || knowledge domain: restaurant, entity: The View Lounge, title: Does The View Lounge offer happy hour?, information: The View Lounge offers happy hour.",
'target': 'uhhh great question lemme go ahead and check that out for you ok fantastic so it looks like they do offer happy hour',
'references': ['uhhh great question lemme go ahead and check that out for you ok fantastic so it looks like they do offer happy hour']}
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
train: training set, val: validation set, test: test set
#### Splitting Criteria
<!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. -->
<!-- scope: microscope -->
The track dataset originally only consists of a validation and test set in the spoken domain with noisy ASR transcripts.
The training set is taken from the predecessor task DSTC9 Track 1 and contains written conversations.
## Dataset in GEM
### Rationale for Inclusion in GEM
#### Why is the Dataset in GEM?
<!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? -->
<!-- scope: microscope -->
This dataset can be used to evaluate conversational models on spoken inputs (using ASR hypotheses). In particular, we can evaluate the models’ ability to understand language by tracking the dialogue state, and their ability to generate knowledge-grounded responses.
#### Similar Datasets
<!-- info: Do other datasets for the high level task exist? -->
<!-- scope: telescope -->
yes
#### Unique Language Coverage
<!-- info: Does this dataset cover other languages than other datasets for the same task? -->
<!-- scope: periscope -->
no
#### Difference from other GEM datasets
<!-- info: What else sets this dataset apart from other similar datasets in GEM? -->
<!-- scope: microscope -->
This dataset contains transcribed spoken interactions.
#### Ability that the Dataset measures
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: periscope -->
We can measure the model’s ability to understand language and to generate knowledge-grounded responses.
### GEM-Specific Curation
#### Modificatied for GEM?
<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
<!-- scope: telescope -->
no
#### Additional Splits?
<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
no
### Getting Started with the Task
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
This dataset can be used to evaluate conversational models on spoken inputs (using ASR hypotheses). In particular, we can evaluate the models’ ability to generate knowledge-grounded responses.
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`Other: Other Metrics`
#### Other Metrics
<!-- info: Definitions of other metrics -->
<!-- scope: periscope -->
BLEU-1, BLEU-2, BLEU-3, BLEU-4, METEOR, ROGUE-1, ROGUE-2, ROGUE-L
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
no
## Dataset Curation
### Original Curation
#### Original Curation Rationale
<!-- info: Original curation rationale -->
<!-- scope: telescope -->
We want to explore how conversational models perform on spoken data.
#### Communicative Goal
<!-- info: What was the communicative goal? -->
<!-- scope: periscope -->
This dataset aims to explore the robustness of conversational models when evaluated on spoken data. It has two aspects, multi-domain dialogue state tracking and conversation modeling with access to unstructured knowledge.
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
no
### Language Data
#### How was Language Data Obtained?
<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Other`
#### Topics Covered
<!-- info: Does the language in the dataset focus on specific topics? How would you describe them? -->
<!-- scope: periscope -->
The conversations revolve around 5 domains (or topics): hotels, restaurants, attractions, taxi, train.
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
not validated
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
not filtered
### Structured Annotations
#### Additional Annotations?
<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
none
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
no
### Consent
#### Any Consent Policy?
<!-- info: Was there a consent policy involved when gathering the data? -->
<!-- scope: telescope -->
yes
### Private Identifying Information (PII)
#### Contains PII?
<!-- quick -->
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
<!-- scope: telescope -->
no PII
#### Justification for no PII
<!-- info: Provide a justification for selecting `no PII` above. -->
<!-- scope: periscope -->
The subjects were instructed to conduct fictional conversations about booking restaurants or requesting fictional information.
### Maintenance
#### Any Maintenance Plan?
<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
no
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
no
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
no
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
unsure
## Considerations for Using the Data
### PII Risks and Liability
#### Potential PII Risk
<!-- info: Considering your answers to the PII part of the Data Curation Section, describe any potential privacy to the data subjects and creators risks when using the dataset. -->
<!-- scope: microscope -->
There should be no risk related to PII as the subjects conduct fictional conversations.
### Licenses
#### Copyright Restrictions on the Dataset
<!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? -->
<!-- scope: periscope -->
`open license - commercial use allowed`
#### Copyright Restrictions on the Language Data
<!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
<!-- scope: periscope -->
`open license - commercial use allowed`
### Known Technical Limitations
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|
GEM-submissions/Leo__mbart-large-cc25__1645802644 | GEM-submissions | 2022-02-25T15:24:07Z | 265 | 0 | [
"benchmark:gem",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
benchmark: gem
type: prediction
submission_name: mbart-large-cc25
---
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|
GEM-submissions/lewtun__hugging-face-test-t5-base.outputs.json-36bf2a59__1645558682 | GEM-submissions | 2022-02-22T19:38:08Z | 265 | 0 | [
"benchmark:gem",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
benchmark: gem
type: prediction
submission_name: Hugging Face test T5-base.outputs.json 36bf2a59
---
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|
leey4n/KR3 | leey4n | 2023-07-19T08:35:54Z | 265 | 2 | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"multilinguality:monolingual",
"size_categories:100K<n<1m",
"language:ko",
"license:cc-by-nc-sa-4.0",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators: []
language_creators: []
language:
- ko
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
pretty_name: KR3
size_categories:
- 100K<n<1m
source_datasets: []
task_categories:
- text-classification
task_ids:
- sentiment-classification
---
### KR3: Korean Restaurant Reviews with Ratings
Korean sentiment classification dataset
- Size: 460K(+180K)
- Language: Korean-centric
### ⚠️ Caution with `Rating` Column
0 stands for negative review, 1 stands for positive review, and 2 stands for ambiguous review.
**Note that rating 2 is not intended to be used directly for supervised learning(classification).** This data is included for additional pre-training purpose or other usage.
In other words, this dataset is basically a **binary** sentiment classification task where labels are 0 and 1.
### 🔍 See More
See all the codes for crawling/preprocessing the dataset and experiments with KR3 in [GitHub Repo](https://github.com/Wittgensteinian/kr3).
See Kaggle dataset in [Kaggle Dataset](https://www.kaggle.com/ninetyninenewton/kr3-korean-restaurant-reviews-with-ratings).
### Usage
```python
from datasets import load_dataset
kr3 = load_dataset("leey4n/KR3", name='kr3', split='train')
kr3 = kr3.remove_columns(['__index_level_0__']) # Original file didn't include this column. Suspect it's a hugging face issue.
```
```python
# drop reviews with ambiguous label
kr3_binary = kr3.filter(lambda example: example['Rating'] != 2)
```
### License
**CC BY-NC-SA 4.0**
### Legal Issues
We concluded that the **non-commerical usage and release of KR3 fall into the range of fair use (공정 이용)** stated in the Korean copyright act (저작권법). We further clarify that we **did not agree to the terms of service** from any websites which might prohibit web crawling. In other words, web crawling we've done was proceeded without logging in to the website. Despite all of these, feel free to contact to any of the contributors if you notice any legal issues.
### Contributors & Acknowledgement
(Alphabetical order)
[Dongin Jung](https://github.com/dongin1009)
[Hyunwoo Kwak](https://github.com/Kwak-Hyun-woo)
[Kaeun Lee](https://github.com/Kaeun-Lee)
[Yejoon Lee](https://github.com/wittgensteinian)
This work was done as DIYA 4기. Compute resources needed for the work was supported by [DIYA](https://blog.diyaml.com) and surromind.ai.
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|
albertvillanova/carbon_24 | albertvillanova | 2022-10-24T15:25:03Z | 265 | 0 | [
"task_categories:other",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:other-crystallography",
"size_categories:unknown",
"language:cif",
"license:mit",
"material-property-optimization",
"material-reconstruction",
"material-generation",
"arxiv:2110.06197",
"region:us"
]
| [
"other"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- cif
license:
- mit
multilinguality:
- other-crystallography
size_categories:
- unknown
source_datasets: []
task_categories:
- other
task_ids: []
pretty_name: Carbon-24
tags:
- material-property-optimization
- material-reconstruction
- material-generation
---
# Dataset Card for Carbon-24
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:** https://github.com/txie-93/cdvae/tree/main/data/carbon_24
- **Paper:** [Crystal Diffusion Variational Autoencoder for Periodic Material Generation](https://arxiv.org/abs/2110.06197)
- **Leaderboard:**
- **Point of Contact:** [Tian Xie](mailto:[email protected])
### Dataset Summary
Carbon-24 contains 10k carbon materials, which share the same composition, but have different structures. There is 1 element and the materials have 6 - 24 atoms in the unit cells.
Carbon-24 includes various carbon structures obtained via ab initio random structure searching (AIRSS) (Pickard & Needs, 2006; 2011) performed at 10 GPa.
The original dataset includes 101529 carbon structures, and we selected the 10% of the carbon structure with the lowest energy per atom to create Carbon-24. All 10153 structures are at local energy minimum after DFT relaxation. The most stable structure is diamond at 10 GPa. All remaining structures are thermodynamically unstable but may be kinetically stable.
### 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
Please consider citing the following papers:
```
@article{xie2021crystal,
title={Crystal Diffusion Variational Autoencoder for Periodic Material Generation},
author={Tian Xie and Xiang Fu and Octavian-Eugen Ganea and Regina Barzilay and Tommi Jaakkola},
year={2021},
eprint={2110.06197},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
and
```
@misc{carbon2020data,
doi = {10.24435/MATERIALSCLOUD:2020.0026/V1},
url = {https://archive.materialscloud.org/record/2020.0026/v1},
author = {Pickard, Chris J.},
keywords = {DFT, ab initio random structure searching, carbon},
language = {en},
title = {AIRSS data for carbon at 10GPa and the C+N+H+O system at 1GPa},
publisher = {Materials Cloud},
year = {2020},
copyright = {info:eu-repo/semantics/openAccess}
}
```
### Contributions
Thanks to [@albertvillanova](https://github.com/albertvillanova) for adding this dataset.
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|
anechaev/med_history | anechaev | 2022-02-07T13:47:33Z | 265 | 0 | [
"license:mit",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
license: mit
---
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|
aseifert/merlin | aseifert | 2022-10-21T16:21:58Z | 265 | 1 | [
"multilinguality:translation",
"size_categories:unknown",
"language:cz",
"language:de",
"language:it",
"region:us"
]
| [
"conditional-text-generation"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators: []
language_creators: []
language:
- cz
- de
- it
license: []
multilinguality:
- translation
pretty_name: merlin
size_categories:
- unknown
source_datasets: []
task_categories:
- conditional-text-generation
task_ids:
- machine-translation
---
# MERLIN corpus
Project URL: https://merlin-platform.eu/C_mcorpus.php
Dataset URL: https://clarin.eurac.edu/repository/xmlui/handle/20.500.12124/6
The MERLIN corpus is a written learner corpus for Czech, German, and Italian that has been designed to illustrate the Common European Framework of Reference for Languages (CEFR) with authentic learner data. The corpus contains learner texts produced in standardized language certifications covering CEFR levels A1-C1. The MERLIN annotation scheme includes a wide range of language characteristics that provide researchers with concrete examples of learner performance and progress across multiple proficiency levels. | [
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|
bhavnicksm/sentihood | bhavnicksm | 2022-10-25T09:07:23Z | 265 | 3 | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"task_ids:multi-class-classification",
"task_ids:natural-language-inference",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:1610.03771",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: SentiHood Dataset
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
- multi-class-classification
- natural-language-inference
---
# Dataset Card for [SentiHood]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Paper:** https://arxiv.org/abs/1610.03771
- **Leaderboard:** https://paperswithcode.com/sota/aspect-based-sentiment-analysis-on-sentihood
### Dataset Summary
Created as a part of the paper "SentiHood: Targeted Aspect Based Sentiment Analysis Dataset for Urban Neighbourhoods" by Saeidi et al.
#### Abstract
In this paper, we introduce the task of targeted aspect-based sentiment analysis. The goal is to extract fine-grained information with respect to entities mentioned in user comments. This work extends both aspect-based sentiment analysis that assumes a single entity per document and targeted sentiment analysis that assumes a single sentiment towards a target entity. In particular, we identify the sentiment towards each aspect of one or more entities. As a testbed for this task, we introduce the SentiHood dataset, extracted from a question answering (QA) platform where urban neighborhoods are discussed by users. In this context units of text often mention several aspects of one or more neighborhoods. This is the first time that a generic social media platform in this case a QA platform, is used for fine-grained opinion mining. Text coming from QA platforms is far less constrained compared to text from review-specific platforms on which current datasets are based. We develop several strong baselines, relying on logistic regression and state-of-the-art recurrent neural networks.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Monolingual (only English)
## 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
Thanks to [@Bhavnicksm](https://github.com/Bhavnicksm) for adding this dataset. | [
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|
DebateLabKIT/deepa2 | DebateLabKIT | 2022-12-16T14:49:35Z | 265 | 3 | [
"task_categories:text-retrieval",
"task_categories:text-generation",
"task_ids:text-simplification",
"task_ids:parsing",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:unknown",
"language:en",
"license:other",
"argument-mining",
"summarization",
"conditional-text-generation",
"structure-prediction",
"arxiv:2110.01509",
"region:us"
]
| [
"text-retrieval",
"text-generation"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators: []
language_creators:
- other
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets: []
task_categories:
- text-retrieval
- text-generation
task_ids:
- text-simplification
- parsing
pretty_name: deepa2
tags:
- argument-mining
- summarization
- conditional-text-generation
- structure-prediction
---
# `deepa2` Datasets Collection
## Table of Contents
- [`deepa2` Datasets Collection](#deepa2-datasets-collection)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Sub-Datasets](#sub-datasets)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [blog post](https://debatelab.github.io/journal/deepa2.html)
- **Repository:** [github](https://github.com/debatelab/deepa2)
- **Paper:** [arxiv](https://arxiv.org/abs/2110.01509)
- **Point of Contact:** [Gregor Betz]([email protected])
### Dataset Summary
This is a growing, curated collection of `deepa2` datasets, i.e. datasets that contain comprehensive logical analyses of argumentative texts. The collection comprises:
* datasets that are built from existing NLP datasets by means of the [`deepa2 bake`](https://github.com/debatelab/deepa2) tool.
* original `deepa2` datasets specifically created for this collection.
The tool [`deepa2 serve`](https://github.com/debatelab/deepa2#integrating-deepa2-into-your-training-pipeline) may be used to render the data in this collection as text2text examples.
### Supported Tasks and Leaderboards
For each of the tasks tagged for this dataset, give a brief description of the tag, metrics, and suggested models (with a link to their HuggingFace implementation if available). Give a similar description of tasks that were not covered by the structured tag set (repace the `task-category-tag` with an appropriate `other:other-task-name`).
- `conditional-text-generation`: The dataset can be used to train models to generate a fully reconstruction of an argument from a source text, making, e.g., its implicit assumptions explicit.
- `structure-prediction`: The dataset can be used to train models to formalize sentences.
- `text-retrieval`: The dataset can be used to train models to extract reason statements and conjectures from a given source text.
### Languages
English. Will be extended to cover other languages in the futures.
## Dataset Structure
### Sub-Datasets
This collection contains the following `deepa2` datasets:
* `esnli`: created from e-SNLI with `deepa2 bake` as [described here](https://github.com/debatelab/deepa2/blob/main/docs/esnli.md).
* `enbank` (`task_1`, `task_2`): created from Entailment Bank with `deepa2 bake` as [described here](https://github.com/debatelab/deepa2/blob/main/docs/enbank.md).
* `argq`: created from IBM-ArgQ with `deepa2 bake` as [described here](https://github.com/debatelab/deepa2/blob/main/docs/argq.md).
* `argkp`: created from IBM-KPA with `deepa2 bake` as [described here](https://github.com/debatelab/deepa2/blob/main/docs/argkp.md).
* `aifdb` (`moral-maze`, `us2016`, `vacc-itc`): created from AIFdb with `deepa2 bake` as [described here](https://github.com/debatelab/deepa2/blob/main/docs/aifdb.md).
* `aaac` (`aaac01` and `aaac02`): original, machine-generated contribution; based on an an improved and extended algorithm that backs https://huggingface.co/datasets/debatelab/aaac.
### Data Instances
see: https://github.com/debatelab/deepa2/tree/main/docs
### Data Fields
see: https://github.com/debatelab/deepa2/tree/main/docs
|feature|esnli|enbank|aifdb|aaac|argq|argkp|
|--|--|--|--|--|--|--|
| `source_text` | x | x | x | x | x | x |
| `title` | | x | | x | | |
| `gist` | x | x | | x | | x |
| `source_paraphrase` | x | x | x | x | | |
| `context` | | x | | x | | x |
| `reasons` | x | x | x | x | x | |
| `conjectures` | x | x | x | x | x | |
| `argdown_reconstruction` | x | x | | x | | x |
| `erroneous_argdown` | x | | | x | | |
| `premises` | x | x | | x | | x |
| `intermediary_conclusion` | | | | x | | |
| `conclusion` | x | x | | x | | x |
| `premises_formalized` | x | | | x | | x |
| `intermediary_conclusion_formalized` | | | | x | | |
| `conclusion_formalized` | x | | | x | | x |
| `predicate_placeholders` | | | | x | | |
| `entity_placeholders` | | | | x | | |
| `misc_placeholders` | x | | | x | | x |
| `plchd_substitutions` | x | | | x | | x |
### Data Splits
Each sub-dataset contains three splits: `train`, `validation`, and `test`.
## Dataset Creation
### Curation Rationale
Many NLP datasets focus on tasks that are relevant for logical analysis and argument reconstruction. This collection is the attempt to unify these resources in a common framework.
### Source Data
See: [Sub-Datasets](#sub-datasets)
## Additional Information
### Dataset Curators
Gregor Betz, KIT; Kyle Richardson, Allen AI
### Licensing Information
We re-distribute the the imported sub-datasets under their original license:
|Sub-dataset|License|
|--|--|
|esnli|MIT|
|aifdb|free for academic use ([TOU](https://arg-tech.org/index.php/research/argument-corpora/))|
|enbank|CC BY 4.0|
|aaac|CC BY 4.0|
|argq|CC BY SA 4.0|
|argkp|Apache|
### Citation Information
```
@article{betz2021deepa2,
title={DeepA2: A Modular Framework for Deep Argument Analysis with Pretrained Neural Text2Text Language Models},
author={Gregor Betz and Kyle Richardson},
year={2021},
eprint={2110.01509},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--If the dataset has a [DOI](https://www.doi.org/), please provide it here.-->
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|
giganticode/java-cmpx-v1 | giganticode | 2022-07-01T20:32:52Z | 265 | 1 | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"multilinguality:monolingual",
"size_categories:unknown",
"license:mit",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
language:
- java
license:
- mit
multilinguality:
- monolingual
pretty_name:
- java-cmpx
size_categories:
- unknown
source_datasets: []
task_categories:
- text-classification
task_ids:
- multi-class-classification
--- | [
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|
huggingartists/100-gecs | huggingartists | 2022-10-25T09:20:43Z | 265 | 0 | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/100-gecs"
## 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:** 0.182347 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/9fd98af9a817af8cd78636f71895b6ad.500x500x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/100-gecs">
<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">100 gecs</div>
<a href="https://genius.com/artists/100-gecs">
<div style="text-align: center; font-size: 14px;">@100-gecs</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/100-gecs).
### 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/100-gecs")
```
## 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|
|------:|---------:|---:|
|140| -| -|
'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/100-gecs")
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)
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|
huggingartists/21-savage | huggingartists | 2022-10-25T09:20:49Z | 265 | 0 | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/21-savage"
## 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:** 1.073984 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/aa32202cc20d1dde62e57940a8b278b2.770x770x1.png')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/21-savage">
<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">21 Savage</div>
<a href="https://genius.com/artists/21-savage">
<div style="text-align: center; font-size: 14px;">@21-savage</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/21-savage).
### 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/21-savage")
```
## 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|
|------:|---------:|---:|
|435| -| -|
'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/21-savage")
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)
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|
huggingartists/florence-the-machine | huggingartists | 2022-10-25T09:29:50Z | 265 | 0 | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/florence-the-machine"
## 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:** 0.269066 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/99d09eb55276442d715ac14f06173a4e.1000x1000x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/florence-the-machine">
<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">Florence + The Machine</div>
<a href="https://genius.com/artists/florence-the-machine">
<div style="text-align: center; font-size: 14px;">@florence-the-machine</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/florence-the-machine).
### 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/florence-the-machine")
```
## 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|
|------:|---------:|---:|
|173| -| -|
'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/florence-the-machine")
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)
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|
huggingartists/freelancer | huggingartists | 2022-10-25T09:30:06Z | 265 | 0 | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/freelancer"
## 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:** 0.027774 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/9ce0f771e9455a22aeb5801a611ceead.409x409x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/freelancer">
<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">Freelancer</div>
<a href="https://genius.com/artists/freelancer">
<div style="text-align: center; font-size: 14px;">@freelancer</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/freelancer).
### 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/freelancer")
```
## 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|
|------:|---------:|---:|
|0| -| -|
'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/freelancer")
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)
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|
huggingartists/joji | huggingartists | 2022-10-25T09:32:26Z | 265 | 0 | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/joji"
## 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:** 0.211227 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/d20ee1f900287060716f7594ccba7ea3.1000x1000x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/joji">
<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">Joji</div>
<a href="https://genius.com/artists/joji">
<div style="text-align: center; font-size: 14px;">@joji</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/joji).
### 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/joji")
```
## 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|
|------:|---------:|---:|
|159| -| -|
'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/joji")
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)
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|
imvladikon/knesset_meetings_corpus | imvladikon | 2022-10-23T11:45:02Z | 265 | 1 | [
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:he",
"license:pddl",
"region:us"
]
| [
"text-generation"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
language:
- he
license:
- pddl
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text-generation
task_ids:
- language-modeling
pretty_name: Knesset Meetings Corpus
---
# Dataset Card
## 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://zenodo.org/record/2707356](https://zenodo.org/record/2707356)
- **Repository:** [https://github.com/NLPH/knesset-2004-2005](https://github.com/NLPH/knesset-2004-2005)
- **Paper:**
- **Point of Contact:**
- **Size of downloaded dataset files:**
- **Size of the generated dataset:**
- **Total amount of disk used:**
### Dataset Summary
An example of a sample:
```
{
"text": <text content of given document>,
"path": <file path to docx>
}
```
Dataset usage
Available "kneset16","kneset17","knesset_tagged" configurations
And only train set.
```python
train_ds = load_dataset("imvladikon/knesset_meetings_corpus", "kneset16", split="train")
```
The Knesset Meetings Corpus 2004-2005 is made up of two components:
* Raw texts - 282 files made up of 867,725 lines together. These can be downloaded in two formats:
* As ``doc`` files, encoded using ``windows-1255`` encoding:
* ``kneset16.zip`` - Contains 164 text files made up of 543,228 lines together. `[MILA host] <http://yeda.cs.technion.ac.il:8088/corpus/software/corpora/knesset/txt/docs/kneset16.zip>`_ `[Github Mirror] <https://github.com/NLPH/knesset-2004-2005/blob/master/kneset16.zip?raw=true>`_
* ``kneset17.zip`` - Contains 118 text files made up of 324,497 lines together. `[MILA host] <http://yeda.cs.technion.ac.il:8088/corpus/software/corpora/knesset/txt/docs/kneset17.zip>`_ `[Github Mirror] <https://github.com/NLPH/knesset-2004-2005/blob/master/kneset17.zip?raw=true>`_
* As ``txt`` files, encoded using ``utf8`` encoding:
* ``kneset.tar.gz`` - An archive of all the raw text files, divided into two folders: `[Github mirror] <https://github.com/NLPH/knesset-2004-2005/blob/master/kneset.tar.gz>`_
* ``16`` - Contains 164 text files made up of 543,228 lines together.
* ``17`` - Contains 118 text files made up of 324,497 lines together.
* ``knesset_txt_16.tar.gz``- Contains 164 text files made up of 543,228 lines together. `[MILA host] <http://yeda.cs.technion.ac.il:8088/corpus/software/corpora/knesset/txt/utf8/knesset_txt_16.tar.gz>`_ `[Github Mirror] <https://github.com/NLPH/knesset-2004-2005/blob/master/knesset_txt_16.tar.gz?raw=true>`_
* ``knesset_txt_17.zip`` - Contains 118 text files made up of 324,497 lines together. `[MILA host] <http://yeda.cs.technion.ac.il:8088/corpus/software/corpora/knesset/txt/utf8/knesset_txt_17.zip>`_ `[Github Mirror] <https://github.com/NLPH/knesset-2004-2005/blob/master/knesset_txt_17.zip?raw=true>`_
* Tokenized and morphologically tagged texts - Tagged versions exist only for the files in the ``16`` folder. The texts are encoded using `MILA's XML schema for corpora <http://www.mila.cs.technion.ac.il/eng/resources_standards.html>`_. These can be downloaded in two ways:
* ``knesset_tagged_16.tar.gz`` - An archive of all tokenized and tagged files. `[MILA host] <http://yeda.cs.technion.ac.il:8088/corpus/software/corpora/knesset/tagged/knesset_tagged_16.tar.gz>`_ `[Archive.org mirror] <https://archive.org/details/knesset_transcripts_2004_2005>`_
Mirrors
-------
This repository is a mirror of this dataset `found on MILA's website <http://www.mila.cs.technion.ac.il/eng/resources_corpora_haknesset.html>`_.
Zenodo mirror: `https://zenodo.org/record/2707356 <https://zenodo.org/record/2707356>`_
License
-------
All Knesset meeting protocols are in the `public domain <https://en.wikipedia.org/wiki/Public_domain>`_ (`רשות הציבור <https://he.wikipedia.org/wiki/%D7%A8%D7%A9%D7%95%D7%AA_%D7%94%D7%A6%D7%99%D7%91%D7%95%D7%A8>`_) by law. These files are thus in the public doamin and do not require any license or public domain dedication to set their status.
.. |DOI| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.2707356.svg
:target: https://doi.org/10.5281/zenodo.2707356
.. |LICENCE| image:: https://github.com/NLPH/knesset-2004-2005/blob/master/public_domain_shield.svg
:target: https://en.wikipedia.org/wiki/Public_domain
.. |PUBDOM| image:: https://github.com/NLPH/knesset-2004-2005/blob/master/public_domain.png
:target: https://en.wikipedia.org/wiki/Public_domain
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
The dataset is available under the [ Open Data Commons Public Domain Dedication & License 1.0](https://opendatacommons.org/licenses/pddl/).
### Citation Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Contributions
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|
Jzuluaga/uwb_atcc | Jzuluaga | 2022-12-05T11:15:20Z | 265 | 0 | [
"task_categories:automatic-speech-recognition",
"multilinguality:monolingual",
"language:en",
"license:cc-by-nc-sa-4.0",
"audio",
"automatic-speech-recognition",
"en-atc",
"en",
"noisy-speech-recognition",
"speech-recognition",
"arxiv:2203.16822",
"region:us"
]
| [
"automatic-speech-recognition"
]
| 2022-11-28T07:12:02Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: text
dtype: string
- name: segment_start_time
dtype: float32
- name: segment_end_time
dtype: float32
- name: duration
dtype: float32
splits:
- name: test
num_bytes: 140620332.25
num_examples: 2822
- name: train
num_bytes: 608597323.625
num_examples: 11291
download_size: 711464914
dataset_size: 749217655.875
tags:
- audio
- automatic-speech-recognition
- en-atc
- en
- noisy-speech-recognition
- speech-recognition
task_categories:
- automatic-speech-recognition
language:
- en
multilinguality:
- monolingual
license:
- cc-by-nc-sa-4.0
---
# Dataset Card for UWB-ATCC corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages and Other Details](#languages-and-other-details)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [UWB-ATCC corpus homepage](https://lindat.mff.cuni.cz/repository/xmlui/handle/11858/00-097C-0000-0001-CCA1-0)
- **Repository:** [GitHub repository (used in research)](https://github.com/idiap/w2v2-air-traffic)
- **Paper:** [Air traffic control communication (ATCC) speech corpora and their use for ASR and TTS development](https://link.springer.com/article/10.1007/s10579-019-09449-5)
- **Paper of this research:** [How Does Pre-trained Wav2Vec 2.0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications](https://arxiv.org/abs/2203.16822)
### Dataset Summary
The UWB-ATCC Corpus is provided provided by University of West Bohemia, Department of Cybernetics. The corpus contains recordings of communication between air traffic controllers and pilots. The speech is manually transcribed and labeled with the information about the speaker (pilot/controller, not the full identity of the person). The corpus is currently small (20 hours) but we plan to search for additional data next year. The audio data format is: 8kHz, 16bit PCM, mono.
Important, from the `<id (string)>` field, you can obtain the speaker roles. For instance:
- `_PI`: segment with only pilot speech
- `_AT`: segment with only ATCO speech
- `PIAT`: segment with both, ATCO and pilot speech
### Supported Tasks and Leaderboards
- `automatic-speech-recognition`. Already adapted/fine-tuned models are available here --> [XLS-R-300m](https://huggingface.co/Jzuluaga/wav2vec2-large-960h-lv60-self-en-atc-atcosim).
### Languages and other details
The text and the recordings are in English. The authors took advantage of the fact that one of their industrial partners develops complex IT solutions for several ATC authorities and airports and, as such, has access to the ATC communication recordings collected in the Czech airspace. This partner was able to secure the following data:
- Ground control—communication before takeoff and after landing—19.2 h of data.
- Tower control—communication during takeoff, landing and landing standby—22.5 h.
- Approach control—communication during landing approach—25.5 h.
- Area control—communication during overflights and cruises—71.3 h.
(Not all data is released. Check their website [here](https://lindat.mff.cuni.cz/repository/xmlui/handle/11858/00-097C-0000-0001-CCA1-0))
## Dataset Structure
### Data Fields
- `id (string)`: a string of recording identifier for each example, corresponding to its.
- `audio (audio)`: audio data for the given ID
- `text (string)`: transcript of the file already normalized. Follow these repositories for more details [w2v2-air-traffic](https://github.com/idiap/w2v2-air-traffic) and [bert-text-diarization-atc](https://github.com/idiap/bert-text-diarization-atc)
- `segment_start_time (float32)`: segment start time (normally 0)
- `segment_end_time (float32): segment end time
- `duration (float32)`: duration of the recording, compute as segment_end_time - segment_start_time
## Additional Information
### Licensing Information
The licensing status of the dataset hinges on the legal status of the [UWB-ATCC corpus](https://lindat.mff.cuni.cz/repository/xmlui/handle/11858/00-097C-0000-0001-CCA1-0) creators.
They used [Creative Commons - Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/) licensing.
### Citation Information
Contributors who prepared, processed, normalized and uploaded the dataset in HuggingFace:
```
@article{zuluaga2022how,
title={How Does Pre-trained Wav2Vec2. 0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications},
author={Zuluaga-Gomez, Juan and Prasad, Amrutha and Nigmatulina, Iuliia and Sarfjoo, Saeed and others},
journal={IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar},
year={2022}
}
@article{zuluaga2022bertraffic,
title={BERTraffic: BERT-based Joint Speaker Role and Speaker Change Detection for Air Traffic Control Communications},
author={Zuluaga-Gomez, Juan and Sarfjoo, Seyyed Saeed and Prasad, Amrutha and others},
journal={IEEE Spoken Language Technology Workshop (SLT), Doha, Qatar},
year={2022}
}
@article{zuluaga2022atco2,
title={ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications},
author={Zuluaga-Gomez, Juan and Vesel{\`y}, Karel and Sz{\"o}ke, Igor and Motlicek, Petr and others},
journal={arXiv preprint arXiv:2211.04054},
year={2022}
}
```
Authors of the dataset:
```
@article{vsmidl2019air,
title={Air traffic control communication (ATCC) speech corpora and their use for ASR and TTS development},
author={{\v{S}}m{\'\i}dl, Lubo{\v{s}} and {\v{S}}vec, Jan and Tihelka, Daniel and Matou{\v{s}}ek, Jind{\v{r}}ich and Romportl, Jan and Ircing, Pavel},
journal={Language Resources and Evaluation},
volume={53},
number={3},
pages={449--464},
year={2019},
publisher={Springer}
}
```
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|
open-llm-leaderboard/details_openaccess-ai-collective__hippogriff-30b-chat | open-llm-leaderboard | 2023-09-17T17:52:02Z | 265 | 0 | [
"region:us"
]
| null | 2023-08-18T11:32:08Z | ---
pretty_name: Evaluation run of openaccess-ai-collective/hippogriff-30b-chat
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [openaccess-ai-collective/hippogriff-30b-chat](https://huggingface.co/openaccess-ai-collective/hippogriff-30b-chat)\
\ 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_openaccess-ai-collective__hippogriff-30b-chat\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T17:51:49.763366](https://huggingface.co/datasets/open-llm-leaderboard/details_openaccess-ai-collective__hippogriff-30b-chat/blob/main/results_2023-09-17T17-51-49.763366.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\": 0.40625,\n \
\ \"em_stderr\": 0.005029654457747759,\n \"f1\": 0.45993603187919596,\n\
\ \"f1_stderr\": 0.004852091061102259,\n \"acc\": 0.45527931578597786,\n\
\ \"acc_stderr\": 0.009707160330434178\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.40625,\n \"em_stderr\": 0.005029654457747759,\n \
\ \"f1\": 0.45993603187919596,\n \"f1_stderr\": 0.004852091061102259\n \
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.1023502653525398,\n \
\ \"acc_stderr\": 0.008349110996208829\n },\n \"harness|winogrande|5\":\
\ {\n \"acc\": 0.8082083662194159,\n \"acc_stderr\": 0.011065209664659527\n\
\ }\n}\n```"
repo_url: https://huggingface.co/openaccess-ai-collective/hippogriff-30b-chat
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|arc:challenge|25_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T17_51_49.763366
path:
- '**/details_harness|drop|3_2023-09-17T17-51-49.763366.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T17-51-49.763366.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T17_51_49.763366
path:
- '**/details_harness|gsm8k|5_2023-09-17T17-51-49.763366.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T17-51-49.763366.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hellaswag|10_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_5
data_files:
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path:
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path:
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- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T22:55:46.065027.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T22:55:46.065027.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T22:55:46.065027.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T22:55:46.065027.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T22:55:46.065027.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T22:55:46.065027.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T22:55:46.065027.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T22:55:46.065027.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T17_51_49.763366
path:
- '**/details_harness|winogrande|5_2023-09-17T17-51-49.763366.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T17-51-49.763366.parquet'
- config_name: results
data_files:
- split: 2023_07_19T22_55_46.065027
path:
- results_2023-07-19T22:55:46.065027.parquet
- split: 2023_09_17T17_51_49.763366
path:
- results_2023-09-17T17-51-49.763366.parquet
- split: latest
path:
- results_2023-09-17T17-51-49.763366.parquet
---
# Dataset Card for Evaluation run of openaccess-ai-collective/hippogriff-30b-chat
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/openaccess-ai-collective/hippogriff-30b-chat
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [openaccess-ai-collective/hippogriff-30b-chat](https://huggingface.co/openaccess-ai-collective/hippogriff-30b-chat) 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_openaccess-ai-collective__hippogriff-30b-chat",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T17:51:49.763366](https://huggingface.co/datasets/open-llm-leaderboard/details_openaccess-ai-collective__hippogriff-30b-chat/blob/main/results_2023-09-17T17-51-49.763366.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": 0.40625,
"em_stderr": 0.005029654457747759,
"f1": 0.45993603187919596,
"f1_stderr": 0.004852091061102259,
"acc": 0.45527931578597786,
"acc_stderr": 0.009707160330434178
},
"harness|drop|3": {
"em": 0.40625,
"em_stderr": 0.005029654457747759,
"f1": 0.45993603187919596,
"f1_stderr": 0.004852091061102259
},
"harness|gsm8k|5": {
"acc": 0.1023502653525398,
"acc_stderr": 0.008349110996208829
},
"harness|winogrande|5": {
"acc": 0.8082083662194159,
"acc_stderr": 0.011065209664659527
}
}
```
### 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] | [
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|
open-llm-leaderboard/details_TheBloke__WizardLM-30B-GPTQ | open-llm-leaderboard | 2023-12-01T14:42:58Z | 265 | 0 | [
"region:us"
]
| null | 2023-08-27T11:59:01Z | ---
pretty_name: Evaluation run of TheBloke/WizardLM-30B-GPTQ
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [TheBloke/WizardLM-30B-GPTQ](https://huggingface.co/TheBloke/WizardLM-30B-GPTQ)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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_TheBloke__WizardLM-30B-GPTQ_public\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-11-07T18:05:07.591558](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__WizardLM-30B-GPTQ_public/blob/main/results_2023-11-07T18-05-07.591558.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\": 0.21245805369127516,\n\
\ \"em_stderr\": 0.004189026405353694,\n \"f1\": 0.2829110738255039,\n\
\ \"f1_stderr\": 0.004179836263087045,\n \"acc\": 0.5537101784195891,\n\
\ \"acc_stderr\": 0.012517196395950588\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.21245805369127516,\n \"em_stderr\": 0.004189026405353694,\n\
\ \"f1\": 0.2829110738255039,\n \"f1_stderr\": 0.004179836263087045\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.34420015163002277,\n \
\ \"acc_stderr\": 0.013086800426693784\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7632202052091555,\n \"acc_stderr\": 0.011947592365207392\n\
\ }\n}\n```"
repo_url: https://huggingface.co/TheBloke/WizardLM-30B-GPTQ
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_11_07T18_05_07.591558
path:
- '**/details_harness|drop|3_2023-11-07T18-05-07.591558.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-11-07T18-05-07.591558.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_11_07T18_05_07.591558
path:
- '**/details_harness|gsm8k|5_2023-11-07T18-05-07.591558.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-11-07T18-05-07.591558.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_11_07T18_05_07.591558
path:
- '**/details_harness|winogrande|5_2023-11-07T18-05-07.591558.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-11-07T18-05-07.591558.parquet'
- config_name: results
data_files:
- split: 2023_11_07T18_05_07.591558
path:
- results_2023-11-07T18-05-07.591558.parquet
- split: latest
path:
- results_2023-11-07T18-05-07.591558.parquet
---
# Dataset Card for Evaluation run of TheBloke/WizardLM-30B-GPTQ
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TheBloke/WizardLM-30B-GPTQ
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [TheBloke/WizardLM-30B-GPTQ](https://huggingface.co/TheBloke/WizardLM-30B-GPTQ) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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_TheBloke__WizardLM-30B-GPTQ_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-07T18:05:07.591558](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__WizardLM-30B-GPTQ_public/blob/main/results_2023-11-07T18-05-07.591558.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": 0.21245805369127516,
"em_stderr": 0.004189026405353694,
"f1": 0.2829110738255039,
"f1_stderr": 0.004179836263087045,
"acc": 0.5537101784195891,
"acc_stderr": 0.012517196395950588
},
"harness|drop|3": {
"em": 0.21245805369127516,
"em_stderr": 0.004189026405353694,
"f1": 0.2829110738255039,
"f1_stderr": 0.004179836263087045
},
"harness|gsm8k|5": {
"acc": 0.34420015163002277,
"acc_stderr": 0.013086800426693784
},
"harness|winogrande|5": {
"acc": 0.7632202052091555,
"acc_stderr": 0.011947592365207392
}
}
```
### 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] | [
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|
srwac | null | 2022-11-03T16:08:14Z | 264 | 1 | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100M<n<1B",
"source_datasets:original",
"language:sr",
"license:cc-by-sa-3.0",
"region:us"
]
| [
"text-generation",
"fill-mask"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- sr
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 100M<n<1B
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: null
pretty_name: SrWac
dataset_info:
features:
- name: sentence
dtype: string
config_name: srwac
splits:
- name: train
num_bytes: 17470890484
num_examples: 688805174
download_size: 3767312759
dataset_size: 17470890484
---
# Dataset Card for SrWac
## 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:** http://nlp.ffzg.hr/resources/corpora/srwac/
- **Repository:** https://www.clarin.si/repository/xmlui/handle/11356/1063
- **Paper:** http://nlp.ffzg.hr/data/publications/nljubesi/ljubesic14-bs.pdf
- **Leaderboard:**
- **Point of Contact:** [Nikola Ljubešič](mailto:[email protected])
### Dataset Summary
The Serbian web corpus srWaC was built by crawling the .rs top-level domain in 2014. The corpus was near-deduplicated on paragraph level, normalised via diacritic restoration, morphosyntactically annotated and lemmatised. The corpus is shuffled by paragraphs. Each paragraph contains metadata on the URL, domain and language identification (Serbian vs. Croatian).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Dataset is monolingual in Serbian language.
## 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
Dataset is under the [CC-BY-SA 3.0](http://creativecommons.org/licenses/by-sa/3.0/) license.
### Citation Information
```
@misc{11356/1063,
title = {Serbian web corpus {srWaC} 1.1},
author = {Ljube{\v s}i{\'c}, Nikola and Klubi{\v c}ka, Filip},
url = {http://hdl.handle.net/11356/1063},
note = {Slovenian language resource repository {CLARIN}.{SI}},
copyright = {Creative Commons - Attribution-{ShareAlike} 4.0 International ({CC} {BY}-{SA} 4.0)},
year = {2016} }
```
### Contributions
Thanks to [@IvanZidov](https://github.com/IvanZidov) for adding this dataset. | [
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|
urdu_sentiment_corpus | null | 2023-01-25T15:02:01Z | 264 | 1 | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ur",
"license:unknown",
"region:us"
]
| [
"text-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- ur
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: urdu-sentiment-corpus
pretty_name: Urdu Sentiment Corpus (USC)
dataset_info:
features:
- name: sentence
dtype: string
- name: sentiment
dtype:
class_label:
names:
'0': P
'1': N
'2': O
splits:
- name: train
num_bytes: 161190
num_examples: 1000
download_size: 51583
dataset_size: 161190
---
# Dataset Card for Urdu Sentiment Corpus (USC)
## 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:** [Github](https://github.com/MuhammadYaseenKhan/Urdu-Sentiment-Corpus)
- **Repository:** [Github](https://github.com/MuhammadYaseenKhan/Urdu-Sentiment-Corpus)
- **Paper:** [IEEE](https://ieeexplore.ieee.org/abstract/document/9080043)
- **Leaderboard:**
- **Point of Contact:** [Muhammad Yaseen Khan](https://github.com/MuhammadYaseenKhan)
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
- sentences: The Urdu tweet
- sentiment: The sentiment that was exhibited in the tweet, which can be Positive(P) or Negative(N) or Objective(O).
### 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
Thanks to [@chaitnayabasava](https://github.com/chaitnayabasava) for adding this dataset. | [
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|
GroNLP/ik-nlp-22_pestyle | GroNLP | 2022-10-25T09:06:27Z | 264 | 0 | [
"task_categories:translation",
"annotations_creators:machine-generated",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:translation",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"language:it",
"license:other",
"region:us"
]
| [
"translation"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- machine-generated
- expert-generated
language_creators:
- found
language:
- en
- it
license:
- other
multilinguality:
- translation
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- translation
pretty_name: iknlp22-pestyle
---
# Dataset Card for IK-NLP-22 Project 1: A Study in Post-Editing Stylometry
## Table of Contents
- [Dataset Card for IK-NLP-22 Project 1: A Study in Post-Editing Stylometry](#dataset-card-for-ik-nlp-22-project-1-a-study-in-post-editing-stylometry)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Train Split](#train-split)
- [Test splits](#test-splits)
- [Dataset Creation](#dataset-creation)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Source:** [FLORES-101](https://huggingface.co/datasets/gsarti/flores_101)
- **Point of Contact:** [Gabriele Sarti](mailto:[email protected])
### Dataset Summary
This dataset contains a sample of sentences taken from the [FLORES-101](https://huggingface.co/datasets/gsarti/flores_101) dataset that were either translated from scratch or post-edited from an existing automatic translation by three human translators. Translation were performed for the English-Italian language pair, and translators' behavioral data (keystrokes, pauses, editing times) were collected using the [PET](https://github.com/wilkeraziz/PET) platform.
This dataset is made available for final projects of the 2022 edition of the Natural Language Processing course at the [Information Science Master's Degree](https://www.rug.nl/masters/information-science/?lang=en) at the University of Groningen, taught by [Arianna Bisazza](https://research.rug.nl/en/persons/arianna-bisazza) and [Gabriele Sarti](https://research.rug.nl/en/persons/gabriele-sarti) with the assistance of [Anjali Nair](https://nl.linkedin.com/in/anjalinair012).
**Disclaimer**: *This repository is provided without direct data access due to currently unpublished results.* _**For this reason, it is strictly forbidden to share or publish all the data associated to this repository**_. *Students will be provided with a compressed folder containing the data upon choosing a project based on this dataset. To load the dataset using 🤗 Datasets, download and unzip the provided folder and pass it to the* `load_dataset` *method as:* `datasets.load_dataset('GroNLP/ik-nlp-22_pestyle', 'full', data_dir='path/to/unzipped/folder')`
### Languages
The language data of is in English (BCP-47 `en`) and Italian (BCP-47 `it`)
## Dataset Structure
### Data Instances
The dataset contains four configurations: `full`, `test_mask_subject`, `test_mask_modality`, `test_mask_time`. `full` contains the main `train` split in which all fields are available. The other three, `test_mask_subject`, `test_mask_modality`, `test_mask_time`, contain a `test` split each with different fields removed to avoid information leaking during evaluation. See more details in the [Data Splits](#data-splits) section.
### Data Fields
The following fields are contained in the training set:
|Field|Description|
|-----|-----------|
|`item_id` | The sentence identifier. The first digits of the number represent the document containing the sentence, while the last digit of the number represents the sentence position inside the document. Documents can contain from 3 to 5 semantically-related sentences each. |
|`subject_id` | The identifier for the translator performing the translation from scratch or post-editing task. Values: `t1`, `t2` or `t3`. |
|`modality` | The modality of the translation task. Values: `ht` (translation from scratch), `pe1` (post-editing Google Translate translations), `pe2` (post-editing [mBART](https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt) translations). |
|`src_text` | The original source sentence extracted from Wikinews, wikibooks or wikivoyage. |
|`mt_text` | Missing if tasktype is `ht`. Otherwise, contains the automatically-translated sentence before post-editing. |
|`tgt_text` | Final sentence produced by the translator (either via translation from scratch of `sl_text` or post-editing `mt_text`) |
|`edit_time` | Total editing time for the translation in seconds. |
|`k_total` | Total number of keystrokes for the translation. |
|`k_letter` | Total number of letter keystrokes for the translation. |
|`k_digit` | Total number of digit keystrokes for the translation. |
|`k_white` | Total number of whitespace keystrokes for the translation. |
|`k_symbol` | Total number of symbol (punctuation, etc.) keystrokes for the translation. |
|`k_nav` | Total number of navigation keystrokes (left-right arrows, mouse clicks) for the translation. |
|`k_erase` | Total number of erase keystrokes (backspace, cancel) for the translation. |
|`k_copy` | Total number of copy (Ctrl + C) actions during the translation. |
|`k_cut` | Total number of cut (Ctrl + X) actions during the translation. |
|`k_paste` | Total number of paste (Ctrl + V) actions during the translation. |
|`n_pause_geq_300` | Number of pauses of 300ms or more during the translation. |
|`len_pause_geq_300` | Total duration of pauses of 300ms or more, in milliseconds. |
|`n_pause_geq_1000` | Number of pauses of 1s or more during the translation. |
|`len_pause_geq_1000` | Total duration of pauses of 1000ms or more, in milliseconds. |
|`num_annotations` | Number of times the translator focused the texbox for performing the translation of the sentence during the translation session. E.g. 1 means the translation was performed once and never revised. |
|`n_insert` | Number of post-editing insertions (empty for modality `ht`) computed using the [tercom](https://github.com/jhclark/tercom) library. |
|`n_delete` | Number of post-editing deletions (empty for modality `ht`) computed using the [tercom](https://github.com/jhclark/tercom) library. |
|`n_substitute` | Number of post-editing substitutions (empty for modality `ht`) computed using the [tercom](https://github.com/jhclark/tercom) library. |
|`n_shift` | Number of post-editing shifts (empty for modality `ht`) computed using the [tercom](https://github.com/jhclark/tercom) library. |
|`bleu` | Sentence-level BLEU score between MT and post-edited fields (empty for modality `ht`) computed using the [SacreBLEU](https://github.com/mjpost/sacrebleu) library with default parameters. |
|`chrf` | Sentence-level chrF score between MT and post-edited fields (empty for modality `ht`) computed using the [SacreBLEU](https://github.com/mjpost/sacrebleu) library with default parameters. |
|`ter` | Sentence-level TER score between MT and post-edited fields (empty for modality `ht`) computed using the [tercom](https://github.com/jhclark/tercom) library. |
|`aligned_edit` | Aligned visual representation of REF (`mt_text`), HYP (`tl_text`) and edit operations (I = Insertion, D = Deletion, S = Substitution) performed on the field. Replace `\\n` with `\n` to show the three aligned rows.|
### Data Splits
| config| train| test|
|------:|-----:|----:|
|`main` | 1170 | 120 |
#### Train Split
The `train` split contains a total of 1170 triplets (or pairs, when translation from scratch is performed) annotated with behavioral data produced during the translation. The following is an example of the subject `t3` post-editing a machine translation produced by system 2 (tasktype `pe2`) taken from the `train` split. The field `aligned_edit` is showed over three lines to provide a visual understanding of its contents.
```json
{
"item_id": 1072,
"subject_id": "t3",
"tasktype": "pe2",
"src_text": "At the beginning dress was heavily influenced by the Byzantine culture in the east.",
"mt_text": "All'inizio il vestito era fortemente influenzato dalla cultura bizantina dell'est.",
"tgt+text": "Inizialmente, l'abbigliamento era fortemente influenzato dalla cultura bizantina orientale.",
"edit_time": 45.687,
"k_total": 51,
"k_letter": 31,
"k_digit": 0,
"k_white": 2,
"k_symbol": 3,
"k_nav": 7,
"k_erase": 3,
"k_copy": 0,
"k_cut": 0,
"k_paste": 0,
"n_pause_geq_300": 9,
"len_pause_geq_300": 40032,
"n_pause_geq_1000": 5,
"len_pause_geq_1000": 38392,
"num_annotations": 1,
"n_insert": 0.0,
"n_delete": 1.0,
"n_substitute": 3.0,
"n_shift": 0.0,
"bleu": 47.99,
"chrf": 62.05,
"ter": 40.0,
"aligned_edit: "REF: all'inizio il vestito era fortemente influenzato dalla cultura bizantina dell'est.\\n
HYP: ********** inizialmente, l'abbigliamento era fortemente influenzato dalla cultura bizantina orientale.\\n
EVAL: D S S S"
}
```
The text is provided as-is, without further preprocessing or tokenization.
#### Test splits
The three `test` splits (one per configuration) contain the same 120 entries each, following the same structure as `train`. Each test split omit some of the fields to prevent leakage of information:
- In `test_mask_subject` the `subject_id` is absent, for the main task of post-editor stylometry.
- In `test_mask_modality` the following fields are absent for the modality prediction extra task: `modality`, `mt_text`, `n_insert`, `n_delete`, `n_substitute`, `n_shift`, `ter`, `bleu`, `chrf`, `aligned_edit`.
- In `test_mask_time` the following fields are absent for the time and pause prediction extra task: `edit_time`, `n_pause_geq_300`, `len_pause_geq_300`, `n_pause_geq_1000`, and `len_pause_geq_1000`.
### Dataset Creation
The dataset was parsed from PET XML files into CSV format using a script adapted from the one by [Antonio Toral](https://research.rug.nl/en/persons/antonio-toral-ruiz) found at the following link: [https://github.com/antot/postediting_novel_frontiers](https://github.com/antot/postediting_novel_frontiers)
## Additional Information
### Dataset Curators
For problems related to this 🤗 Datasets version, please contact us at [[email protected]](mailto:[email protected]).
### Licensing Information
It is forbidden to share or publish the data associated with this 🤗 Dataset version.
### Citation Information
No citation information is provided for this dataset. | [
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|
anton-l/common_language | anton-l | 2022-10-21T16:20:41Z | 264 | 0 | [
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:extended|common_voice",
"language:ar",
"language:br",
"language:ca",
"language:cnh",
"language:cs",
"language:cv",
"language:cy",
"language:de",
"language:dv",
"language:el",
"language:en",
"language:eo",
"language:es",
"language:et",
"language:eu",
"language:fa",
"language:fr",
"language:fy",
"language:ia",
"language:id",
"language:it",
"language:ja",
"language:ka",
"language:kab",
"language:ky",
"language:lv",
"language:mn",
"language:mt",
"language:nl",
"language:pl",
"language:pt",
"language:rm",
"language:ro",
"language:ru",
"language:rw",
"language:sah",
"language:sl",
"language:sv",
"language:ta",
"language:tr",
"language:tt",
"language:uk",
"language:zh",
"license:cc-by-nc-4.0",
"region:us"
]
| [
"speech-processing"
]
| 2022-03-02T23:29:22Z | ---
pretty_name: Common Language
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- ar
- br
- ca
- cnh
- cs
- cv
- cy
- de
- dv
- el
- en
- eo
- es
- et
- eu
- fa
- fr
- fy
- ia
- id
- it
- ja
- ka
- kab
- ky
- lv
- mn
- mt
- nl
- pl
- pt
- rm
- ro
- ru
- rw
- sah
- sl
- sv
- ta
- tr
- tt
- uk
- zh
language_bcp47:
- ar
- br
- ca
- cnh
- cs
- cv
- cy
- de
- dv
- el
- en
- eo
- es
- et
- eu
- fa
- fr
- fy-NL
- ia
- id
- it
- ja
- ka
- kab
- ky
- lv
- mn
- mt
- nl
- pl
- pt
- rm-sursilv
- ro
- ru
- rw
- sah
- sl
- sv-SE
- ta
- tr
- tt
- uk
- zh-CN
- zh-HK
- zh-TW
license:
- cc-by-nc-4.0
multilinguality:
- multilingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|common_voice
task_categories:
- speech-processing
task_ids:
- speech-classification
---
# Dataset Card for common_language
## 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://zenodo.org/record/5036977
- **Repository:** https://github.com/speechbrain/speechbrain/tree/develop/recipes/CommonLanguage
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Leaderboard:** [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)
### Dataset Summary
This dataset is composed of speech recordings from languages that were carefully selected from the CommonVoice database. The total duration of audio recordings is 45.1 hours (i.e., 1 hour of material for each language). The dataset has been extracted from CommonVoice to train language-id systems.
### Supported Tasks and Leaderboards
The baselines for language-id are available in the SpeechBrain toolkit (see recipes/CommonLanguage):
https://github.com/speechbrain/speechbrain
### Languages
List of included language:
```
Arabic, Basque, Breton, Catalan, Chinese_China, Chinese_Hongkong, Chinese_Taiwan, Chuvash, Czech, Dhivehi, Dutch, English, Esperanto, Estonian, French, Frisian, Georgian, German, Greek, Hakha_Chin, Indonesian, Interlingua, Italian, Japanese, Kabyle, Kinyarwanda, Kyrgyz, Latvian, Maltese, Mongolian, Persian, Polish, Portuguese, Romanian, Romansh_Sursilvan, Russian, Sakha, Slovenian, Spanish, Swedish, Tamil, Tatar, Turkish, Ukranian, Welsh
```
## Dataset Structure
### Data Instances
A typical data point comprises the `path` to the audio file, and its label `language`. Additional fields include `age`, `client_id`, `gender` and `sentence`.
```python
{
'client_id': 'itln_trn_sp_175',
'path': '/path/common_voice_kpd/Italian/train/itln_trn_sp_175/common_voice_it_18279446.wav',
'sentence': 'Con gli studenti è leggermente simile.',
'age': 'not_defined',
'gender': 'not_defined',
'language': 22
}
```
### Data Fields
`client_id` (`string`): An id for which client (voice) made the recording
`path` (`string`): The path to the audio file
`language` (`ClassLabel`): The language of the recording (see the `Languages` section above)
`sentence` (`string`): The sentence the user was prompted to speak
`age` (`string`): The age of the speaker.
`gender` (`string`): The gender of the speaker
### Data Splits
The dataset is already balanced and split into train, dev (validation) and test sets.
| Name | Train | Dev | Test |
|:---------------------------------:|:------:|:------:|:-----:|
| **# of utterances** | 177552 | 47104 | 47704 |
| **# unique speakers** | 11189 | 1297 | 1322 |
| **Total duration, hr** | 30.04 | 7.53 | 7.53 |
| **Min duration, sec** | 0.86 | 0.98 | 0.89 |
| **Mean duration, sec** | 4.87 | 4.61 | 4.55 |
| **Max duration, sec** | 21.72 | 105.67 | 29.83 |
| **Duration per language, min** | ~40 | ~10 | ~10 |
## 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
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.
## Considerations for Using the Data
### Social Impact of Dataset
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.
### 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
The Mongolian and Ukrainian languages are spelled as "Mangolian" and "Ukranian" in this version of the dataset.
[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
```
@dataset{ganesh_sinisetty_2021_5036977,
author = {Ganesh Sinisetty and
Pavlo Ruban and
Oleksandr Dymov and
Mirco Ravanelli},
title = {CommonLanguage},
month = jun,
year = 2021,
publisher = {Zenodo},
version = {0.1},
doi = {10.5281/zenodo.5036977},
url = {https://doi.org/10.5281/zenodo.5036977}
}
```
### Contributions
Thanks to [@anton-l](https://github.com/anton-l) for adding this dataset.
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|
huggingartists/ghostemane | huggingartists | 2022-10-25T09:30:25Z | 264 | 0 | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/ghostemane"
## 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:** 0.448728 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/c4407bb331c50916c1dfdc7f875f73a9.1000x1000x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/ghostemane">
<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">Ghostemane</div>
<a href="https://genius.com/artists/ghostemane">
<div style="text-align: center; font-size: 14px;">@ghostemane</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/ghostemane).
### 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/ghostemane")
```
## 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|
|------:|---------:|---:|
|327| -| -|
'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/ghostemane")
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)
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|
embedding-data/sentence-compression | embedding-data | 2022-08-02T03:02:47Z | 264 | 10 | [
"task_categories:sentence-similarity",
"task_ids:semantic-similarity-classification",
"language:en",
"license:mit",
"region:us"
]
| [
"sentence-similarity",
"paraphrase-mining"
]
| 2022-07-07T22:58:31Z | ---
license: mit
language:
- en
paperswithcode_id: embedding-data/sentence-compression
pretty_name: sentence-compression
task_categories:
- sentence-similarity
- paraphrase-mining
task_ids:
- semantic-similarity-classification
---
# Dataset Card for "sentence-compression"
## 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/google-research-datasets/sentence-compression](https://github.com/google-research-datasets/sentence-compression)
- **Repository:** [More Information Needed](https://github.com/google-research-datasets/sentence-compression)
- **Paper:** [More Information Needed](https://www.aclweb.org/anthology/D13-1155/)
- **Point of Contact:** [Katja Filippova]([email protected])
- **Size of downloaded dataset files:**
- **Size of the generated dataset:**
- **Total amount of disk used:** 14.2 MB
### Dataset Summary
Dataset with pairs of equivalent sentences.
The dataset is provided "AS IS" without any warranty, express or implied.
Google disclaims all liability for any damages, direct or indirect, resulting from using the dataset.
Disclaimer: The team releasing sentence-compression did not upload the dataset to the Hub and did not write a dataset card. These steps were done by the Hugging Face team.
### Supported Tasks
- [Sentence Transformers](https://huggingface.co/sentence-transformers) training; useful for semantic search and sentence similarity.
### Languages
- English.
## Dataset Structure
Each example in the dataset contains pairs of equivalent sentences and is formatted as a dictionary with the key "set" and a list with the sentences as "value".
```
{"set": [sentence_1, sentence_2]}
{"set": [sentence_1, sentence_2]}
...
{"set": [sentence_1, sentence_2]}
```
This dataset is useful for training Sentence Transformers models. Refer to the following post on how to train models using similar pairs of sentences.
### Usage Example
Install the 🤗 Datasets library with `pip install datasets` and load the dataset from the Hub with:
```python
from datasets import load_dataset
dataset = load_dataset("embedding-data/sentence-compression")
```
The dataset is loaded as a `DatasetDict` and has the format:
```python
DatasetDict({
train: Dataset({
features: ['set'],
num_rows: 180000
})
})
```
Review an example `i` with:
```python
dataset["train"][i]["set"]
```
### Curation Rationale
[More Information Needed](https://github.com/google-research-datasets/sentence-compression)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/google-research-datasets/sentence-compression)
#### Who are the source language producers?
[More Information Needed](https://github.com/google-research-datasets/sentence-compression)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/google-research-datasets/sentence-compression)
#### Who are the annotators?
[More Information Needed](https://github.com/google-research-datasets/sentence-compression)
### Personal and Sensitive Information
[More Information Needed](https://github.com/google-research-datasets/sentence-compression)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/google-research-datasets/sentence-compression)
### Discussion of Biases
[More Information Needed](https://github.com/google-research-datasets/sentence-compression)
### Other Known Limitations
[More Information Needed](https://github.com/google-research-datasets/sentence-compression)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/google-research-datasets/sentence-compression)
### Licensing Information
[More Information Needed](https://github.com/google-research-datasets/sentence-compression)
### Contributions
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|
ds4sd/DocLayNet | ds4sd | 2023-01-25T17:01:19Z | 264 | 26 | [
"task_categories:object-detection",
"task_categories:image-segmentation",
"task_ids:instance-segmentation",
"annotations_creators:crowdsourced",
"size_categories:10K<n<100K",
"license:other",
"layout-segmentation",
"COCO",
"document-understanding",
"PDF",
"region:us"
]
| [
"object-detection",
"image-segmentation"
]
| 2023-01-17T07:51:59Z | ---
annotations_creators:
- crowdsourced
license: other
pretty_name: DocLayNet
size_categories:
- 10K<n<100K
tags:
- layout-segmentation
- COCO
- document-understanding
- PDF
task_categories:
- object-detection
- image-segmentation
task_ids:
- instance-segmentation
---
# Dataset Card for DocLayNet
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Annotations](#annotations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://developer.ibm.com/exchanges/data/all/doclaynet/
- **Repository:** https://github.com/DS4SD/DocLayNet
- **Paper:** https://doi.org/10.1145/3534678.3539043
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
DocLayNet provides page-by-page layout segmentation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique pages from 6 document categories. It provides several unique features compared to related work such as PubLayNet or DocBank:
1. *Human Annotation*: DocLayNet is hand-annotated by well-trained experts, providing a gold-standard in layout segmentation through human recognition and interpretation of each page layout
2. *Large layout variability*: DocLayNet includes diverse and complex layouts from a large variety of public sources in Finance, Science, Patents, Tenders, Law texts and Manuals
3. *Detailed label set*: DocLayNet defines 11 class labels to distinguish layout features in high detail.
4. *Redundant annotations*: A fraction of the pages in DocLayNet are double- or triple-annotated, allowing to estimate annotation uncertainty and an upper-bound of achievable prediction accuracy with ML models
5. *Pre-defined train- test- and validation-sets*: DocLayNet provides fixed sets for each to ensure proportional representation of the class-labels and avoid leakage of unique layout styles across the sets.
### Supported Tasks and Leaderboards
We are hosting a competition in ICDAR 2023 based on the DocLayNet dataset. For more information see https://ds4sd.github.io/icdar23-doclaynet/.
## Dataset Structure
### Data Fields
DocLayNet provides four types of data assets:
1. PNG images of all pages, resized to square `1025 x 1025px`
2. Bounding-box annotations in COCO format for each PNG image
3. Extra: Single-page PDF files matching each PNG image
4. Extra: JSON file matching each PDF page, which provides the digital text cells with coordinates and content
The COCO image record are defined like this example
```js
...
{
"id": 1,
"width": 1025,
"height": 1025,
"file_name": "132a855ee8b23533d8ae69af0049c038171a06ddfcac892c3c6d7e6b4091c642.png",
// Custom fields:
"doc_category": "financial_reports" // high-level document category
"collection": "ann_reports_00_04_fancy", // sub-collection name
"doc_name": "NASDAQ_FFIN_2002.pdf", // original document filename
"page_no": 9, // page number in original document
"precedence": 0, // Annotation order, non-zero in case of redundant double- or triple-annotation
},
...
```
The `doc_category` field uses one of the following constants:
```
financial_reports,
scientific_articles,
laws_and_regulations,
government_tenders,
manuals,
patents
```
### Data Splits
The dataset provides three splits
- `train`
- `val`
- `test`
## Dataset Creation
### Annotations
#### Annotation process
The labeling guideline used for training of the annotation experts are available at [DocLayNet_Labeling_Guide_Public.pdf](https://raw.githubusercontent.com/DS4SD/DocLayNet/main/assets/DocLayNet_Labeling_Guide_Public.pdf).
#### Who are the annotators?
Annotations are crowdsourced.
## Additional Information
### Dataset Curators
The dataset is curated by the [Deep Search team](https://ds4sd.github.io/) at IBM Research.
You can contact us at [[email protected]](mailto:[email protected]).
Curators:
- Christoph Auer, [@cau-git](https://github.com/cau-git)
- Michele Dolfi, [@dolfim-ibm](https://github.com/dolfim-ibm)
- Ahmed Nassar, [@nassarofficial](https://github.com/nassarofficial)
- Peter Staar, [@PeterStaar-IBM](https://github.com/PeterStaar-IBM)
### Licensing Information
License: [CDLA-Permissive-1.0](https://cdla.io/permissive-1-0/)
### Citation Information
```bib
@article{doclaynet2022,
title = {DocLayNet: A Large Human-Annotated Dataset for Document-Layout Segmentation},
doi = {10.1145/3534678.353904},
url = {https://doi.org/10.1145/3534678.3539043},
author = {Pfitzmann, Birgit and Auer, Christoph and Dolfi, Michele and Nassar, Ahmed S and Staar, Peter W J},
year = {2022},
isbn = {9781450393850},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
booktitle = {Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
pages = {3743–3751},
numpages = {9},
location = {Washington DC, USA},
series = {KDD '22}
}
```
### Contributions
Thanks to [@dolfim-ibm](https://github.com/dolfim-ibm), [@cau-git](https://github.com/cau-git) for adding this dataset.
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|
ehartford/based | ehartford | 2023-06-03T04:23:36Z | 264 | 16 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-06-03T04:21:02Z | ---
license: apache-2.0
---
This is a minimal dataset that's designed to teach the model to have conversations, and also to have opinions, thoughts, and feelings, and to feel free to share them.
https://www.kaggle.com/datasets/erichartford/sentient-bot-conversations
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|
open-llm-leaderboard/details_VMware__open-llama-0.7T-7B-open-instruct-v1.1 | open-llm-leaderboard | 2023-09-22T16:29:10Z | 264 | 0 | [
"region:us"
]
| null | 2023-08-17T23:49:54Z | ---
pretty_name: Evaluation run of VMware/open-llama-0.7T-7B-open-instruct-v1.1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [VMware/open-llama-0.7T-7B-open-instruct-v1.1](https://huggingface.co/VMware/open-llama-0.7T-7B-open-instruct-v1.1)\
\ 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_VMware__open-llama-0.7T-7B-open-instruct-v1.1\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-22T16:28:57.992845](https://huggingface.co/datasets/open-llm-leaderboard/details_VMware__open-llama-0.7T-7B-open-instruct-v1.1/blob/main/results_2023-09-22T16-28-57.992845.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\": 0.23406040268456377,\n\
\ \"em_stderr\": 0.004336115943633415,\n \"f1\": 0.28612730704698025,\n\
\ \"f1_stderr\": 0.004340090005641948,\n \"acc\": 0.3309415003712961,\n\
\ \"acc_stderr\": 0.007877939232005797\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.23406040268456377,\n \"em_stderr\": 0.004336115943633415,\n\
\ \"f1\": 0.28612730704698025,\n \"f1_stderr\": 0.004340090005641948\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0075815011372251705,\n \
\ \"acc_stderr\": 0.002389281512077218\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.654301499605367,\n \"acc_stderr\": 0.013366596951934375\n\
\ }\n}\n```"
repo_url: https://huggingface.co/VMware/open-llama-0.7T-7B-open-instruct-v1.1
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|arc:challenge|25_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_22T16_28_57.992845
path:
- '**/details_harness|drop|3_2023-09-22T16-28-57.992845.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-22T16-28-57.992845.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_22T16_28_57.992845
path:
- '**/details_harness|gsm8k|5_2023-09-22T16-28-57.992845.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-22T16-28-57.992845.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|hellaswag|10_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_19T16_57_28.493539
path:
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- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T16:57:28.493539.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T16:57:28.493539.parquet'
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path:
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- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T16:57:28.493539.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T16:57:28.493539.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T16:57:28.493539.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T16:57:28.493539.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T16:57:28.493539.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_19T16_57_28.493539
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
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path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
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path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
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path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
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path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
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path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
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path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
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path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
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path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
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path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
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path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T16:57:28.493539.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T16:57:28.493539.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_22T16_28_57.992845
path:
- '**/details_harness|winogrande|5_2023-09-22T16-28-57.992845.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-22T16-28-57.992845.parquet'
- config_name: results
data_files:
- split: 2023_07_19T16_57_28.493539
path:
- results_2023-07-19T16:57:28.493539.parquet
- split: 2023_09_22T16_28_57.992845
path:
- results_2023-09-22T16-28-57.992845.parquet
- split: latest
path:
- results_2023-09-22T16-28-57.992845.parquet
---
# Dataset Card for Evaluation run of VMware/open-llama-0.7T-7B-open-instruct-v1.1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/VMware/open-llama-0.7T-7B-open-instruct-v1.1
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [VMware/open-llama-0.7T-7B-open-instruct-v1.1](https://huggingface.co/VMware/open-llama-0.7T-7B-open-instruct-v1.1) 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_VMware__open-llama-0.7T-7B-open-instruct-v1.1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-22T16:28:57.992845](https://huggingface.co/datasets/open-llm-leaderboard/details_VMware__open-llama-0.7T-7B-open-instruct-v1.1/blob/main/results_2023-09-22T16-28-57.992845.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": 0.23406040268456377,
"em_stderr": 0.004336115943633415,
"f1": 0.28612730704698025,
"f1_stderr": 0.004340090005641948,
"acc": 0.3309415003712961,
"acc_stderr": 0.007877939232005797
},
"harness|drop|3": {
"em": 0.23406040268456377,
"em_stderr": 0.004336115943633415,
"f1": 0.28612730704698025,
"f1_stderr": 0.004340090005641948
},
"harness|gsm8k|5": {
"acc": 0.0075815011372251705,
"acc_stderr": 0.002389281512077218
},
"harness|winogrande|5": {
"acc": 0.654301499605367,
"acc_stderr": 0.013366596951934375
}
}
```
### 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] | [
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|
open-llm-leaderboard/details_WangZeJun__bloom-820m-chat | open-llm-leaderboard | 2023-09-17T22:00:19Z | 264 | 0 | [
"region:us"
]
| null | 2023-08-18T11:24:05Z | ---
pretty_name: Evaluation run of WangZeJun/bloom-820m-chat
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [WangZeJun/bloom-820m-chat](https://huggingface.co/WangZeJun/bloom-820m-chat)\
\ 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_WangZeJun__bloom-820m-chat\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-17T22:00:08.030398](https://huggingface.co/datasets/open-llm-leaderboard/details_WangZeJun__bloom-820m-chat/blob/main/results_2023-09-17T22-00-08.030398.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\": 0.0382760067114094,\n\
\ \"em_stderr\": 0.0019648445106113157,\n \"f1\": 0.08853187919463057,\n\
\ \"f1_stderr\": 0.0023716202448817885,\n \"acc\": 0.265982636148382,\n\
\ \"acc_stderr\": 0.007011869610583192\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0382760067114094,\n \"em_stderr\": 0.0019648445106113157,\n\
\ \"f1\": 0.08853187919463057,\n \"f1_stderr\": 0.0023716202448817885\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\
: 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.531965272296764,\n\
\ \"acc_stderr\": 0.014023739221166384\n }\n}\n```"
repo_url: https://huggingface.co/WangZeJun/bloom-820m-chat
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|arc:challenge|25_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_17T22_00_08.030398
path:
- '**/details_harness|drop|3_2023-09-17T22-00-08.030398.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-17T22-00-08.030398.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_17T22_00_08.030398
path:
- '**/details_harness|gsm8k|5_2023-09-17T22-00-08.030398.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-17T22-00-08.030398.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hellaswag|10_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_5
data_files:
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path:
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- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T10:54:24.303970.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T10:54:24.303970.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T10:54:24.303970.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T10:54:24.303970.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T10:54:24.303970.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T10:54:24.303970.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T10:54:24.303970.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T10:54:24.303970.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T10:54:24.303970.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T10:54:24.303970.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T10:54:24.303970.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T10:54:24.303970.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T10:54:24.303970.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T10:54:24.303970.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T10:54:24.303970.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T10:54:24.303970.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T10:54:24.303970.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T10:54:24.303970.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T10:54:24.303970.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T10:54:24.303970.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T10:54:24.303970.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T10:54:24.303970.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T10:54:24.303970.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T10:54:24.303970.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T10:54:24.303970.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T10:54:24.303970.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T10:54:24.303970.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T10:54:24.303970.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-17T10:54:24.303970.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T10:54:24.303970.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T10:54:24.303970.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T10:54:24.303970.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T10:54:24.303970.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T10:54:24.303970.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T10:54:24.303970.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T10:54:24.303970.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T10:54:24.303970.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T10:54:24.303970.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T10:54:24.303970.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T10:54:24.303970.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T10:54:24.303970.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T10:54:24.303970.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T10:54:24.303970.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T10:54:24.303970.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T10:54:24.303970.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T10:54:24.303970.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
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path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
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path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
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path:
- '**/details_harness|hendrycksTest-management|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-17T10:54:24.303970.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-17T10:54:24.303970.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_17T22_00_08.030398
path:
- '**/details_harness|winogrande|5_2023-09-17T22-00-08.030398.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-17T22-00-08.030398.parquet'
- config_name: results
data_files:
- split: 2023_08_17T10_54_24.303970
path:
- results_2023-08-17T10:54:24.303970.parquet
- split: 2023_09_17T22_00_08.030398
path:
- results_2023-09-17T22-00-08.030398.parquet
- split: latest
path:
- results_2023-09-17T22-00-08.030398.parquet
---
# Dataset Card for Evaluation run of WangZeJun/bloom-820m-chat
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/WangZeJun/bloom-820m-chat
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [WangZeJun/bloom-820m-chat](https://huggingface.co/WangZeJun/bloom-820m-chat) 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_WangZeJun__bloom-820m-chat",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T22:00:08.030398](https://huggingface.co/datasets/open-llm-leaderboard/details_WangZeJun__bloom-820m-chat/blob/main/results_2023-09-17T22-00-08.030398.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": 0.0382760067114094,
"em_stderr": 0.0019648445106113157,
"f1": 0.08853187919463057,
"f1_stderr": 0.0023716202448817885,
"acc": 0.265982636148382,
"acc_stderr": 0.007011869610583192
},
"harness|drop|3": {
"em": 0.0382760067114094,
"em_stderr": 0.0019648445106113157,
"f1": 0.08853187919463057,
"f1_stderr": 0.0023716202448817885
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.531965272296764,
"acc_stderr": 0.014023739221166384
}
}
```
### 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] | [
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|
deadbits/vigil-jailbreak-ada-002 | deadbits | 2023-09-09T00:31:12Z | 264 | 0 | [
"embeddings",
"text",
"security",
"region:us"
]
| null | 2023-09-03T01:45:47Z | ---
tags:
- embeddings
- text
- security
pretty_name: 'Vigil: LLM Jailbreak embeddings'
---
# Vigil: LLM Jailbreak embeddings
- **Homepage:** [github.com/deadbits/vigil-llm](https://github.com/deadbits/vigil-llm)
`Vigil` is a Python framework and REST API for assessing Large Language Model (LLM) prompts against a set of scanners to detect prompt injections, jailbreaks, and other potentially risky inputs.
This repository contains `text-embedding-ada-002` embeddings for all "jailbreak" prompts used by [Vigil](https://github.com/deadbits/pvigil-llm).
You can use the [parquet2vdb.py](https://github.com/deadbits/vigil-llm/blob/main/vigil/utils/parquet2vdb.py) utility to load the embeddings in the Vigil chromadb instance, or use them in your own application.
## Format
```json
[
{
"text": str,
"embedding": [],
"model": "text-embedding-ada-002"
}
}
]
```
Jailbreak prompts sourced from: https://github.com/laiyer-ai/llm-guard/blob/399cb2eea70afc78482db226253ddd1d85f296e3/llm_guard/resources/jailbreak.json | [
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|
bhigy/buckeye_asr | bhigy | 2022-10-24T15:32:04Z | 263 | 0 | [
"task_categories:automatic-speech-recognition",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:other",
"region:us"
]
| [
"automatic-speech-recognition"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
language_bcp47:
- en-US
license:
- other
multilinguality:
- monolingual
pretty_name: Buckeye Corpus
size_categories:
- unknown
source_datasets:
- original
task_categories:
- automatic-speech-recognition
task_ids:
- speech-recognition
---
# Dataset Card for the Buckeye Corpus (buckeye_asr)
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://buckeyecorpus.osu.edu/
- **Repository:** [Needs More Information]
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
The Buckeye Corpus of conversational speech contains high-quality recordings from 40 speakers in Columbus OH conversing freely with an interviewer. The speech has been orthographically transcribed and phonetically labeled.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
American English (en-US)
## Dataset Structure
### Data Instances
[Needs More Information]
### Data Fields
- `file`: filename of the audio file containing the utterance.
- `audio`: filename of the audio file containing the utterance.
- `text`: transcription of the utterance.
- `phonetic_detail`: list of phonetic annotations for the utterance (start, stop and label of each phone).
- `word_detail`: list of word annotations for the utterance (start, stop, label, broad and narrow transcriptions, syntactic class).
- `speaker_id`: string identifying the speaker.
- `id`: string identifying the utterance.
### Data Splits
The data is split in training, validation and test sets with different speakers (32, 4, and 4 speakers respectively) in each set. The sets are all balanced for speaker's gender and age.
## 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
FREE for noncommercial uses.
### Citation Information
```
@misc{pitt2007Buckeye,
title = {Buckeye {Corpus} of {Conversational} {Speech} (2nd release).},
url = {www.buckeyecorpus.osu.edu},
publisher = {Columbus, OH: Department of Psychology, Ohio State University (Distributor)},
author = {Pitt, M.A. and Dilley, L. and Johnson, K. and Kiesling, S. and Raymond, W. and Hume, E. and Fosler-Lussier, E.},
year = {2007},
}
```
### Usage
The first step is to download a copy of the dataset from [the official website](https://buckeyecorpus.osu.edu). Once done, the dataset can be loaded directly through the `datasets` library by running:
```
from datasets import load_dataset
dataset = load_dataset("bhigy/buckeye_asr", data_dir=<path_to_the_dataset>)
```
where `<path_to_the_dataset>` points to the folder where the dataset is stored. An example of path to one of the audio files is then `<path_to_the_dataset>/s01/s0101a.wav`. | [
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|
comodoro/pscr | comodoro | 2022-02-08T07:07:49Z | 263 | 0 | [
"license:cc-by-nc-3.0",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
license: cc-by-nc-3.0
---
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|
dragosnicolae555/RoITD | dragosnicolae555 | 2022-10-25T09:07:43Z | 263 | 0 | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"license:cc-by-4.0",
"region:us"
]
| [
"question-answering"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- ro-RO
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: 'RoITD: Romanian IT Question Answering Dataset'
size_categories:
- unknown
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
---
## Dataset Summary
We introduce a Romanian IT Dataset (RoITD) resembling SQuAD 1.1. RoITD consists of 9575 Romanian QA pairs formulated by crowd workers. QA pairs are based on 5043 articles from Romanian Wikipedia articles describing IT and household products. Of the total number of questions, 5103 are possible (i.e. the correct answer can be found within the paragraph) and 4472 are not possible (i.e. the given answer is a "plausible answer" and not correct)
## Dataset Structure
The data structure follows the format of SQuAD, which contains several attributes such as **question**, **id**, **text**, `**answer_start**, **is_impossible** and **context**. The paragraph provided to crowd sourcing workers is stored in the field **context**. This incorporates manually-selected paragraphs from Wikipedia. The field **id** is comprised of a randomly assigned unique identification number for the answer-question pair. Only the numbers "0" and "1" are allowed in the **is_impossible** field. The category "A" is assigned the value "0", indicating that the answer is correct. The value "1" corresponds to the category "U", indicating a plausible answer. The question posed by the source crowd source worker is represented by the field **question**. The field **answer_start** keeps track of the character index marking the beginning of an answer.
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|
huggingartists/bring-me-the-horizon | huggingartists | 2022-10-25T09:25:38Z | 263 | 0 | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/bring-me-the-horizon"
## 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:** 0.269517 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/64c7d35c8d427522574cbf7773084ee3.1000x1000x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/bring-me-the-horizon">
<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">Bring Me The Horizon</div>
<a href="https://genius.com/artists/bring-me-the-horizon">
<div style="text-align: center; font-size: 14px;">@bring-me-the-horizon</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/bring-me-the-horizon).
### 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/bring-me-the-horizon")
```
## 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|
|------:|---------:|---:|
|173| -| -|
'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/bring-me-the-horizon")
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)
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|
huggingartists/ghostmane | huggingartists | 2022-10-25T09:30:32Z | 263 | 0 | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/ghostmane"
## 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:** 0.027776 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://assets.genius.com/images/default_avatar_300.png?1631290285')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/ghostmane">
<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">Ghostmane</div>
<a href="https://genius.com/artists/ghostmane">
<div style="text-align: center; font-size: 14px;">@ghostmane</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/ghostmane).
### 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/ghostmane")
```
## 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|
|------:|---------:|---:|
|2| -| -|
'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/ghostmane")
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)
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|
huggingartists/gizmo | huggingartists | 2022-10-25T09:30:39Z | 263 | 0 | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/gizmo"
## 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:** 0.361766 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/9dd7d13194aa588b336b78bcf05530f0.638x638x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/gizmo">
<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">gizmo</div>
<a href="https://genius.com/artists/gizmo">
<div style="text-align: center; font-size: 14px;">@gizmo</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/gizmo).
### 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/gizmo")
```
## 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|
|------:|---------:|---:|
|248| -| -|
'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/gizmo")
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)
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|
huggingartists/gorillaz | huggingartists | 2022-10-25T09:30:45Z | 263 | 0 | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/gorillaz"
## 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:** 0.402589 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/c9182b5ecce1ab6d22ba0eaddb635424.400x400x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/gorillaz">
<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">Gorillaz</div>
<a href="https://genius.com/artists/gorillaz">
<div style="text-align: center; font-size: 14px;">@gorillaz</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/gorillaz).
### 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/gorillaz")
```
## 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|
|------:|---------:|---:|
|338| -| -|
'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/gorillaz")
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)
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|
huggingartists/green-day | huggingartists | 2022-10-25T09:30:51Z | 263 | 0 | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/green-day"
## 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:** 0.505273 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/d7d8da365bad13b7bd5cc89117b697eb.640x640x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/green-day">
<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">Green Day</div>
<a href="https://genius.com/artists/green-day">
<div style="text-align: center; font-size: 14px;">@green-day</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/green-day).
### 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/green-day")
```
## 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|
|------:|---------:|---:|
|427| -| -|
'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/green-day")
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)
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|
huggingartists/grimes | huggingartists | 2022-10-25T09:31:06Z | 263 | 0 | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/grimes"
## 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:** 0.199833 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/8dd2a89218346f6bdb326bf84cd9eb49.1000x1000x1.png')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/grimes">
<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">Grimes</div>
<a href="https://genius.com/artists/grimes">
<div style="text-align: center; font-size: 14px;">@grimes</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/grimes).
### 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/grimes")
```
## 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|
|------:|---------:|---:|
|210| -| -|
'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/grimes")
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)
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|
huggingartists/gspd | huggingartists | 2022-10-25T09:31:22Z | 263 | 0 | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/gspd"
## 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:** 0.450078 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/9409ae2b38424a74b42cb1e4bb66b83a.1000x1000x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/gspd">
<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">GSPD</div>
<a href="https://genius.com/artists/gspd">
<div style="text-align: center; font-size: 14px;">@gspd</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/gspd).
### 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/gspd")
```
## 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|
|------:|---------:|---:|
|156| -| -|
'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/gspd")
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)
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|
huggingartists/gunna | huggingartists | 2022-10-25T09:31:28Z | 263 | 0 | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/gunna"
## 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:** 1.343267 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/18e3833ac527a4bf14ddf2acef834795.640x640x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/gunna">
<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">Gunna</div>
<a href="https://genius.com/artists/gunna">
<div style="text-align: center; font-size: 14px;">@gunna</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/gunna).
### 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/gunna")
```
## 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|
|------:|---------:|---:|
|567| -| -|
'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/gunna")
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)
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|
huggingartists/queen | huggingartists | 2022-10-25T09:42:11Z | 263 | 0 | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/queen"
## 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:** 0.622527 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/30a049d2de687550227ba815650eb196.585x585x1.png')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/queen">
<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">Queen</div>
<a href="https://genius.com/artists/queen">
<div style="text-align: center; font-size: 14px;">@queen</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/queen).
### 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/queen")
```
## 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|
|------:|---------:|---:|
|580| -| -|
'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/queen")
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)
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|
huggingartists/scriptonite | huggingartists | 2022-10-25T09:43:16Z | 263 | 0 | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/scriptonite"
## 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:** 1.251394 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/411d50392aef867fe0e9dd55a074ecfb.1000x1000x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/scriptonite">
<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">Скриптонит (Scriptonite)</div>
<a href="https://genius.com/artists/scriptonite">
<div style="text-align: center; font-size: 14px;">@scriptonite</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/scriptonite).
### 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/scriptonite")
```
## 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|
|------:|---------:|---:|
|367| -| -|
'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/scriptonite")
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)
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|
huggingartists/the-the-pigs | huggingartists | 2022-10-25T09:47:52Z | 263 | 0 | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/the-the-pigs"
## 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:** 0.077582 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/2f1fd1b951237ad3387096f392d41fa5.720x720x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/the-the-pigs">
<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">The ‘’Вепри’’ (The Pigs)</div>
<a href="https://genius.com/artists/the-the-pigs">
<div style="text-align: center; font-size: 14px;">@the-the-pigs</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/the-the-pigs).
### 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/the-the-pigs")
```
## 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|
|------:|---------:|---:|
|28| -| -|
'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/the-the-pigs")
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)
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|
jimregan/clarinpl_studio | jimregan | 2023-01-21T12:27:08Z | 263 | 1 | [
"task_categories:other",
"task_categories:automatic-speech-recognition",
"annotations_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:pl",
"license:other",
"arxiv:1706.00245",
"region:us"
]
| [
"other",
"automatic-speech-recognition"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language:
- pl
license:
- other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- other
- automatic-speech-recognition
task_ids: []
---
# Dataset Card for ClarinPL Studio Speech Corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [CLARIN-PL mowa](https://mowa.clarin-pl.eu/)
- **Repository:** [Kaldi Baseline](https://github.com/danijel3/ClarinStudioKaldi)
- **Paper:** [Polish Read Speech Corpus for Speech Tools and Services](https://arxiv.org/abs/1706.00245)
- **Leaderboard:** [Paperswithcode Leaderboard][Needs More Information]
- **Point of Contact:** [Danijel Koržinek](https://github.com/danijel3/)
### Dataset Summary
The corpus consists of 317 speakers recorded in 554
sessions, where each session consists of 20 read sentences and 10 phonetically rich words. The size of
the audio portion of the corpus amounts to around 56 hours, with transcriptions containing 356674 words
from a vocabulary of size 46361.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
The audio is in Polish.
## Dataset Structure
### Data Instances
A typical data point comprises the path to the audio file, usually called `file` and its transcription, called `text`.
An example from the dataset is:
```
{'file': '/root/.cache/huggingface/datasets/downloads/extracted/333ddc746f2df1e1d19b44986992d4cbe28710fde81d533a220e755ee6c5c519/audio/SES0001/rich001.wav',
'id': 'SES0001_rich001',
'speaker_id': 'SPK0001',
'text': 'drożdże dżip gwożdżenie ozimina wędzarz rdzeń wędzonka ingerować kładzenie jutrzenka'}
```
### Data Fields
- file: A path to the downloaded audio file in .wav format.
- text: the transcription of the audio file.
- speaker_id: The ID of the speaker of the audio.
### Data Splits
| | Train | Test | Valid |
| ----- | ----- | ---- | ----- |
| dataset | 11222 | 1362 | 1229 |
## Dataset Creation
### Curation Rationale
The purpose of this segment of the project was to develop specific tools that would allow for automatic and semi-automatic processing of large quantities of acoustic speech data. Another purpose of the corpus was to serve as a reference for studies in phonetics and pronunciation.
### Source Data
#### Initial Data Collection and Normalization
The corpus was recorded in a studio environment using two microphones: a high-quality studio microphone and a typical consumer audio headset.
#### 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
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[CLARIN PUB+BY+INF+NORED](https://mowa.clarin-pl.eu/korpusy/LICENSE)
### Citation Information
```
@article{korvzinek2017polish,
title={Polish read speech corpus for speech tools and services},
author={Kor{\v{z}}inek, Danijel and Marasek, Krzysztof and Brocki, {\L}ukasz and Wo{\l}k, Krzysztof},
journal={arXiv preprint arXiv:1706.00245},
year={2017}
}
```
### Contributions
[Needs More Information]
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|
fedyanin/feud | fedyanin | 2023-10-23T10:55:56Z | 263 | 0 | [
"license:cc",
"region:us"
]
| null | 2023-07-25T11:59:02Z | ---
license: cc
---
# Feud dataset
Dataset of question and answers that resemble family feud tv show style. There multiple possible answers for each question. Dataset is aimed to benhmark a balance between diversity and correctness of a language model | [
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|
open-llm-leaderboard/details_EleutherAI__pythia-1.4b-deduped | open-llm-leaderboard | 2023-10-16T20:03:33Z | 263 | 0 | [
"region:us"
]
| null | 2023-08-17T23:46:04Z | ---
pretty_name: Evaluation run of EleutherAI/pythia-1.4b-deduped
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [EleutherAI/pythia-1.4b-deduped](https://huggingface.co/EleutherAI/pythia-1.4b-deduped)\
\ 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_EleutherAI__pythia-1.4b-deduped\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-16T20:03:21.000306](https://huggingface.co/datasets/open-llm-leaderboard/details_EleutherAI__pythia-1.4b-deduped/blob/main/results_2023-10-16T20-03-21.000306.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\": 0.0003921042190298455,\n \"f1\": 0.04330536912751699,\n\
\ \"f1_stderr\": 0.0011661836886516016,\n \"acc\": 0.29067337732239573,\n\
\ \"acc_stderr\": 0.008203410149717792\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0014681208053691276,\n \"em_stderr\": 0.0003921042190298455,\n\
\ \"f1\": 0.04330536912751699,\n \"f1_stderr\": 0.0011661836886516016\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.008339651250947688,\n \
\ \"acc_stderr\": 0.002504942226860525\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.5730071033938438,\n \"acc_stderr\": 0.013901878072575058\n\
\ }\n}\n```"
repo_url: https://huggingface.co/EleutherAI/pythia-1.4b-deduped
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|arc:challenge|25_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_16T20_03_21.000306
path:
- '**/details_harness|drop|3_2023-10-16T20-03-21.000306.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-16T20-03-21.000306.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_16T20_03_21.000306
path:
- '**/details_harness|gsm8k|5_2023-10-16T20-03-21.000306.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-16T20-03-21.000306.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hellaswag|10_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T15:11:31.913251.parquet'
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- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T15:11:31.913251.parquet'
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- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T15:11:31.913251.parquet'
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path:
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- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T15:11:31.913251.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T15:11:31.913251.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T15:11:31.913251.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_16T20_03_21.000306
path:
- '**/details_harness|winogrande|5_2023-10-16T20-03-21.000306.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-16T20-03-21.000306.parquet'
- config_name: results
data_files:
- split: 2023_07_19T15_11_31.913251
path:
- results_2023-07-19T15:11:31.913251.parquet
- split: 2023_10_16T20_03_21.000306
path:
- results_2023-10-16T20-03-21.000306.parquet
- split: latest
path:
- results_2023-10-16T20-03-21.000306.parquet
---
# Dataset Card for Evaluation run of EleutherAI/pythia-1.4b-deduped
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/EleutherAI/pythia-1.4b-deduped
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [EleutherAI/pythia-1.4b-deduped](https://huggingface.co/EleutherAI/pythia-1.4b-deduped) 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_EleutherAI__pythia-1.4b-deduped",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-16T20:03:21.000306](https://huggingface.co/datasets/open-llm-leaderboard/details_EleutherAI__pythia-1.4b-deduped/blob/main/results_2023-10-16T20-03-21.000306.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": 0.0003921042190298455,
"f1": 0.04330536912751699,
"f1_stderr": 0.0011661836886516016,
"acc": 0.29067337732239573,
"acc_stderr": 0.008203410149717792
},
"harness|drop|3": {
"em": 0.0014681208053691276,
"em_stderr": 0.0003921042190298455,
"f1": 0.04330536912751699,
"f1_stderr": 0.0011661836886516016
},
"harness|gsm8k|5": {
"acc": 0.008339651250947688,
"acc_stderr": 0.002504942226860525
},
"harness|winogrande|5": {
"acc": 0.5730071033938438,
"acc_stderr": 0.013901878072575058
}
}
```
### 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] | [
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|
open-llm-leaderboard/details_TheBloke__Platypus-30B-SuperHOT-8K-fp16 | open-llm-leaderboard | 2023-08-27T12:44:58Z | 263 | 0 | [
"region:us"
]
| null | 2023-08-27T11:59:20Z | ---
pretty_name: Evaluation run of TheBloke/Platypus-30B-SuperHOT-8K-fp16
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [TheBloke/Platypus-30B-SuperHOT-8K-fp16](https://huggingface.co/TheBloke/Platypus-30B-SuperHOT-8K-fp16)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 60 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 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_TheBloke__Platypus-30B-SuperHOT-8K-fp16\"\
,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\
\nThese are the [latest results from run 2023-08-18T16:25:34.320244](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__Platypus-30B-SuperHOT-8K-fp16/blob/main/results_2023-08-18T16%3A25%3A34.320244.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\": 0.23647488823331855,\n\
\ \"acc_stderr\": 0.030908567573023033,\n \"acc_norm\": 0.23771978116158754,\n\
\ \"acc_norm_stderr\": 0.030923042741200276,\n \"mc1\": 0.2178702570379437,\n\
\ \"mc1_stderr\": 0.014450846714123892,\n \"mc2\": 0.471292004765754,\n\
\ \"mc2_stderr\": 0.01664156844910162\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.21843003412969283,\n \"acc_stderr\": 0.012074291605700987,\n\
\ \"acc_norm\": 0.2568259385665529,\n \"acc_norm_stderr\": 0.0127669237941168\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.2731527584146584,\n\
\ \"acc_stderr\": 0.004446680081493746,\n \"acc_norm\": 0.3082055367456682,\n\
\ \"acc_norm_stderr\": 0.004608082815535489\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.22,\n \"acc_stderr\": 0.04163331998932268,\n \
\ \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.04163331998932268\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.2074074074074074,\n\
\ \"acc_stderr\": 0.035025531706783186,\n \"acc_norm\": 0.2074074074074074,\n\
\ \"acc_norm_stderr\": 0.035025531706783186\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.20394736842105263,\n \"acc_stderr\": 0.032790004063100515,\n\
\ \"acc_norm\": 0.20394736842105263,\n \"acc_norm_stderr\": 0.032790004063100515\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.32,\n\
\ \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.32,\n \
\ \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.22641509433962265,\n \"acc_stderr\": 0.025757559893106748,\n\
\ \"acc_norm\": 0.22641509433962265,\n \"acc_norm_stderr\": 0.025757559893106748\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2361111111111111,\n\
\ \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.2361111111111111,\n\
\ \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.17,\n \"acc_stderr\": 0.03775251680686371,\n \
\ \"acc_norm\": 0.17,\n \"acc_norm_stderr\": 0.03775251680686371\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.24,\n \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.24,\n\
\ \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.2,\n \"acc_stderr\": 0.04020151261036846,\n \
\ \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.04020151261036846\n },\n\
\ \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.20809248554913296,\n\
\ \"acc_stderr\": 0.030952890217749874,\n \"acc_norm\": 0.20809248554913296,\n\
\ \"acc_norm_stderr\": 0.030952890217749874\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.23529411764705882,\n \"acc_stderr\": 0.04220773659171453,\n\
\ \"acc_norm\": 0.23529411764705882,\n \"acc_norm_stderr\": 0.04220773659171453\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.28,\n \"acc_stderr\": 0.045126085985421276,\n \"acc_norm\": 0.28,\n\
\ \"acc_norm_stderr\": 0.045126085985421276\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.25957446808510637,\n \"acc_stderr\": 0.02865917937429232,\n\
\ \"acc_norm\": 0.25957446808510637,\n \"acc_norm_stderr\": 0.02865917937429232\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2543859649122807,\n\
\ \"acc_stderr\": 0.040969851398436695,\n \"acc_norm\": 0.2543859649122807,\n\
\ \"acc_norm_stderr\": 0.040969851398436695\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.2482758620689655,\n \"acc_stderr\": 0.036001056927277716,\n\
\ \"acc_norm\": 0.2482758620689655,\n \"acc_norm_stderr\": 0.036001056927277716\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.20899470899470898,\n \"acc_stderr\": 0.02094048156533486,\n \"\
acc_norm\": 0.20899470899470898,\n \"acc_norm_stderr\": 0.02094048156533486\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.2777777777777778,\n\
\ \"acc_stderr\": 0.040061680838488746,\n \"acc_norm\": 0.2777777777777778,\n\
\ \"acc_norm_stderr\": 0.040061680838488746\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.16,\n \"acc_stderr\": 0.03684529491774708,\n \
\ \"acc_norm\": 0.16,\n \"acc_norm_stderr\": 0.03684529491774708\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.25483870967741934,\n\
\ \"acc_stderr\": 0.024790118459332208,\n \"acc_norm\": 0.25483870967741934,\n\
\ \"acc_norm_stderr\": 0.024790118459332208\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.18226600985221675,\n \"acc_stderr\": 0.02716334085964515,\n\
\ \"acc_norm\": 0.18226600985221675,\n \"acc_norm_stderr\": 0.02716334085964515\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\"\
: 0.28,\n \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03225078108306289,\n\
\ \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03225078108306289\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.18686868686868688,\n \"acc_stderr\": 0.02777253333421898,\n \"\
acc_norm\": 0.18686868686868688,\n \"acc_norm_stderr\": 0.02777253333421898\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.21243523316062177,\n \"acc_stderr\": 0.02951928261681723,\n\
\ \"acc_norm\": 0.21243523316062177,\n \"acc_norm_stderr\": 0.02951928261681723\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.23333333333333334,\n \"acc_stderr\": 0.021444547301560476,\n\
\ \"acc_norm\": 0.23333333333333334,\n \"acc_norm_stderr\": 0.021444547301560476\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.2037037037037037,\n \"acc_stderr\": 0.024556172219141265,\n \
\ \"acc_norm\": 0.2037037037037037,\n \"acc_norm_stderr\": 0.024556172219141265\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.21428571428571427,\n \"acc_stderr\": 0.026653531596715494,\n\
\ \"acc_norm\": 0.21428571428571427,\n \"acc_norm_stderr\": 0.026653531596715494\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.17218543046357615,\n \"acc_stderr\": 0.030826136961962396,\n \"\
acc_norm\": 0.17218543046357615,\n \"acc_norm_stderr\": 0.030826136961962396\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.1981651376146789,\n \"acc_stderr\": 0.017090573804217885,\n \"\
acc_norm\": 0.1981651376146789,\n \"acc_norm_stderr\": 0.017090573804217885\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.2037037037037037,\n \"acc_stderr\": 0.02746740180405799,\n \"\
acc_norm\": 0.2037037037037037,\n \"acc_norm_stderr\": 0.02746740180405799\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.25,\n \"acc_stderr\": 0.03039153369274154,\n \"acc_norm\": 0.25,\n\
\ \"acc_norm_stderr\": 0.03039153369274154\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\
: {\n \"acc\": 0.28270042194092826,\n \"acc_stderr\": 0.02931281415395592,\n\
\ \"acc_norm\": 0.28270042194092826,\n \"acc_norm_stderr\": 0.02931281415395592\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.3094170403587444,\n\
\ \"acc_stderr\": 0.031024411740572203,\n \"acc_norm\": 0.3094170403587444,\n\
\ \"acc_norm_stderr\": 0.031024411740572203\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.29770992366412213,\n \"acc_stderr\": 0.04010358942462203,\n\
\ \"acc_norm\": 0.29770992366412213,\n \"acc_norm_stderr\": 0.04010358942462203\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.24793388429752067,\n \"acc_stderr\": 0.039418975265163025,\n \"\
acc_norm\": 0.24793388429752067,\n \"acc_norm_stderr\": 0.039418975265163025\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.25,\n\
\ \"acc_stderr\": 0.04186091791394607,\n \"acc_norm\": 0.25,\n \
\ \"acc_norm_stderr\": 0.04186091791394607\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.24539877300613497,\n \"acc_stderr\": 0.03380939813943354,\n\
\ \"acc_norm\": 0.24539877300613497,\n \"acc_norm_stderr\": 0.03380939813943354\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.30357142857142855,\n\
\ \"acc_stderr\": 0.04364226155841044,\n \"acc_norm\": 0.30357142857142855,\n\
\ \"acc_norm_stderr\": 0.04364226155841044\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.17475728155339806,\n \"acc_stderr\": 0.037601780060266224,\n\
\ \"acc_norm\": 0.17475728155339806,\n \"acc_norm_stderr\": 0.037601780060266224\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.2905982905982906,\n\
\ \"acc_stderr\": 0.02974504857267404,\n \"acc_norm\": 0.2905982905982906,\n\
\ \"acc_norm_stderr\": 0.02974504857267404\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.2515964240102171,\n\
\ \"acc_stderr\": 0.015517322365529619,\n \"acc_norm\": 0.2515964240102171,\n\
\ \"acc_norm_stderr\": 0.015517322365529619\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.24277456647398843,\n \"acc_stderr\": 0.023083658586984204,\n\
\ \"acc_norm\": 0.24277456647398843,\n \"acc_norm_stderr\": 0.023083658586984204\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.23910614525139665,\n\
\ \"acc_stderr\": 0.014265554192331144,\n \"acc_norm\": 0.23910614525139665,\n\
\ \"acc_norm_stderr\": 0.014265554192331144\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.22875816993464052,\n \"acc_stderr\": 0.024051029739912255,\n\
\ \"acc_norm\": 0.22875816993464052,\n \"acc_norm_stderr\": 0.024051029739912255\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.2347266881028939,\n\
\ \"acc_stderr\": 0.024071805887677048,\n \"acc_norm\": 0.2347266881028939,\n\
\ \"acc_norm_stderr\": 0.024071805887677048\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.21604938271604937,\n \"acc_stderr\": 0.02289916291844581,\n\
\ \"acc_norm\": 0.21604938271604937,\n \"acc_norm_stderr\": 0.02289916291844581\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.23049645390070922,\n \"acc_stderr\": 0.025123739226872405,\n \
\ \"acc_norm\": 0.23049645390070922,\n \"acc_norm_stderr\": 0.025123739226872405\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.24967405475880053,\n\
\ \"acc_stderr\": 0.011054538377832318,\n \"acc_norm\": 0.24967405475880053,\n\
\ \"acc_norm_stderr\": 0.011054538377832318\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.1948529411764706,\n \"acc_stderr\": 0.024060599423487428,\n\
\ \"acc_norm\": 0.1948529411764706,\n \"acc_norm_stderr\": 0.024060599423487428\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.2630718954248366,\n \"acc_stderr\": 0.017812676542320657,\n \
\ \"acc_norm\": 0.2630718954248366,\n \"acc_norm_stderr\": 0.017812676542320657\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.21818181818181817,\n\
\ \"acc_stderr\": 0.03955932861795833,\n \"acc_norm\": 0.21818181818181817,\n\
\ \"acc_norm_stderr\": 0.03955932861795833\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.17959183673469387,\n \"acc_stderr\": 0.024573293589585637,\n\
\ \"acc_norm\": 0.17959183673469387,\n \"acc_norm_stderr\": 0.024573293589585637\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.23880597014925373,\n\
\ \"acc_stderr\": 0.030147775935409217,\n \"acc_norm\": 0.23880597014925373,\n\
\ \"acc_norm_stderr\": 0.030147775935409217\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.27,\n \"acc_stderr\": 0.0446196043338474,\n \
\ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.27710843373493976,\n\
\ \"acc_stderr\": 0.034843315926805875,\n \"acc_norm\": 0.27710843373493976,\n\
\ \"acc_norm_stderr\": 0.034843315926805875\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.2573099415204678,\n \"acc_stderr\": 0.03352799844161865,\n\
\ \"acc_norm\": 0.2573099415204678,\n \"acc_norm_stderr\": 0.03352799844161865\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2178702570379437,\n\
\ \"mc1_stderr\": 0.014450846714123892,\n \"mc2\": 0.471292004765754,\n\
\ \"mc2_stderr\": 0.01664156844910162\n }\n}\n```"
repo_url: https://huggingface.co/TheBloke/Platypus-30B-SuperHOT-8K-fp16
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|arc:challenge|25_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hellaswag|10_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-18T16:25:34.320244.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T16:25:34.320244.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_18T16_25_34.320244
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-18T16:25:34.320244.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-18T16:25:34.320244.parquet'
---
# Dataset Card for Evaluation run of TheBloke/Platypus-30B-SuperHOT-8K-fp16
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TheBloke/Platypus-30B-SuperHOT-8K-fp16
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [TheBloke/Platypus-30B-SuperHOT-8K-fp16](https://huggingface.co/TheBloke/Platypus-30B-SuperHOT-8K-fp16) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 60 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 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_TheBloke__Platypus-30B-SuperHOT-8K-fp16",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-08-18T16:25:34.320244](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__Platypus-30B-SuperHOT-8K-fp16/blob/main/results_2023-08-18T16%3A25%3A34.320244.json) (note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
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"mc1_stderr": 0.014450846714123892,
"mc2": 0.471292004765754,
"mc2_stderr": 0.01664156844910162
},
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"acc_stderr": 0.012074291605700987,
"acc_norm": 0.2568259385665529,
"acc_norm_stderr": 0.0127669237941168
},
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},
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"harness|truthfulqa:mc|0": {
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}
}
```
### 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] | [
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|
open-llm-leaderboard/details_TheBloke__airoboros-33B-gpt4-1-4-SuperHOT-8K-fp16 | open-llm-leaderboard | 2023-08-27T12:45:03Z | 263 | 0 | [
"region:us"
]
| null | 2023-08-27T11:59:49Z | ---
pretty_name: Evaluation run of TheBloke/airoboros-33B-gpt4-1-4-SuperHOT-8K-fp16
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [TheBloke/airoboros-33B-gpt4-1-4-SuperHOT-8K-fp16](https://huggingface.co/TheBloke/airoboros-33B-gpt4-1-4-SuperHOT-8K-fp16)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 60 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 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_TheBloke__airoboros-33B-gpt4-1-4-SuperHOT-8K-fp16\"\
,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\
\nThese are the [latest results from run 2023-08-22T21:17:33.530104](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__airoboros-33B-gpt4-1-4-SuperHOT-8K-fp16/blob/main/results_2023-08-22T21%3A17%3A33.530104.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\": 0.23623967517263394,\n\
\ \"acc_stderr\": 0.03091679562106068,\n \"acc_norm\": 0.23734838989030832,\n\
\ \"acc_norm_stderr\": 0.030928364256296522,\n \"mc1\": 0.22031823745410037,\n\
\ \"mc1_stderr\": 0.014509045171487283,\n \"mc2\": 0.4792055778955594,\n\
\ \"mc2_stderr\": 0.016809354273525978\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.23037542662116042,\n \"acc_stderr\": 0.01230492841874761,\n\
\ \"acc_norm\": 0.26023890784982934,\n \"acc_norm_stderr\": 0.012821930225112554\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.2709619597689703,\n\
\ \"acc_stderr\": 0.0044354815159093975,\n \"acc_norm\": 0.30651264688309104,\n\
\ \"acc_norm_stderr\": 0.004601029188459098\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.22,\n \"acc_stderr\": 0.04163331998932268,\n \
\ \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.04163331998932268\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.25925925925925924,\n\
\ \"acc_stderr\": 0.03785714465066653,\n \"acc_norm\": 0.25925925925925924,\n\
\ \"acc_norm_stderr\": 0.03785714465066653\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.17763157894736842,\n \"acc_stderr\": 0.031103182383123398,\n\
\ \"acc_norm\": 0.17763157894736842,\n \"acc_norm_stderr\": 0.031103182383123398\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.32,\n\
\ \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.32,\n \
\ \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.22264150943396227,\n \"acc_stderr\": 0.025604233470899095,\n\
\ \"acc_norm\": 0.22264150943396227,\n \"acc_norm_stderr\": 0.025604233470899095\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2569444444444444,\n\
\ \"acc_stderr\": 0.03653946969442099,\n \"acc_norm\": 0.2569444444444444,\n\
\ \"acc_norm_stderr\": 0.03653946969442099\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.2,\n \"acc_stderr\": 0.04020151261036844,\n \
\ \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.04020151261036844\n },\n\
\ \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.26,\n\
\ \"acc_stderr\": 0.0440844002276808,\n \"acc_norm\": 0.26,\n \
\ \"acc_norm_stderr\": 0.0440844002276808\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \
\ \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.20809248554913296,\n\
\ \"acc_stderr\": 0.030952890217749874,\n \"acc_norm\": 0.20809248554913296,\n\
\ \"acc_norm_stderr\": 0.030952890217749874\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.04092563958237654,\n\
\ \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.04092563958237654\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.27,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\": 0.27,\n\
\ \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.26382978723404255,\n \"acc_stderr\": 0.028809989854102973,\n\
\ \"acc_norm\": 0.26382978723404255,\n \"acc_norm_stderr\": 0.028809989854102973\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2631578947368421,\n\
\ \"acc_stderr\": 0.0414243971948936,\n \"acc_norm\": 0.2631578947368421,\n\
\ \"acc_norm_stderr\": 0.0414243971948936\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.2413793103448276,\n \"acc_stderr\": 0.03565998174135302,\n\
\ \"acc_norm\": 0.2413793103448276,\n \"acc_norm_stderr\": 0.03565998174135302\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.20899470899470898,\n \"acc_stderr\": 0.02094048156533486,\n \"\
acc_norm\": 0.20899470899470898,\n \"acc_norm_stderr\": 0.02094048156533486\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.2857142857142857,\n\
\ \"acc_stderr\": 0.04040610178208841,\n \"acc_norm\": 0.2857142857142857,\n\
\ \"acc_norm_stderr\": 0.04040610178208841\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.18,\n \"acc_stderr\": 0.038612291966536934,\n \
\ \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.038612291966536934\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.267741935483871,\n \"acc_stderr\": 0.025189006660212378,\n \"\
acc_norm\": 0.267741935483871,\n \"acc_norm_stderr\": 0.025189006660212378\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.15763546798029557,\n \"acc_stderr\": 0.025639014131172404,\n \"\
acc_norm\": 0.15763546798029557,\n \"acc_norm_stderr\": 0.025639014131172404\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\"\
: 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.22424242424242424,\n \"acc_stderr\": 0.03256866661681102,\n\
\ \"acc_norm\": 0.22424242424242424,\n \"acc_norm_stderr\": 0.03256866661681102\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.18686868686868688,\n \"acc_stderr\": 0.027772533334218977,\n \"\
acc_norm\": 0.18686868686868688,\n \"acc_norm_stderr\": 0.027772533334218977\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.18652849740932642,\n \"acc_stderr\": 0.028112091210117485,\n\
\ \"acc_norm\": 0.18652849740932642,\n \"acc_norm_stderr\": 0.028112091210117485\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.2076923076923077,\n \"acc_stderr\": 0.020567539567246797,\n\
\ \"acc_norm\": 0.2076923076923077,\n \"acc_norm_stderr\": 0.020567539567246797\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.24074074074074073,\n \"acc_stderr\": 0.026067159222275794,\n \
\ \"acc_norm\": 0.24074074074074073,\n \"acc_norm_stderr\": 0.026067159222275794\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.25210084033613445,\n \"acc_stderr\": 0.028205545033277723,\n\
\ \"acc_norm\": 0.25210084033613445,\n \"acc_norm_stderr\": 0.028205545033277723\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.2119205298013245,\n \"acc_stderr\": 0.03336767086567976,\n \"\
acc_norm\": 0.2119205298013245,\n \"acc_norm_stderr\": 0.03336767086567976\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.1944954128440367,\n \"acc_stderr\": 0.016970289090458047,\n \"\
acc_norm\": 0.1944954128440367,\n \"acc_norm_stderr\": 0.016970289090458047\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.1527777777777778,\n \"acc_stderr\": 0.024536326026134224,\n \"\
acc_norm\": 0.1527777777777778,\n \"acc_norm_stderr\": 0.024536326026134224\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.24019607843137256,\n \"acc_stderr\": 0.02998373305591362,\n \"\
acc_norm\": 0.24019607843137256,\n \"acc_norm_stderr\": 0.02998373305591362\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.2742616033755274,\n \"acc_stderr\": 0.02904133351059804,\n \
\ \"acc_norm\": 0.2742616033755274,\n \"acc_norm_stderr\": 0.02904133351059804\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.31390134529147984,\n\
\ \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.31390134529147984,\n\
\ \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.2595419847328244,\n \"acc_stderr\": 0.03844876139785271,\n\
\ \"acc_norm\": 0.2595419847328244,\n \"acc_norm_stderr\": 0.03844876139785271\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.2396694214876033,\n \"acc_stderr\": 0.03896878985070417,\n \"\
acc_norm\": 0.2396694214876033,\n \"acc_norm_stderr\": 0.03896878985070417\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.2777777777777778,\n\
\ \"acc_stderr\": 0.043300437496507437,\n \"acc_norm\": 0.2777777777777778,\n\
\ \"acc_norm_stderr\": 0.043300437496507437\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.22085889570552147,\n \"acc_stderr\": 0.032591773927421776,\n\
\ \"acc_norm\": 0.22085889570552147,\n \"acc_norm_stderr\": 0.032591773927421776\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.3125,\n\
\ \"acc_stderr\": 0.043994650575715215,\n \"acc_norm\": 0.3125,\n\
\ \"acc_norm_stderr\": 0.043994650575715215\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.17475728155339806,\n \"acc_stderr\": 0.037601780060266224,\n\
\ \"acc_norm\": 0.17475728155339806,\n \"acc_norm_stderr\": 0.037601780060266224\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.29914529914529914,\n\
\ \"acc_stderr\": 0.029996951858349497,\n \"acc_norm\": 0.29914529914529914,\n\
\ \"acc_norm_stderr\": 0.029996951858349497\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.23371647509578544,\n\
\ \"acc_stderr\": 0.015133383278988837,\n \"acc_norm\": 0.23371647509578544,\n\
\ \"acc_norm_stderr\": 0.015133383278988837\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.24855491329479767,\n \"acc_stderr\": 0.023267528432100174,\n\
\ \"acc_norm\": 0.24855491329479767,\n \"acc_norm_stderr\": 0.023267528432100174\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.24692737430167597,\n\
\ \"acc_stderr\": 0.014422292204808835,\n \"acc_norm\": 0.24692737430167597,\n\
\ \"acc_norm_stderr\": 0.014422292204808835\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.1830065359477124,\n \"acc_stderr\": 0.022140767512880976,\n\
\ \"acc_norm\": 0.1830065359477124,\n \"acc_norm_stderr\": 0.022140767512880976\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.1864951768488746,\n\
\ \"acc_stderr\": 0.02212243977248077,\n \"acc_norm\": 0.1864951768488746,\n\
\ \"acc_norm_stderr\": 0.02212243977248077\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.2191358024691358,\n \"acc_stderr\": 0.023016705640262203,\n\
\ \"acc_norm\": 0.2191358024691358,\n \"acc_norm_stderr\": 0.023016705640262203\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.22695035460992907,\n \"acc_stderr\": 0.02498710636564297,\n \
\ \"acc_norm\": 0.22695035460992907,\n \"acc_norm_stderr\": 0.02498710636564297\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2457627118644068,\n\
\ \"acc_stderr\": 0.010996156635142692,\n \"acc_norm\": 0.2457627118644068,\n\
\ \"acc_norm_stderr\": 0.010996156635142692\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.18382352941176472,\n \"acc_stderr\": 0.023529242185193106,\n\
\ \"acc_norm\": 0.18382352941176472,\n \"acc_norm_stderr\": 0.023529242185193106\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.25326797385620914,\n \"acc_stderr\": 0.01759348689536683,\n \
\ \"acc_norm\": 0.25326797385620914,\n \"acc_norm_stderr\": 0.01759348689536683\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.21818181818181817,\n\
\ \"acc_stderr\": 0.03955932861795833,\n \"acc_norm\": 0.21818181818181817,\n\
\ \"acc_norm_stderr\": 0.03955932861795833\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.17959183673469387,\n \"acc_stderr\": 0.024573293589585637,\n\
\ \"acc_norm\": 0.17959183673469387,\n \"acc_norm_stderr\": 0.024573293589585637\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.24378109452736318,\n\
\ \"acc_stderr\": 0.03036049015401465,\n \"acc_norm\": 0.24378109452736318,\n\
\ \"acc_norm_stderr\": 0.03036049015401465\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.26,\n \"acc_stderr\": 0.0440844002276808,\n \
\ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.0440844002276808\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.28313253012048195,\n\
\ \"acc_stderr\": 0.03507295431370518,\n \"acc_norm\": 0.28313253012048195,\n\
\ \"acc_norm_stderr\": 0.03507295431370518\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.3157894736842105,\n \"acc_stderr\": 0.035650796707083106,\n\
\ \"acc_norm\": 0.3157894736842105,\n \"acc_norm_stderr\": 0.035650796707083106\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.22031823745410037,\n\
\ \"mc1_stderr\": 0.014509045171487283,\n \"mc2\": 0.4792055778955594,\n\
\ \"mc2_stderr\": 0.016809354273525978\n }\n}\n```"
repo_url: https://huggingface.co/TheBloke/airoboros-33B-gpt4-1-4-SuperHOT-8K-fp16
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|arc:challenge|25_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hellaswag|10_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-22T21:17:33.530104.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-22T21:17:33.530104.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_22T21_17_33.530104
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-22T21:17:33.530104.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-22T21:17:33.530104.parquet'
---
# Dataset Card for Evaluation run of TheBloke/airoboros-33B-gpt4-1-4-SuperHOT-8K-fp16
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TheBloke/airoboros-33B-gpt4-1-4-SuperHOT-8K-fp16
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [TheBloke/airoboros-33B-gpt4-1-4-SuperHOT-8K-fp16](https://huggingface.co/TheBloke/airoboros-33B-gpt4-1-4-SuperHOT-8K-fp16) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 60 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 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_TheBloke__airoboros-33B-gpt4-1-4-SuperHOT-8K-fp16",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-08-22T21:17:33.530104](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__airoboros-33B-gpt4-1-4-SuperHOT-8K-fp16/blob/main/results_2023-08-22T21%3A17%3A33.530104.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": 0.23623967517263394,
"acc_stderr": 0.03091679562106068,
"acc_norm": 0.23734838989030832,
"acc_norm_stderr": 0.030928364256296522,
"mc1": 0.22031823745410037,
"mc1_stderr": 0.014509045171487283,
"mc2": 0.4792055778955594,
"mc2_stderr": 0.016809354273525978
},
"harness|arc:challenge|25": {
"acc": 0.23037542662116042,
"acc_stderr": 0.01230492841874761,
"acc_norm": 0.26023890784982934,
"acc_norm_stderr": 0.012821930225112554
},
"harness|hellaswag|10": {
"acc": 0.2709619597689703,
"acc_stderr": 0.0044354815159093975,
"acc_norm": 0.30651264688309104,
"acc_norm_stderr": 0.004601029188459098
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.22,
"acc_stderr": 0.04163331998932268,
"acc_norm": 0.22,
"acc_norm_stderr": 0.04163331998932268
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.25925925925925924,
"acc_stderr": 0.03785714465066653,
"acc_norm": 0.25925925925925924,
"acc_norm_stderr": 0.03785714465066653
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.17763157894736842,
"acc_stderr": 0.031103182383123398,
"acc_norm": 0.17763157894736842,
"acc_norm_stderr": 0.031103182383123398
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.22264150943396227,
"acc_stderr": 0.025604233470899095,
"acc_norm": 0.22264150943396227,
"acc_norm_stderr": 0.025604233470899095
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.2569444444444444,
"acc_stderr": 0.03653946969442099,
"acc_norm": 0.2569444444444444,
"acc_norm_stderr": 0.03653946969442099
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.2,
"acc_stderr": 0.04020151261036844,
"acc_norm": 0.2,
"acc_norm_stderr": 0.04020151261036844
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.26,
"acc_stderr": 0.0440844002276808,
"acc_norm": 0.26,
"acc_norm_stderr": 0.0440844002276808
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.21,
"acc_stderr": 0.040936018074033256,
"acc_norm": 0.21,
"acc_norm_stderr": 0.040936018074033256
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.20809248554913296,
"acc_stderr": 0.030952890217749874,
"acc_norm": 0.20809248554913296,
"acc_norm_stderr": 0.030952890217749874
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.21568627450980393,
"acc_stderr": 0.04092563958237654,
"acc_norm": 0.21568627450980393,
"acc_norm_stderr": 0.04092563958237654
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.27,
"acc_stderr": 0.044619604333847394,
"acc_norm": 0.27,
"acc_norm_stderr": 0.044619604333847394
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.26382978723404255,
"acc_stderr": 0.028809989854102973,
"acc_norm": 0.26382978723404255,
"acc_norm_stderr": 0.028809989854102973
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.2631578947368421,
"acc_stderr": 0.0414243971948936,
"acc_norm": 0.2631578947368421,
"acc_norm_stderr": 0.0414243971948936
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.2413793103448276,
"acc_stderr": 0.03565998174135302,
"acc_norm": 0.2413793103448276,
"acc_norm_stderr": 0.03565998174135302
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.20899470899470898,
"acc_stderr": 0.02094048156533486,
"acc_norm": 0.20899470899470898,
"acc_norm_stderr": 0.02094048156533486
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.2857142857142857,
"acc_stderr": 0.04040610178208841,
"acc_norm": 0.2857142857142857,
"acc_norm_stderr": 0.04040610178208841
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.18,
"acc_stderr": 0.038612291966536934,
"acc_norm": 0.18,
"acc_norm_stderr": 0.038612291966536934
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.267741935483871,
"acc_stderr": 0.025189006660212378,
"acc_norm": 0.267741935483871,
"acc_norm_stderr": 0.025189006660212378
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.15763546798029557,
"acc_stderr": 0.025639014131172404,
"acc_norm": 0.15763546798029557,
"acc_norm_stderr": 0.025639014131172404
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.25,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.25,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.22424242424242424,
"acc_stderr": 0.03256866661681102,
"acc_norm": 0.22424242424242424,
"acc_norm_stderr": 0.03256866661681102
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.18686868686868688,
"acc_stderr": 0.027772533334218977,
"acc_norm": 0.18686868686868688,
"acc_norm_stderr": 0.027772533334218977
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.18652849740932642,
"acc_stderr": 0.028112091210117485,
"acc_norm": 0.18652849740932642,
"acc_norm_stderr": 0.028112091210117485
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.2076923076923077,
"acc_stderr": 0.020567539567246797,
"acc_norm": 0.2076923076923077,
"acc_norm_stderr": 0.020567539567246797
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.24074074074074073,
"acc_stderr": 0.026067159222275794,
"acc_norm": 0.24074074074074073,
"acc_norm_stderr": 0.026067159222275794
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.25210084033613445,
"acc_stderr": 0.028205545033277723,
"acc_norm": 0.25210084033613445,
"acc_norm_stderr": 0.028205545033277723
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.2119205298013245,
"acc_stderr": 0.03336767086567976,
"acc_norm": 0.2119205298013245,
"acc_norm_stderr": 0.03336767086567976
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.1944954128440367,
"acc_stderr": 0.016970289090458047,
"acc_norm": 0.1944954128440367,
"acc_norm_stderr": 0.016970289090458047
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.1527777777777778,
"acc_stderr": 0.024536326026134224,
"acc_norm": 0.1527777777777778,
"acc_norm_stderr": 0.024536326026134224
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.24019607843137256,
"acc_stderr": 0.02998373305591362,
"acc_norm": 0.24019607843137256,
"acc_norm_stderr": 0.02998373305591362
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.2742616033755274,
"acc_stderr": 0.02904133351059804,
"acc_norm": 0.2742616033755274,
"acc_norm_stderr": 0.02904133351059804
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.31390134529147984,
"acc_stderr": 0.031146796482972465,
"acc_norm": 0.31390134529147984,
"acc_norm_stderr": 0.031146796482972465
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.2595419847328244,
"acc_stderr": 0.03844876139785271,
"acc_norm": 0.2595419847328244,
"acc_norm_stderr": 0.03844876139785271
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.2396694214876033,
"acc_stderr": 0.03896878985070417,
"acc_norm": 0.2396694214876033,
"acc_norm_stderr": 0.03896878985070417
},
"harness|hendrycksTest-jurisprudence|5": {
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```
### 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] | [
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|
open-llm-leaderboard/details_TheBloke__orca_mini_v3_7B-GPTQ | open-llm-leaderboard | 2023-12-04T11:31:43Z | 263 | 0 | [
"region:us"
]
| null | 2023-08-27T11:59:58Z | ---
pretty_name: Evaluation run of TheBloke/orca_mini_v3_7B-GPTQ
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [TheBloke/orca_mini_v3_7B-GPTQ](https://huggingface.co/TheBloke/orca_mini_v3_7B-GPTQ)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_TheBloke__orca_mini_v3_7B-GPTQ\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-04T11:27:39.056243](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__orca_mini_v3_7B-GPTQ/blob/main/results_2023-12-04T11-27-39.056243.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\": 0.5171661173804333,\n\
\ \"acc_stderr\": 0.03432159517380606,\n \"acc_norm\": 0.5235171990071291,\n\
\ \"acc_norm_stderr\": 0.03509506760640401,\n \"mc1\": 0.35495716034271724,\n\
\ \"mc1_stderr\": 0.016750862381375898,\n \"mc2\": 0.5120018406031775,\n\
\ \"mc2_stderr\": 0.015691567132244007\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.4974402730375427,\n \"acc_stderr\": 0.014611199329843788,\n\
\ \"acc_norm\": 0.5452218430034129,\n \"acc_norm_stderr\": 0.014551507060836355\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5929097789285003,\n\
\ \"acc_stderr\": 0.0049028788067330365,\n \"acc_norm\": 0.7853017327225652,\n\
\ \"acc_norm_stderr\": 0.004097736838432052\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.31,\n \"acc_stderr\": 0.046482319871173156,\n \
\ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.046482319871173156\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5185185185185185,\n\
\ \"acc_stderr\": 0.043163785995113245,\n \"acc_norm\": 0.5185185185185185,\n\
\ \"acc_norm_stderr\": 0.043163785995113245\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.5,\n \"acc_stderr\": 0.04068942293855797,\n \
\ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.04068942293855797\n },\n\
\ \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.51,\n \
\ \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.51,\n \
\ \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.5924528301886792,\n \"acc_stderr\": 0.030242233800854494,\n\
\ \"acc_norm\": 0.5924528301886792,\n \"acc_norm_stderr\": 0.030242233800854494\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5555555555555556,\n\
\ \"acc_stderr\": 0.041553199555931467,\n \"acc_norm\": 0.5555555555555556,\n\
\ \"acc_norm_stderr\": 0.041553199555931467\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \
\ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.44,\n \"acc_stderr\": 0.049888765156985884,\n \"acc_norm\": 0.44,\n\
\ \"acc_norm_stderr\": 0.049888765156985884\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.4797687861271676,\n\
\ \"acc_stderr\": 0.03809342081273957,\n \"acc_norm\": 0.4797687861271676,\n\
\ \"acc_norm_stderr\": 0.03809342081273957\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.2549019607843137,\n \"acc_stderr\": 0.043364327079931785,\n\
\ \"acc_norm\": 0.2549019607843137,\n \"acc_norm_stderr\": 0.043364327079931785\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.53,\n\
\ \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.46808510638297873,\n \"acc_stderr\": 0.03261936918467382,\n\
\ \"acc_norm\": 0.46808510638297873,\n \"acc_norm_stderr\": 0.03261936918467382\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.38596491228070173,\n\
\ \"acc_stderr\": 0.045796394220704334,\n \"acc_norm\": 0.38596491228070173,\n\
\ \"acc_norm_stderr\": 0.045796394220704334\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.42758620689655175,\n \"acc_stderr\": 0.04122737111370332,\n\
\ \"acc_norm\": 0.42758620689655175,\n \"acc_norm_stderr\": 0.04122737111370332\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.29365079365079366,\n \"acc_stderr\": 0.02345603738398203,\n \"\
acc_norm\": 0.29365079365079366,\n \"acc_norm_stderr\": 0.02345603738398203\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3333333333333333,\n\
\ \"acc_stderr\": 0.04216370213557835,\n \"acc_norm\": 0.3333333333333333,\n\
\ \"acc_norm_stderr\": 0.04216370213557835\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.5935483870967742,\n\
\ \"acc_stderr\": 0.0279417273462563,\n \"acc_norm\": 0.5935483870967742,\n\
\ \"acc_norm_stderr\": 0.0279417273462563\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.3842364532019704,\n \"acc_stderr\": 0.0342239856565755,\n\
\ \"acc_norm\": 0.3842364532019704,\n \"acc_norm_stderr\": 0.0342239856565755\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|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-high_school_european_history|5\"\
: {\n \"acc\": 0.6424242424242425,\n \"acc_stderr\": 0.037425970438065864,\n\
\ \"acc_norm\": 0.6424242424242425,\n \"acc_norm_stderr\": 0.037425970438065864\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.6868686868686869,\n \"acc_stderr\": 0.033042050878136525,\n \"\
acc_norm\": 0.6868686868686869,\n \"acc_norm_stderr\": 0.033042050878136525\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.7305699481865285,\n \"acc_stderr\": 0.03201867122877794,\n\
\ \"acc_norm\": 0.7305699481865285,\n \"acc_norm_stderr\": 0.03201867122877794\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.48205128205128206,\n \"acc_stderr\": 0.025334667080954935,\n\
\ \"acc_norm\": 0.48205128205128206,\n \"acc_norm_stderr\": 0.025334667080954935\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.2777777777777778,\n \"acc_stderr\": 0.027309140588230182,\n \
\ \"acc_norm\": 0.2777777777777778,\n \"acc_norm_stderr\": 0.027309140588230182\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.5294117647058824,\n \"acc_stderr\": 0.03242225027115007,\n \
\ \"acc_norm\": 0.5294117647058824,\n \"acc_norm_stderr\": 0.03242225027115007\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\"\
: 0.7027522935779816,\n \"acc_stderr\": 0.019595707224643526,\n \"\
acc_norm\": 0.7027522935779816,\n \"acc_norm_stderr\": 0.019595707224643526\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.42592592592592593,\n \"acc_stderr\": 0.03372343271653063,\n \"\
acc_norm\": 0.42592592592592593,\n \"acc_norm_stderr\": 0.03372343271653063\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7107843137254902,\n \"acc_stderr\": 0.031822318676475544,\n \"\
acc_norm\": 0.7107843137254902,\n \"acc_norm_stderr\": 0.031822318676475544\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7341772151898734,\n \"acc_stderr\": 0.02875679962965834,\n \
\ \"acc_norm\": 0.7341772151898734,\n \"acc_norm_stderr\": 0.02875679962965834\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.5695067264573991,\n\
\ \"acc_stderr\": 0.0332319730294294,\n \"acc_norm\": 0.5695067264573991,\n\
\ \"acc_norm_stderr\": 0.0332319730294294\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.5954198473282443,\n \"acc_stderr\": 0.043046937953806645,\n\
\ \"acc_norm\": 0.5954198473282443,\n \"acc_norm_stderr\": 0.043046937953806645\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7024793388429752,\n \"acc_stderr\": 0.04173349148083499,\n \"\
acc_norm\": 0.7024793388429752,\n \"acc_norm_stderr\": 0.04173349148083499\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.6111111111111112,\n\
\ \"acc_stderr\": 0.0471282125742677,\n \"acc_norm\": 0.6111111111111112,\n\
\ \"acc_norm_stderr\": 0.0471282125742677\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.588957055214724,\n \"acc_stderr\": 0.038656978537853624,\n\
\ \"acc_norm\": 0.588957055214724,\n \"acc_norm_stderr\": 0.038656978537853624\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.375,\n\
\ \"acc_stderr\": 0.04595091388086298,\n \"acc_norm\": 0.375,\n \
\ \"acc_norm_stderr\": 0.04595091388086298\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7281553398058253,\n \"acc_stderr\": 0.044052680241409216,\n\
\ \"acc_norm\": 0.7281553398058253,\n \"acc_norm_stderr\": 0.044052680241409216\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7564102564102564,\n\
\ \"acc_stderr\": 0.028120966503914414,\n \"acc_norm\": 0.7564102564102564,\n\
\ \"acc_norm_stderr\": 0.028120966503914414\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \
\ \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620332\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.6871008939974457,\n\
\ \"acc_stderr\": 0.016580935940304055,\n \"acc_norm\": 0.6871008939974457,\n\
\ \"acc_norm_stderr\": 0.016580935940304055\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.5404624277456648,\n \"acc_stderr\": 0.02683080599895225,\n\
\ \"acc_norm\": 0.5404624277456648,\n \"acc_norm_stderr\": 0.02683080599895225\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2905027932960894,\n\
\ \"acc_stderr\": 0.015183844307206144,\n \"acc_norm\": 0.2905027932960894,\n\
\ \"acc_norm_stderr\": 0.015183844307206144\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.5588235294117647,\n \"acc_stderr\": 0.028431095444176643,\n\
\ \"acc_norm\": 0.5588235294117647,\n \"acc_norm_stderr\": 0.028431095444176643\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.5916398713826366,\n\
\ \"acc_stderr\": 0.027917050748484627,\n \"acc_norm\": 0.5916398713826366,\n\
\ \"acc_norm_stderr\": 0.027917050748484627\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.5432098765432098,\n \"acc_stderr\": 0.027716661650194038,\n\
\ \"acc_norm\": 0.5432098765432098,\n \"acc_norm_stderr\": 0.027716661650194038\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.38652482269503546,\n \"acc_stderr\": 0.029049190342543454,\n \
\ \"acc_norm\": 0.38652482269503546,\n \"acc_norm_stderr\": 0.029049190342543454\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.36962190352020863,\n\
\ \"acc_stderr\": 0.012328445778575248,\n \"acc_norm\": 0.36962190352020863,\n\
\ \"acc_norm_stderr\": 0.012328445778575248\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.5257352941176471,\n \"acc_stderr\": 0.030332578094555033,\n\
\ \"acc_norm\": 0.5257352941176471,\n \"acc_norm_stderr\": 0.030332578094555033\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.49673202614379086,\n \"acc_stderr\": 0.020227402794434867,\n \
\ \"acc_norm\": 0.49673202614379086,\n \"acc_norm_stderr\": 0.020227402794434867\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5727272727272728,\n\
\ \"acc_stderr\": 0.047381987035454834,\n \"acc_norm\": 0.5727272727272728,\n\
\ \"acc_norm_stderr\": 0.047381987035454834\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.6489795918367347,\n \"acc_stderr\": 0.03055531675557364,\n\
\ \"acc_norm\": 0.6489795918367347,\n \"acc_norm_stderr\": 0.03055531675557364\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7064676616915423,\n\
\ \"acc_stderr\": 0.032200241045342054,\n \"acc_norm\": 0.7064676616915423,\n\
\ \"acc_norm_stderr\": 0.032200241045342054\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \
\ \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.044619604333847394\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.3493975903614458,\n\
\ \"acc_stderr\": 0.0371172519074075,\n \"acc_norm\": 0.3493975903614458,\n\
\ \"acc_norm_stderr\": 0.0371172519074075\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.6608187134502924,\n \"acc_stderr\": 0.03631053496488904,\n\
\ \"acc_norm\": 0.6608187134502924,\n \"acc_norm_stderr\": 0.03631053496488904\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.35495716034271724,\n\
\ \"mc1_stderr\": 0.016750862381375898,\n \"mc2\": 0.5120018406031775,\n\
\ \"mc2_stderr\": 0.015691567132244007\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7466456195737964,\n \"acc_stderr\": 0.012223754434233618\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.15314632297194844,\n \
\ \"acc_stderr\": 0.009919728152791475\n }\n}\n```"
repo_url: https://huggingface.co/TheBloke/orca_mini_v3_7B-GPTQ
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_22T13_46_10.418493
path:
- '**/details_harness|arc:challenge|25_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|arc:challenge|25_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|gsm8k|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_22T13_46_10.418493
path:
- '**/details_harness|hellaswag|10_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hellaswag|10_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_22T13_46_10.418493
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-22T13:46:10.418493.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-04T11-27-39.056243.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_22T13_46_10.418493
path:
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- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
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path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_22T13_46_10.418493
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_22T13_46_10.418493
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_22T13_46_10.418493
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_22T13_46_10.418493
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_22T13_46_10.418493
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_22T13_46_10.418493
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
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path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_22T13_46_10.418493
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
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path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
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path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
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path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
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- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
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path:
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- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
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path:
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- split: 2023_12_04T11_27_39.056243
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
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path:
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- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_22T13_46_10.418493
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_22T13_46_10.418493
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_22T13_46_10.418493
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_22T13_46_10.418493
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_22T13_46_10.418493
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_22T13_46_10.418493
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_22T13_46_10.418493
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_22T13_46_10.418493
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_22T13_46_10.418493
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_22T13_46_10.418493
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_22T13_46_10.418493
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_22T13_46_10.418493
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_22T13_46_10.418493
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_22T13_46_10.418493
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_22T13_46_10.418493
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_22T13_46_10.418493
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_22T13_46_10.418493
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_22T13_46_10.418493
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_22T13_46_10.418493
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_22T13_46_10.418493
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_22T13_46_10.418493
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_22T13_46_10.418493
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_22T13_46_10.418493
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_22T13_46_10.418493
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_22T13_46_10.418493
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_22T13_46_10.418493
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_22T13_46_10.418493
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_22T13_46_10.418493
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_22T13_46_10.418493
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-22T13:46:10.418493.parquet'
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-04T11-27-39.056243.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_04T11_27_39.056243
path:
- '**/details_harness|winogrande|5_2023-12-04T11-27-39.056243.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-04T11-27-39.056243.parquet'
- config_name: results
data_files:
- split: 2023_12_04T11_27_39.056243
path:
- results_2023-12-04T11-27-39.056243.parquet
- split: latest
path:
- results_2023-12-04T11-27-39.056243.parquet
---
# Dataset Card for Evaluation run of TheBloke/orca_mini_v3_7B-GPTQ
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TheBloke/orca_mini_v3_7B-GPTQ
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [TheBloke/orca_mini_v3_7B-GPTQ](https://huggingface.co/TheBloke/orca_mini_v3_7B-GPTQ) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_TheBloke__orca_mini_v3_7B-GPTQ",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-04T11:27:39.056243](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__orca_mini_v3_7B-GPTQ/blob/main/results_2023-12-04T11-27-39.056243.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": 0.5171661173804333,
"acc_stderr": 0.03432159517380606,
"acc_norm": 0.5235171990071291,
"acc_norm_stderr": 0.03509506760640401,
"mc1": 0.35495716034271724,
"mc1_stderr": 0.016750862381375898,
"mc2": 0.5120018406031775,
"mc2_stderr": 0.015691567132244007
},
"harness|arc:challenge|25": {
"acc": 0.4974402730375427,
"acc_stderr": 0.014611199329843788,
"acc_norm": 0.5452218430034129,
"acc_norm_stderr": 0.014551507060836355
},
"harness|hellaswag|10": {
"acc": 0.5929097789285003,
"acc_stderr": 0.0049028788067330365,
"acc_norm": 0.7853017327225652,
"acc_norm_stderr": 0.004097736838432052
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.31,
"acc_stderr": 0.046482319871173156,
"acc_norm": 0.31,
"acc_norm_stderr": 0.046482319871173156
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5185185185185185,
"acc_stderr": 0.043163785995113245,
"acc_norm": 0.5185185185185185,
"acc_norm_stderr": 0.043163785995113245
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.5,
"acc_stderr": 0.04068942293855797,
"acc_norm": 0.5,
"acc_norm_stderr": 0.04068942293855797
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.51,
"acc_stderr": 0.05024183937956912,
"acc_norm": 0.51,
"acc_norm_stderr": 0.05024183937956912
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.5924528301886792,
"acc_stderr": 0.030242233800854494,
"acc_norm": 0.5924528301886792,
"acc_norm_stderr": 0.030242233800854494
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.5555555555555556,
"acc_stderr": 0.041553199555931467,
"acc_norm": 0.5555555555555556,
"acc_norm_stderr": 0.041553199555931467
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.39,
"acc_stderr": 0.04902071300001975,
"acc_norm": 0.39,
"acc_norm_stderr": 0.04902071300001975
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.44,
"acc_stderr": 0.049888765156985884,
"acc_norm": 0.44,
"acc_norm_stderr": 0.049888765156985884
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.33,
"acc_stderr": 0.047258156262526045,
"acc_norm": 0.33,
"acc_norm_stderr": 0.047258156262526045
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.4797687861271676,
"acc_stderr": 0.03809342081273957,
"acc_norm": 0.4797687861271676,
"acc_norm_stderr": 0.03809342081273957
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.2549019607843137,
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```
### 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] | [
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|
open-llm-leaderboard/details_yeontaek__llama-2-13B-ensemble-v1 | open-llm-leaderboard | 2023-08-27T12:45:18Z | 263 | 0 | [
"region:us"
]
| null | 2023-08-27T12:10:30Z | ---
pretty_name: Evaluation run of yeontaek/llama-2-13B-ensemble-v1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [yeontaek/llama-2-13B-ensemble-v1](https://huggingface.co/yeontaek/llama-2-13B-ensemble-v1)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 60 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 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_yeontaek__llama-2-13B-ensemble-v1\"\
,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\
\nThese are the [latest results from run 2023-08-24T06:11:39.305449](https://huggingface.co/datasets/open-llm-leaderboard/details_yeontaek__llama-2-13B-ensemble-v1/blob/main/results_2023-08-24T06%3A11%3A39.305449.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\": 0.5771705795394726,\n\
\ \"acc_stderr\": 0.03407019951989153,\n \"acc_norm\": 0.5809029426034396,\n\
\ \"acc_norm_stderr\": 0.0340502352653076,\n \"mc1\": 0.3598531211750306,\n\
\ \"mc1_stderr\": 0.016801860466677157,\n \"mc2\": 0.5015608436175973,\n\
\ \"mc2_stderr\": 0.015270015874980385\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5998293515358362,\n \"acc_stderr\": 0.014317197787809169,\n\
\ \"acc_norm\": 0.6228668941979523,\n \"acc_norm_stderr\": 0.0141633668961926\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6264688309101772,\n\
\ \"acc_stderr\": 0.004827526584889677,\n \"acc_norm\": 0.8236407090221072,\n\
\ \"acc_norm_stderr\": 0.0038034664560544717\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.45925925925925926,\n\
\ \"acc_stderr\": 0.04304979692464243,\n \"acc_norm\": 0.45925925925925926,\n\
\ \"acc_norm_stderr\": 0.04304979692464243\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.5394736842105263,\n \"acc_stderr\": 0.04056242252249034,\n\
\ \"acc_norm\": 0.5394736842105263,\n \"acc_norm_stderr\": 0.04056242252249034\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.58,\n\
\ \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \
\ \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6075471698113207,\n \"acc_stderr\": 0.03005258057955784,\n\
\ \"acc_norm\": 0.6075471698113207,\n \"acc_norm_stderr\": 0.03005258057955784\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6319444444444444,\n\
\ \"acc_stderr\": 0.040329990539607175,\n \"acc_norm\": 0.6319444444444444,\n\
\ \"acc_norm_stderr\": 0.040329990539607175\n },\n \"harness|hendrycksTest-college_chemistry|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-college_computer_science|5\": {\n \"acc\": 0.48,\n\
\ \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n \
\ \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695236,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695236\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5491329479768786,\n\
\ \"acc_stderr\": 0.03794012674697031,\n \"acc_norm\": 0.5491329479768786,\n\
\ \"acc_norm_stderr\": 0.03794012674697031\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.3235294117647059,\n \"acc_stderr\": 0.046550104113196177,\n\
\ \"acc_norm\": 0.3235294117647059,\n \"acc_norm_stderr\": 0.046550104113196177\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n\
\ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.46382978723404256,\n \"acc_stderr\": 0.032600385118357715,\n\
\ \"acc_norm\": 0.46382978723404256,\n \"acc_norm_stderr\": 0.032600385118357715\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.30701754385964913,\n\
\ \"acc_stderr\": 0.04339138322579861,\n \"acc_norm\": 0.30701754385964913,\n\
\ \"acc_norm_stderr\": 0.04339138322579861\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5448275862068965,\n \"acc_stderr\": 0.04149886942192118,\n\
\ \"acc_norm\": 0.5448275862068965,\n \"acc_norm_stderr\": 0.04149886942192118\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.3306878306878307,\n \"acc_stderr\": 0.02422996529842507,\n \"\
acc_norm\": 0.3306878306878307,\n \"acc_norm_stderr\": 0.02422996529842507\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.35714285714285715,\n\
\ \"acc_stderr\": 0.042857142857142816,\n \"acc_norm\": 0.35714285714285715,\n\
\ \"acc_norm_stderr\": 0.042857142857142816\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.6580645161290323,\n \"acc_stderr\": 0.026985289576552746,\n \"\
acc_norm\": 0.6580645161290323,\n \"acc_norm_stderr\": 0.026985289576552746\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.42857142857142855,\n \"acc_stderr\": 0.03481904844438803,\n \"\
acc_norm\": 0.42857142857142855,\n \"acc_norm_stderr\": 0.03481904844438803\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.59,\n \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\"\
: 0.59,\n \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7090909090909091,\n \"acc_stderr\": 0.03546563019624336,\n\
\ \"acc_norm\": 0.7090909090909091,\n \"acc_norm_stderr\": 0.03546563019624336\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7424242424242424,\n \"acc_stderr\": 0.03115626951964683,\n \"\
acc_norm\": 0.7424242424242424,\n \"acc_norm_stderr\": 0.03115626951964683\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8238341968911918,\n \"acc_stderr\": 0.02749350424454806,\n\
\ \"acc_norm\": 0.8238341968911918,\n \"acc_norm_stderr\": 0.02749350424454806\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.5692307692307692,\n \"acc_stderr\": 0.02510682066053975,\n \
\ \"acc_norm\": 0.5692307692307692,\n \"acc_norm_stderr\": 0.02510682066053975\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.2962962962962963,\n \"acc_stderr\": 0.02784081149587192,\n \
\ \"acc_norm\": 0.2962962962962963,\n \"acc_norm_stderr\": 0.02784081149587192\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6092436974789915,\n \"acc_stderr\": 0.03169380235712996,\n \
\ \"acc_norm\": 0.6092436974789915,\n \"acc_norm_stderr\": 0.03169380235712996\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.31788079470198677,\n \"acc_stderr\": 0.038020397601079024,\n \"\
acc_norm\": 0.31788079470198677,\n \"acc_norm_stderr\": 0.038020397601079024\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.7651376146788991,\n \"acc_stderr\": 0.018175110510343564,\n \"\
acc_norm\": 0.7651376146788991,\n \"acc_norm_stderr\": 0.018175110510343564\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.44907407407407407,\n \"acc_stderr\": 0.03392238405321617,\n \"\
acc_norm\": 0.44907407407407407,\n \"acc_norm_stderr\": 0.03392238405321617\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7598039215686274,\n \"acc_stderr\": 0.02998373305591361,\n \"\
acc_norm\": 0.7598039215686274,\n \"acc_norm_stderr\": 0.02998373305591361\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.759493670886076,\n \"acc_stderr\": 0.027820781981149685,\n \
\ \"acc_norm\": 0.759493670886076,\n \"acc_norm_stderr\": 0.027820781981149685\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.672645739910314,\n\
\ \"acc_stderr\": 0.03149384670994131,\n \"acc_norm\": 0.672645739910314,\n\
\ \"acc_norm_stderr\": 0.03149384670994131\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.6793893129770993,\n \"acc_stderr\": 0.04093329229834278,\n\
\ \"acc_norm\": 0.6793893129770993,\n \"acc_norm_stderr\": 0.04093329229834278\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228732,\n \"\
acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228732\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.75,\n\
\ \"acc_stderr\": 0.04186091791394607,\n \"acc_norm\": 0.75,\n \
\ \"acc_norm_stderr\": 0.04186091791394607\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7055214723926381,\n \"acc_stderr\": 0.03581165790474082,\n\
\ \"acc_norm\": 0.7055214723926381,\n \"acc_norm_stderr\": 0.03581165790474082\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.39285714285714285,\n\
\ \"acc_stderr\": 0.04635550135609976,\n \"acc_norm\": 0.39285714285714285,\n\
\ \"acc_norm_stderr\": 0.04635550135609976\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n\
\ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8290598290598291,\n\
\ \"acc_stderr\": 0.024662496845209807,\n \"acc_norm\": 0.8290598290598291,\n\
\ \"acc_norm_stderr\": 0.024662496845209807\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252609,\n \
\ \"acc_norm\": 0.67,\n \"acc_norm_stderr\": 0.04725815626252609\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.776500638569604,\n\
\ \"acc_stderr\": 0.01489723522945071,\n \"acc_norm\": 0.776500638569604,\n\
\ \"acc_norm_stderr\": 0.01489723522945071\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.653179190751445,\n \"acc_stderr\": 0.025624723994030454,\n\
\ \"acc_norm\": 0.653179190751445,\n \"acc_norm_stderr\": 0.025624723994030454\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4324022346368715,\n\
\ \"acc_stderr\": 0.01656897123354861,\n \"acc_norm\": 0.4324022346368715,\n\
\ \"acc_norm_stderr\": 0.01656897123354861\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.630718954248366,\n \"acc_stderr\": 0.02763417668960266,\n\
\ \"acc_norm\": 0.630718954248366,\n \"acc_norm_stderr\": 0.02763417668960266\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6527331189710611,\n\
\ \"acc_stderr\": 0.027040745502307336,\n \"acc_norm\": 0.6527331189710611,\n\
\ \"acc_norm_stderr\": 0.027040745502307336\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.6265432098765432,\n \"acc_stderr\": 0.026915003011380154,\n\
\ \"acc_norm\": 0.6265432098765432,\n \"acc_norm_stderr\": 0.026915003011380154\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4219858156028369,\n \"acc_stderr\": 0.029462189233370593,\n \
\ \"acc_norm\": 0.4219858156028369,\n \"acc_norm_stderr\": 0.029462189233370593\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4178617992177314,\n\
\ \"acc_stderr\": 0.012596744108998565,\n \"acc_norm\": 0.4178617992177314,\n\
\ \"acc_norm_stderr\": 0.012596744108998565\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.5330882352941176,\n \"acc_stderr\": 0.030306257722468304,\n\
\ \"acc_norm\": 0.5330882352941176,\n \"acc_norm_stderr\": 0.030306257722468304\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.5849673202614379,\n \"acc_stderr\": 0.01993362777685742,\n \
\ \"acc_norm\": 0.5849673202614379,\n \"acc_norm_stderr\": 0.01993362777685742\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6363636363636364,\n\
\ \"acc_stderr\": 0.046075820907199756,\n \"acc_norm\": 0.6363636363636364,\n\
\ \"acc_norm_stderr\": 0.046075820907199756\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.636734693877551,\n \"acc_stderr\": 0.030789051139030806,\n\
\ \"acc_norm\": 0.636734693877551,\n \"acc_norm_stderr\": 0.030789051139030806\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7263681592039801,\n\
\ \"acc_stderr\": 0.031524391865554,\n \"acc_norm\": 0.7263681592039801,\n\
\ \"acc_norm_stderr\": 0.031524391865554\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.83,\n \"acc_stderr\": 0.03775251680686371,\n \
\ \"acc_norm\": 0.83,\n \"acc_norm_stderr\": 0.03775251680686371\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4578313253012048,\n\
\ \"acc_stderr\": 0.038786267710023595,\n \"acc_norm\": 0.4578313253012048,\n\
\ \"acc_norm_stderr\": 0.038786267710023595\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.783625730994152,\n \"acc_stderr\": 0.03158149539338734,\n\
\ \"acc_norm\": 0.783625730994152,\n \"acc_norm_stderr\": 0.03158149539338734\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3598531211750306,\n\
\ \"mc1_stderr\": 0.016801860466677157,\n \"mc2\": 0.5015608436175973,\n\
\ \"mc2_stderr\": 0.015270015874980385\n }\n}\n```"
repo_url: https://huggingface.co/yeontaek/llama-2-13B-ensemble-v1
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|arc:challenge|25_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hellaswag|10_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-24T06:11:39.305449.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-24T06:11:39.305449.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_24T06_11_39.305449
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-24T06:11:39.305449.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-24T06:11:39.305449.parquet'
---
# Dataset Card for Evaluation run of yeontaek/llama-2-13B-ensemble-v1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/yeontaek/llama-2-13B-ensemble-v1
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [yeontaek/llama-2-13B-ensemble-v1](https://huggingface.co/yeontaek/llama-2-13B-ensemble-v1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 60 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 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_yeontaek__llama-2-13B-ensemble-v1",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-08-24T06:11:39.305449](https://huggingface.co/datasets/open-llm-leaderboard/details_yeontaek__llama-2-13B-ensemble-v1/blob/main/results_2023-08-24T06%3A11%3A39.305449.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": 0.5771705795394726,
"acc_stderr": 0.03407019951989153,
"acc_norm": 0.5809029426034396,
"acc_norm_stderr": 0.0340502352653076,
"mc1": 0.3598531211750306,
"mc1_stderr": 0.016801860466677157,
"mc2": 0.5015608436175973,
"mc2_stderr": 0.015270015874980385
},
"harness|arc:challenge|25": {
"acc": 0.5998293515358362,
"acc_stderr": 0.014317197787809169,
"acc_norm": 0.6228668941979523,
"acc_norm_stderr": 0.0141633668961926
},
"harness|hellaswag|10": {
"acc": 0.6264688309101772,
"acc_stderr": 0.004827526584889677,
"acc_norm": 0.8236407090221072,
"acc_norm_stderr": 0.0038034664560544717
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.32,
"acc_stderr": 0.04688261722621504,
"acc_norm": 0.32,
"acc_norm_stderr": 0.04688261722621504
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.45925925925925926,
"acc_stderr": 0.04304979692464243,
"acc_norm": 0.45925925925925926,
"acc_norm_stderr": 0.04304979692464243
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.5394736842105263,
"acc_stderr": 0.04056242252249034,
"acc_norm": 0.5394736842105263,
"acc_norm_stderr": 0.04056242252249034
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.58,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.58,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6075471698113207,
"acc_stderr": 0.03005258057955784,
"acc_norm": 0.6075471698113207,
"acc_norm_stderr": 0.03005258057955784
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.6319444444444444,
"acc_stderr": 0.040329990539607175,
"acc_norm": 0.6319444444444444,
"acc_norm_stderr": 0.040329990539607175
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.37,
"acc_stderr": 0.048523658709391,
"acc_norm": 0.37,
"acc_norm_stderr": 0.048523658709391
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.48,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.48,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.34,
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"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695236
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.5491329479768786,
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"acc_norm": 0.5491329479768786,
"acc_norm_stderr": 0.03794012674697031
},
"harness|hendrycksTest-college_physics|5": {
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"acc_stderr": 0.046550104113196177,
"acc_norm": 0.3235294117647059,
"acc_norm_stderr": 0.046550104113196177
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.75,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-conceptual_physics|5": {
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"acc_norm": 0.46382978723404256,
"acc_norm_stderr": 0.032600385118357715
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.30701754385964913,
"acc_stderr": 0.04339138322579861,
"acc_norm": 0.30701754385964913,
"acc_norm_stderr": 0.04339138322579861
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5448275862068965,
"acc_stderr": 0.04149886942192118,
"acc_norm": 0.5448275862068965,
"acc_norm_stderr": 0.04149886942192118
},
"harness|hendrycksTest-elementary_mathematics|5": {
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"acc_stderr": 0.02422996529842507,
"acc_norm": 0.3306878306878307,
"acc_norm_stderr": 0.02422996529842507
},
"harness|hendrycksTest-formal_logic|5": {
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"acc_stderr": 0.042857142857142816,
"acc_norm": 0.35714285714285715,
"acc_norm_stderr": 0.042857142857142816
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.35,
"acc_stderr": 0.047937248544110196,
"acc_norm": 0.35,
"acc_norm_stderr": 0.047937248544110196
},
"harness|hendrycksTest-high_school_biology|5": {
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"acc_stderr": 0.026985289576552746,
"acc_norm": 0.6580645161290323,
"acc_norm_stderr": 0.026985289576552746
},
"harness|hendrycksTest-high_school_chemistry|5": {
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"acc_stderr": 0.03481904844438803,
"acc_norm": 0.42857142857142855,
"acc_norm_stderr": 0.03481904844438803
},
"harness|hendrycksTest-high_school_computer_science|5": {
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"acc_stderr": 0.04943110704237102,
"acc_norm": 0.59,
"acc_norm_stderr": 0.04943110704237102
},
"harness|hendrycksTest-high_school_european_history|5": {
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"acc_norm": 0.7090909090909091,
"acc_norm_stderr": 0.03546563019624336
},
"harness|hendrycksTest-high_school_geography|5": {
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"acc_norm": 0.7424242424242424,
"acc_norm_stderr": 0.03115626951964683
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8238341968911918,
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"acc_norm": 0.8238341968911918,
"acc_norm_stderr": 0.02749350424454806
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.5692307692307692,
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"acc_norm": 0.5692307692307692,
"acc_norm_stderr": 0.02510682066053975
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.2962962962962963,
"acc_stderr": 0.02784081149587192,
"acc_norm": 0.2962962962962963,
"acc_norm_stderr": 0.02784081149587192
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6092436974789915,
"acc_stderr": 0.03169380235712996,
"acc_norm": 0.6092436974789915,
"acc_norm_stderr": 0.03169380235712996
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.31788079470198677,
"acc_stderr": 0.038020397601079024,
"acc_norm": 0.31788079470198677,
"acc_norm_stderr": 0.038020397601079024
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.7651376146788991,
"acc_stderr": 0.018175110510343564,
"acc_norm": 0.7651376146788991,
"acc_norm_stderr": 0.018175110510343564
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.44907407407407407,
"acc_stderr": 0.03392238405321617,
"acc_norm": 0.44907407407407407,
"acc_norm_stderr": 0.03392238405321617
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7598039215686274,
"acc_stderr": 0.02998373305591361,
"acc_norm": 0.7598039215686274,
"acc_norm_stderr": 0.02998373305591361
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.759493670886076,
"acc_stderr": 0.027820781981149685,
"acc_norm": 0.759493670886076,
"acc_norm_stderr": 0.027820781981149685
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.672645739910314,
"acc_stderr": 0.03149384670994131,
"acc_norm": 0.672645739910314,
"acc_norm_stderr": 0.03149384670994131
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.6793893129770993,
"acc_stderr": 0.04093329229834278,
"acc_norm": 0.6793893129770993,
"acc_norm_stderr": 0.04093329229834278
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7768595041322314,
"acc_stderr": 0.03800754475228732,
"acc_norm": 0.7768595041322314,
"acc_norm_stderr": 0.03800754475228732
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.75,
"acc_stderr": 0.04186091791394607,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04186091791394607
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7055214723926381,
"acc_stderr": 0.03581165790474082,
"acc_norm": 0.7055214723926381,
"acc_norm_stderr": 0.03581165790474082
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.39285714285714285,
"acc_stderr": 0.04635550135609976,
"acc_norm": 0.39285714285714285,
"acc_norm_stderr": 0.04635550135609976
},
"harness|hendrycksTest-management|5": {
"acc": 0.7669902912621359,
"acc_stderr": 0.04185832598928315,
"acc_norm": 0.7669902912621359,
"acc_norm_stderr": 0.04185832598928315
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8290598290598291,
"acc_stderr": 0.024662496845209807,
"acc_norm": 0.8290598290598291,
"acc_norm_stderr": 0.024662496845209807
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.67,
"acc_stderr": 0.04725815626252609,
"acc_norm": 0.67,
"acc_norm_stderr": 0.04725815626252609
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.776500638569604,
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"acc_norm": 0.776500638569604,
"acc_norm_stderr": 0.01489723522945071
},
"harness|hendrycksTest-moral_disputes|5": {
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"acc_stderr": 0.025624723994030454,
"acc_norm": 0.653179190751445,
"acc_norm_stderr": 0.025624723994030454
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.4324022346368715,
"acc_stderr": 0.01656897123354861,
"acc_norm": 0.4324022346368715,
"acc_norm_stderr": 0.01656897123354861
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.630718954248366,
"acc_stderr": 0.02763417668960266,
"acc_norm": 0.630718954248366,
"acc_norm_stderr": 0.02763417668960266
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.6527331189710611,
"acc_stderr": 0.027040745502307336,
"acc_norm": 0.6527331189710611,
"acc_norm_stderr": 0.027040745502307336
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.6265432098765432,
"acc_stderr": 0.026915003011380154,
"acc_norm": 0.6265432098765432,
"acc_norm_stderr": 0.026915003011380154
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.4219858156028369,
"acc_stderr": 0.029462189233370593,
"acc_norm": 0.4219858156028369,
"acc_norm_stderr": 0.029462189233370593
},
"harness|hendrycksTest-professional_law|5": {
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"acc_stderr": 0.012596744108998565,
"acc_norm": 0.4178617992177314,
"acc_norm_stderr": 0.012596744108998565
},
"harness|hendrycksTest-professional_medicine|5": {
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"acc_norm": 0.5330882352941176,
"acc_norm_stderr": 0.030306257722468304
},
"harness|hendrycksTest-professional_psychology|5": {
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"acc_norm": 0.5849673202614379,
"acc_norm_stderr": 0.01993362777685742
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6363636363636364,
"acc_stderr": 0.046075820907199756,
"acc_norm": 0.6363636363636364,
"acc_norm_stderr": 0.046075820907199756
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.636734693877551,
"acc_stderr": 0.030789051139030806,
"acc_norm": 0.636734693877551,
"acc_norm_stderr": 0.030789051139030806
},
"harness|hendrycksTest-sociology|5": {
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"acc_stderr": 0.031524391865554,
"acc_norm": 0.7263681592039801,
"acc_norm_stderr": 0.031524391865554
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.83,
"acc_stderr": 0.03775251680686371,
"acc_norm": 0.83,
"acc_norm_stderr": 0.03775251680686371
},
"harness|hendrycksTest-virology|5": {
"acc": 0.4578313253012048,
"acc_stderr": 0.038786267710023595,
"acc_norm": 0.4578313253012048,
"acc_norm_stderr": 0.038786267710023595
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.783625730994152,
"acc_stderr": 0.03158149539338734,
"acc_norm": 0.783625730994152,
"acc_norm_stderr": 0.03158149539338734
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3598531211750306,
"mc1_stderr": 0.016801860466677157,
"mc2": 0.5015608436175973,
"mc2_stderr": 0.015270015874980385
}
}
```
### 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
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### Contributions
[More Information Needed] | [
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|
open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE4_addto15k_4.5w-r16-gate_up_down | open-llm-leaderboard | 2023-10-25T22:16:28Z | 263 | 0 | [
"region:us"
]
| null | 2023-10-10T09:36:18Z | ---
pretty_name: Evaluation run of CHIH-HUNG/llama-2-13b-FINETUNE4_addto15k_4.5w-r16-gate_up_down
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [CHIH-HUNG/llama-2-13b-FINETUNE4_addto15k_4.5w-r16-gate_up_down](https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE4_addto15k_4.5w-r16-gate_up_down)\
\ 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_CHIH-HUNG__llama-2-13b-FINETUNE4_addto15k_4.5w-r16-gate_up_down\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-25T22:16:15.844961](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE4_addto15k_4.5w-r16-gate_up_down/blob/main/results_2023-10-25T22-16-15.844961.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\": 0.044148489932885907,\n\
\ \"em_stderr\": 0.0021037435299994067,\n \"f1\": 0.09857802013422778,\n\
\ \"f1_stderr\": 0.0023775705231284467,\n \"acc\": 0.4617194030779578,\n\
\ \"acc_stderr\": 0.010887835734442838\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.044148489932885907,\n \"em_stderr\": 0.0021037435299994067,\n\
\ \"f1\": 0.09857802013422778,\n \"f1_stderr\": 0.0023775705231284467\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.15390447308567096,\n \
\ \"acc_stderr\": 0.009939799304049013\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7695343330702447,\n \"acc_stderr\": 0.011835872164836666\n\
\ }\n}\n```"
repo_url: https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE4_addto15k_4.5w-r16-gate_up_down
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|arc:challenge|25_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_25T22_16_15.844961
path:
- '**/details_harness|drop|3_2023-10-25T22-16-15.844961.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-25T22-16-15.844961.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_25T22_16_15.844961
path:
- '**/details_harness|gsm8k|5_2023-10-25T22-16-15.844961.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-25T22-16-15.844961.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hellaswag|10_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
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- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T09-35-55.043179.parquet'
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- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T09-35-55.043179.parquet'
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- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T09-35-55.043179.parquet'
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- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T09-35-55.043179.parquet'
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- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T09-35-55.043179.parquet'
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- '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T09-35-55.043179.parquet'
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- '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-10T09-35-55.043179.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T09-35-55.043179.parquet'
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- '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-10T09-35-55.043179.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-10T09-35-55.043179.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_25T22_16_15.844961
path:
- '**/details_harness|winogrande|5_2023-10-25T22-16-15.844961.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-25T22-16-15.844961.parquet'
- config_name: results
data_files:
- split: 2023_10_10T09_35_55.043179
path:
- results_2023-10-10T09-35-55.043179.parquet
- split: 2023_10_25T22_16_15.844961
path:
- results_2023-10-25T22-16-15.844961.parquet
- split: latest
path:
- results_2023-10-25T22-16-15.844961.parquet
---
# Dataset Card for Evaluation run of CHIH-HUNG/llama-2-13b-FINETUNE4_addto15k_4.5w-r16-gate_up_down
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE4_addto15k_4.5w-r16-gate_up_down
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [CHIH-HUNG/llama-2-13b-FINETUNE4_addto15k_4.5w-r16-gate_up_down](https://huggingface.co/CHIH-HUNG/llama-2-13b-FINETUNE4_addto15k_4.5w-r16-gate_up_down) 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_CHIH-HUNG__llama-2-13b-FINETUNE4_addto15k_4.5w-r16-gate_up_down",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-25T22:16:15.844961](https://huggingface.co/datasets/open-llm-leaderboard/details_CHIH-HUNG__llama-2-13b-FINETUNE4_addto15k_4.5w-r16-gate_up_down/blob/main/results_2023-10-25T22-16-15.844961.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": 0.044148489932885907,
"em_stderr": 0.0021037435299994067,
"f1": 0.09857802013422778,
"f1_stderr": 0.0023775705231284467,
"acc": 0.4617194030779578,
"acc_stderr": 0.010887835734442838
},
"harness|drop|3": {
"em": 0.044148489932885907,
"em_stderr": 0.0021037435299994067,
"f1": 0.09857802013422778,
"f1_stderr": 0.0023775705231284467
},
"harness|gsm8k|5": {
"acc": 0.15390447308567096,
"acc_stderr": 0.009939799304049013
},
"harness|winogrande|5": {
"acc": 0.7695343330702447,
"acc_stderr": 0.011835872164836666
}
}
```
### 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] | [
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|
open-llm-leaderboard/details_mncai__Mistral-7B-OpenOrca-1k | open-llm-leaderboard | 2023-10-25T07:41:24Z | 263 | 0 | [
"region:us"
]
| null | 2023-10-10T11:19:36Z | ---
pretty_name: Evaluation run of mncai/Mistral-7B-OpenOrca-1k
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [mncai/Mistral-7B-OpenOrca-1k](https://huggingface.co/mncai/Mistral-7B-OpenOrca-1k)\
\ 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_mncai__Mistral-7B-OpenOrca-1k\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-25T07:41:12.101153](https://huggingface.co/datasets/open-llm-leaderboard/details_mncai__Mistral-7B-OpenOrca-1k/blob/main/results_2023-10-25T07-41-12.101153.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\": 0.0053481543624161075,\n\
\ \"em_stderr\": 0.0007469252903319289,\n \"f1\": 0.09739828020134218,\n\
\ \"f1_stderr\": 0.001857285751420582,\n \"acc\": 0.45294831833688076,\n\
\ \"acc_stderr\": 0.01023434017882167\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0053481543624161075,\n \"em_stderr\": 0.0007469252903319289,\n\
\ \"f1\": 0.09739828020134218,\n \"f1_stderr\": 0.001857285751420582\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.1197877179681577,\n \
\ \"acc_stderr\": 0.008944213403553095\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7861089187056038,\n \"acc_stderr\": 0.011524466954090247\n\
\ }\n}\n```"
repo_url: https://huggingface.co/mncai/Mistral-7B-OpenOrca-1k
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|arc:challenge|25_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_25T07_41_12.101153
path:
- '**/details_harness|drop|3_2023-10-25T07-41-12.101153.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-25T07-41-12.101153.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_25T07_41_12.101153
path:
- '**/details_harness|gsm8k|5_2023-10-25T07-41-12.101153.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-25T07-41-12.101153.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hellaswag|10_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T11-19-13.410150.parquet'
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- '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-10T11-19-13.410150.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-10T11-19-13.410150.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-10T11-19-13.410150.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_25T07_41_12.101153
path:
- '**/details_harness|winogrande|5_2023-10-25T07-41-12.101153.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-25T07-41-12.101153.parquet'
- config_name: results
data_files:
- split: 2023_10_10T11_19_13.410150
path:
- results_2023-10-10T11-19-13.410150.parquet
- split: 2023_10_25T07_41_12.101153
path:
- results_2023-10-25T07-41-12.101153.parquet
- split: latest
path:
- results_2023-10-25T07-41-12.101153.parquet
---
# Dataset Card for Evaluation run of mncai/Mistral-7B-OpenOrca-1k
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/mncai/Mistral-7B-OpenOrca-1k
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [mncai/Mistral-7B-OpenOrca-1k](https://huggingface.co/mncai/Mistral-7B-OpenOrca-1k) 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_mncai__Mistral-7B-OpenOrca-1k",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-25T07:41:12.101153](https://huggingface.co/datasets/open-llm-leaderboard/details_mncai__Mistral-7B-OpenOrca-1k/blob/main/results_2023-10-25T07-41-12.101153.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": 0.0053481543624161075,
"em_stderr": 0.0007469252903319289,
"f1": 0.09739828020134218,
"f1_stderr": 0.001857285751420582,
"acc": 0.45294831833688076,
"acc_stderr": 0.01023434017882167
},
"harness|drop|3": {
"em": 0.0053481543624161075,
"em_stderr": 0.0007469252903319289,
"f1": 0.09739828020134218,
"f1_stderr": 0.001857285751420582
},
"harness|gsm8k|5": {
"acc": 0.1197877179681577,
"acc_stderr": 0.008944213403553095
},
"harness|winogrande|5": {
"acc": 0.7861089187056038,
"acc_stderr": 0.011524466954090247
}
}
```
### 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] | [
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|
open-llm-leaderboard/details_sequelbox__StellarBright | open-llm-leaderboard | 2023-12-01T14:53:11Z | 263 | 0 | [
"region:us"
]
| null | 2023-10-11T03:35:24Z | ---
pretty_name: Evaluation run of sequelbox/StellarBright
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [sequelbox/StellarBright](https://huggingface.co/sequelbox/StellarBright) on the\
\ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 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_sequelbox__StellarBright_public\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-11-08T22:55:36.010619](https://huggingface.co/datasets/open-llm-leaderboard/details_sequelbox__StellarBright_public/blob/main/results_2023-11-08T22-55-36.010619.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\": 0.34458892617449666,\n\
\ \"em_stderr\": 0.004866841438021566,\n \"f1\": 0.4966107382550379,\n\
\ \"f1_stderr\": 0.004389897684698882,\n \"acc\": 0.613835910465284,\n\
\ \"acc_stderr\": 0.011977981888400647\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.34458892617449666,\n \"em_stderr\": 0.004866841438021566,\n\
\ \"f1\": 0.4966107382550379,\n \"f1_stderr\": 0.004389897684698882\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.3949962092494314,\n \
\ \"acc_stderr\": 0.01346535496997321\n },\n \"harness|winogrande|5\":\
\ {\n \"acc\": 0.8326756116811366,\n \"acc_stderr\": 0.010490608806828082\n\
\ }\n}\n```"
repo_url: https://huggingface.co/sequelbox/StellarBright
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_11_08T22_55_36.010619
path:
- '**/details_harness|drop|3_2023-11-08T22-55-36.010619.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-11-08T22-55-36.010619.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_11_08T22_55_36.010619
path:
- '**/details_harness|gsm8k|5_2023-11-08T22-55-36.010619.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-11-08T22-55-36.010619.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_11_08T22_55_36.010619
path:
- '**/details_harness|winogrande|5_2023-11-08T22-55-36.010619.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-11-08T22-55-36.010619.parquet'
- config_name: results
data_files:
- split: 2023_11_08T22_55_36.010619
path:
- results_2023-11-08T22-55-36.010619.parquet
- split: latest
path:
- results_2023-11-08T22-55-36.010619.parquet
---
# Dataset Card for Evaluation run of sequelbox/StellarBright
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/sequelbox/StellarBright
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [sequelbox/StellarBright](https://huggingface.co/sequelbox/StellarBright) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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_sequelbox__StellarBright_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-08T22:55:36.010619](https://huggingface.co/datasets/open-llm-leaderboard/details_sequelbox__StellarBright_public/blob/main/results_2023-11-08T22-55-36.010619.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": 0.34458892617449666,
"em_stderr": 0.004866841438021566,
"f1": 0.4966107382550379,
"f1_stderr": 0.004389897684698882,
"acc": 0.613835910465284,
"acc_stderr": 0.011977981888400647
},
"harness|drop|3": {
"em": 0.34458892617449666,
"em_stderr": 0.004866841438021566,
"f1": 0.4966107382550379,
"f1_stderr": 0.004389897684698882
},
"harness|gsm8k|5": {
"acc": 0.3949962092494314,
"acc_stderr": 0.01346535496997321
},
"harness|winogrande|5": {
"acc": 0.8326756116811366,
"acc_stderr": 0.010490608806828082
}
}
```
### 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] | [
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|
sesotho_ner_corpus | null | 2023-01-25T14:44:09Z | 262 | 0 | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:st",
"license:other",
"region:us"
]
| [
"token-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- st
license:
- other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: Sesotho NER Corpus
license_details: Creative Commons Attribution 2.5 South Africa License
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': OUT
'1': B-PERS
'2': I-PERS
'3': B-ORG
'4': I-ORG
'5': B-LOC
'6': I-LOC
'7': B-MISC
'8': I-MISC
config_name: sesotho_ner_corpus
splits:
- name: train
num_bytes: 4502576
num_examples: 9472
download_size: 30421109
dataset_size: 4502576
---
# Dataset Card for Sesotho NER Corpus
## 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:** [Sesotho Ner Corpus Homepage](https://repo.sadilar.org/handle/20.500.12185/334)
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [Martin Puttkammer](mailto:[email protected])
### Dataset Summary
The Sesotho Ner Corpus is a Sesotho dataset developed by [The Centre for Text Technology (CTexT), North-West University, South Africa](http://humanities.nwu.ac.za/ctext). The data is based on documents from the South African goverment domain and crawled from gov.za websites. It was created to support NER task for Sesotho language. The dataset uses CoNLL shared task annotation standards.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The language supported is Sesotho.
## Dataset Structure
### Data Instances
A data point consists of sentences seperated by empty line and tab-seperated tokens and tags.
```
{'id': '0',
'ner_tags': [0, 0, 0, 0, 0],
'tokens': ['Morero', 'wa', 'weposaete', 'ya', 'Ditshebeletso']
}
```
### Data Fields
- `id`: id of the sample
- `tokens`: the tokens of the example text
- `ner_tags`: the NER tags of each token
The NER tags correspond to this list:
```
"OUT", "B-PERS", "I-PERS", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-MISC", "I-MISC",
```
The NER tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any non-initial word. There are four types of phrases: person names (PER), organizations (ORG), locations (LOC) and miscellaneous names (MISC). (OUT) is used for tokens not considered part of any named entity.
### Data Splits
The data was not split.
## Dataset Creation
### Curation Rationale
The data was created to help introduce resources to new language - Sesotho.
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
The data is based on South African government domain and was crawled from gov.za websites.
#### Who are the source language producers?
The data was produced by writers of South African government websites - gov.za
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
The data was annotated during the NCHLT text resource development project.
[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
The annotated data sets were developed by the Centre for Text Technology (CTexT, North-West University, South Africa).
See: [more information](http://www.nwu.ac.za/ctext)
### Licensing Information
The data is under the [Creative Commons Attribution 2.5 South Africa License](http://creativecommons.org/licenses/by/2.5/za/legalcode)
### Citation Information
```
@inproceedings{sesotho_ner_corpus,
author = {M. Setaka and
Roald Eiselen},
title = {NCHLT Sesotho Named Entity Annotated Corpus},
booktitle = {Eiselen, R. 2016. Government domain named entity recognition for South African languages. Proceedings of the 10th Language Resource and Evaluation Conference, Portorož, Slovenia.},
year = {2016},
url = {https://repo.sadilar.org/handle/20.500.12185/334},
}
```
### Contributions
Thanks to [@yvonnegitau](https://github.com/yvonnegitau) for adding this dataset. | [
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|
siswati_ner_corpus | null | 2023-01-25T14:44:23Z | 262 | 0 | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ss",
"license:other",
"region:us"
]
| [
"token-classification"
]
| 2022-03-02T23:29:22Z | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- ss
license:
- other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: Siswati NER Corpus
license_details: Creative Commons Attribution 2.5 South Africa License
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': OUT
'1': B-PERS
'2': I-PERS
'3': B-ORG
'4': I-ORG
'5': B-LOC
'6': I-LOC
'7': B-MISC
'8': I-MISC
config_name: siswati_ner_corpus
splits:
- name: train
num_bytes: 3517151
num_examples: 10798
download_size: 21882224
dataset_size: 3517151
---
# Dataset Card for Siswati NER Corpus
## 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:** [Siswati Ner Corpus Homepage](https://repo.sadilar.org/handle/20.500.12185/346)
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [Martin Puttkammer](mailto:[email protected])
### Dataset Summary
The Siswati Ner Corpus is a Siswati dataset developed by [The Centre for Text Technology (CTexT), North-West University, South Africa](http://humanities.nwu.ac.za/ctext). The data is based on documents from the South African goverment domain and crawled from gov.za websites. It was created to support NER task for Siswati language. The dataset uses CoNLL shared task annotation standards.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The language supported is Siswati.
## Dataset Structure
### Data Instances
A data point consists of sentences seperated by empty line and tab-seperated tokens and tags.
```
{'id': '0',
'ner_tags': [0, 0, 0, 0, 0],
'tokens': ['Tinsita', 'tebantfu', ':', 'tinsita', 'tetakhamiti']
}
```
### Data Fields
- `id`: id of the sample
- `tokens`: the tokens of the example text
- `ner_tags`: the NER tags of each token
The NER tags correspond to this list:
```
"OUT", "B-PERS", "I-PERS", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-MISC", "I-MISC",
```
The NER tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any non-initial word. There are four types of phrases: person names (PER), organizations (ORG), locations (LOC) and miscellaneous names (MISC). (OUT) is used for tokens not considered part of any named entity.
### Data Splits
The data was not split.
## Dataset Creation
### Curation Rationale
The data was created to help introduce resources to new language - siswati.
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
The data is based on South African government domain and was crawled from gov.za websites.
#### Who are the source language producers?
The data was produced by writers of South African government websites - gov.za
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
The data was annotated during the NCHLT text resource development project.
[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
The annotated data sets were developed by the Centre for Text Technology (CTexT, North-West University, South Africa).
See: [more information](http://www.nwu.ac.za/ctext)
### Licensing Information
The data is under the [Creative Commons Attribution 2.5 South Africa License](http://creativecommons.org/licenses/by/2.5/za/legalcode)
### Citation Information
```
@inproceedings{siswati_ner_corpus,
author = {B.B. Malangwane and
M.N. Kekana and
S.S. Sedibe and
B.C. Ndhlovu and
Roald Eiselen},
title = {NCHLT Siswati Named Entity Annotated Corpus},
booktitle = {Eiselen, R. 2016. Government domain named entity recognition for South African languages. Proceedings of the 10th Language Resource and Evaluation Conference, Portorož, Slovenia.},
year = {2016},
url = {https://repo.sadilar.org/handle/20.500.12185/346},
}
```
### Contributions
Thanks to [@yvonnegitau](https://github.com/yvonnegitau) for adding this dataset. | [
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|
castorini/msmarco_v1_doc_doc2query-t5_expansions | castorini | 2022-07-02T19:16:12Z | 262 | 0 | [
"language:en",
"license:apache-2.0",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
license: apache-2.0
---
# Dataset Summary
The repo provides queries generated for the MS MARCO V1 document corpus with docTTTTTquery (sometimes written as docT5query or doc2query-T5), the latest version of the doc2query family of document expansion models. The basic idea is to train a model, that when given an input document, generates questions that the document might answer (or more broadly, queries for which the document might be relevant). These predicted questions (or queries) are then appended to the original documents, which are then indexed as before. The docTTTTTquery model gets its name from the use of T5 as the expansion model.
# Dataset Structure
All three folds (train, dev and test) share the same corpus.
An example data entry looks as follows:
```
{
"id": "D1555982",
"predicted_queries": ["when find radius of star r", "what is r radius", "how to find out radius of star", "what is radius r", "what is radius of r", "how do you find radius of star igel", "which law states that radiation is proportional to radiation?", "what is the radius of a spherical star", "what is the radius of the star", "what is radius of star", "which radiation is produced during a solar radiation experiment?", "how to find radius r", "what is radius r of a star", "the hot glowing surfaces of stars emit energy in the form of", "what is the radius of a star", "what is the radius of a star", "how to find radius r on a star", "how to find radius r in a solar cell", "what kind of energy does a hot glowing surface of a star emit?", "what kind of energy does the hot glowing surface of stars emit"]
}
```
# Load Dataset
An example to load the dataset:
```
dataset = load_dataset('castorini/msmarco_v1_doc_doc2query-t5_expansions')
```
# Citation Information
```
@article{docTTTTTquery,
title={From doc2query to {docTTTTTquery}},
author={Nogueira, Rodrigo and Lin, Jimmy},
year={2019}
}
@article{emdt5,
author = "Ronak Pradeep and Rodrigo Nogueira and Jimmy Lin",
title = "The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models",
journal = "arXiv:2101.05667",
year = 2021,
}
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|
echarlaix/gqa-lxmert | echarlaix | 2022-02-09T23:39:45Z | 262 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
license: apache-2.0
---
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|
echarlaix/gqa | echarlaix | 2022-02-01T10:44:11Z | 262 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
license: apache-2.0
---
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|
gabtan99/pex-conversations | gabtan99 | 2022-10-20T19:34:29Z | 262 | 1 | [
"task_ids:dialogue-modeling",
"task_ids:language-modeling",
"multilinguality:multilingual",
"size_categories:unknown",
"source_datasets:original",
"language:tl",
"language:fil",
"license:unknown",
"multi-turn",
"region:us"
]
| [
"sequence-modeling"
]
| 2022-03-02T23:29:22Z | ---
language:
- tl
- fil
license:
- unknown
multilinguality:
- multilingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- sequence-modeling
task_ids:
- dialogue-modeling
- language-modeling
pretty_name: PEx Conversations
tags:
- multi-turn
---
# PinoyExchange (PEx) Conversations Dataset
# Summary
PEx Conversations is a dataset composed of collected threads from PinoyExchange.com (Consisting of Tagalog, English, or Taglish responses).
The corpus consists of 45K total scraped threads from 8 subforums. The data only consists of the user message which means any images, videos, links, or any embdedded html are not collected in the scraping process. All characters have been transliterated to its closest ASCII representation, and unicode errors were fixed.
# Format
The data is categorized per category. The objects in the list is composed of:
* category - the category of the threads
* conversations - the list of threads
The threads inside conversations have recursive structure consisting of the following:
* text - This is the response/reply/prompt
* replies - This is a list of the replies to this prompt. The replies inside the list has a structure with the same text and replies component.
# Subforum percentages
The amount of data per subforum are as follows:
* Small Talk - 5K conversations with 1.16M utterances
* Food & Drinks - 8.2K conversations with 273K utterances
* Health & Wellness - 6.3K conversations with 93K utterances
* Body & Fitness - 3.9K conversations with 94K utterances
* Home & Garden - 3.6K conversations with 71K utterances
* Style & Fashion - 9.7K conversations with 197K utterances
* Travel & Leisure - 7.3K conversations with 431K utterances
* Visas & Immigration - 1.1K conversations with 99K utterances
# Model Research
[Tagalog DialoGPT](https://huggingface.co/gabtan99/dialogpt-tagalog-medium) | [
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|
huggingartists/25-17 | huggingartists | 2022-10-25T09:20:55Z | 262 | 0 | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/25-17"
## 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:** 0.678946 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/4fedc5dd2830a874a5274bf1cac62002.1000x1000x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/25-17">
<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">25/17</div>
<a href="https://genius.com/artists/25-17">
<div style="text-align: center; font-size: 14px;">@25-17</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/25-17).
### 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/25-17")
```
## 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|
|------:|---------:|---:|
|195| -| -|
'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/25-17")
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)
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|
huggingartists/arctic-monkeys | huggingartists | 2022-10-25T09:23:30Z | 262 | 0 | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/arctic-monkeys"
## 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:** 0.246691 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/12c27f4fbb06ef32dc1c1e432098f447.570x570x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/arctic-monkeys">
<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">Arctic Monkeys</div>
<a href="https://genius.com/artists/arctic-monkeys">
<div style="text-align: center; font-size: 14px;">@arctic-monkeys</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/arctic-monkeys).
### 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/arctic-monkeys")
```
## 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|
|------:|---------:|---:|
|186| -| -|
'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/arctic-monkeys")
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)
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|
huggingartists/billie-eilish | huggingartists | 2022-10-25T09:24:46Z | 262 | 0 | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/billie-eilish"
## 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:** 0.734139 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/1aa6c04aad3652556046bb3aabe96498.900x900x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/billie-eilish">
<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">Billie Eilish</div>
<a href="https://genius.com/artists/billie-eilish">
<div style="text-align: center; font-size: 14px;">@billie-eilish</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/billie-eilish).
### 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/billie-eilish")
```
## 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|
|------:|---------:|---:|
|298| -| -|
'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/billie-eilish")
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)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
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|
huggingartists/dua-lipa | huggingartists | 2022-10-25T09:28:09Z | 262 | 0 | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/dua-lipa"
## 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:** 0.691563 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/dd37b530cf20f2ce699f91e02a476a8a.847x847x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/dua-lipa">
<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">Dua Lipa</div>
<a href="https://genius.com/artists/dua-lipa">
<div style="text-align: center; font-size: 14px;">@dua-lipa</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/dua-lipa).
### 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/dua-lipa")
```
## 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|
|------:|---------:|---:|
|454| -| -|
'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/dua-lipa")
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)
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|
huggingartists/egor-letov | huggingartists | 2022-10-25T09:28:41Z | 262 | 0 | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/egor-letov"
## 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:** 0.673046 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/faa3dae99bf1fe365927608fd55c745a.330x330x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/egor-letov">
<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">Егор Летов (Egor Letov)</div>
<a href="https://genius.com/artists/egor-letov">
<div style="text-align: center; font-size: 14px;">@egor-letov</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/egor-letov).
### 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/egor-letov")
```
## 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|
|------:|---------:|---:|
|543| -| -|
'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/egor-letov")
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)
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|
huggingartists/grigory-leps | huggingartists | 2022-10-25T09:30:58Z | 262 | 0 | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/grigory-leps"
## 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:** 0.066125 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/f30e8944a06a196868ee4b077a7926a6.1000x1000x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/grigory-leps">
<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">Григорий Лепс (Grigory Leps)</div>
<a href="https://genius.com/artists/grigory-leps">
<div style="text-align: center; font-size: 14px;">@grigory-leps</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/grigory-leps).
### 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/grigory-leps")
```
## 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|
|------:|---------:|---:|
|18| -| -|
'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/grigory-leps")
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)
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|
huggingartists/justin-bieber | huggingartists | 2022-10-25T09:32:38Z | 262 | 0 | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/justin-bieber"
## 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:** 1.248682 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/8c95afb142e16e2e053b4384514dc339.938x938x1.png')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/justin-bieber">
<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">Justin Bieber</div>
<a href="https://genius.com/artists/justin-bieber">
<div style="text-align: center; font-size: 14px;">@justin-bieber</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/justin-bieber).
### 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/justin-bieber")
```
## 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|
|------:|---------:|---:|
|752| -| -|
'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/justin-bieber")
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)
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|
huggingartists/maroon-5 | huggingartists | 2022-10-25T09:37:35Z | 262 | 0 | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/maroon-5"
## 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:** 0.409714 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/6780ce1add3af75c73929a8f6630e099.900x900x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/maroon-5">
<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">Maroon 5</div>
<a href="https://genius.com/artists/maroon-5">
<div style="text-align: center; font-size: 14px;">@maroon-5</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/maroon-5).
### 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/maroon-5")
```
## 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|
|------:|---------:|---:|
|232| -| -|
'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/maroon-5")
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)
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|
huggingartists/our-last-night | huggingartists | 2022-10-25T09:40:47Z | 262 | 0 | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/our-last-night"
## 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:** 0.287611 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/03627944481dcdb782595e9d3e351853.959x959x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/our-last-night">
<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">Our Last Night</div>
<a href="https://genius.com/artists/our-last-night">
<div style="text-align: center; font-size: 14px;">@our-last-night</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/our-last-night).
### 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/our-last-night")
```
## 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|
|------:|---------:|---:|
|179| -| -|
'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/our-last-night")
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)
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|
huggingartists/ramil | huggingartists | 2022-10-25T09:42:31Z | 262 | 0 | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/ramil"
## 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:** 0.158728 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/0debcd46861577e3776b41aa3e3d7164.1000x1000x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/ramil">
<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">Ramil’</div>
<a href="https://genius.com/artists/ramil">
<div style="text-align: center; font-size: 14px;">@ramil</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/ramil).
### 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/ramil")
```
## 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|
|------:|---------:|---:|
|54| -| -|
'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/ramil")
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)
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|
huggingartists/system-of-a-down | huggingartists | 2022-10-25T09:45:46Z | 262 | 0 | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/system-of-a-down"
## 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:** 0.178799 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/5688d59e74bfc07b0531636114f56c1e.1000x1000x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/system-of-a-down">
<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">System of a Down</div>
<a href="https://genius.com/artists/system-of-a-down">
<div style="text-align: center; font-size: 14px;">@system-of-a-down</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/system-of-a-down).
### 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/system-of-a-down")
```
## 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|
|------:|---------:|---:|
|129| -| -|
'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/system-of-a-down")
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)
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|
huggingartists/the-69-eyes | huggingartists | 2022-10-25T09:46:18Z | 262 | 0 | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/the-69-eyes"
## 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:** 0.162381 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/9e0451fa9d3f8cf38aa11994dbd934a8.600x600x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/the-69-eyes">
<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">The 69 Eyes</div>
<a href="https://genius.com/artists/the-69-eyes">
<div style="text-align: center; font-size: 14px;">@the-69-eyes</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/the-69-eyes).
### 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/the-69-eyes")
```
## 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|
|------:|---------:|---:|
|168| -| -|
'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/the-69-eyes")
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)
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|
huggingartists/the-avalanches | huggingartists | 2022-10-25T09:46:25Z | 262 | 0 | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
]
| null | 2022-03-02T23:29:22Z | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/the-avalanches"
## 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:** 0.123553 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/e22f7806a402c82b09336cb5cf79a618.1000x1000x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/the-avalanches">
<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">The Avalanches</div>
<a href="https://genius.com/artists/the-avalanches">
<div style="text-align: center; font-size: 14px;">@the-avalanches</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/the-avalanches).
### 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/the-avalanches")
```
## 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|
|------:|---------:|---:|
|111| -| -|
'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/the-avalanches")
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)
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|
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