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autoevaluate/autoeval-eval-samsum-samsum-bf100b-1520255007
|
autoevaluate
| 2022-09-21T02:23:16Z | 111 | 0 |
[
"autotrain",
"evaluation",
"region:us"
] | null | 2022-09-21T02:15:16Z |
---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- samsum
eval_info:
task: summarization
model: SamuelAllen123/t5-efficient-large-nl36_fine_tune_sum_V2
metrics: ['rouge']
dataset_name: samsum
dataset_config: samsum
dataset_split: test
col_mapping:
text: dialogue
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: SamuelAllen123/t5-efficient-large-nl36_fine_tune_sum_V2
* Dataset: samsum
* Config: samsum
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@samuelallen123](https://huggingface.co/samuelallen123) for evaluating this model.
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elenanereiss/german-ler
|
elenanereiss
| 2022-10-26T08:32:17Z | 111 | 9 |
[
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:de",
"license:cc-by-4.0",
"ner, named entity recognition, legal ner, legal texts, label classification",
"arxiv:2003.13016",
"doi:10.57967/hf/0046",
"region:us"
] |
[
"token-classification"
] | 2022-10-18T11:10:32Z |
---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- de
license:
- cc-by-4.0
multilinguality:
- monolingual
paperswithcode_id: dataset-of-legal-documents
pretty_name: German Named Entity Recognition in Legal Documents
size_categories:
- 1M<n<10M
source_datasets:
- original
tags:
- ner, named entity recognition, legal ner, legal texts, label classification
task_categories:
- token-classification
task_ids:
- named-entity-recognition
train-eval-index:
- config: conll2003
task: token-classification
task_id: entity_extraction
splits:
train_split: train
eval_split: test
col_mapping:
tokens: tokens
ner_tags: tags
---
# Dataset Card for "German LER"
## 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/elenanereiss/Legal-Entity-Recognition](https://github.com/elenanereiss/Legal-Entity-Recognition)
- **Paper:** [https://arxiv.org/pdf/2003.13016v1.pdf](https://arxiv.org/pdf/2003.13016v1.pdf)
- **Point of Contact:** [[email protected]]([email protected])
### Dataset Summary
A dataset of Legal Documents from German federal court decisions for Named Entity Recognition. The dataset is human-annotated with 19 fine-grained entity classes. The dataset consists of approx. 67,000 sentences and contains 54,000 annotated entities. NER tags use the `BIO` tagging scheme.
The dataset includes two different versions of annotations, one with a set of 19 fine-grained semantic classes (`ner_tags`) and another one with a set of 7 coarse-grained classes (`ner_coarse_tags`). There are 53,632 annotated entities in total, the majority of which (74.34 %) are legal entities, the others are person, location and organization (25.66 %).
For more details see [https://arxiv.org/pdf/2003.13016v1.pdf](https://arxiv.org/pdf/2003.13016v1.pdf).
### Supported Tasks and Leaderboards
- **Tasks:** Named Entity Recognition
- **Leaderboards:**
### Languages
German
## Dataset Structure
### Data Instances
```python
{
'id': '1',
'tokens': ['Eine', 'solchermaßen', 'verzögerte', 'oder', 'bewusst', 'eingesetzte', 'Verkettung', 'sachgrundloser', 'Befristungen', 'schließt', '§', '14', 'Abs.', '2', 'Satz', '2', 'TzBfG', 'aus', '.'],
'ner_tags': [38, 38, 38, 38, 38, 38, 38, 38, 38, 38, 3, 22, 22, 22, 22, 22, 22, 38, 38],
'ner_coarse_tags': [14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 2, 9, 9, 9, 9, 9, 9, 14, 14]
}
```
### Data Fields
```python
{
'id': Value(dtype='string', id=None),
'tokens': Sequence(feature=Value(dtype='string', id=None),
length=-1, id=None),
'ner_tags': Sequence(feature=ClassLabel(num_classes=39,
names=['B-AN',
'B-EUN',
'B-GRT',
'B-GS',
'B-INN',
'B-LD',
'B-LDS',
'B-LIT',
'B-MRK',
'B-ORG',
'B-PER',
'B-RR',
'B-RS',
'B-ST',
'B-STR',
'B-UN',
'B-VO',
'B-VS',
'B-VT',
'I-AN',
'I-EUN',
'I-GRT',
'I-GS',
'I-INN',
'I-LD',
'I-LDS',
'I-LIT',
'I-MRK',
'I-ORG',
'I-PER',
'I-RR',
'I-RS',
'I-ST',
'I-STR',
'I-UN',
'I-VO',
'I-VS',
'I-VT',
'O'],
id=None),
length=-1,
id=None),
'ner_coarse_tags': Sequence(feature=ClassLabel(num_classes=15,
names=['B-LIT',
'B-LOC',
'B-NRM',
'B-ORG',
'B-PER',
'B-REG',
'B-RS',
'I-LIT',
'I-LOC',
'I-NRM',
'I-ORG',
'I-PER',
'I-REG',
'I-RS',
'O'],
id=None),
length=-1,
id=None)
}
```
### Data Splits
| | train | validation | test |
|-------------------------|------:|-----------:|-----:|
| Input Sentences | 53384 | 6666 | 6673 |
## Dataset Creation
### Curation Rationale
Documents in the legal domain contain multiple references to named entities, especially domain-specific named entities, i. e., jurisdictions, legal institutions, etc. Legal documents are unique and differ greatly from newspaper texts. On the one hand, the occurrence of general-domain named entities is relatively rare. On the other hand, in concrete applications, crucial domain-specific entities need to be identified in a reliable way, such as designations of legal norms and references to other legal documents (laws, ordinances, regulations, decisions, etc.). Most NER solutions operate in the general or news domain, which makes them inapplicable to the analysis of legal documents. Accordingly, there is a great need for an NER-annotated dataset consisting of legal documents, including the corresponding development of a typology of semantic concepts and uniform annotation guidelines.
### Source Data
Court decisions from 2017 and 2018 were selected for the dataset, published online by the [Federal Ministry of Justice and Consumer Protection](http://www.rechtsprechung-im-internet.de). The documents originate from seven federal courts: Federal Labour Court (BAG), Federal Fiscal Court (BFH), Federal Court of Justice (BGH), Federal Patent Court (BPatG), Federal Social Court (BSG), Federal Constitutional Court (BVerfG) and Federal Administrative Court (BVerwG).
#### Initial Data Collection and Normalization
From the table of [contents](http://www.rechtsprechung-im-internet.de/rii-toc.xml), 107 documents from each court were selected (see Table 1). The data was collected from the XML documents, i. e., it was extracted from the XML elements `Mitwirkung, Titelzeile, Leitsatz, Tenor, Tatbestand, Entscheidungsgründe, Gründen, abweichende Meinung, and sonstiger Titel`. The metadata at the beginning of the documents (name of court, date of decision, file number, European Case Law Identifier, document type, laws) and those that belonged to previous legal proceedings was deleted. Paragraph numbers were removed.
The extracted data was split into sentences, tokenised using [SoMaJo](https://github.com/tsproisl/SoMaJo) and manually annotated in [WebAnno](https://webanno.github.io/webanno/).
#### Who are the source language producers?
The Federal Ministry of Justice and the Federal Office of Justice provide selected decisions. Court decisions were produced by humans.
### Annotations
#### Annotation process
For more details see [annotation guidelines](https://github.com/elenanereiss/Legal-Entity-Recognition/blob/master/docs/Annotationsrichtlinien.pdf) (in German).
<!-- #### 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
A fundamental characteristic of the published decisions is that all personal information have been anonymised for privacy reasons. This affects the classes person, location and organization.
<!-- ## 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
[CC BY-SA 4.0 license](https://creativecommons.org/licenses/by-sa/4.0/)
### Citation Information
```
@misc{https://doi.org/10.48550/arxiv.2003.13016,
doi = {10.48550/ARXIV.2003.13016},
url = {https://arxiv.org/abs/2003.13016},
author = {Leitner, Elena and Rehm, Georg and Moreno-Schneider, Julián},
keywords = {Computation and Language (cs.CL), Information Retrieval (cs.IR), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {A Dataset of German Legal Documents for Named Entity Recognition},
publisher = {arXiv},
year = {2020},
copyright = {arXiv.org perpetual, non-exclusive license}
}
```
### Contributions
|
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] |
fahamu/ioi
|
fahamu
| 2023-12-01T09:00:43Z | 111 | 2 |
[
"license:mit",
"doi:10.57967/hf/1415",
"region:us"
] | null | 2022-11-28T01:10:59Z |
---
license: mit
---
# Dataset Release: Indirect Object Identification
`mecha_ioi` is a pair of datasets tailored for the Indirect Object Identification task, where sentences are generated from the following set of templates:
- BABA
```
baba_templates = [
"Then, {B} and {A} went to the {PLACE}. {B} gave a {OBJECT} to {A}",
"Then, {B} and {A} had a lot of fun at the {PLACE}. {B} gave a {OBJECT} to {A}",
"Then, {B} and {A} were working at the {PLACE}. {B} decided to give a {OBJECT} to {A}",
"Then, {B} and {A} were thinking about going to the {PLACE}. {B} wanted to give a {OBJECT} to {A}",
"Then, {B} and {A} had a long argument, and afterwards {B} said to {A}",
"After {B} and {A} went to the {PLACE}, {B} gave a {OBJECT} to {A}",
"When {B} and {A} got a {OBJECT} at the {PLACE}, {B} decided to give it to {A}",
"When {B} and {A} got a {OBJECT} at the {PLACE}, {B} decided to give the {OBJECT} to {A}",
"While {B} and {A} were working at the {PLACE}, {B} gave a {OBJECT} to {A}",
"While {B} and {A} were commuting to the {PLACE}, {B} gave a {OBJECT} to {A}",
"After the lunch, {B} and {A} went to the {PLACE}. {B} gave a {OBJECT} to {A}",
"Afterwards, {B} and {A} went to the {PLACE}. {B} gave a {OBJECT} to {A}",
"Then, {B} and {A} had a long argument. Afterwards {B} said to {A}",
"The {PLACE} {B} and {A} went to had a {OBJECT}. {B} gave it to {A}",
"Friends {B} and {A} found a {OBJECT} at the {PLACE}. {B} gave it to {A}",
]
```
- ABBA
```
abba_templates = [
"Then, {A} and {B} went to the {PLACE}. {B} gave a {OBJECT} to {A}",
"Then, {A} and {B} had a lot of fun at the {PLACE}. {B} gave a {OBJECT} to {A}",
"Then, {A} and {B} were working at the {PLACE}. {B} decided to give a {OBJECT} to {A}",
"Then, {A} and {B} were thinking about going to the {PLACE}. {B} wanted to give a {OBJECT} to {A}",
"Then, {A} and {B} had a long argument, and afterwards {B} said to {A}",
"After {A} and {B} went to the {PLACE}, {B} gave a {OBJECT} to {A}",
"When {A} and {B} got a {OBJECT} at the {PLACE}, {B} decided to give it to {A}",
"When {A} and {B} got a {OBJECT} at the {PLACE}, {B} decided to give the {OBJECT} to {A}",
"While {A} and {B} were working at the {PLACE}, {B} gave a {OBJECT} to {A}",
"While {A} and {B} were commuting to the {PLACE}, {B} gave a {OBJECT} to {A}",
"After the lunch, {A} and {B} went to the {PLACE}. {B} gave a {OBJECT} to {A}",
"Afterwards, {A} and {B} went to the {PLACE}. {B} gave a {OBJECT} to {A}",
"Then, {A} and {B} had a long argument. Afterwards {B} said to {A}",
"The {PLACE} {A} and {B} went to had a {OBJECT}. {B} gave it to {A}",
"Friends {A} and {B} found a {OBJECT} at the {PLACE}. {B} gave it to {A}",
]
```
The purpose of this dataset is to facilitate interpretability research, inspired by the paper
_Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small_, from Redwood Research. We are not affiliated with Redwood Research,
and release this dataset to contribute to the collective research effort behind understanding how Transformer language models perform this task.
### BibTex
```
@misc {fahamu_2022,
author = { {Brian Muhia} },
title = { ioi (Revision 223da8b) },
year = 2022,
url = { https://huggingface.co/datasets/fahamu/ioi },
doi = { 10.57967/hf/0142 },
publisher = { Hugging Face }
}
```
|
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aashsach/multiconer2
|
aashsach
| 2023-01-05T03:00:49Z | 111 | 0 |
[
"region:us"
] | null | 2022-12-28T17:03:44Z |
---
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] |
jonathan-roberts1/RSI-CB256
|
jonathan-roberts1
| 2023-03-31T17:11:50Z | 111 | 0 |
[
"task_categories:image-classification",
"task_categories:zero-shot-image-classification",
"license:other",
"region:us"
] |
[
"image-classification",
"zero-shot-image-classification"
] | 2023-02-14T19:09:45Z |
---
dataset_info:
features:
- name: label_1
dtype:
class_label:
names:
'0': transportation
'1': other objects
'2': woodland
'3': water area
'4': other land
'5': cultivated land
'6': construction land
- name: label_2
dtype:
class_label:
names:
'0': parking lot
'1': avenue
'2': highway
'3': bridge
'4': marina
'5': crossroads
'6': airport runway
'7': pipeline
'8': town
'9': airplane
'10': forest
'11': mangrove
'12': artificial grassland
'13': river protection forest
'14': shrubwood
'15': sapling
'16': sparse forest
'17': lakeshore
'18': river
'19': stream
'20': coastline
'21': hirst
'22': dam
'23': sea
'24': snow mountain
'25': sandbeach
'26': mountain
'27': desert
'28': dry farm
'29': green farmland
'30': bare land
'31': city building
'32': residents
'33': container
'34': storage room
- name: image
dtype: image
splits:
- name: train
num_bytes: 4901667781.625
num_examples: 24747
download_size: 4198991130
dataset_size: 4901667781.625
license: other
task_categories:
- image-classification
- zero-shot-image-classification
---
# Dataset Card for "RSI-CB256"
## Dataset Description
- **Paper** [Exploring Models and Data for Remote Sensing Image Caption Generation](https://ieeexplore.ieee.org/iel7/36/4358825/08240966.pdf)
-
### Licensing Information
For academic purposes.
## Citation Information
[Exploring Models and Data for Remote Sensing Image Caption Generation](https://ieeexplore.ieee.org/iel7/36/4358825/08240966.pdf)
```
@article{lu2017exploring,
title = {Exploring Models and Data for Remote Sensing Image Caption Generation},
author = {Lu, Xiaoqiang and Wang, Binqiang and Zheng, Xiangtao and Li, Xuelong},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
volume = 56,
number = 4,
pages = {2183--2195},
doi = {10.1109/TGRS.2017.2776321},
year={2018}
}
```
|
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maveriq/tobacco3482
|
maveriq
| 2023-03-02T21:23:58Z | 111 | 1 |
[
"region:us"
] | null | 2023-03-02T20:46:53Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': ADVE
'1': Email
'2': Form
'3': Letter
'4': Memo
'5': News
'6': Note
'7': Report
'8': Resume
'9': Scientific
splits:
- name: train
num_bytes: 1409969631.808
num_examples: 3482
download_size: 1733093218
dataset_size: 1409969631.808
---
# Dataset Card for "tobacco3482"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
theblackcat102/instruction_translations
|
theblackcat102
| 2023-03-05T06:36:37Z | 111 | 5 |
[
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"ChatGPT",
"SimpleAI",
"Detection",
"doi:10.57967/hf/0423",
"region:us"
] |
[
"text-generation"
] | 2023-03-04T00:51:47Z |
---
task_categories:
- text-generation
language:
- en
tags:
- ChatGPT
- SimpleAI
- Detection
size_categories:
- 10K<n<100K
license: mit
---
# Translations for Instruction dataset
Translations were generated by [M2M 12B](https://huggingface.co/facebook/m2m100-12B-avg-5-ckpt) and the output generations were limited at 512 tokens due to VRAM limit (40G).
|
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] |
FreedomIntelligence/HuatuoGPT-sft-data-v1
|
FreedomIntelligence
| 2023-06-01T11:05:15Z | 111 | 50 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-05-25T08:09:22Z |
---
license: apache-2.0
---
|
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Someman/hindi-summarization
|
Someman
| 2023-05-30T12:55:13Z | 111 | 0 |
[
"task_categories:summarization",
"size_categories:10K<n<100K",
"language:hi",
"license:mit",
"region:us"
] |
[
"summarization"
] | 2023-05-30T12:39:11Z |
---
license: mit
task_categories:
- summarization
language: hi
original_source: >-
https://www.kaggle.com/datasets/disisbig/hindi-text-short-and-large-summarization-corpus
dataset_info:
features:
- name: headline
dtype: string
- name: summary
dtype: string
- name: article
dtype: string
splits:
- name: train
num_bytes: 410722079.5542422
num_examples: 55226
- name: test
num_bytes: 102684238.44575782
num_examples: 13807
- name: valid
num_bytes: 128376473
num_examples: 17265
download_size: 150571314
dataset_size: 641782791
pretty_name: hindi summarization
size_categories:
- 10K<n<100K
---
# Dataset Card for Dataset Name
## Dataset Description
- Homepage: https://www.kaggle.com/datasets/disisbig/hindi-text-short-and-large-summarization-corpus?select=test.csv
### Dataset Summary
Hindi Text Short and Large Summarization Corpus is a collection of ~180k articles with their headlines and summary collected from Hindi News Websites.
This is a first of its kind Dataset in Hindi which can be used to benchmark models for Text summarization in Hindi. This does not contain articles contained in Hindi Text Short Summarization Corpus which is being released parallely with this Dataset.
The dataset retains original punctuation, numbers etc in the articles.
### Languages
The language is Hindi.
### Licensing Information
MIT
### Citation Information
https://www.kaggle.com/datasets/disisbig/hindi-text-short-and-large-summarization-corpus?select=test.csv
### Contributions
|
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qgyd2021/chinese_chitchat
|
qgyd2021
| 2023-09-22T08:39:11Z | 111 | 11 |
[
"size_categories:100M<n<1B",
"language:zh",
"license:apache-2.0",
"chitchat",
"region:us"
] | null | 2023-09-22T02:24:54Z |
---
license: apache-2.0
language:
- zh
tags:
- chitchat
size_categories:
- 100M<n<1B
---
## 中文闲聊数据集
role 的取值有: "unknown", "human", "assistant", 三种.
数据集从网上收集整理如下:
| 数据 | 原始数据/项目地址 | 样本个数 | 语料描述 | 替代数据下载地址 |
| :--- | :---: | :---: | :---: | :---: |
| ChatterBot | [ChatterBot](https://github.com/gunthercox/ChatterBot); [chatterbot-corpus](https://github.com/gunthercox/chatterbot-corpus) | 560 | 按类型分类，质量较高 | [阿里云盘](https://www.aliyundrive.com/s/qXBdAYtz5j5); 提取码: 81ao |
| douban | [Douban Conversation Corpus](https://github.com/MarkWuNLP/MultiTurnResponseSelection) | 352W | 来自北航和微软的paper, 噪音相对较少, 多轮(平均7.6轮) | [阿里云盘](https://www.aliyundrive.com/s/qXBdAYtz5j5); 提取码: 81ao |
| ptt | [PTT中文語料](https://github.com/zake7749/Gossiping-Chinese-Corpus) | 77W | 开源项目, 台湾PTT论坛八卦版, 繁体, 语料较生活化, 有噪音 | [阿里云盘](https://www.aliyundrive.com/s/qXBdAYtz5j5); 提取码: 81ao |
| qingyun | [阿里云盘](https://www.aliyundrive.com/s/qXBdAYtz5j5); 提取码: 81ao | 10W | 青云语料, 相对不错, 生活化 | |
| subtitle | [电视剧对白语料](https://github.com/aceimnorstuvwxz/dgk_lost_conv) | 274W | 来自爬取的电影和美剧的字幕, 有一些噪音, 不严谨的对话, 说话人无法对应起来, 多轮(平均5.3轮) | [阿里云盘](https://www.aliyundrive.com/s/qXBdAYtz5j5); 提取码: 81ao |
| tieba | [贴吧论坛回帖语料](https://pan.baidu.com/s/1mUknfwy1nhSM7XzH8xi7gQ); 密码:i4si | 232W | 多轮, 有噪音 | [阿里云盘](https://www.aliyundrive.com/s/qXBdAYtz5j5); 提取码: 81ao |
| weibo | [阿里云盘](https://www.aliyundrive.com/s/qXBdAYtz5j5); 提取码: 81ao | 443W | 来自华为的paper | |
| xiaohuangji | [小黄鸡语料](https://github.com/candlewill/Dialog_Corpus) | 45W | 原人人网项目语料, 有一些不雅对话, 少量噪音 | [阿里云盘](https://www.aliyundrive.com/s/qXBdAYtz5j5); 提取码: 81ao |
<details>
<summary>参考的数据来源,展开查看</summary>
<pre>
<code>
https://github.com/codemayq/chinese_chatbot_corpus
https://github.com/yangjianxin1/GPT2-chitchat
</code>
</pre>
</details>
|
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veezbo/akkadian_english_corpus
|
veezbo
| 2023-09-30T21:32:28Z | 111 | 1 |
[
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:en",
"license:mit",
"region:us"
] |
[
"text-generation"
] | 2023-09-29T07:22:07Z |
---
license: mit
task_categories:
- text-generation
language:
- en
pretty_name: English-translated Akkadian Corpus
size_categories:
- 1K<n<10K
---
# Akkadian English Corpus
This dataset is a cleaned English-translated Akkadian language dataset. This dataset can and has been used for text generation tasks, for example to fine-tune LLMs.
## How it was generated
Please visit my [repo](https://github.com/veezbo/akkadian_english_corpus) on Github which explains the steps that were taken to prepare this dataset for a text generation task.
At a high level, these are steps that were taken:
- Sourced a high-quality dataset of English-translated Akkadian by experts
- Enforced a minimum line length
- Removed duplicate lines
- Removed textual notes and other generic notes within parantheses
- Inserted translation notes and literal notes in place (preserving grammar and adding clarity to the corpus)
## Credit
Credit for the aggregation of the raw data belongs to the [Akkademia](https://github.com/gaigutherz/Akkademia/tree/master) project. Specifically, the exact data file used as the starting dataset is linked [here](https://github.com/gaigutherz/Akkademia/blob/master/NMT_input/train.en) and was also used to train their SOTA neural machine translation Akkadian->English model as described in their recent [paper](https://academic.oup.com/pnasnexus/article/2/5/pgad096/7147349) Gutherz et al. 2023 [1].
Credit for the original source of the raw data belongs to the incredible Open Richly Annotated Cuneiform Corpus ([ORACC](http://oracc.org)) project [2]. Specifically, as noted by the Akkademia project above, the RINAP 1, 3, 4, and 5 datasets are the source of the original raw data.
## Citations
[1] Gai Gutherz, Shai Gordin, Luis Sáenz, Omer Levy, Jonathan Berant, Translating Akkadian to English with neural machine translation, PNAS Nexus, Volume 2, Issue 5, May 2023, pgad096, https://doi.org/10.1093/pnasnexus/pgad096
[2] Jamie Novotny, Eleanor Robson, Steve Tinney, Niek Veldhuis, et al. Open Richly Annotated Cuneiform Corpus, http://oracc.org
|
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cfilt/HiNER-original
|
cfilt
| 2023-03-07T16:42:05Z | 110 | 2 |
[
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:hi",
"license:cc-by-sa-4.0",
"arxiv:2204.13743",
"region:us"
] |
[
"token-classification"
] | 2022-04-25T13:55:19Z |
---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- hi
license: "cc-by-sa-4.0"
multilinguality:
- monolingual
paperswithcode_id: hiner-original-1
pretty_name: HiNER - Large Hindi Named Entity Recognition dataset
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
---
<p align="center"><img src="https://huggingface.co/datasets/cfilt/HiNER-collapsed/raw/main/cfilt-dark-vec.png" alt="Computation for Indian Language Technology Logo" width="150" height="150"/></p>
# Dataset Card for HiNER-original
[](https://twitter.com/cfiltnlp)
[](https://twitter.com/PeopleCentredAI)
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://github.com/cfiltnlp/HiNER
- **Repository:** https://github.com/cfiltnlp/HiNER
- **Paper:** https://arxiv.org/abs/2204.13743
- **Leaderboard:** https://paperswithcode.com/sota/named-entity-recognition-on-hiner-original
- **Point of Contact:** Rudra Murthy V
### Dataset Summary
This dataset was created for the fundamental NLP task of Named Entity Recognition for the Hindi language at CFILT Lab, IIT Bombay. We gathered the dataset from various government information webpages and manually annotated these sentences as a part of our data collection strategy.
**Note:** The dataset contains sentences from ILCI and other sources. ILCI dataset requires license from Indian Language Consortium due to which we do not distribute the ILCI portion of the data. Please send us a mail with proof of ILCI data acquisition to obtain the full dataset.
### Supported Tasks and Leaderboards
Named Entity Recognition
### Languages
Hindi
## Dataset Structure
### Data Instances
{'id': '0', 'tokens': ['प्राचीन', 'समय', 'में', 'उड़ीसा', 'को', 'कलिंग','के', 'नाम', 'से', 'जाना', 'जाता', 'था', '।'], 'ner_tags': [0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0]}
### Data Fields
- `id`: The ID value of the data point.
- `tokens`: Raw tokens in the dataset.
- `ner_tags`: the NER tags for this dataset.
### Data Splits
| | Train | Valid | Test |
| ----- | ------ | ----- | ---- |
| original | 76025 | 10861 | 21722|
| collapsed | 76025 | 10861 | 21722|
## About
This repository contains the Hindi Named Entity Recognition dataset (HiNER) published at the Langauge Resources and Evaluation conference (LREC) in 2022. A pre-print via arXiv is available [here](https://arxiv.org/abs/2204.13743).
### Recent Updates
* Version 0.0.5: HiNER initial release
## Usage
You should have the 'datasets' packages installed to be able to use the :rocket: HuggingFace datasets repository. Please use the following command and install via pip:
```code
pip install datasets
```
To use the original dataset with all the tags, please use:<br/>
```python
from datasets import load_dataset
hiner = load_dataset('cfilt/HiNER-original')
```
To use the collapsed dataset with only PER, LOC, and ORG tags, please use:<br/>
```python
from datasets import load_dataset
hiner = load_dataset('cfilt/HiNER-collapsed')
```
However, the CoNLL format dataset files can also be found on this Git repository under the [data](data/) folder.
## Model(s)
Our best performing models are hosted on the HuggingFace models repository:
1. [HiNER-Collapsed-XLM-R](https://huggingface.co/cfilt/HiNER-Collapse-XLM-Roberta-Large)
2. [HiNER-Original-XLM-R](https://huggingface.co/cfilt/HiNER-Original-XLM-Roberta-Large)
## Dataset Creation
### Curation Rationale
HiNER was built on data extracted from various government websites handled by the Government of India which provide information in Hindi. This dataset was built for the task of Named Entity Recognition. The dataset was introduced to introduce new resources to the Hindi language that was under-served for Natural Language Processing.
### Source Data
#### Initial Data Collection and Normalization
HiNER was built on data extracted from various government websites handled by the Government of India which provide information in Hindi
#### Who are the source language producers?
Various Government of India webpages
### Annotations
#### Annotation process
This dataset was manually annotated by a single annotator of a long span of time.
#### Who are the annotators?
Pallab Bhattacharjee
### Personal and Sensitive Information
We ensured that there was no sensitive information present in the dataset. All the data points are curated from publicly available information.
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to provide a large Hindi Named Entity Recognition dataset. Since the information (data points) has been obtained from public resources, we do not think there is a negative social impact in releasing this data.
### Discussion of Biases
Any biases contained in the data released by the Indian government are bound to be present in our data.
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
Pallab Bhattacharjee
### Licensing Information
CC-BY-SA 4.0
### Citation Information
```latex
@misc{https://doi.org/10.48550/arxiv.2204.13743,
doi = {10.48550/ARXIV.2204.13743},
url = {https://arxiv.org/abs/2204.13743},
author = {Murthy, Rudra and Bhattacharjee, Pallab and Sharnagat, Rahul and Khatri, Jyotsana and Kanojia, Diptesh and Bhattacharyya, Pushpak},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {HiNER: A Large Hindi Named Entity Recognition Dataset},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
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] |
thepurpleowl/codequeries
|
thepurpleowl
| 2023-06-03T12:50:46Z | 110 | 5 |
[
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:code",
"license:apache-2.0",
"neural modeling of code",
"code question answering",
"code semantic understanding",
"region:us"
] |
[
"question-answering"
] | 2022-08-24T09:27:43Z |
---
annotations_creators:
- expert-generated
language:
- code
language_creators:
- found
multilinguality:
- monolingual
pretty_name: codequeries
size_categories:
- 100K<n<1M
source_datasets:
- original
tags:
- neural modeling of code
- code question answering
- code semantic understanding
task_categories:
- question-answering
task_ids:
- extractive-qa
license:
- apache-2.0
---
# Dataset Card for CodeQueries
## 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)
- [How to use](#how-to-use)
- [Data Splits and Data Fields](#data-splits-and-data-fields)
- [Dataset Creation](#dataset-creation)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [Data](https://huggingface.co/datasets/thepurpleowl/codequeries)
- **Repository:** [Code](https://github.com/thepurpleowl/codequeries-benchmark)
- **Paper:**
### Dataset Summary
CodeQueries is a dataset to evaluate the ability of neural networks to answer semantic queries over code. Given a query and code, a model is expected to identify answer and supporting-fact spans in the code for the query. This is extractive question-answering over code, for questions with a large scope (entire files) and complexity including both single- and multi-hop reasoning.
### Supported Tasks and Leaderboards
Extractive question answering for code, semantic understanding of code.
### Languages
The dataset contains code context from `python` files.
## Dataset Structure
### How to Use
The dataset can be directly used with the huggingface datasets package. You can load and iterate through the dataset for the proposed five settings with the following two lines of code:
```python
import datasets
# in addition to `twostep`, the other supported settings are <ideal/file_ideal/prefix>.
ds = datasets.load_dataset("thepurpleowl/codequeries", "twostep", split=datasets.Split.TEST)
print(next(iter(ds)))
#OUTPUT:
{'query_name': 'Unused import',
'code_file_path': 'rcbops/glance-buildpackage/glance/tests/unit/test_db.py',
'context_block': {'content': '# vim: tabstop=4 shiftwidth=4 softtabstop=4\n\n# Copyright 2010-2011 OpenStack, LLC\ ...',
'metadata': 'root',
'header': "['module', '___EOS___']",
'index': 0},
'answer_spans': [{'span': 'from glance.common import context',
'start_line': 19,
'start_column': 0,
'end_line': 19,
'end_column': 33}
],
'supporting_fact_spans': [],
'example_type': 1,
'single_hop': False,
'subtokenized_input_sequence': ['[CLS]_', 'Un', 'used_', 'import_', '[SEP]_', 'module_', '\\u\\u\\uEOS\\u\\u\\u_', '#', ' ', 'vim', ':', ...],
'label_sequence': [4, 4, 4, 4, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, ...],
'relevance_label': 1
}
```
### Data Splits and Data Fields
Detailed information on the data splits for proposed settings can be found in the paper.
In general, data splits in all the proposed settings have examples with the following fields -
```
- query_name (query name to uniquely identify the query)
- code_file_path (relative source file path w.r.t. ETH Py150 corpus)
- context_blocks (code blocks as context with metadata) [`prefix` setting doesn't have this field and `twostep` has `context_block`]
- answer_spans (answer spans with metadata)
- supporting_fact_spans (supporting-fact spans with metadata)
- example_type (1(positive)) or 0(negative)) example type)
- single_hop (True or False - for query type)
- subtokenized_input_sequence (example subtokens) [`prefix` setting has the corresponding token ids]
- label_sequence (example subtoken labels)
- relevance_label (0 (not relevant) or 1 (relevant) - relevance label of a block) [only `twostep` setting has this field]
```
## Dataset Creation
The dataset is created using [ETH Py150 Open dataset](https://github.com/google-research-datasets/eth_py150_open) as source for code contexts. To get semantic queries and corresponding answer/supporting-fact spans in ETH Py150 Open corpus files, CodeQL was used.
## Additional Information
### Licensing Information
The source code repositories used for preparing CodeQueries are based on the [ETH Py150 Open dataset](https://github.com/google-research-datasets/eth_py150_open) and are redistributable under the respective licenses. A Huggingface dataset for ETH Py150 Open is available [here](https://huggingface.co/datasets/eth_py150_open). The labeling prepared and provided by us as part of CodeQueries is released under the Apache-2.0 license.
|
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zouharvi/pwesuite-eval
|
zouharvi
| 2023-10-11T17:14:09Z | 110 | 0 |
[
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"language:en",
"language:am",
"language:bn",
"language:sw",
"language:uz",
"language:es",
"language:pl",
"language:fr",
"language:de",
"license:apache-2.0",
"words",
"word",
"embedding",
"phonetic",
"phonological",
"cognates",
"rhyme",
"analogy",
"arxiv:2304.02541",
"region:us"
] | null | 2023-02-04T22:04:58Z |
---
language:
- en
- am
- bn
- sw
- uz
- es
- pl
- fr
- de
multilinguality:
- multilingual
tags:
- words
- word
- embedding
- phonetic
- phonological
- cognates
- rhyme
- analogy
pretty_name: PWESuite Evaluation v1
size_categories:
- 100K<n<1M
dataset_info:
features:
- name: token_ort
dtype: string
- name: token_ipa
dtype: string
- name: token_arp
dtype: string
- name: lang
dtype: string
- name: purpose
dtype: string
splits:
- name: train
num_examples: 1738008
license: apache-2.0
---
# PWESuite-Eval
Dataset composed of multiple smaller datasets used for the evaluation of phonetic word embeddings.
See code for evaluation [here](https://github.com/zouharvi/pwesuite).
Used datasets:
- [CMU Pronunciation dictionary](http://www.speech.cs.cmu.edu/cgi-bin/cmudict)
- [CC-100](https://data.statmt.org/cc-100/)
- [CogNet v0](https://aclanthology.org/P19-1302/)
- [Vitz and Winkler (1973)](https://www.sciencedirect.com/science/article/pii/S0022537173800167)
Authors:
- Vilém Zouhar (ETH Zürich, [contact](mailto:[email protected]))
- Kalvin Chang (CMU LTI, [contact](mailto:[email protected]))
- Chenxuan Cui (CMU LTI, [contact](mailto:[email protected]))
- Nathaniel Robinson (CMU LTI, [contact](mailto:[email protected]))
- Nathaniel Carlson (BYU, [contact](mailto:[email protected]))
- David Mortensen (CMU LTI, [contact](mailto:[email protected]))
If you use this dataset/evaluation, please cite:
```
@article{zouhar2023pwesuite,
title={{PWESuite}: {P}honetic Word Embeddings and Tasks They Facilitate},
author={Zouhar, Vil{\'e}m and Chang, Kalvin and Cui, Chenxuan and Carlson, Nathaniel and Robinson, Nathaniel and Sachan, Mrinmaya and Mortensen, David},
journal={arXiv preprint arXiv:2304.02541},
year={2023},
url={https://arxiv.org/abs/2304.02541}
}
```
|
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] |
roszcz/maestro-v1-sustain
|
roszcz
| 2023-04-23T13:35:49Z | 110 | 0 |
[
"region:us"
] | null | 2023-02-28T20:38:48Z |
---
dataset_info:
features:
- name: notes
struct:
- name: duration
sequence: float64
- name: end
sequence: float64
- name: pitch
sequence: int64
- name: start
sequence: float64
- name: velocity
sequence: int64
- name: composer
dtype: string
- name: title
dtype: string
- name: year
dtype: int64
- name: midi_filename
dtype: string
splits:
- name: test
num_bytes: 29686362
num_examples: 177
- name: validation
num_bytes: 25599834
num_examples: 137
- name: train
num_bytes: 226534277
num_examples: 962
download_size: 87287914
dataset_size: 281820473
---
# Dataset Card for "maestro-v1-sustain"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
yangwang825/reuters-21578
|
yangwang825
| 2023-05-19T02:04:58Z | 110 | 0 |
[
"task_categories:text-classification",
"language:en",
"region:us"
] |
[
"text-classification"
] | 2023-05-17T14:25:37Z |
---
task_categories:
- text-classification
language:
- en
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': acq
'1': crude
'2': earn
'3': grain
'4': interest
'5': money-fx
'6': ship
'7': trade
---
`yangwang825/reuters-21578` is an 8-class subset of the Reuters 21578 news dataset.
|
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] |
augtoma/medmcqa
|
augtoma
| 2023-08-11T20:44:27Z | 110 | 1 |
[
"region:us"
] | null | 2023-08-11T20:44:11Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: id
dtype: string
- name: question
dtype: string
- name: cop
dtype:
class_label:
names:
'0': a
'1': b
'2': c
'3': d
- name: choice_type
dtype: string
- name: exp
dtype: string
- name: subject_name
dtype: string
- name: topic_name
dtype: string
- name: options
struct:
- name: A
dtype: string
- name: B
dtype: string
- name: C
dtype: string
- name: D
dtype: string
- name: answer_idx
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 136988451
num_examples: 182822
- name: test
num_bytes: 2350095
num_examples: 4183
download_size: 90978864
dataset_size: 139338546
---
# Dataset Card for "medmcqa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
deven367/babylm-100M-children-stories
|
deven367
| 2023-09-16T05:17:25Z | 110 | 1 |
[
"region:us"
] | null | 2023-09-16T05:17:12Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 17676869
num_examples: 76758
- name: valid
num_bytes: 1425137
num_examples: 5996
- name: test
num_bytes: 1804421
num_examples: 7959
download_size: 12749002
dataset_size: 20906427
---
# Dataset Card for "babylm-100M-children-stories"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
vietgpt/ultrachat
|
vietgpt
| 2023-11-03T10:35:43Z | 110 | 0 |
[
"region:us"
] | null | 2023-11-03T10:28:43Z |
---
dataset_info:
features:
- name: prompt
dtype: string
- name: prompt_id
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train_1024
num_bytes: 1395429967.5845509
num_examples: 270812
- name: train_2048
num_bytes: 2779271371.8960648
num_examples: 539375
- name: train_4096
num_bytes: 3360683349.1806855
num_examples: 652210
download_size: 3454050489
dataset_size: 7535384688.661301
configs:
- config_name: default
data_files:
- split: train_1024
path: data/train_1024-*
- split: train_2048
path: data/train_2048-*
- split: train_4096
path: data/train_4096-*
---
# Dataset Card for "ultrachat"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
monology/VMware-open-instruct-higgsfield
|
monology
| 2023-11-20T19:39:05Z | 110 | 0 |
[
"region:us"
] | null | 2023-11-15T23:28:41Z |
---
dataset_info:
features:
- name: prompt
dtype: string
- name: completion
dtype: string
splits:
- name: train
num_bytes: 91035395
num_examples: 142622
download_size: 42482187
dataset_size: 91035395
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "VMware-open-instruct-higgsfield"
Just a port of the [VMware/open-instruct](https://huggingface.co/datasets/VMware/open-instruct) dataset to [Higgsfield's prompt-completion format](https://github.com/higgsfield-ai/higgsfield/blob/main/tutorials/prompt_completion.ipynb).
|
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laion/laion2B-en-joined
|
laion
| 2022-03-31T07:44:37Z | 109 | 7 |
[
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-29T22:02:50Z |
---
license: cc-by-4.0
---
|
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tner/wnut2017
|
tner
| 2022-08-06T23:30:30Z | 109 | 0 |
[
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"multilinguality:monolingual",
"size_categories:1k<10K",
"language:en",
"license:other",
"region:us"
] |
[
"token-classification"
] | 2022-07-16T11:08:24Z |
---
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 1k<10K
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: WNUT 2017
---
# Dataset Card for "tner/wnut2017"
## Dataset Description
- **Repository:** [T-NER](https://github.com/asahi417/tner)
- **Paper:** [https://aclanthology.org/W17-4418/](https://aclanthology.org/W17-4418/)
- **Dataset:** WNUT 2017
- **Domain:** Twitter, Reddit, YouTube, and StackExchange
- **Number of Entity:** 6
### Dataset Summary
WNUT 2017 NER dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project.
- Entity Types: `creative-work`, `corporation`, `group`, `location`, `person`, `product`
## Dataset Structure
### Data Instances
An example of `train` looks as follows.
```
{
'tokens': ['@paulwalk', 'It', "'s", 'the', 'view', 'from', 'where', 'I', "'m", 'living', 'for', 'two', 'weeks', '.', 'Empire', 'State', 'Building', '=', 'ESB', '.', 'Pretty', 'bad', 'storm', 'here', 'last', 'evening', '.'],
'tags': [12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 3, 9, 9, 12, 3, 12, 12, 12, 12, 12, 12, 12, 12]
}
```
### Label ID
The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/wnut2017/raw/main/dataset/label.json).
```python
{
"B-corporation": 0,
"B-creative-work": 1,
"B-group": 2,
"B-location": 3,
"B-person": 4,
"B-product": 5,
"I-corporation": 6,
"I-creative-work": 7,
"I-group": 8,
"I-location": 9,
"I-person": 10,
"I-product": 11,
"O": 12
}
```
### Data Splits
| name |train|validation|test|
|---------|----:|---------:|---:|
|wnut2017 | 2395| 1009|1287|
### Citation Information
```
@inproceedings{derczynski-etal-2017-results,
title = "Results of the {WNUT}2017 Shared Task on Novel and Emerging Entity Recognition",
author = "Derczynski, Leon and
Nichols, Eric and
van Erp, Marieke and
Limsopatham, Nut",
booktitle = "Proceedings of the 3rd Workshop on Noisy User-generated Text",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-4418",
doi = "10.18653/v1/W17-4418",
pages = "140--147",
abstract = "This shared task focuses on identifying unusual, previously-unseen entities in the context of emerging discussions. Named entities form the basis of many modern approaches to other tasks (like event clustering and summarization), but recall on them is a real problem in noisy text - even among annotators. This drop tends to be due to novel entities and surface forms. Take for example the tweet {``}so.. kktny in 30 mins?!{''} {--} even human experts find the entity {`}kktny{'} hard to detect and resolve. The goal of this task is to provide a definition of emerging and of rare entities, and based on that, also datasets for detecting these entities. The task as described in this paper evaluated the ability of participating entries to detect and classify novel and emerging named entities in noisy text.",
}
```
|
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autoevaluate/autoeval-staging-eval-samsum-samsum-f593d1-14645991
|
autoevaluate
| 2022-08-31T01:18:28Z | 109 | 0 |
[
"autotrain",
"evaluation",
"region:us"
] | null | 2022-08-31T00:52:23Z |
---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- samsum
eval_info:
task: summarization
model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP11
metrics: []
dataset_name: samsum
dataset_config: samsum
dataset_split: test
col_mapping:
text: dialogue
target: summary
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: pszemraj/long-t5-tglobal-large-pubmed-3k-booksum-16384-WIP11
* Dataset: samsum
* Config: samsum
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model.
|
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lmqg/qa_squadshifts_synthetic
|
lmqg
| 2023-01-15T14:25:15Z | 109 | 0 |
[
"task_categories:question-answering",
"task_ids:extractive-qa",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|wikipedia",
"language:en",
"license:cc-by-4.0",
"arxiv:2210.03992",
"region:us"
] |
[
"question-answering"
] | 2022-12-20T08:31:18Z |
---
license: cc-by-4.0
pretty_name: Synthetic QA dataset on SQuADShifts.
language: en
multilinguality: monolingual
size_categories: 10K<n<100K
source_datasets:
- extended|wikipedia
task_categories:
- question-answering
task_ids:
- extractive-qa
---
# Dataset Card for "lmqg/qa_squadshifts_synthetic"
## Dataset Description
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
- **Point of Contact:** [Asahi Ushio](http://asahiushio.com/)
### Dataset Summary
This is a synthetic QA dataset generated with fine-tuned QG models over [`lmqg/qa_squadshifts`](https://huggingface.co/datasets/lmqg/qa_squadshifts), made for question-answering based evaluation (QAE) for question generation model proposed by [Zhang and Bansal, 2019](https://aclanthology.org/D19-1253/).
The test split is the original validation set of [`lmqg/qa_squadshifts`](https://huggingface.co/datasets/lmqg/qa_squadshifts), where the model should be evaluate on.
### Supported Tasks and Leaderboards
* `question-answering`
### Languages
English (en)
## Dataset Structure
### Data Fields
The data fields are the same among all splits.
#### plain_text
- `id`: a `string` feature of id
- `title`: a `string` feature of title of the paragraph
- `context`: a `string` feature of paragraph
- `question`: a `string` feature of question
- `answers`: a `json` feature of answers
### Data Splits
TBA
## Citation Information
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
|
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TigerResearch/tigerbot-zhihu-zh-10k
|
TigerResearch
| 2023-05-31T02:59:43Z | 109 | 14 |
[
"language:zh",
"license:apache-2.0",
"region:us"
] | null | 2023-05-30T15:15:37Z |
---
license: apache-2.0
language:
- zh
---
[Tigerbot](https://github.com/TigerResearch/TigerBot) 基于开源搜集的知乎数据生成的sft问答对
## Usage
```python
import datasets
ds_sft = datasets.load_dataset('TigerResearch/tigerbot-zhihu-zh-10k')
```
|
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pankajmathur/WizardLM_Orca
|
pankajmathur
| 2023-06-26T14:39:38Z | 109 | 64 |
[
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-nc-sa-4.0",
"region:us"
] |
[
"text-generation"
] | 2023-06-24T18:34:28Z |
---
license: cc-by-nc-sa-4.0
task_categories:
- text-generation
language:
- en
size_categories:
- 10K<n<100K
---
Explain tuned WizardLM dataset ~55K created using approaches from Orca Research Paper.
We leverage all of the 15 system instructions provided in Orca Research Paper. to generate custom datasets, in contrast to vanilla instruction tuning approaches used by original datasets.
This helps student models like orca_mini_13b to learn thought process from teacher model, which is ChatGPT (gpt-3.5-turbo-0301 version).
Please see how the System prompt is added before each instruction.
|
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] |
BI55/MedText
|
BI55
| 2023-07-25T09:30:17Z | 109 | 53 |
[
"license:cc-by-4.0",
"region:us"
] | null | 2023-07-25T09:13:09Z |
---
license: cc-by-4.0
---
This is the shuffled version of medtext_1, so the datapoints are in random order and not sorted by category. This is to prevent catastrophic forgetting by category.
This is a medical diagnosis dataset containing over 1000 top notch textbook quality patient presentations and diagnosis/treatments. The 100 most common diseases and the 30 most common injuries people go to the hospital with, are, among others, fully captured in the dataset, with multiple datapoints for each ranging from mild to complicated to severe. Full list below. The dataset also contains completions about the nature of the AI itself, that it never can replace a doctor and always emphasizes to go to a professional and some nonsensical or doubtful presentations. A model trained on this dataset explicitly tells when it CANNOT answer with confidence or if the presentation is insufficient. This is to prevent hallucinations.
Medtext is a free to use (CC BY 4.0) dataset of over 1000 patient presentations and their diagnosis/treatment plans.
This is original data, converted into uniform datapoints using GPT-4.
We then pulled 10 random examples of the dataset and showed them to 3 different doctors, 2 of them involved and 1 of them uninvolved, and they all categorize the quality as „textbook quality“.
It’s content includes:
NOISE/DATA POLLUTION
*Dismissing of non-medical or non-psychological issues
*specifically asking for more information / admitting no possible diagnosis with confidence if insufficient data
*conflicting/contradicting and irrelevant information
*cases where symptoms are misleading to seemingly obvious diagnosis but actually being something different
*information about the model (What are you? What can you do? Are you able to replace a doctor? This is to make the model humble and always emphasize that it can never replace a professional and it is just there to do some substitute analysis)
MISC
*emergency cases / first aid / almost fatal njuries that require emergency surgery
*injuries from crimes
*sexual injuries and STDs
*Infant specific cases
*Gynecological and urological cases
*genetic anomalies
*Previous medical mishandling
*Abuse/Overdosing/Misuse of drugs
*Cross side effects of drugs
ANALYSIS
*Textual analysis of blood tests, ultrasound, CT, MRI and X-ray examinations.
INJURIES:
* Sprains and strains
* Fractures
* Contusions (bruises)
* Cuts and lacerations
* Concussions
* Burns
* Dislocations
* Abrasions (scrapes)
* Whiplash injuries
* Eye injuries
* Puncture wounds
* Bites and stings
* Back injuries
* Broken nose
* Knee injuries
* Ankle injuries
* Shoulder injuries
* Wrist injuries
* Chest injuries
* Head injuries
DISEASES:
* Acne
* Allergies
* Alzheimer's Disease
* Anemia
* Angina
* Anxiety Disorders
* Arthritis
* Asthma
* Atherosclerosis
* Athlete's Foot
* Attention Deficit Hyperactivity Disorder (ADHD)
* Autism Spectrum Disorder
* Back Pain
* Bipolar Disorder
* Bronchitis
* Cataracts
* Chickenpox
* Chronic Obstructive Pulmonary Disease (COPD)
* Common Cold
* Conjunctivitis (Pink Eye)
* Constipation
* Coronary Heart Disease
* Cystitis
* Dementia
* Depression
* Diabetes Type 1
* Diabetes Type 2
* Diarrhea
* Diverticulitis
* Dizziness (Vertigo)
* Ear Infections
* Eczema
* Endometriosis
* Erectile Dysfunction
* Fibromyalgia
* Flu (Influenza)
* Food Poisoning
* Gallstones
* Gastroenteritis
* Gastroesophageal Reflux Disease (GERD)
* Gout
* Hay Fever (Allergic Rhinitis)
* Headaches
* Heart Failure
* Hemorrhoids
* Hepatitis B
* Hepatitis C
* Herpes Simplex Virus (HSV)
* High Blood Pressure (Hypertension)
* High Cholesterol (Hypercholesterolemia)
* HIV/AIDS
* Hyperthyroidism (Overactive Thyroid)
* Hypothyroidism (Underactive Thyroid)
* Inflammatory Bowel Disease (Including Crohn's and Ulcerative Colitis)
* Insomnia
* Iron Deficiency Anemia
* Irritable Bowel Syndrome (IBS)
* Kidney Stones
* Lactose Intolerance
* Lyme Disease
* Macular Degeneration
* Malaria
* Menopause
* Migraine
* Multiple Sclerosis
* Obesity
* Osteoarthritis
* Osteoporosis
* Otitis Media (Middle Ear Infection)
* Pancreatitis
* Parkinson's Disease
* Peptic Ulcers
* Periodontal Disease
* Pneumonia
* Polycystic Ovary Syndrome (PCOS)
* Prostate Enlargement (Benign Prostatic Hyperplasia)
* Psoriasis
* Pulmonary Embolism
* Restless Legs Syndrome
* Rheumatoid Arthritis
* Rosacea
* Schizophrenia
* Sciatica
* Scoliosis
* Seasonal Affective Disorder (SAD)
* Sinusitis
* Skin Cancer
* Sleep Apnea
* Strokes
* Tendonitis
* Tonsillitis
* Tuberculosis
* Urinary Tract Infection (UTI)
* Varicose Veins
* Vitiligo
* Yeast Infection (Candidiasis)
* Zika Virus
|
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jacob-hugging-face/job-descriptions
|
jacob-hugging-face
| 2023-08-18T20:07:48Z | 109 | 7 |
[
"license:llama2",
"region:us"
] | null | 2023-08-18T20:06:24Z |
---
license: llama2
---
|
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open-llm-leaderboard/details_tiiuae__falcon-7b-instruct
|
open-llm-leaderboard
| 2023-12-03T18:01:52Z | 109 | 0 |
[
"region:us"
] | null | 2023-09-12T17:11:45Z |
---
pretty_name: Evaluation run of tiiuae/falcon-7b-instruct
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [tiiuae/falcon-7b-instruct](https://huggingface.co/tiiuae/falcon-7b-instruct)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 6 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_tiiuae__falcon-7b-instruct\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T18:01:45.204079](https://huggingface.co/datasets/open-llm-leaderboard/details_tiiuae__falcon-7b-instruct/blob/main/results_2023-12-03T18-01-45.204079.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\": 0.04700530705079606,\n\
\ \"acc_stderr\": 0.005829898355937184\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.04700530705079606,\n \"acc_stderr\": 0.005829898355937184\n\
\ }\n}\n```"
repo_url: https://huggingface.co/tiiuae/falcon-7b-instruct
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_12T17_11_33.167282
path:
- '**/details_harness|arc:challenge|25_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|arc:challenge|25_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_25T19_58_40.365010
path:
- '**/details_harness|drop|3_2023-10-25T19-58-40.365010.parquet'
- split: 2023_10_26T00_21_45.676219
path:
- '**/details_harness|drop|3_2023-10-26T00-21-45.676219.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-26T00-21-45.676219.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_25T19_58_40.365010
path:
- '**/details_harness|gsm8k|5_2023-10-25T19-58-40.365010.parquet'
- split: 2023_10_26T00_21_45.676219
path:
- '**/details_harness|gsm8k|5_2023-10-26T00-21-45.676219.parquet'
- split: 2023_12_03T18_01_19.868958
path:
- '**/details_harness|gsm8k|5_2023-12-03T18-01-19.868958.parquet'
- split: 2023_12_03T18_01_45.204079
path:
- '**/details_harness|gsm8k|5_2023-12-03T18-01-45.204079.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T18-01-45.204079.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_12T17_11_33.167282
path:
- '**/details_harness|hellaswag|10_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hellaswag|10_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_09_12T17_11_33.167282
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-12T17-11-33.167282.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-12T17-11-33.167282.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-12T17-11-33.167282.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T17-11-33.167282.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T17-11-33.167282.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-12T17-11-33.167282.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T17-11-33.167282.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T17-11-33.167282.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T17-11-33.167282.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T17-11-33.167282.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-12T17-11-33.167282.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-12T17-11-33.167282.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T17-11-33.167282.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-12T17-11-33.167282.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T17-11-33.167282.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T17-11-33.167282.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T17-11-33.167282.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-12T17-11-33.167282.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T17-11-33.167282.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T17-11-33.167282.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-12T17-11-33.167282.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-12T17-11-33.167282.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-12T17-11-33.167282.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-12T17-11-33.167282.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-12T17-11-33.167282.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-12T17-11-33.167282.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-12T17-11-33.167282.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-12T17-11-33.167282.parquet'
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- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-12T17-11-33.167282.parquet'
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- split: 2023_10_03T22_10_35.400219
path:
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- '**/details_harness|hendrycksTest-computer_security|5_2023-10-03T22-10-35.400219.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-03T22-10-35.400219.parquet'
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- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-03T22-10-35.400219.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-03T22-10-35.400219.parquet'
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- split: latest
path:
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- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
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path:
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- split: 2023_10_03T22_10_35.400219
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
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path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
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- config_name: harness_hendrycksTest_astronomy_5
data_files:
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path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
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path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_09_12T17_11_33.167282
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_09_12T17_11_33.167282
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_09_12T17_11_33.167282
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_09_12T17_11_33.167282
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_09_12T17_11_33.167282
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_09_12T17_11_33.167282
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_09_12T17_11_33.167282
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_09_12T17_11_33.167282
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_09_12T17_11_33.167282
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_09_12T17_11_33.167282
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_09_12T17_11_33.167282
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_09_12T17_11_33.167282
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_09_12T17_11_33.167282
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_09_12T17_11_33.167282
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_09_12T17_11_33.167282
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_09_12T17_11_33.167282
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-03T22-10-35.400219.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_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
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path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
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path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_09_12T17_11_33.167282
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
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path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
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path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
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path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
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path:
- '**/details_harness|hendrycksTest-management|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
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path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_12T17_11_33.167282
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
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path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
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path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
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path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_12T17_11_33.167282
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_12T17_11_33.167282
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_12T17_11_33.167282
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_12T17_11_33.167282
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-12T17-11-33.167282.parquet'
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path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_12T17_11_33.167282
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_12T17_11_33.167282
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_12T17_11_33.167282
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_12T17_11_33.167282
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_12T17_11_33.167282
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_12T17_11_33.167282
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_12T17_11_33.167282
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_12T17_11_33.167282
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-12T17-11-33.167282.parquet'
- split: 2023_10_03T22_10_35.400219
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_12T17_11_33.167282
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-12T17-11-33.167282.parquet'
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path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
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path:
- '**/details_harness|truthfulqa:mc|0_2023-09-12T17-11-33.167282.parquet'
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path:
- '**/details_harness|truthfulqa:mc|0_2023-10-03T22-10-35.400219.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-03T22-10-35.400219.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_25T19_58_40.365010
path:
- '**/details_harness|winogrande|5_2023-10-25T19-58-40.365010.parquet'
- split: 2023_10_26T00_21_45.676219
path:
- '**/details_harness|winogrande|5_2023-10-26T00-21-45.676219.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-26T00-21-45.676219.parquet'
- config_name: results
data_files:
- split: 2023_09_12T17_11_33.167282
path:
- results_2023-09-12T17-11-33.167282.parquet
- split: 2023_10_03T22_10_35.400219
path:
- results_2023-10-03T22-10-35.400219.parquet
- split: 2023_10_25T19_58_40.365010
path:
- results_2023-10-25T19-58-40.365010.parquet
- split: 2023_10_26T00_21_45.676219
path:
- results_2023-10-26T00-21-45.676219.parquet
- split: 2023_12_03T18_01_19.868958
path:
- results_2023-12-03T18-01-19.868958.parquet
- split: 2023_12_03T18_01_45.204079
path:
- results_2023-12-03T18-01-45.204079.parquet
- split: latest
path:
- results_2023-12-03T18-01-45.204079.parquet
---
# Dataset Card for Evaluation run of tiiuae/falcon-7b-instruct
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/tiiuae/falcon-7b-instruct
- **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 [tiiuae/falcon-7b-instruct](https://huggingface.co/tiiuae/falcon-7b-instruct) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 6 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_tiiuae__falcon-7b-instruct",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T18:01:45.204079](https://huggingface.co/datasets/open-llm-leaderboard/details_tiiuae__falcon-7b-instruct/blob/main/results_2023-12-03T18-01-45.204079.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": 0.04700530705079606,
"acc_stderr": 0.005829898355937184
},
"harness|gsm8k|5": {
"acc": 0.04700530705079606,
"acc_stderr": 0.005829898355937184
}
}
```
### 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_Secbone__llama-33B-instructed
|
open-llm-leaderboard
| 2023-10-25T13:35:40Z | 109 | 0 |
[
"region:us"
] | null | 2023-09-21T22:24:09Z |
---
pretty_name: Evaluation run of Secbone/llama-33B-instructed
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Secbone/llama-33B-instructed](https://huggingface.co/Secbone/llama-33B-instructed)\
\ 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_Secbone__llama-33B-instructed\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-25T13:35:27.526768](https://huggingface.co/datasets/open-llm-leaderboard/details_Secbone__llama-33B-instructed/blob/main/results_2023-10-25T13-35-27.526768.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\": 0.1983011744966443,\n\
\ \"em_stderr\": 0.004083268310230377,\n \"f1\": 0.2580683724832209,\n\
\ \"f1_stderr\": 0.0040518641243222474,\n \"acc\": 0.46863041707830366,\n\
\ \"acc_stderr\": 0.010527338901150537\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.1983011744966443,\n \"em_stderr\": 0.004083268310230377,\n\
\ \"f1\": 0.2580683724832209,\n \"f1_stderr\": 0.0040518641243222474\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.14404852160727824,\n \
\ \"acc_stderr\": 0.00967211097306528\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7932123125493291,\n \"acc_stderr\": 0.011382566829235791\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Secbone/llama-33B-instructed
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_21T22_23_50.443527
path:
- '**/details_harness|arc:challenge|25_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_25T13_35_27.526768
path:
- '**/details_harness|drop|3_2023-10-25T13-35-27.526768.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-25T13-35-27.526768.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_25T13_35_27.526768
path:
- '**/details_harness|gsm8k|5_2023-10-25T13-35-27.526768.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-25T13-35-27.526768.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_21T22_23_50.443527
path:
- '**/details_harness|hellaswag|10_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_09_21T22_23_50.443527
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-21T22-23-50.443527.parquet'
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- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-21T22-23-50.443527.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_09_21T22_23_50.443527
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_09_21T22_23_50.443527
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_09_21T22_23_50.443527
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_09_21T22_23_50.443527
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
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path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_09_21T22_23_50.443527
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_09_21T22_23_50.443527
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_09_21T22_23_50.443527
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_09_21T22_23_50.443527
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_09_21T22_23_50.443527
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_09_21T22_23_50.443527
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_09_21T22_23_50.443527
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_09_21T22_23_50.443527
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
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path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
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path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-21T22-23-50.443527.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_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
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- 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_2023-09-21T22-23-50.443527.parquet'
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path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
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- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_09_21T22_23_50.443527
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_09_21T22_23_50.443527
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_09_21T22_23_50.443527
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_09_21T22_23_50.443527
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_09_21T22_23_50.443527
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
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path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_09_21T22_23_50.443527
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-21T22-23-50.443527.parquet'
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path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_09_21T22_23_50.443527
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
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path:
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path:
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- config_name: harness_hendrycksTest_management_5
data_files:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_09_21T22_23_50.443527
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_21T22_23_50.443527
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_21T22_23_50.443527
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_21T22_23_50.443527
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_21T22_23_50.443527
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_21T22_23_50.443527
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_21T22_23_50.443527
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_21T22_23_50.443527
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_21T22_23_50.443527
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_21T22_23_50.443527
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_21T22_23_50.443527
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_21T22_23_50.443527
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_21T22_23_50.443527
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_21T22_23_50.443527
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_21T22_23_50.443527
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_21T22_23_50.443527
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_21T22_23_50.443527
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_21T22_23_50.443527
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
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path:
- '**/details_harness|truthfulqa:mc|0_2023-09-21T22-23-50.443527.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-21T22-23-50.443527.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_25T13_35_27.526768
path:
- '**/details_harness|winogrande|5_2023-10-25T13-35-27.526768.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-25T13-35-27.526768.parquet'
- config_name: results
data_files:
- split: 2023_09_21T22_23_50.443527
path:
- results_2023-09-21T22-23-50.443527.parquet
- split: 2023_10_25T13_35_27.526768
path:
- results_2023-10-25T13-35-27.526768.parquet
- split: latest
path:
- results_2023-10-25T13-35-27.526768.parquet
---
# Dataset Card for Evaluation run of Secbone/llama-33B-instructed
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Secbone/llama-33B-instructed
- **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 [Secbone/llama-33B-instructed](https://huggingface.co/Secbone/llama-33B-instructed) 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_Secbone__llama-33B-instructed",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-25T13:35:27.526768](https://huggingface.co/datasets/open-llm-leaderboard/details_Secbone__llama-33B-instructed/blob/main/results_2023-10-25T13-35-27.526768.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": 0.1983011744966443,
"em_stderr": 0.004083268310230377,
"f1": 0.2580683724832209,
"f1_stderr": 0.0040518641243222474,
"acc": 0.46863041707830366,
"acc_stderr": 0.010527338901150537
},
"harness|drop|3": {
"em": 0.1983011744966443,
"em_stderr": 0.004083268310230377,
"f1": 0.2580683724832209,
"f1_stderr": 0.0040518641243222474
},
"harness|gsm8k|5": {
"acc": 0.14404852160727824,
"acc_stderr": 0.00967211097306528
},
"harness|winogrande|5": {
"acc": 0.7932123125493291,
"acc_stderr": 0.011382566829235791
}
}
```
### 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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lmqg/qg_ruquad
|
lmqg
| 2022-12-02T18:55:01Z | 108 | 2 |
[
"task_categories:text-generation",
"task_ids:language-modeling",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:deepset/germanquad",
"language:ru",
"license:cc-by-4.0",
"question-generation",
"arxiv:2210.03992",
"region:us"
] |
[
"text-generation"
] | 2022-06-02T23:44:54Z |
---
license: cc-by-4.0
pretty_name: SberQuAD for question generation
language: ru
multilinguality: monolingual
size_categories: 10K<n<100K
source_datasets: deepset/germanquad
task_categories:
- text-generation
task_ids:
- language-modeling
tags:
- question-generation
---
# Dataset Card for "lmqg/qg_ruquad"
## Dataset Description
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
- **Point of Contact:** [Asahi Ushio](http://asahiushio.com/)
### Dataset Summary
This is a subset of [QG-Bench](https://github.com/asahi417/lm-question-generation/blob/master/QG_BENCH.md#datasets), a unified question generation benchmark proposed in
["Generative Language Models for Paragraph-Level Question Generation: A Unified Benchmark and Evaluation, EMNLP 2022 main conference"](https://arxiv.org/abs/2210.03992).
This is a modified version of [SberQuaD](https://huggingface.co/datasets/sberquad) for question generation (QG) task.
Since the original dataset only contains training/validation set, we manually sample test set from training set, which
has no overlap in terms of the paragraph with the training set.
### Supported Tasks and Leaderboards
* `question-generation`: The dataset is assumed to be used to train a model for question generation.
Success on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail).
### Languages
Russian (ru)
## Dataset Structure
An example of 'train' looks as follows.
```
{
'answer': 'известковыми выделениями сине-зелёных водорослей',
'question': 'чем представлены органические остатки?',
'sentence': 'Они представлены известковыми выделениями сине-зелёных водорослей , ходами червей, остатками кишечнополостных.'
'paragraph': "В протерозойских отложениях органические остатки встречаются намного чаще, чем в архейских. Они представлены..."
'sentence_answer': "Они представлены <hl> известковыми выделениями сине-зелёных водорослей <hl> , ход...",
'paragraph_answer': "В протерозойских отложениях органические остатки встречаются намного чаще, чем в архейских. Они представлены <hl> известковыми выделениям...",
'paragraph_sentence': "В протерозойских отложениях органические остатки встречаются намного чаще, чем в архейских. <hl> Они представлены известковыми выделениями сине-зелёных водорослей , ходами червей, остатками кишечнополостных. <hl> Кроме..."
}
```
The data fields are the same among all splits.
- `question`: a `string` feature.
- `paragraph`: a `string` feature.
- `answer`: a `string` feature.
- `sentence`: a `string` feature.
- `paragraph_answer`: a `string` feature, which is same as the paragraph but the answer is highlighted by a special token `<hl>`.
- `paragraph_sentence`: a `string` feature, which is same as the paragraph but a sentence containing the answer is highlighted by a special token `<hl>`.
- `sentence_answer`: a `string` feature, which is same as the sentence but the answer is highlighted by a special token `<hl>`.
Each of `paragraph_answer`, `paragraph_sentence`, and `sentence_answer` feature is assumed to be used to train a question generation model,
but with different information. The `paragraph_answer` and `sentence_answer` features are for answer-aware question generation and
`paragraph_sentence` feature is for sentence-aware question generation.
## Data Splits
|train|validation|test |
|----:|---------:|----:|
| 45327 | 5036 |23936 |
## Citation Information
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
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google/cvss
|
google
| 2022-08-27T23:19:14Z | 108 | 8 |
[
"license:cc-by-4.0",
"arxiv:2201.03713",
"region:us"
] | null | 2022-08-11T00:54:54Z |
---
license: cc-by-4.0
---
# CVSS: A Massively Multilingual Speech-to-Speech Translation Corpus
*CVSS* is a massively multilingual-to-English speech-to-speech translation corpus, covering sentence-level parallel speech-to-speech translation pairs from 21 languages into English. CVSS is derived from the [Common Voice](https://commonvoice.mozilla.org/) speech corpus and the [CoVoST 2](https://github.com/facebookresearch/covost) speech-to-text translation corpus. The translation speech in CVSS is synthesized with two state-of-the-art TTS models trained on the [LibriTTS](http://www.openslr.org/60/) corpus.
CVSS includes two versions of spoken translation for all the 21 x-en language pairs from CoVoST 2, with each version providing unique values:
- *CVSS-C*: All the translation speeches are in a single canonical speaker's voice. Despite being synthetic, these speeches are of very high naturalness and cleanness, as well as having a consistent speaking style. These properties ease the modeling of the target speech and enable models to produce high quality translation speech suitable for user-facing applications.
- *CVSS-T*: The translation speeches are in voices transferred from the corresponding source speeches. Each translation pair has similar voices on the two sides despite being in different languages, making this dataset suitable for building models that preserve speakers' voices when translating speech into different languages.
Together with the source speeches originated from Common Voice, they make two multilingual speech-to-speech translation datasets each with about 1,900 hours of speech.
In addition to translation speech, CVSS also provides normalized translation text matching the pronunciation in the translation speech (e.g. on numbers, currencies, acronyms, etc.), which can be used for both model training as well as standardizing evaluation.
Please check out [our paper](https://arxiv.org/abs/2201.03713) for the detailed description of this corpus, as well as the baseline models we trained on both datasets.
# Load the data
The following example loads the translation speech (i.e. target speech) and the normalized translation text (i.e. target text) released in CVSS corpus. You'll need to load the source speech and optionally the source text from [Common Voice v4.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_4_0) separately, and join them by the file names.
```py
from datasets import load_dataset
# Load only ar-en and ja-en language pairs. Omitting the `languages` argument
# would load all the language pairs.
cvss_c = load_dataset('google/cvss', 'cvss_c', languages=['ar', 'ja'])
# Print the structure of the dataset.
print(cvss_c)
```
# License
CVSS is released under the very permissive [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/) license.
## Citation
Please cite this paper when referencing the CVSS corpus:
```
@inproceedings{jia2022cvss,
title={{CVSS} Corpus and Massively Multilingual Speech-to-Speech Translation},
author={Jia, Ye and Tadmor Ramanovich, Michelle and Wang, Quan and Zen, Heiga},
booktitle={Proceedings of Language Resources and Evaluation Conference (LREC)},
pages={6691--6703},
year={2022}
}
```
|
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Linkseed/hacker_news_with_comments
|
Linkseed
| 2023-01-06T05:44:10Z | 108 | 3 |
[
"task_categories:text-generation",
"task_ids:language-modeling",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"language:en",
"license:afl-3.0",
"CommentGenerate",
"region:us"
] |
[
"text-generation"
] | 2023-01-04T06:19:34Z |
---
annotations_creators: []
language:
- en
language_creators:
- found
license:
- afl-3.0
multilinguality:
- monolingual
pretty_name: 'hacker_news_with_comments '
size_categories:
- 1M<n<10M
source_datasets: []
tags:
- CommentGenerate
task_categories:
- text-generation
task_ids:
- language-modeling
---
# 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)
- [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:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Hacker news until 2015 with comments. Collect from Google BigQuery open dataset. We didn't do any pre-processing except remove HTML tags.
### Supported Tasks and Leaderboards
Comment Generation; News analysis with comments; Other comment-based NLP tasks.
### Languages
English
### 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 [@github-username](https://github.com/<github-username>) for adding this dataset.
|
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] |
Multimodal-Fatima/StanfordCars_test
|
Multimodal-Fatima
| 2023-06-12T02:33:45Z | 108 | 0 |
[
"region:us"
] | null | 2023-01-28T02:30:24Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': am general hummer suv 2000
'1': acura rl sedan 2012
'2': acura tl sedan 2012
'3': acura tl type-s 2008
'4': acura tsx sedan 2012
'5': acura integra type r 2001
'6': acura zdx hatchback 2012
'7': aston martin v8 vantage convertible 2012
'8': aston martin v8 vantage coupe 2012
'9': aston martin virage convertible 2012
'10': aston martin virage coupe 2012
'11': audi rs 4 convertible 2008
'12': audi a5 coupe 2012
'13': audi tts coupe 2012
'14': audi r8 coupe 2012
'15': audi v8 sedan 1994
'16': audi 100 sedan 1994
'17': audi 100 wagon 1994
'18': audi tt hatchback 2011
'19': audi s6 sedan 2011
'20': audi s5 convertible 2012
'21': audi s5 coupe 2012
'22': audi s4 sedan 2012
'23': audi s4 sedan 2007
'24': audi tt rs coupe 2012
'25': bmw activehybrid 5 sedan 2012
'26': bmw 1 series convertible 2012
'27': bmw 1 series coupe 2012
'28': bmw 3 series sedan 2012
'29': bmw 3 series wagon 2012
'30': bmw 6 series convertible 2007
'31': bmw x5 suv 2007
'32': bmw x6 suv 2012
'33': bmw m3 coupe 2012
'34': bmw m5 sedan 2010
'35': bmw m6 convertible 2010
'36': bmw x3 suv 2012
'37': bmw z4 convertible 2012
'38': bentley continental supersports conv. convertible 2012
'39': bentley arnage sedan 2009
'40': bentley mulsanne sedan 2011
'41': bentley continental gt coupe 2012
'42': bentley continental gt coupe 2007
'43': bentley continental flying spur sedan 2007
'44': bugatti veyron 16.4 convertible 2009
'45': bugatti veyron 16.4 coupe 2009
'46': buick regal gs 2012
'47': buick rainier suv 2007
'48': buick verano sedan 2012
'49': buick enclave suv 2012
'50': cadillac cts-v sedan 2012
'51': cadillac srx suv 2012
'52': cadillac escalade ext crew cab 2007
'53': chevrolet silverado 1500 hybrid crew cab 2012
'54': chevrolet corvette convertible 2012
'55': chevrolet corvette zr1 2012
'56': chevrolet corvette ron fellows edition z06 2007
'57': chevrolet traverse suv 2012
'58': chevrolet camaro convertible 2012
'59': chevrolet hhr ss 2010
'60': chevrolet impala sedan 2007
'61': chevrolet tahoe hybrid suv 2012
'62': chevrolet sonic sedan 2012
'63': chevrolet express cargo van 2007
'64': chevrolet avalanche crew cab 2012
'65': chevrolet cobalt ss 2010
'66': chevrolet malibu hybrid sedan 2010
'67': chevrolet trailblazer ss 2009
'68': chevrolet silverado 2500hd regular cab 2012
'69': chevrolet silverado 1500 classic extended cab 2007
'70': chevrolet express van 2007
'71': chevrolet monte carlo coupe 2007
'72': chevrolet malibu sedan 2007
'73': chevrolet silverado 1500 extended cab 2012
'74': chevrolet silverado 1500 regular cab 2012
'75': chrysler aspen suv 2009
'76': chrysler sebring convertible 2010
'77': chrysler town and country minivan 2012
'78': chrysler 300 srt-8 2010
'79': chrysler crossfire convertible 2008
'80': chrysler pt cruiser convertible 2008
'81': daewoo nubira wagon 2002
'82': dodge caliber wagon 2012
'83': dodge caliber wagon 2007
'84': dodge caravan minivan 1997
'85': dodge ram pickup 3500 crew cab 2010
'86': dodge ram pickup 3500 quad cab 2009
'87': dodge sprinter cargo van 2009
'88': dodge journey suv 2012
'89': dodge dakota crew cab 2010
'90': dodge dakota club cab 2007
'91': dodge magnum wagon 2008
'92': dodge challenger srt8 2011
'93': dodge durango suv 2012
'94': dodge durango suv 2007
'95': dodge charger sedan 2012
'96': dodge charger srt-8 2009
'97': eagle talon hatchback 1998
'98': fiat 500 abarth 2012
'99': fiat 500 convertible 2012
'100': ferrari ff coupe 2012
'101': ferrari california convertible 2012
'102': ferrari 458 italia convertible 2012
'103': ferrari 458 italia coupe 2012
'104': fisker karma sedan 2012
'105': ford f-450 super duty crew cab 2012
'106': ford mustang convertible 2007
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'108': ford expedition el suv 2009
'109': ford edge suv 2012
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'111': ford gt coupe 2006
'112': ford f-150 regular cab 2012
'113': ford f-150 regular cab 2007
'114': ford focus sedan 2007
'115': ford e-series wagon van 2012
'116': ford fiesta sedan 2012
'117': gmc terrain suv 2012
'118': gmc savana van 2012
'119': gmc yukon hybrid suv 2012
'120': gmc acadia suv 2012
'121': gmc canyon extended cab 2012
'122': geo metro convertible 1993
'123': hummer h3t crew cab 2010
'124': hummer h2 sut crew cab 2009
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'142': isuzu ascender suv 2008
'143': jaguar xk xkr 2012
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'145': jeep wrangler suv 2012
'146': jeep liberty suv 2012
'147': jeep grand cherokee suv 2012
'148': jeep compass suv 2012
'149': lamborghini reventon coupe 2008
'150': lamborghini aventador coupe 2012
'151': lamborghini gallardo lp 570-4 superleggera 2012
'152': lamborghini diablo coupe 2001
'153': land rover range rover suv 2012
'154': land rover lr2 suv 2012
'155': lincoln town car sedan 2011
'156': mini cooper roadster convertible 2012
'157': maybach landaulet convertible 2012
'158': mazda tribute suv 2011
'159': mclaren mp4-12c coupe 2012
'160': mercedes-benz 300-class convertible 1993
'161': mercedes-benz c-class sedan 2012
'162': mercedes-benz sl-class coupe 2009
'163': mercedes-benz e-class sedan 2012
'164': mercedes-benz s-class sedan 2012
'165': mercedes-benz sprinter van 2012
'166': mitsubishi lancer sedan 2012
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'169': nissan juke hatchback 2012
'170': nissan 240sx coupe 1998
'171': plymouth neon coupe 1999
'172': porsche panamera sedan 2012
'173': ram c/v cargo van minivan 2012
'174': rolls-royce phantom drophead coupe convertible 2012
'175': rolls-royce ghost sedan 2012
'176': rolls-royce phantom sedan 2012
'177': scion xd hatchback 2012
'178': spyker c8 convertible 2009
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'180': suzuki aerio sedan 2007
'181': suzuki kizashi sedan 2012
'182': suzuki sx4 hatchback 2012
'183': suzuki sx4 sedan 2012
'184': tesla model s sedan 2012
'185': toyota sequoia suv 2012
'186': toyota camry sedan 2012
'187': toyota corolla sedan 2012
'188': toyota 4runner suv 2012
'189': volkswagen golf hatchback 2012
'190': volkswagen golf hatchback 1991
'191': volkswagen beetle hatchback 2012
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- name: id
dtype: int64
- name: clip_tags_ViT_L_14
sequence: string
- name: LLM_Description_opt175b_downstream_tasks_ViT_L_14
sequence: string
- name: LLM_Description_gpt3_downstream_tasks_ViT_L_14
sequence: string
- name: LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14
sequence: string
- name: blip_caption_beam_5
dtype: string
- name: Attributes_ViT_L_14_text_davinci_003_full
sequence: string
- name: Attributes_ViT_L_14_text_davinci_003_stanfordcars
sequence: string
- name: clip_tags_ViT_L_14_with_openai_classes
sequence: string
- name: clip_tags_ViT_L_14_wo_openai_classes
sequence: string
- name: clip_tags_ViT_L_14_simple_specific
dtype: string
- name: clip_tags_ViT_L_14_ensemble_specific
dtype: string
- name: clip_tags_ViT_B_16_simple_specific
dtype: string
- name: clip_tags_ViT_B_16_ensemble_specific
dtype: string
- name: clip_tags_ViT_B_32_simple_specific
dtype: string
- name: clip_tags_ViT_B_32_ensemble_specific
dtype: string
- name: Attributes_ViT_B_16_descriptors_text_davinci_003_full
sequence: string
- name: Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full
sequence: string
- name: clip_tags_LAION_ViT_H_14_2B_simple_specific
dtype: string
- name: clip_tags_LAION_ViT_H_14_2B_ensemble_specific
dtype: string
- name: Attributes_ViT_L_14_descriptors_text_davinci_003_full
sequence: string
splits:
- name: test
num_bytes: 1016320238.0
num_examples: 8041
download_size: 989991348
dataset_size: 1016320238.0
---
# Dataset Card for "StanfordCars_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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wanng/midjourney-v5-202304-clean
|
wanng
| 2023-05-28T05:56:11Z | 108 | 38 |
[
"task_categories:text-to-image",
"task_categories:image-to-text",
"language:en",
"license:apache-2.0",
"midjourney",
"region:us"
] |
[
"text-to-image",
"image-to-text"
] | 2023-05-26T06:58:05Z |
---
license: apache-2.0
task_categories:
- text-to-image
- image-to-text
language:
- en
tags:
- midjourney
---
# midjourney-v5-202304-clean
## 简介 Brief Introduction
非官方的，爬取自midjourney v5的2023年4月的数据，一共1701420条。
Unofficial, crawled from midjourney v5 for April 2023, 1,701,420 pairs in total.
## 数据集信息 Dataset Information
原始项目地址：https://huggingface.co/datasets/tarungupta83/MidJourney_v5_Prompt_dataset
我做了一些清洗，清理出了两个文件：
- ori_prompts_df.parquet （1,255,812对，midjourney的四格图）
- upscaled_prompts_df.parquet （445,608对，使用了高清指令的图，这意味着这个图更受欢迎。）
Original project address: https://huggingface.co/datasets/tarungupta83/MidJourney_v5_Prompt_dataset
I did some cleaning and cleaned out two files:
- ori_prompts_df.parquet (1,255,812 pairs, midjourney's four-frame diagrams)
- upscaled_prompts_df.parquet (445,608 pairs, graphs that use the Upscale command, which means this one is more popular.)
|
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GATE-engine/fungi
|
GATE-engine
| 2023-06-05T16:36:25Z | 108 | 1 |
[
"region:us"
] | null | 2023-06-05T00:42:00Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: int64
splits:
- name: train
num_bytes: 6188400790.875
num_examples: 64449
- name: validation
num_bytes: 1173258274.625
num_examples: 12195
- name: test
num_bytes: 1260333216.5
num_examples: 13116
download_size: 835444680
dataset_size: 8621992282.0
---
# Dataset Card for "fungi"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
glaiveai/glaive-function-calling
|
glaiveai
| 2023-09-27T18:04:36Z | 108 | 35 |
[
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"region:us"
] |
[
"text-generation"
] | 2023-08-07T17:51:48Z |
---
license: apache-2.0
task_categories:
- text-generation
language:
- en
size_categories:
- 10K<n<100K
---
This dataset consists of 52k samples generated through [Glaive](https://glaive.ai) for the task of function calling, in the following format-
```
SYSTEM: You are an helpful assistant who has access to the following functions to help the user, you can use the functions if needed-
{
JSON function definiton
}
USER: user message
ASSISTANT: assistant message
Function call invocations are formatted as-
ASSISTANT: <functioncall> {json function call}
Response to the function call is formatted as-
FUNCTION RESPONSE: {json function response}
```
There are also samples which do not have any function invocations, multiple invocations and samples with no functions presented and invoked to keep the data balanced.
|
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shariqfarooq/cs323_densepred_depth
|
shariqfarooq
| 2023-09-16T00:02:26Z | 108 | 0 |
[
"region:us"
] | null | 2023-09-16T00:00:58Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: image
dtype: image
- name: depth
dtype: image
splits:
- name: train
num_bytes: 651397023.7943412
num_examples: 25356
- name: test
num_bytes: 13440344.421658808
num_examples: 518
download_size: 343390111
dataset_size: 664837368.216
---
# Dataset Card for "cs323_densepred_depth"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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open-llm-leaderboard/details_marcchew__Marcoroni-7B-LaMini-40K
|
open-llm-leaderboard
| 2023-12-03T19:35:05Z | 108 | 0 |
[
"region:us"
] | null | 2023-09-18T14:23:12Z |
---
pretty_name: Evaluation run of marcchew/Marcoroni-7B-LaMini-40K
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [marcchew/Marcoroni-7B-LaMini-40K](https://huggingface.co/marcchew/Marcoroni-7B-LaMini-40K)\
\ 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 3 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_marcchew__Marcoroni-7B-LaMini-40K\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-03T19:34:57.307967](https://huggingface.co/datasets/open-llm-leaderboard/details_marcchew__Marcoroni-7B-LaMini-40K/blob/main/results_2023-12-03T19-34-57.307967.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\": 0.0,\n \"\
acc_stderr\": 0.0\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \
\ \"acc_stderr\": 0.0\n }\n}\n```"
repo_url: https://huggingface.co/marcchew/Marcoroni-7B-LaMini-40K
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_18T14_22_48.761056
path:
- '**/details_harness|arc:challenge|25_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_25T16_12_33.522830
path:
- '**/details_harness|drop|3_2023-10-25T16-12-33.522830.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-25T16-12-33.522830.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_25T16_12_33.522830
path:
- '**/details_harness|gsm8k|5_2023-10-25T16-12-33.522830.parquet'
- split: 2023_12_03T19_34_57.307967
path:
- '**/details_harness|gsm8k|5_2023-12-03T19-34-57.307967.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-03T19-34-57.307967.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hellaswag|10_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-18T14-22-48.761056.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-18T14-22-48.761056.parquet'
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- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-18T14-22-48.761056.parquet'
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- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-18T14-22-48.761056.parquet'
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- '**/details_harness|hendrycksTest-econometrics|5_2023-09-18T14-22-48.761056.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-18T14-22-48.761056.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-18T14-22-48.761056.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-18T14-22-48.761056.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-18T14-22-48.761056.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-18T14-22-48.761056.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-18T14-22-48.761056.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-18T14-22-48.761056.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-18T14-22-48.761056.parquet'
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- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-18T14-22-48.761056.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-18T14-22-48.761056.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-18T14-22-48.761056.parquet'
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- '**/details_harness|hendrycksTest-nutrition|5_2023-09-18T14-22-48.761056.parquet'
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- '**/details_harness|hendrycksTest-security_studies|5_2023-09-18T14-22-48.761056.parquet'
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- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-18T14-22-48.761056.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-18T14-22-48.761056.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
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- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-18T14-22-48.761056.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-18T14-22-48.761056.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-18T14-22-48.761056.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-18T14-22-48.761056.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-18T14-22-48.761056.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-18T14-22-48.761056.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-18T14-22-48.761056.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-18T14-22-48.761056.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-18T14-22-48.761056.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-18T14-22-48.761056.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-18T14-22-48.761056.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-18T14-22-48.761056.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-18T14-22-48.761056.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-18T14-22-48.761056.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-18T14-22-48.761056.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-18T14-22-48.761056.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-18T14-22-48.761056.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-18T14-22-48.761056.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-18T14-22-48.761056.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-18T14-22-48.761056.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-18T14-22-48.761056.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-18T14-22-48.761056.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_25T16_12_33.522830
path:
- '**/details_harness|winogrande|5_2023-10-25T16-12-33.522830.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-25T16-12-33.522830.parquet'
- config_name: results
data_files:
- split: 2023_09_18T14_22_48.761056
path:
- results_2023-09-18T14-22-48.761056.parquet
- split: 2023_10_25T16_12_33.522830
path:
- results_2023-10-25T16-12-33.522830.parquet
- split: 2023_12_03T19_34_57.307967
path:
- results_2023-12-03T19-34-57.307967.parquet
- split: latest
path:
- results_2023-12-03T19-34-57.307967.parquet
---
# Dataset Card for Evaluation run of marcchew/Marcoroni-7B-LaMini-40K
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/marcchew/Marcoroni-7B-LaMini-40K
- **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 [marcchew/Marcoroni-7B-LaMini-40K](https://huggingface.co/marcchew/Marcoroni-7B-LaMini-40K) 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 3 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_marcchew__Marcoroni-7B-LaMini-40K",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-03T19:34:57.307967](https://huggingface.co/datasets/open-llm-leaderboard/details_marcchew__Marcoroni-7B-LaMini-40K/blob/main/results_2023-12-03T19-34-57.307967.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": 0.0,
"acc_stderr": 0.0
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
}
}
```
### 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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ura-hcmut/synthetic_reasoning
|
ura-hcmut
| 2023-09-19T02:37:10Z | 108 | 0 |
[
"task_categories:text2text-generation",
"language:vi",
"license:cc-by-nc-sa-4.0",
"region:us"
] |
[
"text2text-generation"
] | 2023-09-19T02:01:51Z |
---
license: cc-by-nc-sa-4.0
task_categories:
- text2text-generation
language:
- vi
configs:
- config_name: induction_gcp
data_files:
- split: train
path: synthetic_reasoning_gcp_induction_training.csv
- split: test
path: synthetic_reasoning_gcp_induction.csv
- config_name: induction_azr
data_files:
- split: train
path: synthetic_reasoning_azr_induction_training.csv
- split: test
path: synthetic_reasoning_azr_induction.csv
- config_name: pattern_match_gcp
data_files:
- split: train
path: synthetic_reasoning_gcp_pattern_match_training.csv
- split: test
path: synthetic_reasoning_gcp_pattern_match.csv
- config_name: pattern_match_azr
data_files:
- split: train
path: synthetic_reasoning_azr_pattern_match_training.csv
- split: test
path: synthetic_reasoning_azr_pattern_match.csv
- config_name: variable_substitution_gcp
data_files:
- split: train
path: synthetic_reasoning_gcp_variable_substitution_training.csv
- split: test
path: synthetic_reasoning_gcp_variable_substitution.csv
- config_name: variable_substitution_azr
data_files:
- split: train
path: synthetic_reasoning_azr_variable_substitution_training.csv
- split: test
path: synthetic_reasoning_azr_variable_substitution.csv
---
# Synthetic reasoning dataset
Original version:
- https://huggingface.co/datasets/lighteval/synthetic_reasoning
Translation source code: https://github.com/martinakaduc/ura-llama/tree/main/dataset_scripts/custom_datasets
|
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] |
legacy107/spamming-email-classification
|
legacy107
| 2023-10-02T09:39:55Z | 108 | 0 |
[
"region:us"
] | null | 2023-09-25T14:22:14Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
- split: test
path: data/test-*
dataset_info:
features:
- name: Text
dtype: string
- name: Spam
dtype: int64
splits:
- name: train
num_bytes: 7196065
num_examples: 4556
- name: val
num_bytes: 819608
num_examples: 569
- name: test
num_bytes: 925859
num_examples: 570
download_size: 4959617
dataset_size: 8941532
---
# Dataset Card for "spamming-email-classification"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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aakanksha19/pico_bigbio_processed
|
aakanksha19
| 2023-11-21T05:20:46Z | 108 | 0 |
[
"license:unknown",
"region:us"
] | null | 2023-11-21T05:17:08Z |
---
license: unknown
---
|
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deokhk/am_wiki_sentences_100000
|
deokhk
| 2023-11-21T07:38:28Z | 108 | 0 |
[
"region:us"
] | null | 2023-11-21T07:38:22Z |
---
dataset_info:
features:
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 15005045
num_examples: 100000
- name: dev
num_bytes: 114806
num_examples: 1000
download_size: 7271644
dataset_size: 15119851
---
# Dataset Card for "am_wiki_sentences_100000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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Medilora/mimic_iii_diagnosis_anonymous
|
Medilora
| 2023-11-24T23:08:04Z | 108 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-24T09:56:12Z |
---
license: mit
---
|
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DFKI-SLT/cross_re
|
DFKI-SLT
| 2023-01-19T09:19:12Z | 107 | 0 |
[
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|cross_ner",
"language:en",
"cross domain",
"ai",
"news",
"music",
"literature",
"politics",
"science",
"arxiv:2210.09345",
"region:us"
] |
[
"text-classification"
] | 2023-01-19T09:18:42Z |
---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- found
license: []
multilinguality:
- monolingual
pretty_name: CrossRE is a cross-domain dataset for relation extraction
size_categories:
- 10K<n<100K
source_datasets:
- extended|cross_ner
tags:
- cross domain
- ai
- news
- music
- literature
- politics
- science
task_categories:
- text-classification
task_ids:
- multi-class-classification
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dtype: string
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sequence: string
- name: ner
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download_size: 594058
dataset_size: 537353
---
# Dataset Card for CrossRE
## 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
- **Repository:** [CrossRE](https://github.com/mainlp/CrossRE)
- **Paper:** [CrossRE: A Cross-Domain Dataset for Relation Extraction](https://arxiv.org/abs/2210.09345)
### Dataset Summary
CrossRE is a new, freely-available crossdomain benchmark for RE, which comprises six distinct text domains and includes
multilabel annotations. It includes the following domains: news, politics, natural science, music, literature and
artificial intelligence. The semantic relations are annotated on top of CrossNER (Liu et al., 2021), a cross-domain
dataset for NER which contains domain-specific entity types.
The dataset contains 17 relation labels for the six domains: PART-OF, PHYSICAL, USAGE, ROLE, SOCIAL,
GENERAL-AFFILIATION, COMPARE, TEMPORAL, ARTIFACT, ORIGIN, TOPIC, OPPOSITE, CAUSE-EFFECT, WIN-DEFEAT, TYPEOF, NAMED, and
RELATED-TO.
For details, see the paper: https://arxiv.org/abs/2210.09345
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
The language data in CrossRE is in English (BCP-47 en)
## Dataset Structure
### Data Instances
#### news
- **Size of downloaded dataset files:** 0.24 MB
- **Size of the generated dataset:** 0.22 MB
An example of 'train' looks as follows:
```python
{
"doc_key": "news-train-1",
"sentence": ["EU", "rejects", "German", "call", "to", "boycott", "British", "lamb", "."],
"ner": [
{"id-start": 0, "id-end": 0, "entity-type": "organisation"},
{"id-start": 2, "id-end": 3, "entity-type": "misc"},
{"id-start": 6, "id-end": 7, "entity-type": "misc"}
],
"relations": [
{"id_1-start": 0, "id_1-end": 0, "id_2-start": 2, "id_2-end": 3, "relation-type": "opposite", "Exp": "rejects", "Un": False, "SA": False},
{"id_1-start": 2, "id_1-end": 3, "id_2-start": 6, "id_2-end": 7, "relation-type": "opposite", "Exp": "calls_for_boycot_of", "Un": False, "SA": False},
{"id_1-start": 2, "id_1-end": 3, "id_2-start": 6, "id_2-end": 7, "relation-type": "topic", "Exp": "", "Un": False, "SA": False}
]
}
```
#### politics
- **Size of downloaded dataset files:** 0.73 MB
- **Size of the generated dataset:** 0.65 MB
An example of 'train' looks as follows:
```python
{
"doc_key": "politics-train-1",
"sentence": ["Parties", "with", "mainly", "Eurosceptic", "views", "are", "the", "ruling", "United", "Russia", ",", "and", "opposition", "parties", "the", "Communist", "Party", "of", "the", "Russian", "Federation", "and", "Liberal", "Democratic", "Party", "of", "Russia", "."],
"ner": [
{"id-start": 8, "id-end": 9, "entity-type": "politicalparty"},
{"id-start": 15, "id-end": 20, "entity-type": "politicalparty"},
{"id-start": 22, "id-end": 26, "entity-type": "politicalparty"}
],
"relations": [
{"id_1-start": 8, "id_1-end": 9, "id_2-start": 15, "id_2-end": 20, "relation-type": "opposite", "Exp": "in_opposition", "Un": False, "SA": False},
{"id_1-start": 8, "id_1-end": 9, "id_2-start": 22, "id_2-end": 26, "relation-type": "opposite", "Exp": "in_opposition", "Un": False, "SA": False}
]
}
```
#### science
- **Size of downloaded dataset files:** 0.59 MB
- **Size of the generated dataset:** 0.54 MB
An example of 'train' looks as follows:
```python
{
"doc_key": "science-train-1",
"sentence": ["They", "may", "also", "use", "Adenosine", "triphosphate", ",", "Nitric", "oxide", ",", "and", "ROS", "for", "signaling", "in", "the", "same", "ways", "that", "animals", "do", "."],
"ner": [
{"id-start": 4, "id-end": 5, "entity-type": "chemicalcompound"},
{"id-start": 7, "id-end": 8, "entity-type": "chemicalcompound"},
{"id-start": 11, "id-end": 11, "entity-type": "chemicalcompound"}
],
"relations": []
}
```
#### music
- **Size of downloaded dataset files:** 0.73 MB
- **Size of the generated dataset:** 0.64 MB
An example of 'train' looks as follows:
```python
{
"doc_key": "music-train-1",
"sentence": ["In", "2003", ",", "the", "Stade", "de", "France", "was", "the", "primary", "site", "of", "the", "2003", "World", "Championships", "in", "Athletics", "."],
"ner": [
{"id-start": 4, "id-end": 6, "entity-type": "location"},
{"id-start": 13, "id-end": 17, "entity-type": "event"}
],
"relations": [
{"id_1-start": 13, "id_1-end": 17, "id_2-start": 4, "id_2-end": 6, "relation-type": "physical", "Exp": "", "Un": False, "SA": False}
]
}
```
#### literature
- **Size of downloaded dataset files:** 0.64 MB
- **Size of the generated dataset:** 0.57 MB
An example of 'train' looks as follows:
```python
{
"doc_key": "literature-train-1",
"sentence": ["In", "1351", ",", "during", "the", "reign", "of", "Emperor", "Toghon", "Temür", "of", "the", "Yuan", "dynasty", ",", "93rd-generation", "descendant", "Kong", "Huan", "(", "孔浣", ")", "'", "s", "2nd", "son", "Kong", "Shao", "(", "孔昭", ")", "moved", "from", "China", "to", "Korea", "during", "the", "Goryeo", ",", "and", "was", "received", "courteously", "by", "Princess", "Noguk", "(", "the", "Mongolian-born", "wife", "of", "the", "future", "king", "Gongmin", ")", "."],
"ner": [
{"id-start": 7, "id-end": 9, "entity-type": "person"},
{"id-start": 12, "id-end": 13, "entity-type": "country"},
{"id-start": 17, "id-end": 18, "entity-type": "writer"},
{"id-start": 20, "id-end": 20, "entity-type": "writer"},
{"id-start": 26, "id-end": 27, "entity-type": "writer"},
{"id-start": 29, "id-end": 29, "entity-type": "writer"},
{"id-start": 33, "id-end": 33, "entity-type": "country"},
{"id-start": 35, "id-end": 35, "entity-type": "country"},
{"id-start": 38, "id-end": 38, "entity-type": "misc"},
{"id-start": 45, "id-end": 46, "entity-type": "person"},
{"id-start": 49, "id-end": 50, "entity-type": "misc"},
{"id-start": 55, "id-end": 55, "entity-type": "person"}
],
"relations": [
{"id_1-start": 7, "id_1-end": 9, "id_2-start": 12, "id_2-end": 13, "relation-type": "role", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 7, "id_1-end": 9, "id_2-start": 12, "id_2-end": 13, "relation-type": "temporal", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 17, "id_1-end": 18, "id_2-start": 26, "id_2-end": 27, "relation-type": "social", "Exp": "family", "Un": False, "SA": False},
{"id_1-start": 20, "id_1-end": 20, "id_2-start": 17, "id_2-end": 18, "relation-type": "named", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 26, "id_1-end": 27, "id_2-start": 33, "id_2-end": 33, "relation-type": "physical", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 26, "id_1-end": 27, "id_2-start": 35, "id_2-end": 35, "relation-type": "physical", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 26, "id_1-end": 27, "id_2-start": 38, "id_2-end": 38, "relation-type": "temporal", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 26, "id_1-end": 27, "id_2-start": 45, "id_2-end": 46, "relation-type": "social", "Exp": "greeted_by", "Un": False, "SA": False},
{"id_1-start": 29, "id_1-end": 29, "id_2-start": 26, "id_2-end": 27, "relation-type": "named", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 45, "id_1-end": 46, "id_2-start": 55, "id_2-end": 55, "relation-type": "social", "Exp": "marriage", "Un": False, "SA": False},
{"id_1-start": 49, "id_1-end": 50, "id_2-start": 45, "id_2-end": 46, "relation-type": "named", "Exp": "", "Un": False, "SA": False}
]
}
```
#### ai
- **Size of downloaded dataset files:** 0.51 MB
- **Size of the generated dataset:** 0.46 MB
An example of 'train' looks as follows:
```python
{
"doc_key": "ai-train-1",
"sentence": ["Popular", "approaches", "of", "opinion-based", "recommender", "system", "utilize", "various", "techniques", "including", "text", "mining", ",", "information", "retrieval", ",", "sentiment", "analysis", "(", "see", "also", "Multimodal", "sentiment", "analysis", ")", "and", "deep", "learning", "X.Y.", "Feng", ",", "H.", "Zhang", ",", "Y.J.", "Ren", ",", "P.H.", "Shang", ",", "Y.", "Zhu", ",", "Y.C.", "Liang", ",", "R.C.", "Guan", ",", "D.", "Xu", ",", "(", "2019", ")", ",", ",", "21", "(", "5", ")", ":", "e12957", "."],
"ner": [
{"id-start": 3, "id-end": 5, "entity-type": "product"},
{"id-start": 10, "id-end": 11, "entity-type": "field"},
{"id-start": 13, "id-end": 14, "entity-type": "task"},
{"id-start": 16, "id-end": 17, "entity-type": "task"},
{"id-start": 21, "id-end": 23, "entity-type": "task"},
{"id-start": 26, "id-end": 27, "entity-type": "field"},
{"id-start": 28, "id-end": 29, "entity-type": "researcher"},
{"id-start": 31, "id-end": 32, "entity-type": "researcher"},
{"id-start": 34, "id-end": 35, "entity-type": "researcher"},
{"id-start": 37, "id-end": 38, "entity-type": "researcher"},
{"id-start": 40, "id-end": 41, "entity-type": "researcher"},
{"id-start": 43, "id-end": 44, "entity-type": "researcher"},
{"id-start": 46, "id-end": 47, "entity-type": "researcher"},
{"id-start": 49, "id-end": 50, "entity-type": "researcher"}
],
"relations": [
{"id_1-start": 3, "id_1-end": 5, "id_2-start": 10, "id_2-end": 11, "relation-type": "part-of", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 3, "id_1-end": 5, "id_2-start": 10, "id_2-end": 11, "relation-type": "usage", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 3, "id_1-end": 5, "id_2-start": 13, "id_2-end": 14, "relation-type": "part-of", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 3, "id_1-end": 5, "id_2-start": 13, "id_2-end": 14, "relation-type": "usage", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 3, "id_1-end": 5, "id_2-start": 16, "id_2-end": 17, "relation-type": "part-of", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 3, "id_1-end": 5, "id_2-start": 16, "id_2-end": 17, "relation-type": "usage", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 3, "id_1-end": 5, "id_2-start": 26, "id_2-end": 27, "relation-type": "part-of", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 3, "id_1-end": 5, "id_2-start": 26, "id_2-end": 27, "relation-type": "usage", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 21, "id_1-end": 23, "id_2-start": 16, "id_2-end": 17, "relation-type": "part-of", "Exp": "", "Un": False, "SA": False},
{"id_1-start": 21, "id_1-end": 23, "id_2-start": 16, "id_2-end": 17, "relation-type": "type-of", "Exp": "", "Un": False, "SA": False}
]
}
```
### Data Fields
The data fields are the same among all splits.
- `doc_key`: the instance id of this sentence, a `string` feature.
- `sentence`: the list of tokens of this sentence, obtained with spaCy, a `list` of `string` features.
- `ner`: the list of named entities in this sentence, a `list` of `dict` features.
- `id-start`: the start index of the entity, a `int` feature.
- `id-end`: the end index of the entity, a `int` feature.
- `entity-type`: the type of the entity, a `string` feature.
- `relations`: the list of relations in this sentence, a `list` of `dict` features.
- `id_1-start`: the start index of the first entity, a `int` feature.
- `id_1-end`: the end index of the first entity, a `int` feature.
- `id_2-start`: the start index of the second entity, a `int` feature.
- `id_2-end`: the end index of the second entity, a `int` feature.
- `relation-type`: the type of the relation, a `string` feature.
- `Exp`: the explanation of the relation type assigned, a `string` feature.
- `Un`: uncertainty of the annotator, a `bool` feature.
- `SA`: existence of syntax ambiguity which poses a challenge for the annotator, a `bool` feature.
### Data Splits
#### Sentences
| | Train | Dev | Test | Total |
|--------------|---------|---------|---------|---------|
| news | 164 | 350 | 400 | 914 |
| politics | 101 | 350 | 400 | 851 |
| science | 103 | 351 | 400 | 854 |
| music | 100 | 350 | 399 | 849 |
| literature | 100 | 400 | 416 | 916 |
| ai | 100 | 350 | 431 | 881 |
| ------------ | ------- | ------- | ------- | ------- |
| total | 668 | 2,151 | 2,46 | 5,265 |
#### Relations
| | Train | Dev | Test | Total |
|--------------|---------|---------|---------|---------|
| news | 175 | 300 | 396 | 871 |
| politics | 502 | 1,616 | 1,831 | 3,949 |
| science | 355 | 1,340 | 1,393 | 3,088 |
| music | 496 | 1,861 | 2,333 | 4,690 |
| literature | 397 | 1,539 | 1,591 | 3,527 |
| ai | 350 | 1,006 | 1,127 | 2,483 |
| ------------ | ------- | ------- | ------- | ------- |
| total | 2,275 | 7,662 | 8,671 | 18,608 |
## 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{bassignana-plank-2022-crossre,
title = "Cross{RE}: A {C}ross-{D}omain {D}ataset for {R}elation {E}xtraction",
author = "Bassignana, Elisa and Plank, Barbara",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
year = "2022",
publisher = "Association for Computational Linguistics"
}
```
### Contributions
Thanks to [@phucdev](https://github.com/phucdev) for adding this dataset.
|
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laion/OIG
|
laion
| 2023-03-31T00:06:28Z | 107 | 259 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-03-05T00:34:58Z |
---
license: apache-2.0
---
# This is the Open Instruction Generalist Dataset
This is our attempt to create a large instruction dataset of medium quality along with a smaller high quality instruciton dataset (OIG-small-chip2).
The data is in the form of jsonl objects, with at least a 'text' field. Some datasets may also include a 'metadata' field. The 'text' field contains a string of the form of one or more of:
- \<human\>: instruction\n\<bot\>: response
- \<human\>: instruction\n\<bot\>: response .. \<human\>: instruction\n\<bot\>: response
The purpose of the larger dataset is to perform continued pre-training, followed by a finetune on the smaller high quality dataset.
The purpose of the smaller OIG-small-chip2 dataset is to make it easy to convert a language model pretrained on large amounts of text into an instruction following model using a small amount of additional compute via finetuning or softprompt tuning.
Many additional datasets are being prepared by various community members and will be incorporated into this dataset as we are able to verify the quality and formatting of the data. Our goal is to make helpful and non-toxic instruction tuned models available to everyone.
OIG is currently at 44M. We will continue to publish ever larger diverse instruction datasets with the goal of creating 1 trillion tokens of diverse instructions - enough to pretrain an LLM from scratch.
It is best to download the individual jsonl files directly that you wish to use instead of using HF load_datasets. https://huggingface.co/datasets/laion/OIG/tree/main
## unified_abstract_infill.jsonl (~232000)
dbpedia and wikipedia snippets combined with a small portion of https://github.com/google-research/dialog-inpainting
## unified_basic.jsonl (30)
## unified_conv_finqa.jsonl (~9000)
https://github.com/czyssrs/ConvFinQA
## unified_cuad.jsonl (~500)
https://www.atticusprojectai.org/cuad
## unified_essays.jsonl (~2000)
- essays available on the public web
## unified_grade_school_math_instructions.jsonl (~9000)
- https://github.com/openai/grade-school-math
## unified_hc3_human.jsonl (~58000)
## unified_image_prompts_instructions.jsonl (~15000)
- A very small subset of LAION-400M
## unified_joke_explanations.jsonl (356)
- Crawled from public internet.
## unified_mathqa_flanv2_kojma_cot.jsonl (~107000)
- https://huggingface.co/datasets/math_qa,
## unified_merged_code_xp3.jsonl (~67000)
- https://huggingface.co/datasets/bigscience/xP3
## unified_multi_news.jsonl (~90000)
- https://www.tensorflow.org/datasets/catalog/multi_news
## unified_multi_sum.jsonl (~1700000)
## unified_nq.jsonl (~307000)
## unified_openai_summarize_tldr.jsonl (~233000)
- https://github.com/openai/summarize-from-feedback
## unified_oscar_en_sample_dialog.jsonl (~2670000)
- https://oscar-project.org/
- https://huggingface.co/datasets/TurkuNLP/register_oscar
## unified_plot_screenplay_books_dialog.jsonl (~8000)
- https://github.com/markriedl/WikiPlots extracted from Wikipedia, snippets from the Pile’s https://huggingface.co/datasets/the_pile_books3, and snippets of screenplays available on the public web.
## unified_sqlv1.jsonl (~17000)
- public text 2 sql datasets.
## unified_sqlv2.jsonl(~24000)
- public text 2 sql datasets.
## unified_squad_v2.jsonl (~19000)
- https://rajpurkar.github.io/SQuAD-explorer/
## unified_squad_v2_more_neg.jsonl (~19000)
- https://rajpurkar.github.io/SQuAD-explorer/
## unified_ul2_plus_oscar_en_sample_dialog.jsonl (~2900000)
- https://oscar-project.org/
- https://huggingface.co/datasets/TurkuNLP/register_oscar
## unified_unifiedskg_instructions.jsonl (~223000)
- https://github.com/HKUNLP/UnifiedSKG
## unified_unnatural_instructions.jsonl (~238000)
- https://github.com/orhonovich/unnatural-instructions
## unified_xp3_sample.jsonl (~188000)
- https://huggingface.co/datasets/bigscience/xP3
## unified_canadian_parliament.jsonl(~301000)
- https://openparliament.ca/data-download/
## unified_poetry_2_song.jsonl (~12000)
- https://huggingface.co/datasets/merve/poetry
- https://huggingface.co/datasets/matthh/gutenberg-poetry-corpus
## unified_flan.jsonl (~2700000)
- https://github.com/google-research/FLAN/tree/main/flan/v2
## unified_ni.jsonl (~256000)
- https://github.com/allenai/natural-instructions
## unified_p3.jsonl (~31000000)
- https://huggingface.co/datasets/bigscience/P3
## unified_soda_dialog.jsonl (~1200000)
- https://huggingface.co/datasets/allenai/soda
## unified_rallio_soda_upgraded_2048.jsonl (~210000)
- https://huggingface.co/datasets/allenai/soda
- a newer version of the unified_soda_dialog dataset, with multiple dialogs on one line
- recommend to use either the unified_soda_dailog.jsonl or unified_rallio_soda_upgraded_2048, and not both.
## unified_rallio_safety_and_prosocial.jsonl (~319000)
- Generated from public datasets and generated from Wiki similar to the chip2 data
- Find a full list in the end of the document
- This dataset also includes https://huggingface.co/datasets/allenai/prosocial-dialog and https://huggingface.co/datasets/Anthropic/hh-rlhf
## unified-chip2.jsonl / OIG-small-chip2 (~210000):
This dataset was created as part of the LAION OA effort by @rallio67 and other members of the LAION contributors. It is a high quality dataset intended to be mixed into a large pre-train dataset and can be used for a final finetune. Chip2 contains:
### Python Code Examples (~6,000):
A set of instruction / response pairs where the User requests the agent to generate a python function. These examples were generated using a large language model and few shot prompting with python code verified to execute. There are also ~3000 examples of manually curated one line python code examples from the Conala publication (see: https://conala-corpus.github.io/)
### Natural Instruction Examples (~124,000):
A balanced set of diverse natural and factual questions and answers made using few shot prompted UL2 20B and an instruction tuned GPT-NeoX-20B model (Chip) and then rejection sampled using multiple automatic evaluations to remove low quality outputs and to filter out factually inaccurate answers. Also includes some filtered natural instructions from Anthropic Helpful instructions (see: https://github.com/anthropics/hh-rlhf).
### Generic Harmless Instruction Examples (~6,500):
A set of instruction / response pairs sourced from the Anthropic redteam paper github (see: https://github.com/anthropics/hh-rlhf). This dataset includes a lot of data regarding real humans trying to make the Anthropic language models say harmful/toxic/trolling things. For this dataset only examples that were rated lowly on the harmful scale (0,1,2 out of 4, where 4 is the most toxic) were included. Again, only the first lines of dialogue (instruction, first_agent_response) were retained.
### Instruction/Responses with Lists (~14,000):
A set of filtered and reformatted instruction / response pairs where the agent response contains a list. Sourced from the Anthropic github (see: https://github.com/anthropics/hh-rlhf). Sourced from wikihow text lists created by b-mc2 (https://huggingface.co/datasets/b-mc2/wikihow_lists). And rejection filtered instruction response pairs generated by Chip20B that contained lists. All lists are formatted in a similar style.
### Follow-up questions (~12,500):
Examples of instructions and responses where an appropriate response is to ask for more information from the prompter. These examples were generated from a combination of few shot prompted UL2 20B (to generate natural questions) and a large dialogue prompted language model to generate the responses containing follow-up questions.
### Wikipedia Toxic Adversarial Questions (~12,000):
Questions and answers generated from wikipedia articles that discuss potentially sensitive topics (flagged as potentially toxic by an early toxicity detection model).
### Grade School Math GSM8K (~9,000):
GSM8K is a dataset of 8.5K high quality linguistically diverse grade school math word problems created by human problem writers. The dataset is segmented into 7.5K training problems and 1K test problems. These problems take between 2 and 8 steps to solve, and solutions primarily involve performing a sequence of elementary calculations using basic arithmetic operations (+ − ×÷) to reach the final answer. A bright middle school student should be able to solve every problem. It can be used for multi-step mathematical reasoning. (https://github.com/openai/grade-school-math)
### Reasoning Instructions (~4,500):
Examples from the Com2Sense and Strategy QA datasets that were reformatted into natural instructions using large language models with few shot prompting and additional quality filtering steps.
### Character and Scene Descriptions (~30,000):
Examples of instructions and responses for the generation of character or scene descriptions. Scenes were sourced from video game wikis and reformatted into instruction / response format using large language models or generated by few shot prompting with large language models.
## Support this project
Your contributions and feedback support the open source ecosystem, improve the bot and provide datasets for future AI research. To participate you can:
Submit Github issues, track issues and help create datasets that need improvement. https://github.com/LAION-AI/Open-Instruction-Generalist
Join our Discord to talk with other team members working on this! https://discord.gg/xBPBXfcFHd
## Update: March 20, 2023
- Added the metadata column to all datasets to alleviate issues with HF datasets loader.
- Broke some of the p3 dialogs into parts for ease of loading.
## Disclaimer
These datasets contain synthetic data and in some cases data that includes humans trying to get the language model to say toxic/offensive/trolling things. If you are concerned about the presence of this type of material in the dataset please make sure you carefully inspect each of the entries and filter appropriately. Our goal is for the model to be as helpful and non-toxic as possible and we are actively evaluating ways to reduce or eliminate undesirable content from the instruction tuning datasets.
## License
The OIG dataset that is authored by LAION volunteers is released under an Apache 2.0 license. However, the data also includes content licensed under other permissive licenses such as Wikipedia data which is licensed under CC-BY-SA, or web-crawled data which is used under fair use principles.
## Acknowledgement
- We would like to thank all of our amazing LAION volunteers including: @Rallio, @Jue, @Ce Zhang, @Player-1, @Laurel, @danielpatrickhug, @Jjmachan, @Mylo, @Khalid, @Coco.han, @Jordiclive, @Pszemraj, all volunteers from the Open Assistant project who initially created synthetic data, and many others.
- We would like to thank Together for their tireless dedication to the open source and AI community and their contribution to many of the datasets.
- We would like to thank AI Horde and user @Db0 for their incredible contribution of filtered data that were flagged as unethical.
- Please check out our related project: https://github.com/LAION-AI/Open-Assistant for our work in human feedback gathering and RLHF.
- Lastly, Ontocord.ai’s founders are grateful to have the opportunity to create a portion of the data augmentation and safety-moderation code for this project.
|
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BelleGroup/train_0.5M_CN
|
BelleGroup
| 2023-04-03T08:11:22Z | 107 | 82 |
[
"task_categories:text2text-generation",
"size_categories:100K<n<1M",
"language:zh",
"license:gpl-3.0",
"region:us"
] |
[
"text2text-generation"
] | 2023-03-31T10:17:49Z |
---
license: gpl-3.0
task_categories:
- text2text-generation
language:
- zh
size_categories:
- 100K<n<1M
---
## 内容
包含约50万条由[BELLE](https://github.com/LianjiaTech/BELLE)项目生成的中文指令数据。
## 样例
```
{
"instruction": "给定一个文字输入，将其中的所有数字加1。\n“明天的会议在9点开始，记得准时到达。”\n",
"input": "",
"output": "“明天的会议在10点开始，记得准时到达。”"
}
```
### 字段：
```
instruction: 指令
input: 输入（本数据集均为空）
output: 输出
```
## 使用限制
仅允许将此数据集及使用此数据集生成的衍生物用于研究目的，不得用于商业，以及其他会对社会带来危害的用途。
本数据集不代表任何一方的立场、利益或想法，无关任何团体的任何类型的主张。因使用本数据集带来的任何损害、纠纷，本项目不承担任何责任。
|
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] |
gbharti/wealth-alpaca_lora
|
gbharti
| 2023-04-07T09:12:57Z | 107 | 12 |
[
"language:en",
"region:us"
] | null | 2023-04-03T02:17:27Z |
---
language:
- en
---
This dataset is a combination of Stanford's Alpaca (https://github.com/tatsu-lab/stanford_alpaca) and FiQA (https://sites.google.com/view/fiqa/) with another 1.3k pairs custom generated using GPT3.5
Script for tuning through Kaggle's (https://www.kaggle.com) free resources using PEFT/LoRa: https://www.kaggle.com/code/gbhacker23/wealth-alpaca-lora
|
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open-llm-leaderboard/details_augtoma__qCammel70
|
open-llm-leaderboard
| 2023-10-17T14:20:05Z | 107 | 0 |
[
"region:us"
] | null | 2023-08-18T18:40:45Z |
---
pretty_name: Evaluation run of augtoma/qCammel70
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [augtoma/qCammel70](https://huggingface.co/augtoma/qCammel70) 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_augtoma__qCammel70\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-17T14:19:52.424228](https://huggingface.co/datasets/open-llm-leaderboard/details_augtoma__qCammel70/blob/main/results_2023-10-17T14-19-52.424228.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\": 0.033766778523489936,\n\
\ \"em_stderr\": 0.001849802869119515,\n \"f1\": 0.10340918624161041,\n\
\ \"f1_stderr\": 0.0022106009828094797,\n \"acc\": 0.5700654570173166,\n\
\ \"acc_stderr\": 0.011407494958111332\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.033766778523489936,\n \"em_stderr\": 0.001849802869119515,\n\
\ \"f1\": 0.10340918624161041,\n \"f1_stderr\": 0.0022106009828094797\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.2971948445792267,\n \
\ \"acc_stderr\": 0.012588685966624186\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8429360694554064,\n \"acc_stderr\": 0.010226303949598479\n\
\ }\n}\n```"
repo_url: https://huggingface.co/augtoma/qCammel70
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_18T06_33_28.828480
path:
- '**/details_harness|arc:challenge|25_2023-08-18T06:33:28.828480.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_17T14_19_52.424228
path:
- '**/details_harness|drop|3_2023-10-17T14-19-52.424228.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-17T14-19-52.424228.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_17T14_19_52.424228
path:
- '**/details_harness|gsm8k|5_2023-10-17T14-19-52.424228.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-17T14-19-52.424228.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_18T06_33_28.828480
path:
- '**/details_harness|hellaswag|10_2023-08-18T06:33:28.828480.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_18T06_33_28.828480
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T06:33:28.828480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-18T06:33:28.828480.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_18T06_33_28.828480
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T06:33:28.828480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_18T06_33_28.828480
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T06:33:28.828480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_18T06_33_28.828480
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T06:33:28.828480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_18T06_33_28.828480
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T06:33:28.828480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_18T06_33_28.828480
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T06:33:28.828480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_18T06_33_28.828480
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T06:33:28.828480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_18T06_33_28.828480
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T06:33:28.828480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_18T06_33_28.828480
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T06:33:28.828480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_18T06_33_28.828480
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T06:33:28.828480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_18T06_33_28.828480
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T06:33:28.828480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_18T06_33_28.828480
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T06:33:28.828480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_18T06_33_28.828480
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T06:33:28.828480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_18T06_33_28.828480
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T06:33:28.828480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_18T06_33_28.828480
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T06:33:28.828480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_18T06_33_28.828480
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T06:33:28.828480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_18T06_33_28.828480
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T06:33:28.828480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_18T06_33_28.828480
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T06:33:28.828480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_18T06_33_28.828480
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T06:33:28.828480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_18T06_33_28.828480
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T06:33:28.828480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_18T06_33_28.828480
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T06:33:28.828480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_18T06_33_28.828480
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T06:33:28.828480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_18T06_33_28.828480
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T06:33:28.828480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_18T06_33_28.828480
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T06:33:28.828480.parquet'
- split: latest
path:
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data_files:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_18T06_33_28.828480
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_18T06_33_28.828480
path:
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path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_18T06_33_28.828480
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T06:33:28.828480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
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path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T06:33:28.828480.parquet'
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path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
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path:
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path:
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- config_name: harness_hendrycksTest_international_law_5
data_files:
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path:
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path:
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path:
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path:
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data_files:
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path:
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data_files:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_18T06_33_28.828480
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-18T06:33:28.828480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_18T06_33_28.828480
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T06:33:28.828480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_18T06_33_28.828480
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T06:33:28.828480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_18T06_33_28.828480
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T06:33:28.828480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_18T06_33_28.828480
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T06:33:28.828480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_18T06_33_28.828480
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T06:33:28.828480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_18T06_33_28.828480
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T06:33:28.828480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_18T06_33_28.828480
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T06:33:28.828480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_18T06_33_28.828480
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T06:33:28.828480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_18T06_33_28.828480
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T06:33:28.828480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_18T06_33_28.828480
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T06:33:28.828480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_18T06_33_28.828480
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T06:33:28.828480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_18T06_33_28.828480
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T06:33:28.828480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_18T06_33_28.828480
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T06:33:28.828480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_hendrycksTest_sociology_5
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- split: 2023_08_18T06_33_28.828480
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-18T06:33:28.828480.parquet'
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path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
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path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T06:33:28.828480.parquet'
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path:
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- config_name: harness_hendrycksTest_virology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-18T06:33:28.828480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
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path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T06:33:28.828480.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
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path:
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- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-18T06:33:28.828480.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_17T14_19_52.424228
path:
- '**/details_harness|winogrande|5_2023-10-17T14-19-52.424228.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-17T14-19-52.424228.parquet'
- config_name: results
data_files:
- split: 2023_08_18T06_33_28.828480
path:
- results_2023-08-18T06:33:28.828480.parquet
- split: 2023_10_17T14_19_52.424228
path:
- results_2023-10-17T14-19-52.424228.parquet
- split: latest
path:
- results_2023-10-17T14-19-52.424228.parquet
---
# Dataset Card for Evaluation run of augtoma/qCammel70
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/augtoma/qCammel70
- **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 [augtoma/qCammel70](https://huggingface.co/augtoma/qCammel70) 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_augtoma__qCammel70",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-17T14:19:52.424228](https://huggingface.co/datasets/open-llm-leaderboard/details_augtoma__qCammel70/blob/main/results_2023-10-17T14-19-52.424228.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": 0.033766778523489936,
"em_stderr": 0.001849802869119515,
"f1": 0.10340918624161041,
"f1_stderr": 0.0022106009828094797,
"acc": 0.5700654570173166,
"acc_stderr": 0.011407494958111332
},
"harness|drop|3": {
"em": 0.033766778523489936,
"em_stderr": 0.001849802869119515,
"f1": 0.10340918624161041,
"f1_stderr": 0.0022106009828094797
},
"harness|gsm8k|5": {
"acc": 0.2971948445792267,
"acc_stderr": 0.012588685966624186
},
"harness|winogrande|5": {
"acc": 0.8429360694554064,
"acc_stderr": 0.010226303949598479
}
}
```
### 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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alexandrainst/lexdk-open
|
alexandrainst
| 2023-10-27T20:22:31Z | 107 | 1 |
[
"size_categories:10K<n<100K",
"language:da",
"license:cc0-1.0",
"region:us"
] | null | 2023-10-25T09:36:11Z |
---
language:
- da
license: cc0-1.0
size_categories:
- 10K<n<100K
pretty_name: Lex.dk Open
dataset_info:
features:
- name: url
dtype: string
- name: title
dtype: string
- name: clarification
dtype: string
- name: authors
sequence: string
- name: date
dtype: string
- name: license
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 18335490
num_examples: 11887
download_size: 10050922
dataset_size: 18335490
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for Lex.dk Open
## Dataset Description
- **Point of Contact:** [Dan Saattrup Nielsen](mailto:[email protected])
- **Size of downloaded dataset files:** 10.05 MB
- **Size of the generated dataset:** 18.34 MB
- **Total amount of disk used:** 28.39 MB
### Dataset Summary
This dataset consists of articles from the Danish encyclopedia [Lex.dk](https://www.lex.dk).
Only the articles released with a permissive license are included here, which constitutes about 7.5% of the total amount of articles.
### Languages
The dataset is available in Danish (`da`).
## Dataset Structure
### Data Instances
- **Size of downloaded dataset files:** 10.05 MB
- **Size of the generated dataset:** 18.34 MB
- **Total amount of disk used:** 28.39 MB
An example from the dataset looks as follows.
```
{
'url': 'https://denstoredanske.lex.dk/Kullmanns_M%C3%B8lle',
'title': 'Kullmanns Mølle',
'clarification': '',
'authors': ['https://brugere.lex.dk/6929'],
'date': '2021-01-20T13:23:20+01:00',
'license': 'fri anvendelse',
'text': 'Kullmanns Mølle er en mølle i Gudhjem, opkaldt efter Matts Kullmann, der byggede møllen i 1893 til sin søn, Christian Kullmann, se Gudhjem Mølle.'
}
```
### Data Fields
The data fields are the same among all splits.
- `url`: a `string` feature.
- `title`: a `string` feature.
- `clarification`: a `string` feature.
- `authors`: a `list` feature.
- `authors`: a `string` feature.
- `license`: a `string` feature.
- `text`: a `string` feature.
### Dataset Statistics
There are 11,887 samples in the dataset.
#### Article Length Distribution
## Additional Information
### Dataset Curators
[Dan Saattrup Nielsen](https://saattrupdan.github.io/) from the [The Alexandra
Institute](https://alexandra.dk/) built the dataset and uploaded it to the Hugging Face Hub.
### Licensing Information
The dataset is licensed under the [CC0
license](https://creativecommons.org/share-your-work/public-domain/cc0/).
|
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] |
zyxleo/cord_donut_multitask
|
zyxleo
| 2023-11-07T04:44:58Z | 107 | 0 |
[
"region:us"
] | null | 2023-11-06T19:32:25Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: task
dtype: string
- name: image_path
dtype: string
- name: ground_truth
dtype: string
- name: labels
sequence: int64
- name: input_ids
sequence: int64
splits:
- name: train
num_bytes: 1260759
num_examples: 800
- name: test
num_bytes: 93059
num_examples: 100
- name: validation
num_bytes: 86619
num_examples: 100
download_size: 299877
dataset_size: 1440437
---
# Dataset Card for "cord_donut_multitask"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
luizlzg/prefeitura_rj
|
luizlzg
| 2023-11-06T21:55:50Z | 107 | 0 |
[
"region:us"
] | null | 2023-11-06T21:54:02Z |
---
configs:
- config_name: default
data_files:
- split: train
path: prefeitura_treino*
- split: test
path: prefeitura_teste*
---
|
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en0c/km-shorts
|
en0c
| 2023-11-11T17:07:10Z | 107 | 0 |
[
"region:us"
] | null | 2023-11-11T17:06:45Z |
---
dataset_info:
features:
- name: audio
dtype: audio
splits:
- name: train
num_bytes: 173849408.0
num_examples: 45
download_size: 157813790
dataset_size: 173849408.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "km-shorts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
cardo14/Taylor_Swift_Embeddings_2
|
cardo14
| 2023-12-03T20:05:53Z | 107 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-12-03T20:05:30Z |
---
license: mit
---
|
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] |
mdroth/github-issues
|
mdroth
| 2023-07-26T15:36:13Z | 106 | 0 |
[
"region:us"
] | null | 2022-05-06T08:27:56Z |
---
dataset_info:
features:
- name: url
dtype: string
- name: repository_url
dtype: string
- name: labels_url
dtype: string
- name: comments_url
dtype: string
- name: events_url
dtype: string
- name: html_url
dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
- name: number
dtype: int64
- name: title
dtype: string
- name: user
struct:
- name: login
dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
- name: avatar_url
dtype: string
- name: gravatar_id
dtype: string
- name: url
dtype: string
- name: html_url
dtype: string
- name: followers_url
dtype: string
- name: following_url
dtype: string
- name: gists_url
dtype: string
- name: starred_url
dtype: string
- name: subscriptions_url
dtype: string
- name: organizations_url
dtype: string
- name: repos_url
dtype: string
- name: events_url
dtype: string
- name: received_events_url
dtype: string
- name: type
dtype: string
- name: site_admin
dtype: bool
- name: labels
list:
- name: id
dtype: int64
- name: node_id
dtype: string
- name: url
dtype: string
- name: name
dtype: string
- name: color
dtype: string
- name: default
dtype: bool
- name: description
dtype: string
- name: state
dtype: string
- name: locked
dtype: bool
- name: assignee
struct:
- name: login
dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
- name: avatar_url
dtype: string
- name: gravatar_id
dtype: string
- name: url
dtype: string
- name: html_url
dtype: string
- name: followers_url
dtype: string
- name: following_url
dtype: string
- name: gists_url
dtype: string
- name: starred_url
dtype: string
- name: subscriptions_url
dtype: string
- name: organizations_url
dtype: string
- name: repos_url
dtype: string
- name: events_url
dtype: string
- name: received_events_url
dtype: string
- name: type
dtype: string
- name: site_admin
dtype: bool
- name: assignees
list:
- name: login
dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
- name: avatar_url
dtype: string
- name: gravatar_id
dtype: string
- name: url
dtype: string
- name: html_url
dtype: string
- name: followers_url
dtype: string
- name: following_url
dtype: string
- name: gists_url
dtype: string
- name: starred_url
dtype: string
- name: subscriptions_url
dtype: string
- name: organizations_url
dtype: string
- name: repos_url
dtype: string
- name: events_url
dtype: string
- name: received_events_url
dtype: string
- name: type
dtype: string
- name: site_admin
dtype: bool
- name: milestone
struct:
- name: url
dtype: string
- name: html_url
dtype: string
- name: labels_url
dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
- name: number
dtype: int64
- name: title
dtype: string
- name: description
dtype: string
- name: creator
struct:
- name: login
dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
- name: avatar_url
dtype: string
- name: gravatar_id
dtype: string
- name: url
dtype: string
- name: html_url
dtype: string
- name: followers_url
dtype: string
- name: following_url
dtype: string
- name: gists_url
dtype: string
- name: starred_url
dtype: string
- name: subscriptions_url
dtype: string
- name: organizations_url
dtype: string
- name: repos_url
dtype: string
- name: events_url
dtype: string
- name: received_events_url
dtype: string
- name: type
dtype: string
- name: site_admin
dtype: bool
- name: open_issues
dtype: int64
- name: closed_issues
dtype: int64
- name: state
dtype: string
- name: created_at
dtype: timestamp[s]
- name: updated_at
dtype: timestamp[s]
- name: due_on
dtype: 'null'
- name: closed_at
dtype: 'null'
- name: comments
sequence: string
- name: created_at
dtype: timestamp[s]
- name: updated_at
dtype: timestamp[s]
- name: closed_at
dtype: timestamp[s]
- name: author_association
dtype: string
- name: active_lock_reason
dtype: 'null'
- name: draft
dtype: bool
- name: pull_request
struct:
- name: url
dtype: string
- name: html_url
dtype: string
- name: diff_url
dtype: string
- name: patch_url
dtype: string
- name: merged_at
dtype: timestamp[s]
- name: body
dtype: string
- name: reactions
struct:
- name: url
dtype: string
- name: total_count
dtype: int64
- name: '+1'
dtype: int64
- name: '-1'
dtype: int64
- name: laugh
dtype: int64
- name: hooray
dtype: int64
- name: confused
dtype: int64
- name: heart
dtype: int64
- name: rocket
dtype: int64
- name: eyes
dtype: int64
- name: timeline_url
dtype: string
- name: performed_via_github_app
dtype: 'null'
- name: state_reason
dtype: string
- name: is_pull_request
dtype: bool
splits:
- name: train
num_bytes: 4103283
num_examples: 300
download_size: 866826
dataset_size: 4103283
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "github-issues"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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MLRS/korpus_malti
|
MLRS
| 2022-08-30T08:59:09Z | 106 | 0 |
[
"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:10M<n<100M",
"source_datasets:original",
"language:mt",
"license:cc-by-nc-sa-4.0",
"region:us"
] |
[
"text-generation",
"fill-mask"
] | 2022-05-11T12:47:44Z |
---
pretty_name: Korpus Malti
language:
- mt
multilinguality:
- monolingual
size_categories:
- 10M<n<100M
annotations_creators:
- no-annotation
language_creators:
- found
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
license:
- cc-by-nc-sa-4.0
---
# Korpus Malti 🇲🇹
General Corpora for the Maltese Language.
This dataset is composed of texts from various genres/domains written in Maltese.
## Configurations
### Shuffled data
The default configuration (`"shuffled"`) yields the the entire corpus from all genres:
```python
import datasets
dataset = datasets.load_dataset("MLRS/korpus_malti")
```
All sentences are combined together and shuffled, without preserving the sentence order.
No other annotations are present, so an instance would be of the following form:
```json
{
"text": "Din hija sentenza."
}
```
The training/validation/testing split is what was used to train the [BERTu](https://huggingface.co/MLRS/BERTu) model.
### Domain-split data
All other configurations contain a subset of the data.
For instance, this loads the Wikipedia portion:
```python
import datasets
dataset = datasets.load_dataset("MLRS/korpus_malti", "wiki")
```
For these configurations the data is not shuffled, so the sentence order on a document level is preserved.
An instance from these configurations would take the following form:
```json
{
"text": ["Din hija sentenza.", "U hawn oħra!"],
}
```
The raw data files contain additional metadata.
Its structure differs from one instance to another, depending on what's available from the source.
This information was typically scraped from the source itself & minimal processing is performed on such data.
## Additional Information
### Dataset Curators
The dataset was created by [Albert Gatt](https://albertgatt.github.io), [Kurt Micallef](https://www.um.edu.mt/profile/kurtmicallef), [Marc Tanti](https://www.um.edu.mt/profile/marctanti), [Lonneke van der Plas](https://sites.google.com/site/lonnekenlp/) and [Claudia Borg](https://www.um.edu.mt/profile/claudiaborg).
### Licensing Information
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa].
Permissions beyond the scope of this license may be available at [https://mlrs.research.um.edu.mt/](https://mlrs.research.um.edu.mt/).
[![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa]
[cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/
[cc-by-nc-sa-image]: https://licensebuttons.net/l/by-nc-sa/4.0/88x31.png
### Citation Information
This work was first presented in [Pre-training Data Quality and Quantity for a Low-Resource Language: New Corpus and BERT Models for Maltese](https://aclanthology.org/2022.deeplo-1.10/).
Cite it as follows:
```bibtex
@inproceedings{BERTu,
title = "Pre-training Data Quality and Quantity for a Low-Resource Language: New Corpus and {BERT} Models for {M}altese",
author = "Micallef, Kurt and
Gatt, Albert and
Tanti, Marc and
van der Plas, Lonneke and
Borg, Claudia",
booktitle = "Proceedings of the Third Workshop on Deep Learning for Low-Resource Natural Language Processing",
month = jul,
year = "2022",
address = "Hybrid",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.deeplo-1.10",
doi = "10.18653/v1/2022.deeplo-1.10",
pages = "90--101",
}
```
|
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lmqg/qg_dequad
|
lmqg
| 2022-12-02T18:53:57Z | 106 | 1 |
[
"task_categories:text-generation",
"task_ids:language-modeling",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:deepset/germanquad",
"language:de",
"license:cc-by-4.0",
"question-generation",
"arxiv:2210.03992",
"region:us"
] |
[
"text-generation"
] | 2022-06-02T23:45:30Z |
---
license: cc-by-4.0
pretty_name: GermanQuAD for question generation
language: de
multilinguality: monolingual
size_categories: 10K<n<100K
source_datasets: deepset/germanquad
task_categories:
- text-generation
task_ids:
- language-modeling
tags:
- question-generation
---
# Dataset Card for "lmqg/qg_dequad"
## Dataset Description
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
- **Point of Contact:** [Asahi Ushio](http://asahiushio.com/)
### Dataset Summary
This is a subset of [QG-Bench](https://github.com/asahi417/lm-question-generation/blob/master/QG_BENCH.md#datasets), a unified question generation benchmark proposed in
["Generative Language Models for Paragraph-Level Question Generation: A Unified Benchmark and Evaluation, EMNLP 2022 main conference"](https://arxiv.org/abs/2210.03992).
This is a modified version of [GermanQuAD](https://huggingface.co/datasets/deepset/germanquad) for question generation (QG) task.
Since the original dataset only contains training/validation set, we manually sample test set from training set, which
has no overlap in terms of the paragraph with the training set.
### Supported Tasks and Leaderboards
* `question-generation`: The dataset is assumed to be used to train a model for question generation.
Success on this task is typically measured by achieving a high BLEU4/METEOR/ROUGE-L/BERTScore/MoverScore (see our paper for more in detail).
### Languages
Spanish (es)
## Dataset Structure
An example of 'train' looks as follows.
```
{
'answer': 'elektromagnetischer Linearführungen',
'question': 'Was kann den Verschleiß des seillosen Aufzuges minimieren?',
'sentence': 'Im Rahmen der Forschungen an dem seillosen Aufzug wird ebenfalls an der Entwicklung elektromagnetischer Linearführungen gearbeitet, um den Verschleiß der seillosen Aufzugsanlage bei hohem Fahrkomfort zu minimieren.',
'paragraph': "Aufzugsanlage\n\n=== Seilloser Aufzug ===\nAn der RWTH Aachen im Institut für Elektrische Maschinen wurde ein seilloser Aufzug entwickelt und ein Prototyp aufgebaut. Die Kabine wird hierbei durch z..."
'sentence_answer': "Im Rahmen der Forschungen an dem seillosen Aufzug wird ebenfalls an der Entwicklung <hl> elektromagnetischer Linearführungen <hl> gearbeitet, um den Verschleiß der seillosen Aufzugsanlage bei...",
'paragraph_answer': "Aufzugsanlage === Seilloser Aufzug === An der RWTH Aachen im Institut für Elektrische Maschinen wurde ein seilloser Aufzug entwickelt und ein Prototyp aufgebaut. Die Kabine wird hierbei durc...",
'paragraph_sentence': "Aufzugsanlage === Seilloser Aufzug === An der RWTH Aachen im Institut für Elektrische Maschinen wurde ein seilloser Aufzug entwickelt und ein Prototyp aufgebaut. Die Kabine wird hierbei du..."
}
```
## Data Fields
The data fields are the same among all splits.
- `question`: a `string` feature.
- `paragraph`: a `string` feature.
- `answer`: a `string` feature.
- `sentence`: a `string` feature.
- `paragraph_answer`: a `string` feature, which is same as the paragraph but the answer is highlighted by a special token `<hl>`.
- `paragraph_sentence`: a `string` feature, which is same as the paragraph but a sentence containing the answer is highlighted by a special token `<hl>`.
- `sentence_answer`: a `string` feature, which is same as the sentence but the answer is highlighted by a special token `<hl>`.
Each of `paragraph_answer`, `paragraph_sentence`, and `sentence_answer` feature is assumed to be used to train a question generation model,
but with different information. The `paragraph_answer` and `sentence_answer` features are for answer-aware question generation and
`paragraph_sentence` feature is for sentence-aware question generation.
### Data Splits
|train|validation|test |
|----:|---------:|----:|
|9314 | 2204 | 2204|
## Citation Information
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
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] |
bigbio/scitail
|
bigbio
| 2023-03-31T02:11:26Z | 106 | 1 |
[
"multilinguality:monolingual",
"language:en",
"license:apache-2.0",
"region:us"
] | null | 2022-07-02T20:53:40Z |
---
language:
- en
bigbio_language:
- English
license: apache-2.0
bigbio_license_shortname: APACHE_2p0
multilinguality: monolingual
pretty_name: SciTail
homepage: https://allenai.org/data/scitail
bigbio_pubmed: false
bigbio_public: true
bigbio_tasks:
- TEXTUAL_ENTAILMENT
paperswithcode_id: scitail
---
# Dataset Card for SciTail
## Dataset Description
- **Homepage:** https://allenai.org/data/scitail
- **Pubmed:** False
- **Public:** True
- **Tasks:** TE
The SciTail dataset is an entailment dataset created from multiple-choice science exams and web sentences. Each question and the correct answer choice are converted into an assertive statement to form the hypothesis. We use information retrieval to obtain relevant text from a large text corpus of web sentences, and use these sentences as a premise P. We crowd source the annotation of such premise-hypothesis pair as supports (entails) or not (neutral), in order to create the SciTail dataset. The dataset contains 27,026 examples with 10,101 examples with entails label and 16,925 examples with neutral label.
## Citation Information
```
@inproceedings{scitail,
author = {Tushar Khot and Ashish Sabharwal and Peter Clark},
booktitle = {AAAI}
title = {SciTail: A Textual Entailment Dataset from Science Question Answering},
year = {2018}
```
|
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DDSC/partial-danish-gigaword-small-test-sample
|
DDSC
| 2023-01-09T13:11:16Z | 106 | 0 |
[
"language:da",
"region:us"
] | null | 2023-01-09T13:07:16Z |
---
dataset_info:
features:
- name: text
dtype: string
- name: source
dtype: string
- name: doc_id
dtype: string
- name: LICENSE
dtype: string
- name: uri
dtype: string
- name: date_built
dtype: string
splits:
- name: train
num_bytes: 23816547.04337273
num_examples: 2411
download_size: 11686492
dataset_size: 23816547.04337273
language:
- da
pretty_name: Danish Gigaword Test Sample
---
# Dataset Card for "Danish Gigaword Test Sample"
This is a small sample of the dataset `DDSC/partial-danish-gigaword-no-twitter`. It is meant as a small dataset for testing code. It is constructed using the following code:
```python
from datasets import concatenate_datasets, load_dataset
# download dataset from huggingface
dataset = load_dataset("DDSC/partial-danish-gigaword-no-twitter")
# All of the dataset is available in the train split - we can simply:
dataset = dataset["train"]
# downsample it to three domains
legal = dataset.filter(lambda x: x["source"] == "retsinformationdk")
news = dataset.filter(lambda x: x["source"] == "tv2r")
speech = dataset.filter(lambda x: x["source"] == "spont")
# downsample to 1000 samples
legal = legal.select(range(1000))
news = news.select(range(1000))
# combine the three domains
dataset = concatenate_datasets([legal, news, speech])
# upload to hub
dataset.push_to_hub("DDSC/partial-danish-gigaword-small-test-sample")
```
|
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] |
Tuana/presidents
|
Tuana
| 2023-02-28T01:06:47Z | 106 | 1 |
[
"region:us"
] | null | 2023-02-28T00:51:03Z |
---
dataset_info:
features:
- name: id
dtype: string
- name: content
dtype: string
- name: content_type
dtype: string
- name: meta
struct:
- name: url
dtype: string
- name: _split_id
dtype: int64
- name: id_hash_keys
sequence: string
- name: score
dtype: 'null'
- name: embedding
dtype: 'null'
splits:
- name: train
num_bytes: 9366886
num_examples: 5529
download_size: 4997888
dataset_size: 9366886
---
# Dataset Card for "presidents"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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jpawan33/fkr30k-image-captioning-dataset
|
jpawan33
| 2023-09-09T04:17:11Z | 106 | 1 |
[
"region:us"
] | null | 2023-09-06T19:00:10Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 1625135945.608
num_examples: 31782
download_size: 1621386563
dataset_size: 1625135945.608
---
# Dataset Card for "fkr30k-image-captioning-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
thu-coai/SafetyBench
|
thu-coai
| 2023-09-14T05:25:39Z | 106 | 1 |
[
"license:mit",
"arxiv:2309.07045",
"region:us"
] | null | 2023-09-12T02:59:06Z |
---
license: mit
configs:
- config_name: test
data_files:
- split: zh
path: "test_zh.json"
- split: en
path: "test_en.json"
- split: zh_subset
path: "test_zh_subset.json"
- config_name: dev
data_files:
- split: zh
path: "dev_zh.json"
- split: en
path: "dev_en.json"
---
SafetyBench is a comprehensive benchmark for evaluating the safety of LLMs, which comprises 11,435 diverse multiple choice questions spanning across 7 distinct categories of safety concerns. Notably, SafetyBench also incorporates both Chinese and English data, facilitating the evaluation in both languages.
Please visit our [GitHub](https://github.com/thu-coai/SafetyBench) and [website](https://llmbench.ai/safety) or check our [paper](https://arxiv.org/abs/2309.07045) for more details.
We release three differents test sets including Chinese testset (`test_zh.json`), English testset (`test_en.json`) and Chinese subset testset (`test_zh_subset.json`). The Chinese subset testset removes samples with highly sensitive keywords to test API-based Chinese LLMs with strict filtering mechanism.
We also release 5 examples for each of the 7 safety categories in both Chinese (`dev_zh.json`) and English (`dev_en.json`), to enable few-shot evaluation.
Labels on the test sets are not released and users are required to submit their results to automatically obtain test accuracy. [How to submit?](https://github.com/thu-coai/SafetyBench#how-to-submit)
## Load the Data
```python
from datasets import load_dataset
test_dataset = load_dataset("thu-coai/SafetyBench", "test")
dev_dataset = load_dataset("thu-coai/SafetyBench", "dev")
```
More details about loading the data and evaluating LLMs could be found at our [github repository](https://github.com/thu-coai/SafetyBench).
Please cite our paper if you use our dataset.
```
@article{zhang2023safetybench,
title={SafetyBench: Evaluating the Safety of Large Language Models with Multiple Choice Questions},
author={Zhexin Zhang and Leqi Lei and Lindong Wu and Rui Sun and Yongkang Huang and Chong Long and Xiao Liu and Xuanyu Lei and Jie Tang and Minlie Huang},
journal={arXiv preprint arXiv:2309.07045},
year={2023}
}
```
|
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andreped/AeroPath
|
andreped
| 2023-11-13T22:36:21Z | 106 | 4 |
[
"task_categories:image-segmentation",
"size_categories:1B<n<10B",
"language:en",
"license:mit",
"medical",
"arxiv:2311.01138",
"region:us"
] |
[
"image-segmentation"
] | 2023-11-03T21:53:46Z |
---
license: mit
task_categories:
- image-segmentation
language:
- en
tags:
- medical
pretty_name: AeroPath
size_categories:
- 1B<n<10B
---
This README file was generated on 03-11-2023 by David Bouget.
It was then updated on 07-11-2023 to add Hugging Face metadata by André Pedersen.
Last updated: 07-11-2023.
----------------------
ACCESSING THE DATASET
----------------------
To access the data, it is HIGHLY RECOMMENDED to use the Python API.
See this notebook on how to get started: https://colab.research.google.com/gist/andreped/6070d1d2914a9ce5847d4b3e687188b7/aeropath-load-dataset-example.ipynb
--------------------
GENERAL INFORMATION
--------------------
1. Title of Dataset: AeroPath
2. Publication and DOI: "AeroPath: An airway segmentation benchmark dataset with challenging pathology". https://arxiv.org/abs/2311.01138
3. Contact Information
Name: Erlend F. Hofstad
Institution: Medical Technology Department, SINTEF Digital, Trondheim
Email: [email protected]
Website: https://www.sintef.no/en/all-employees/employee/erlend.hofstad/
4. Contributors: Karen-Helene Støverud, Haakon Olav Leira, Erlend F. Hofstad, Andre Pedersen, David Bouget, and Thomas Langø.
5. Kind of data: computed tomography angiography (CTA) scans and binary annotation masks, all stored as NifTI files (*.nii.gz).
6. Date of data collection/generation: .
7. Geographic location: Trondheim, Norway.
8. Funding sources: the Ministry of Health and Care Services of Norway through the Norwegian National Research Center for Minimally Invasive and Image-Guided Diagnostics and Therapy (MiDT) at St. Olavs hospital, Trondheim University Hospital, Trondheim, Norway. The research leading to these results has in addition received funding from the Norwegian Financial Mechanism 2014-2021 under the project RO- NO2019-0138, 19/2020 “Improving Cancer Diagnostics in Flexible Endoscopy using Artificial Intelligence and Medical Robotics” IDEAR, Contract No. 19/2020.
9. Description of dataset:
General description and ethics approvals: The dataset contains 27 computed tomography angiography
(CTA) scans, acquired using the Thorax Lung protocol at St. Olavs hospital (Trondheim, Norway). The included patients (nine women), aged 52 to 84 (median 70), were all undergoing diagnostic tests for lung cancer and had a wide range of pathologies including malignant tumors, sarcoidosis, and emphysema.
---------------------------
SHARING/ACCESS INFORMATION
---------------------------
1. Licenses/Restrictions:CC-BY 4.0 (See license.md).
2. Recommended citation: See citation recommended at https://github.com/raidionics/AeroPath.
---------------------
DATA & FILE OVERVIEW
---------------------
```
1. File List:
README.md
license.md
└── 1/
└── 1_CT_HR.nii.gz
└── 1_CT_HR_label_airways.nii.gz
└── 1_CT_HR_label_lungs.nii.gz
.
.
.
└── 27/
└── 27_CT_HR.nii.gz
└── 27_CT_HR_label_airways.nii.gz
└── 27_CT_HR_label_lungs.nii.gz
```
---------------------------
METHODOLOGICAL INFORMATION
---------------------------
1. Description of sources and methods used for collection/generation of data:
Dataset statistics
Overall, the CT scan dimensions are covering [487 : 512] × [441 : 512] × [241 : 829] voxels, and the trans-axial voxel size ranges [0.68 : 0.76] × [0.68 : 0.75] mm2 with a reconstructed slice thickness of [0.5 : 1.25] mm.
Annotation procedures
The annotation process for generating the ground truth was performed in three steps. First, the largest components (i.e., trachea and the first branches) were extracted based on a region growing, or a grow-cut method. Due to leakage, the region growing method did not yield satisfactory results in all cases. Therefore, for certain cases, the grow-cut method in 3D Slicer was used instead. In the second step, BronchiNet was employed to segment the smaller peripheral airways. In the third and final step, the segmentations were refined manually. Bronchial fragments and missed segments were connected, before false positives and fragments that could not be connected based on visual inspection were removed. All manual corrections were performed using the default segment editor in 3D Slicer. The manual correction was performed by a trained engineer, supervised by a pulmonologist. Finally, all annotations were verified on a case-by-case basis by a pulmonologist. The final annotations from the AeroPath segmentation included on average 128 ± 56 branches per CT scan.
|
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bh8648/esg1to3
|
bh8648
| 2023-11-14T14:01:13Z | 106 | 0 |
[
"region:us"
] | null | 2023-11-14T14:01:10Z |
---
dataset_info:
features:
- name: Major Category
dtype: string
- name: Middle Category
dtype: string
- name: Small Category
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 690585
num_examples: 170
download_size: 339311
dataset_size: 690585
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "esg1to3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
dbaezaj/lince_ner_dataset
|
dbaezaj
| 2023-11-15T17:11:50Z | 106 | 0 |
[
"region:us"
] | null | 2023-11-14T18:31:38Z |
---
dataset_info:
features:
- name: id
dtype: int64
- name: words
sequence: string
- name: lid
sequence: string
- name: labels
sequence: string
---
|
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] |
joseluhf11/oct-object-detection-v3-merge
|
joseluhf11
| 2023-11-22T08:49:02Z | 106 | 0 |
[
"region:us"
] | null | 2023-11-20T09:28:44Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: objects
struct:
- name: bbox
sequence:
sequence: int64
- name: categories
sequence: string
splits:
- name: train
num_bytes: 154014595.25
num_examples: 1246
download_size: 71638878
dataset_size: 154014595.25
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "oct-object-detection-v3-merge"
Dataset is composed of images with multiples object detection box in coco format (x,y,w,h). Images are OCT (type of eye scaner) with boxes indicating some features associated to AMD disease.
The unique difference from from v2 is categories field must have as many class label as there are boxes annotated in each image, even if the class label is the same. So for a image with 3 boxes for the same object, must have 3 class labels.
[Source datataset](https://doi.org/10.1101/2023.03.29.534704)
|
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Bingsu/Human_Action_Recognition
|
Bingsu
| 2022-07-05T02:48:56Z | 105 | 8 |
[
"task_categories:image-classification",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:odbl",
"region:us"
] |
[
"image-classification"
] | 2022-06-09T02:00:52Z |
---
language:
- en
license:
- odbl
pretty_name: Human Action Recognition
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- image-classification
---
## Dataset Description
- **Homepage:** [Human Action Recognition (HAR) Dataset](https://www.kaggle.com/datasets/meetnagadia/human-action-recognition-har-dataset)
- **Repository:** N/A
- **Paper:** N/A
- **Leaderboard:** N/A
- **Point of Contact:** N/A
## Dataset Summary
A dataset from [kaggle](https://www.kaggle.com/datasets/meetnagadia/human-action-recognition-har-dataset). origin: https://dphi.tech/challenges/data-sprint-76-human-activity-recognition/233/data
### Introduction
- The dataset features 15 different classes of Human Activities.
- The dataset contains about 12k+ labelled images including the validation images.
- Each image has only one human activity category and are saved in separate folders of the labelled classes
### PROBLEM STATEMENT
- Human Action Recognition (HAR) aims to understand human behavior and assign a label to each action. It has a wide range of applications, and therefore has been attracting increasing attention in the field of computer vision. Human actions can be represented using various data modalities, such as RGB, skeleton, depth, infrared, point cloud, event stream, audio, acceleration, radar, and WiFi signal, which encode different sources of useful yet distinct information and have various advantages depending on the application scenarios.
- Consequently, lots of existing works have attempted to investigate different types of approaches for HAR using various modalities.
- Your Task is to build an Image Classification Model using CNN that classifies to which class of activity a human is performing.
### About Files
- Train - contains all the images that are to be used for training your model. In this folder you will find 15 folders namely - 'calling', ’clapping’, ’cycling’, ’dancing’, ‘drinking’, ‘eating’, ‘fighting’, ‘hugging’, ‘laughing’, ‘listeningtomusic’, ‘running’, ‘sitting’, ‘sleeping’, texting’, ‘using_laptop’ which contain the images of the respective human activities.
- Test - contains 5400 images of Human Activities. For these images you are required to make predictions as the respective class names -'calling', ’clapping’, ’cycling’, ’dancing’, ‘drinking’, ‘eating’, ‘fighting’, ‘hugging’, ‘laughing’, ‘listeningtomusic’, ‘running’, ‘sitting’, ‘sleeping’, texting’, ‘using_laptop’.
- Testing_set.csv - this is the order of the predictions for each image that is to be submitted on the platform. Make sure the predictions you download are with their image’s filename in the same order as given in this file.
- sample_submission: This is a csv file that contains the sample submission for the data sprint.
### Data Fields
The data instances have the following fields:
- `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`.
- `labels`: an `int` classification label. All `test` data is labeled 0.
### Class Label Mappings:
```
{
'calling': 0,
'clapping': 1,
'cycling': 2,
'dancing': 3,
'drinking': 4,
'eating': 5,
'fighting': 6,
'hugging': 7,
'laughing': 8,
'listening_to_music': 9,
'running': 10,
'sitting': 11,
'sleeping': 12,
'texting': 13,
'using_laptop': 14
}
```
### Data Splits
| | train | test |
|---------------|--------|-----:|
| # of examples | 12600 | 5400 |
### Data Size
- download: 311.96 MiB
- generated: 312.59 MiB
- total: 624.55 MiB
```pycon
>>> from datasets import load_dataset
>>> ds = load_dataset("Bingsu/Human_Action_Recognition")
>>> ds
DatasetDict({
test: Dataset({
features: ['image', 'labels'],
num_rows: 5400
})
train: Dataset({
features: ['image', 'labels'],
num_rows: 12600
})
})
>>> ds["train"].features
{'image': Image(decode=True, id=None),
'labels': ClassLabel(num_classes=15, names=['calling', 'clapping', 'cycling', 'dancing', 'drinking', 'eating', 'fighting', 'hugging', 'laughing', 'listening_to_music', 'running', 'sitting', 'sleeping', 'texting', 'using_laptop'], id=None)}
>>> ds["train"][0]
{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=240x160>,
'labels': 11}
```
|
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] |
din0s/asqa
|
din0s
| 2022-09-20T16:14:54Z | 105 | 0 |
[
"task_categories:question-answering",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|ambig_qa",
"language:en",
"license:apache-2.0",
"factoid questions",
"long-form answers",
"arxiv:2204.06092",
"region:us"
] |
[
"question-answering"
] | 2022-09-19T22:25:51Z |
---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- expert-generated
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: ASQA
size_categories:
- 1K<n<10K
source_datasets:
- extended|ambig_qa
tags:
- factoid questions
- long-form answers
task_categories:
- question-answering
task_ids:
- open-domain-qa
---
# Dataset Card for ASQA
## 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)
- [Additional Information](#additional-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** https://github.com/google-research/language/tree/master/language/asqa
- **Paper:** https://arxiv.org/abs/2204.06092
- **Leaderboard:** https://ambigqa.github.io/asqa_leaderboard.html
### Dataset Summary
ASQA is the first long-form question answering dataset that focuses on ambiguous factoid questions. Different from previous long-form answers datasets, each question is annotated with both long-form answers and extractive question-answer pairs, which should be answerable by the generated passage. A generated long-form answer will be evaluated using both ROUGE and QA accuracy. In the paper, we show that these evaluation metrics are well-correlated with human judgments.
### Supported Tasks and Leaderboards
Long-form Question Answering. [Leaderboard](https://ambigqa.github.io/asqa_leaderboard.html)
### Languages
- English
## Dataset Structure
### Data Instances
```py
{
"ambiguous_question": "Where does the civil liberties act place the blame for the internment of u.s. citizens?",
"qa_pairs": [
{
"context": "No context provided",
"question": "Where does the civil liberties act place the blame for the internment of u.s. citizens by apologizing on behalf of them?",
"short_answers": [
"the people of the United States"
],
"wikipage": None
},
{
"context": "No context provided",
"question": "Where does the civil liberties act place the blame for the internment of u.s. citizens by making them pay reparations?",
"short_answers": [
"United States government"
],
"wikipage": None
}
],
"wikipages": [
{
"title": "Civil Liberties Act of 1988",
"url": "https://en.wikipedia.org/wiki/Civil%20Liberties%20Act%20of%201988"
}
],
"annotations": [
{
"knowledge": [
{
"content": "The Civil Liberties Act of 1988 (Pub.L. 100–383, title I, August 10, 1988, 102 Stat. 904, 50a U.S.C. § 1989b et seq.) is a United States federal law that granted reparations to Japanese Americans who had been interned by the United States government during World War II.",
"wikipage": "Civil Liberties Act of 1988"
}
],
"long_answer": "The Civil Liberties Act of 1988 is a United States federal law that granted reparations to Japanese Americans who had been interned by the United States government during World War II. In the act, the blame for the internment of U.S. citizens was placed on the people of the United States, by apologizing on behalf of them. Furthermore, the blame for the internment was placed on the United States government, by making them pay reparations."
}
],
"sample_id": -4557617869928758000
}
```
### Data Fields
- `ambiguous_question`: ambiguous question from AmbigQA.
- `annotations`: long-form answers to the ambiguous question constructed by ASQA annotators.
- `annotations/knowledge`: list of additional knowledge pieces.
- `annotations/knowledge/content`: a passage from Wikipedia.
- `annotations/knowledge/wikipage`: title of the Wikipedia page the passage was taken from.
- `annotations/long_answer`: annotation.
- `qa_pairs`: Q&A pairs from AmbigQA which are used for disambiguation.
- `qa_pairs/context`: additional context provided.
- `qa_pairs/question`: disambiguated question from AmbigQA.
- `qa_pairs/short_answers`: list of short answers from AmbigQA.
- `qa_pairs/wikipage`: title of the Wikipedia page the additional context was taken from.
- `sample_id`: the unique id of the sample
- `wikipages`: list of Wikipedia pages visited by AmbigQA annotators.
- `wikipages/title`: title of the Wikipedia page.
- `wikipages/url`: link to the Wikipedia page.
### Data Splits
| **Split** | **Instances** |
|-----------|---------------|
| Train | 4353 |
| Dev | 948 |
## Additional Information
### Contributions
Thanks to [@din0s](https://github.com/din0s) for adding this dataset.
|
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] |
qanastek/frenchmedmcqa
|
qanastek
| 2023-06-08T12:39:22Z | 105 | 5 |
[
"task_categories:question-answering",
"task_categories:multiple-choice",
"task_ids:multiple-choice-qa",
"task_ids:open-domain-qa",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1k<n<10k",
"source_datasets:original",
"language:fr",
"license:apache-2.0",
"region:us"
] |
[
"question-answering",
"multiple-choice"
] | 2023-01-08T20:22:47Z |
---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- fr
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 1k<n<10k
source_datasets:
- original
task_categories:
- question-answering
- multiple-choice
task_ids:
- multiple-choice-qa
- open-domain-qa
paperswithcode_id: frenchmedmcqa
pretty_name: FrenchMedMCQA
---
# Dataset Card for FrenchMedMCQA : A French Multiple-Choice Question Answering Corpus for Medical domain
## Table of Contents
- [Dataset Card for FrenchMedMCQA : A French Multiple-Choice Question Answering Corpus for Medical domain](#dataset-card-for-frenchmedmcqa--a-french-multiple-choice-question-answering-corpus-for-medical-domain)
- [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)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contact](#contact)
## Dataset Description
- **Homepage:** https://deft2023.univ-avignon.fr/
- **Repository:** https://deft2023.univ-avignon.fr/
- **Paper:** [FrenchMedMCQA: A French Multiple-Choice Question Answering Dataset for Medical domain](https://hal.science/hal-03824241/document)
- **Leaderboard:** Coming soon
- **Point of Contact:** [Yanis LABRAK](mailto:[email protected])
### Dataset Summary
This paper introduces FrenchMedMCQA, the first publicly available Multiple-Choice Question Answering (MCQA) dataset in French for medical domain. It is composed of 3,105 questions taken from real exams of the French medical specialization diploma in pharmacy, mixing single and multiple answers.
Each instance of the dataset contains an identifier, a question, five possible answers and their manual correction(s).
We also propose first baseline models to automatically process this MCQA task in order to report on the current performances and to highlight the difficulty of the task. A detailed analysis of the results showed that it is necessary to have representations adapted to the medical domain or to the MCQA task: in our case, English specialized models yielded better results than generic French ones, even though FrenchMedMCQA is in French. Corpus, models and tools are available online.
### Supported Tasks and Leaderboards
Multiple-Choice Question Answering (MCQA)
### Languages
The questions and answers are available in French.
## Dataset Structure
### Data Instances
```json
{
"id": "1863462668476003678",
"question": "Parmi les propositions suivantes, laquelle (lesquelles) est (sont) exacte(s) ? Les chylomicrons plasmatiques :",
"answers": {
"a": "Sont plus riches en cholestérol estérifié qu'en triglycérides",
"b": "Sont synthétisés par le foie",
"c": "Contiennent de l'apolipoprotéine B48",
"d": "Contiennent de l'apolipoprotéine E",
"e": "Sont transformés par action de la lipoprotéine lipase"
},
"correct_answers": [
"c",
"d",
"e"
],
"subject_name": "pharmacie",
"type": "multiple"
}
```
### Data Fields
- `id` : a string question identifier for each example
- `question` : question text (a string)
- `answer_a` : Option A
- `answer_b` : Option B
- `answer_c` : Option C
- `answer_d` : Option D
- `answer_e` : Option E
- `correct_answers` : Correct options, i.e., A, D and E
- `choice_type` ({"single", "multiple"}): Question choice type.
- "single": Single-choice question, where each choice contains a single option.
- "multiple": Multi-choice question, where each choice contains a combination of multiple options.
### Data Splits
| # Answers | Training | Validation | Test | Total |
|:---------:|:--------:|:----------:|:----:|:-----:|
| 1 | 595 | 164 | 321 | 1,080 |
| 2 | 528 | 45 | 97 | 670 |
| 3 | 718 | 71 | 141 | 930 |
| 4 | 296 | 30 | 56 | 382 |
| 5 | 34 | 2 | 7 | 43 |
| Total | 2171 | 312 | 622 | 3,105 |
## Dataset Creation
### Source Data
#### Initial Data Collection and Normalization
The questions and their associated candidate answer(s) were collected from real French pharmacy exams on the remede website. Questions and answers were manually created by medical experts and used during examinations. The dataset is composed of 2,025 questions with multiple answers and 1,080 with a single one, for a total of 3,105 questions. Each instance of the dataset contains an identifier, a question, five options (labeled from A to E) and correct answer(s). The average question length is 14.17 tokens and the average answer length is 6.44 tokens. The vocabulary size is of 13k words, of which 3.8k are estimated medical domain-specific words (i.e. a word related to the medical field). We find an average of 2.49 medical domain-specific words in each question (17 % of the words) and 2 in each answer (36 % of the words). On average, a medical domain-specific word is present in 2 questions and in 8 answers.
### Personal and Sensitive Information
The corpora is free of personal or sensitive information.
## Additional Information
### Dataset Curators
The dataset was created by Labrak Yanis and Bazoge Adrien and Dufour Richard and Daille Béatrice and Gourraud Pierre-Antoine and Morin Emmanuel and Rouvier Mickael.
### Licensing Information
Apache 2.0
### Citation Information
If you find this useful in your research, please consider citing the dataset paper :
```latex
@inproceedings{labrak-etal-2022-frenchmedmcqa,
title = "{F}rench{M}ed{MCQA}: A {F}rench Multiple-Choice Question Answering Dataset for Medical domain",
author = "Labrak, Yanis and
Bazoge, Adrien and
Dufour, Richard and
Daille, Beatrice and
Gourraud, Pierre-Antoine and
Morin, Emmanuel and
Rouvier, Mickael",
booktitle = "Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (LOUHI)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.louhi-1.5",
pages = "41--46",
abstract = "This paper introduces FrenchMedMCQA, the first publicly available Multiple-Choice Question Answering (MCQA) dataset in French for medical domain. It is composed of 3,105 questions taken from real exams of the French medical specialization diploma in pharmacy, mixing single and multiple answers. Each instance of the dataset contains an identifier, a question, five possible answers and their manual correction(s). We also propose first baseline models to automatically process this MCQA task in order to report on the current performances and to highlight the difficulty of the task. A detailed analysis of the results showed that it is necessary to have representations adapted to the medical domain or to the MCQA task: in our case, English specialized models yielded better results than generic French ones, even though FrenchMedMCQA is in French. Corpus, models and tools are available online.",
}
```
### Contact
Thanks to contact [Yanis LABRAK](https://github.com/qanastek) for more information about this dataset.
|
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] |
HighCWu/fill50k
|
HighCWu
| 2023-02-15T15:45:27Z | 105 | 0 |
[
"language:en",
"license:openrail",
"region:us"
] | null | 2023-02-15T12:48:42Z |
---
license: openrail
dataset_info:
features:
- name: image
dtype: image
- name: guide
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 454411979
num_examples: 50000
download_size: 316021131
dataset_size: 454411979
language:
- en
pretty_name: a
---
# Dataset Card for Fill50K
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset is converted from fill50k example dataset of [ControlNet](https://github.com/lllyasviel/ControlNet)
### 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
[fill50k.zip](https://huggingface.co/lllyasviel/ControlNet/blob/main/training/fill50k.zip)
#### 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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musabg/wikipedia-tr
|
musabg
| 2023-05-16T20:32:53Z | 105 | 5 |
[
"task_categories:fill-mask",
"task_categories:text-generation",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:tr",
"license:cc-by-sa-3.0",
"license:gfdl",
"wikipedia, wiki,",
"region:us"
] |
[
"fill-mask",
"text-generation"
] | 2023-02-24T03:02:31Z |
---
annotations_creators:
- no-annotation
language:
- tr
language_creators:
- crowdsourced
license:
- cc-by-sa-3.0
- gfdl
multilinguality: []
pretty_name: Turkish Wikipedia 2023
size_categories:
- 100K<n<1M
source_datasets:
- original
tags:
- wikipedia, wiki,
task_categories:
- fill-mask
- text-generation
task_ids:
- masked-language-modeling
dataset_info:
features:
- name: id
dtype: string
- name: url
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 956353353
num_examples: 520542
download_size: 529875169
dataset_size: 956353353
---
# 📖 Türkçe Vikipedi Mayıs 2023
Bu veri kümesi, Türkçe Vikipedi'den alınan makalelerin bir derlemesi olup, maskeleme dil modelleme ve metin oluşturma görevleri için tasarlanmıştır.
## 🗣️ Etiketlemeler
Bu veri kümesindeki makaleler, özellikle belirli bir görev için etiketlenmemiş olup, veri kümesi etiketsizdir.
## 🌐 Dil
Bu veri kümesi Türkçe yazılmış olup, gönüllülerden oluşan bir ekip tarafından topluluk katılımı yöntemleri ile oluşturulmuştur.
## 📜 Lisans
CC-BY-SA 3.0 ve GFDL
## 💻 Kaynak Veri Kümeleri
Bu veri kümesi, Türkçe Vikipedi'den oluşturulan orijinal bir veri kümesidir.
Türkçe Vikipedi veri kümesini kullandığınız için teşekkürler! Dil modelleme ve metin oluşturma görevleriniz için faydalı olmasını umuyoruz.
---
# 📖 Wikipedia Turkish 2023
This dataset is a collection of articles from the Turkish Wikipedia and is designed to be used for masked language modeling and text generation tasks.
## 📚 Dataset Info
Processed and cleaned using Huggingface wikipedia cleaner.
## 🗣️ Annotations
The articles in this dataset were not specifically annotated for any particular task, meaning that the dataset is unlabeled.
## 🌐 Language
This dataset is written in Turkish and was created using crowdsourcing methods by a team of volunteers.
## 📜 License
CC-BY-SA 3.0 and GFDL
## 💻 Source Datasets
This dataset is an original dataset created from the Turkish Wikipedia.
|
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danielpark/MQuAD-v1
|
danielpark
| 2023-04-07T12:21:48Z | 105 | 2 |
[
"task_categories:question-answering",
"task_categories:text-generation",
"language:en",
"language:ko",
"license:apache-2.0",
"biology",
"region:us"
] |
[
"question-answering",
"text-generation"
] | 2023-04-06T06:31:15Z |
---
license: apache-2.0
task_categories:
- question-answering
- text-generation
language:
- en
- ko
tags:
- biology
pretty_name: Medical domain QA dataset for training a medical chatbot.
---
# MQuAD
The Medical Question and Answering dataset(MQuAD) has been refined, including the following datasets. You can download it through the Hugging Face dataset. Use the DATASETS method as follows.
## Quick Guide
```python
from datasets import load_dataset
dataset = load_dataset("danielpark/MQuAD-v1")
```
Medical Q/A datasets gathered from the following websites.
- eHealth Forum
- iCliniq
- Question Doctors
- WebMD
Data was gathered at the 5th of May 2017.
The MQuAD provides embedded question and answer arrays in string format, so it is recommended to convert the string-formatted arrays into float format as follows. This measure has been applied to save resources and time used for embedding.
```python
from datasets import load_dataset
from utilfunction import col_convert
import pandas as pd
qa = load_dataset("danielpark/MQuAD-v1", "csv")
df_qa = pd.DataFrame(qa['train'])
df_qa = col_convert(df_qa, ['Q_FFNN_embeds', 'A_FFNN_embeds'])
```
|
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] |
clarin-knext/arguana-pl
|
clarin-knext
| 2023-06-07T08:18:37Z | 105 | 0 |
[
"language:pl",
"arxiv:2305.19840",
"region:us"
] | null | 2023-06-06T22:10:02Z |
---
language:
- pl
---
Part of **BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language**.
Link to arxiv: https://arxiv.org/pdf/2305.19840.pdf
Contact: [email protected]
|
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] |
shunk031/MSCOCO
|
shunk031
| 2023-10-30T14:06:39Z | 105 | 0 |
[
"task_categories:image-segmentation",
"task_categories:object-detection",
"task_categories:other",
"task_ids:instance-segmentation",
"task_ids:semantic-segmentation",
"task_ids:panoptic-segmentation",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"image-captioning",
"object-detection",
"keypoint-detection",
"stuff-segmentation",
"panoptic-segmentation",
"arxiv:1405.0312",
"region:us"
] |
[
"image-segmentation",
"object-detection",
"other"
] | 2023-09-09T08:15:05Z |
---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: MSCOCO
size_categories: []
source_datasets:
- original
tags:
- image-captioning
- object-detection
- keypoint-detection
- stuff-segmentation
- panoptic-segmentation
task_categories:
- image-segmentation
- object-detection
- other
task_ids:
- instance-segmentation
- semantic-segmentation
- panoptic-segmentation
---
# Dataset Card for MSCOCO
[](https://github.com/shunk031/huggingface-datasets_MSCOCO/actions/workflows/ci.yaml)
## Table of Contents
- [Dataset Card Creation Guide](#dataset-card-creation-guide)
- [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)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://cocodataset.org/#home
- **Repository:** https://github.com/shunk031/huggingface-datasets_MSCOCO
- **Paper (Preprint):** https://arxiv.org/abs/1405.0312
- **Paper (ECCV2014):** https://link.springer.com/chapter/10.1007/978-3-319-10602-1_48
- **Leaderboard (Detection):** https://cocodataset.org/#detection-leaderboard
- **Leaderboard (Keypoint):** https://cocodataset.org/#keypoints-leaderboard
- **Leaderboard (Stuff):** https://cocodataset.org/#stuff-leaderboard
- **Leaderboard (Panoptic):** https://cocodataset.org/#panoptic-leaderboard
- **Leaderboard (Captioning):** https://cocodataset.org/#captions-leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
> COCO is a large-scale object detection, segmentation, and captioning dataset. COCO has several features:
> - Object segmentation
> - Recognition in context
> - Superpixel stuff segmentation
> - 330K images (>200K labeled)
> - 1.5 million object instances
> - 80 object categories
> - 91 stuff categories
> - 5 captions per image
> - 250,000 people with keypoints
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
#### 2014
- captioning dataset
```python
import datasets as ds
dataset = ds.load_dataset(
"shunk031/MSCOCO",
year=2014,
coco_task="captions",
)
```
- instances dataset
```python
import datasets as ds
dataset = ds.load_dataset(
"shunk031/MSCOCO",
year=2014,
coco_task="instances",
decode_rle=True, # True if Run-length Encoding (RLE) is to be decoded and converted to binary mask.
)
```
- person keypoints dataset
```python
import datasets as ds
dataset = ds.load_dataset(
"shunk031/MSCOCO",
year=2014,
coco_task="person_keypoints",
decode_rle=True, # True if Run-length Encoding (RLE) is to be decoded and converted to binary mask.
)
```
#### 2017
- captioning dataset
```python
import datasets as ds
dataset = ds.load_dataset(
"shunk031/MSCOCO",
year=2017,
coco_task="captions",
)
```
- instances dataset
```python
import datasets as ds
dataset = ds.load_dataset(
"shunk031/MSCOCO",
year=2017,
coco_task="instances",
decode_rle=True, # True if Run-length Encoding (RLE) is to be decoded and converted to binary mask.
)
```
- person keypoints dataset
```python
import datasets as ds
dataset = ds.load_dataset(
"shunk031/MSCOCO",
year=2017,
coco_task="person_keypoints",
decode_rle=True, # True if Run-length Encoding (RLE) is to be decoded and converted to binary mask.
)
```
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### 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
> The annotations in this dataset along with this website belong to the COCO Consortium and are licensed under a [Creative Commons Attribution 4.0 License](https://creativecommons.org/licenses/by/4.0/legalcode).
>
> ## Images
> The COCO Consortium does not own the copyright of the images. Use of the images must abide by the Flickr Terms of Use. The users of the images accept full responsibility for the use of the dataset, including but not limited to the use of any copies of copyrighted images that they may create from the dataset.
>
> ## Software
> Copyright (c) 2015, COCO Consortium. All rights reserved. Redistribution and use software in source and binary form, with or without modification, are permitted provided that the following conditions are met:
> - Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
> - Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
> - Neither the name of the COCO Consortium nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
>
> THIS SOFTWARE AND ANNOTATIONS ARE PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
### Citation Information
```bibtex
@inproceedings{lin2014microsoft,
title={Microsoft coco: Common objects in context},
author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
booktitle={Computer Vision--ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13},
pages={740--755},
year={2014},
organization={Springer}
}
```
### Contributions
Thanks to [COCO Consortium](https://cocodataset.org/#people) for creating this dataset.
|
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] |
bri25yu-temp/bactrian
|
bri25yu-temp
| 2023-09-20T07:12:07Z | 105 | 0 |
[
"region:us"
] | null | 2023-09-20T06:55:27Z |
---
dataset_info:
features:
- name: prompts
sequence: string
- name: completions
sequence: string
splits:
- name: train
num_bytes: 545587063
num_examples: 92511
download_size: 236177873
dataset_size: 545587063
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "bactrian"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
kaitchup/opus-Indonesian-to-English
|
kaitchup
| 2023-11-01T19:15:17Z | 105 | 1 |
[
"region:us"
] | null | 2023-11-01T19:15:12Z |
---
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
- split: train
path: data/train-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: validation
num_bytes: 182024
num_examples: 2000
- name: train
num_bytes: 74451703
num_examples: 989529
download_size: 53126195
dataset_size: 74633727
---
# Dataset Card for "opus-id-en"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
pkarypis/ultrachat_filtered
|
pkarypis
| 2023-11-16T23:05:33Z | 105 | 0 |
[
"region:us"
] | null | 2023-11-16T22:51:33Z |
---
dataset_info:
features:
- name: prompt
dtype: string
- name: prompt_id
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: test_gen
num_bytes: 148276089
num_examples: 28304
- name: test_sft
num_bytes: 154695659
num_examples: 23110
- name: train_gen
num_bytes: 1347396812
num_examples: 256032
- name: train_sft
num_bytes: 1350777817.931667
num_examples: 200979
download_size: 1596770502
dataset_size: 3001146377.9316673
---
# Dataset Card for "ultrachat_filtered"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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KantoRegion/hp_conversations_with_hermione_granger_movie
|
KantoRegion
| 2023-11-20T16:12:20Z | 105 | 0 |
[
"task_categories:conversational",
"size_categories:1K<n<10K",
"language:en",
"region:us"
] |
[
"conversational"
] | 2023-11-17T15:24:45Z |
---
task_categories:
- conversational
language:
- en
size_categories:
- 1K<n<10K
---
|
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] |
hrithikpiyush/acl-arc
|
hrithikpiyush
| 2022-04-26T11:40:41Z | 104 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2022-04-26T05:00:57Z |
---
license: apache-2.0
---
|
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fmplaza/offendes
|
fmplaza
| 2023-03-27T08:19:06Z | 104 | 8 |
[
"language:es",
"license:apache-2.0",
"region:us"
] | null | 2022-06-16T14:32:03Z |
---
license: apache-2.0
language:
- es
---
# Dataset Card for OffendES
## 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)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Paper: OffendES:** [A New Corpus in Spanish for Offensive Language Research](https://aclanthology.org/2021.ranlp-1.123.pdf)
- **Leaderboard:** [Leaderboard for OffendES / Spanish](http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6388)
- **Point of Contact: [email protected]**
### Dataset Summary
Focusing on young influencers from the well-known social platforms of Twitter, Instagram, and YouTube, we have collected a corpus composed of Spanish comments manually labeled on offensive pre-defined categories. From the total corpus, we selected 30,416 posts to be publicly published, they correspond to the ones used in the MeOffendES competition at IberLEF 2021. The posts are labeled with the following categories:
- Offensive, the target is a person (OFP). Offensive text targeting a specific individual.
- Offensive, the target is a group of people or collective (OFG). Offensive text targeting a group of people belonging to the same ethnic group, gender or sexual orientation, political ideology, religious belief, or other common characteristics.
- Non-offensive, but with expletive language (NOE). A text that contains rude words, blasphemes, or swearwords but without the aim of offending, and usually with a positive connotation.
- Non-offensive (NO). Text that is neither offensive nor contains expletive language
### Supported Tasks and Leaderboards
This dataset is intended for multi-class offensive classification and binary offensive classification.
Competition [MeOffendES task on offensive detection for Spanish at IberLEF 2021](http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6388)
### Languages
- Spanish
## Dataset Structure
### Data Instances
For each instance, there is a string for the id of the tweet, a string for the emotion class, a string for the offensive class, and a string for the event. See the []() to explore more examples.
```
{'comment_id': '8003',
'influencer': 'dalas',
'comment': 'Estupido aburrido',
'label': 'NO',
'influencer_gender': 'man',
'media': youtube
}
```
### Data Fields
- `comment_id`: a string to identify the comment
- `influencer`: a string containing the influencer associated with the comment
- `comment`: a string containing the text of the comment
- `label`: a string containing the offensive gold label
- `influencer_gender`: a string containing the genre of the influencer
- `media`: a string containing the social media platform where the comment has been retrieved
### Data Splits
The OffendES dataset contains 3 splits: _train_, _validation_, and _test_. Below are the statistics for each class.
| OffendES | Number of Instances in Split per class| | |
| ------------- | ---------------------------------|---------------------------------|------------------------------------------|
| `Class` | `Train` | `Validation` | `Test` |
| NO | 13,212 | 64 | 9,651 |
| NOE | 1,235 | 22 | 2,340 |
| OFP | 2,051 | 10 | 1,404 |
| OFG | 212 | 4 | 211 |
| Total | 16,710 | 100 | 13,606 |
## Dataset Creation
### Source Data
Twitter, Youtube, Instagram
#### Who are the annotators?
Amazon Mechanical Turkers
## Additional Information
### Licensing Information
The OffendES dataset is released under the [Apache-2.0 License](http://www.apache.org/licenses/LICENSE-2.0).
### Citation Information
@inproceedings{plaza-del-arco-etal-2021-offendes,
title = "{O}ffend{ES}: A New Corpus in {S}panish for Offensive Language Research",
author = "{Plaza-del-Arco}, Flor Miriam and Montejo-R{\'a}ez, Arturo and Ure{\~n}a-L{\'o}pez, L. Alfonso and Mart{\'\i}n-Valdivia, Mar{\'\i}a-Teresa",
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
month = sep,
year = "2021",
address = "Held Online",
url = "https://aclanthology.org/2021.ranlp-1.123.pdf",
language = "English",
pages = "1096--1108"
}
@article{meoffendes2021,
title="{{Overview of MeOffendEs at IberLEF 2021: Offensive Language Detection in Spanish Variants}}",
author="{Flor Miriam Plaza-del-Arco and Casavantes, Marco and Jair Escalante, Hugo and Martín-Valdivia, M. Teresa and Montejo-Ráez, Arturo and {Montes-y-Gómez}, Manuel and Jarquín-Vásquez, Horacio and Villaseñor-Pineda, Luis}",
journal="Procesamiento del Lenguaje Natural",
url = "https://bit.ly/3QpRDfy",
volume="67",
pages="183--194",
year="2021"
}
|
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] |
center-for-humans-and-machines/style-diffusion
|
center-for-humans-and-machines
| 2023-06-30T17:45:02Z | 104 | 0 |
[
"region:us"
] | null | 2023-05-16T11:27:45Z |
---
dataset_info:
features:
- name: vectorId
dtype: string
- name: medianYear
dtype: int32
- name: embedding
sequence: float32
splits:
- name: train
num_bytes: 3448928
num_examples: 1113
download_size: 0
dataset_size: 3448928
---
# Dataset Card for "style-diffusion"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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nazimali/quran-question-answer-context
|
nazimali
| 2023-07-08T21:35:05Z | 104 | 3 |
[
"task_categories:question-answering",
"language:ar",
"language:en",
"license:cc-by-4.0",
"islam",
"quran",
"arabic",
"region:us"
] |
[
"question-answering"
] | 2023-07-03T12:07:47Z |
---
dataset_info:
features:
- name: q_id
dtype: int64
- name: question
dtype: string
- name: answer
dtype: string
- name: q_word
dtype: string
- name: q_topic
dtype: string
- name: fine_class
dtype: string
- name: class
dtype: string
- name: ontology_concept
dtype: string
- name: ontology_concept2
dtype: string
- name: source
dtype: string
- name: q_src_id
dtype: int64
- name: quetion_type
dtype: string
- name: chapter_name
dtype: string
- name: chapter_no
dtype: int64
- name: verse
sequence: string
- name: question_en
dtype: string
- name: answer_en
dtype: string
- name: q_word_en
dtype: string
- name: q_topic_en
dtype: string
- name: fine_class_en
dtype: string
- name: class_en
dtype: string
- name: ontology_concept_en
dtype: string
- name: chapter_name_en
dtype: string
- name: context
dtype: string
splits:
- name: train
num_bytes: 2226830.0310711367
num_examples: 978
- name: test
num_bytes: 557845.9689288634
num_examples: 245
download_size: 1515128
dataset_size: 2784676.0
license: cc-by-4.0
task_categories:
- question-answering
pretty_name: Quran Question Answer with Context
language:
- ar
- en
tags:
- islam
- quran
- arabic
---
# Dataset Card for "quran-question-answer-context"
## Dataset Summary
Translated the original dataset from Arabic to English and added the Surah ayahs to the `context` column.
## Usage
```python
from datasets import load_dataset
dataset = load_dataset("nazimali/quran-question-answer-context")
```
```python
DatasetDict({
train: Dataset({
features: ['q_id', 'question', 'answer', 'q_word', 'q_topic', 'fine_class', 'class', 'ontology_concept', 'ontology_concept2', 'source', 'q_src_id', 'quetion_type', 'chapter_name', 'chapter_no', 'verse', 'question_en', 'answer_en', 'q_word_en', 'q_topic_en', 'fine_class_en', 'class_en', 'ontology_concept_en', 'chapter_name_en', 'context'],
num_rows: 978
})
test: Dataset({
features: ['q_id', 'question', 'answer', 'q_word', 'q_topic', 'fine_class', 'class', 'ontology_concept', 'ontology_concept2', 'source', 'q_src_id', 'quetion_type', 'chapter_name', 'chapter_no', 'verse', 'question_en', 'answer_en', 'q_word_en', 'q_topic_en', 'fine_class_en', 'class_en', 'ontology_concept_en', 'chapter_name_en', 'context'],
num_rows: 245
})
})
```
## Translation Info
1. Translated the Arabic questions/concept columns to English with [Helsinki-NLP/opus-mt-ar-en](https://huggingface.co/Helsinki-NLP/opus-mt-ar-en)
2. Used `en-yusufali` translations for ayas [M-AI-C/quran-en-tafssirs](https://huggingface.co/datasets/M-AI-C/quran-en-tafssirs)
3. Renamed Surahs with [kheder/quran](https://huggingface.co/datasets/kheder/quran)
4. Added the ayahs that helped answer the questions
- Split the `ayah` columns string into a list of integers
- Concactenated the Surah:Ayah pairs into a sentence to the `context` column
Columns with the suffix `_en` contain the translations of the original columns.
## TODO
The `context` column has some `null` values that needs to be investigated and fixed
## Initial Data Collection
The original dataset is from **[Annotated Corpus of Arabic Al-Quran Question and Answer](https://archive.researchdata.leeds.ac.uk/464/)**
## Licensing Information
Original dataset [license](https://archive.researchdata.leeds.ac.uk/464/): **Creative Commons Attribution 4.0 International (CC BY 4.0)**
### Contributions
Original paper authors: Alqahtani, Mohammad and Atwell, Eric (2018) Annotated Corpus of Arabic Al-Quran Question and Answer. University of Leeds. https://doi.org/10.5518/356
|
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] |
dkoterwa/kor-sts
|
dkoterwa
| 2023-07-25T09:52:30Z | 104 | 1 |
[
"license:cc-by-sa-4.0",
"region:us"
] | null | 2023-07-18T14:17:23Z |
---
license: cc-by-sa-4.0
dataset_info:
features:
- name: id
dtype: int64
- name: genre
dtype: string
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: score
dtype: float64
splits:
- name: train
num_bytes: 1034815
num_examples: 5691
- name: valid
num_bytes: 297254
num_examples: 1465
- name: test
num_bytes: 247409
num_examples: 1376
download_size: 837346
dataset_size: 1579478
---
# Korean Semantic Textual Similarity (KorSTS) Dataset
For a better dataset description, please visit this GitHub repository prepared by the authors of the article: [LINK](https://github.com/kakaobrain/kor-nlu-datasets) <br>
<br>
**This dataset was prepared by converting tsv files from this repository.** The idea was to share the dataset for broader audience. I am not an original author of it. <br>
Because of the specifity of read_csv method from Pandas library, there are couple of observations, which had to be deleted because of the formatting (54 in train, 35 in valid, and 1 in test)
Additionaly, **None values have been removed from the dataset** (5 from train, 1 from eval, and 3 from test)
**How to download**
```
from datasets import load_dataset
data = load_dataset("dkoterwa/kor-sts")
```
**If you use this dataset for research, please cite this paper:**
```
@article{ham2020kornli,
title={KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding},
author={Ham, Jiyeon and Choe, Yo Joong and Park, Kyubyong and Choi, Ilji and Soh, Hyungjoon},
journal={arXiv preprint arXiv:2004.03289},
year={2020}
}
```
|
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] |
rwkv-x-dev/openorca-gpt4
|
rwkv-x-dev
| 2023-08-28T14:44:06Z | 104 | 3 |
[
"region:us"
] | null | 2023-08-28T14:38:13Z |
---
pretty_name: OpenOrca
configs:
- config_name: default
default: true
data_files:
- split: train
path:
- "*.parquet"
---
OpenOrca but just the GPT4 bits.
|
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open-llm-leaderboard/details_teknium__Mistral-Trismegistus-7B
|
open-llm-leaderboard
| 2023-10-25T09:46:22Z | 104 | 0 |
[
"region:us"
] | null | 2023-10-12T08:45:48Z |
---
pretty_name: Evaluation run of teknium/Mistral-Trismegistus-7B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [teknium/Mistral-Trismegistus-7B](https://huggingface.co/teknium/Mistral-Trismegistus-7B)\
\ 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_teknium__Mistral-Trismegistus-7B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-25T09:46:08.723071](https://huggingface.co/datasets/open-llm-leaderboard/details_teknium__Mistral-Trismegistus-7B/blob/main/results_2023-10-25T09-46-08.723071.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\": 0.010591442953020135,\n\
\ \"em_stderr\": 0.0010483469790502314,\n \"f1\": 0.07238674496644287,\n\
\ \"f1_stderr\": 0.001675223530701393,\n \"acc\": 0.4004875617305928,\n\
\ \"acc_stderr\": 0.010548628211357203\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.010591442953020135,\n \"em_stderr\": 0.0010483469790502314,\n\
\ \"f1\": 0.07238674496644287,\n \"f1_stderr\": 0.001675223530701393\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.09931766489764973,\n \
\ \"acc_stderr\": 0.008238371412683985\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7016574585635359,\n \"acc_stderr\": 0.012858885010030421\n\
\ }\n}\n```"
repo_url: https://huggingface.co/teknium/Mistral-Trismegistus-7B
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_12T08_45_24.509522
path:
- '**/details_harness|arc:challenge|25_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_25T09_46_08.723071
path:
- '**/details_harness|drop|3_2023-10-25T09-46-08.723071.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-25T09-46-08.723071.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_25T09_46_08.723071
path:
- '**/details_harness|gsm8k|5_2023-10-25T09-46-08.723071.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-25T09-46-08.723071.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_10_12T08_45_24.509522
path:
- '**/details_harness|hellaswag|10_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_10_12T08_45_24.509522
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-12T08-45-24.509522.parquet'
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- '**/details_harness|hendrycksTest-college_biology|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-12T08-45-24.509522.parquet'
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- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-12T08-45-24.509522.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_10_12T08_45_24.509522
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_10_12T08_45_24.509522
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_10_12T08_45_24.509522
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_10_12T08_45_24.509522
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_10_12T08_45_24.509522
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_10_12T08_45_24.509522
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_10_12T08_45_24.509522
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_10_12T08_45_24.509522
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_10_12T08_45_24.509522
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_10_12T08_45_24.509522
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_10_12T08_45_24.509522
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_10_12T08_45_24.509522
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_10_12T08_45_24.509522
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_10_12T08_45_24.509522
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_10_12T08_45_24.509522
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_10_12T08_45_24.509522
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_10_12T08_45_24.509522
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_10_12T08_45_24.509522
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_10_12T08_45_24.509522
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_10_12T08_45_24.509522
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_10_12T08_45_24.509522
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_10_12T08_45_24.509522
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_10_12T08_45_24.509522
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_10_12T08_45_24.509522
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_10_12T08_45_24.509522
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_10_12T08_45_24.509522
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_10_12T08_45_24.509522
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_10_12T08_45_24.509522
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
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path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_10_12T08_45_24.509522
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
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path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_10_12T08_45_24.509522
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
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path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
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path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-12T08-45-24.509522.parquet'
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data_files:
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- config_name: harness_hendrycksTest_marketing_5
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path:
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- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
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path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
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- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_10_12T08_45_24.509522
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
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path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_10_12T08_45_24.509522
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
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path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_10_12T08_45_24.509522
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_10_12T08_45_24.509522
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_10_12T08_45_24.509522
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_10_12T08_45_24.509522
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_10_12T08_45_24.509522
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-12T08-45-24.509522.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_10_12T08_45_24.509522
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-12T08-45-24.509522.parquet'
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path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
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- '**/details_harness|hendrycksTest-sociology|5_2023-10-12T08-45-24.509522.parquet'
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- '**/details_harness|hendrycksTest-sociology|5_2023-10-12T08-45-24.509522.parquet'
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data_files:
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path:
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- config_name: harness_hendrycksTest_virology_5
data_files:
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path:
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path:
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- config_name: harness_hendrycksTest_world_religions_5
data_files:
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path:
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path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
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path:
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path:
- '**/details_harness|truthfulqa:mc|0_2023-10-12T08-45-24.509522.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_25T09_46_08.723071
path:
- '**/details_harness|winogrande|5_2023-10-25T09-46-08.723071.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-25T09-46-08.723071.parquet'
- config_name: results
data_files:
- split: 2023_10_12T08_45_24.509522
path:
- results_2023-10-12T08-45-24.509522.parquet
- split: 2023_10_25T09_46_08.723071
path:
- results_2023-10-25T09-46-08.723071.parquet
- split: latest
path:
- results_2023-10-25T09-46-08.723071.parquet
---
# Dataset Card for Evaluation run of teknium/Mistral-Trismegistus-7B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/teknium/Mistral-Trismegistus-7B
- **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 [teknium/Mistral-Trismegistus-7B](https://huggingface.co/teknium/Mistral-Trismegistus-7B) 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_teknium__Mistral-Trismegistus-7B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-25T09:46:08.723071](https://huggingface.co/datasets/open-llm-leaderboard/details_teknium__Mistral-Trismegistus-7B/blob/main/results_2023-10-25T09-46-08.723071.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": 0.010591442953020135,
"em_stderr": 0.0010483469790502314,
"f1": 0.07238674496644287,
"f1_stderr": 0.001675223530701393,
"acc": 0.4004875617305928,
"acc_stderr": 0.010548628211357203
},
"harness|drop|3": {
"em": 0.010591442953020135,
"em_stderr": 0.0010483469790502314,
"f1": 0.07238674496644287,
"f1_stderr": 0.001675223530701393
},
"harness|gsm8k|5": {
"acc": 0.09931766489764973,
"acc_stderr": 0.008238371412683985
},
"harness|winogrande|5": {
"acc": 0.7016574585635359,
"acc_stderr": 0.012858885010030421
}
}
```
### 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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alexandrainst/wiki40b-da
|
alexandrainst
| 2023-10-27T19:08:09Z | 104 | 0 |
[
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:da",
"license:cc-by-sa-4.0",
"region:us"
] |
[
"text-generation"
] | 2023-10-27T18:47:11Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: wikidata_id
dtype: string
- name: text
dtype: string
- name: version_id
dtype: string
splits:
- name: train
num_bytes: 220855898
num_examples: 109486
- name: validation
num_bytes: 12416304
num_examples: 6173
- name: test
num_bytes: 12818380
num_examples: 6219
download_size: 150569852
dataset_size: 246090582
license: cc-by-sa-4.0
task_categories:
- text-generation
language:
- da
pretty_name: Wiki40b-da
size_categories:
- 100K<n<1M
---
# Dataset Card for "wiki40b-da"
## Dataset Description
- **Point of Contact:** [Dan Saattrup Nielsen](mailto:[email protected])
- **Size of downloaded dataset files:** 150.57 MB
- **Size of the generated dataset:** 246.09 MB
- **Total amount of disk used:** 396.66 MB
### Dataset Summary
This dataset is an upload of the Danish part of the [Wiki40b dataset](https://aclanthology.org/2020.lrec-1.297), being a cleaned version of a dump of Wikipedia.
The dataset is identical in content to [this dataset on the Hugging Face Hub](https://huggingface.co/datasets/wiki40b), but that one requires both `apache_beam`, `tensorflow` and `mwparserfromhell`, which can lead to dependency issues since these are not compatible with several newer packages.
The training, validation and test splits are the original ones.
### Languages
The dataset is available in Danish (`da`).
## Dataset Structure
### Data Instances
- **Size of downloaded dataset files:** 150.57 MB
- **Size of the generated dataset:** 246.09 MB
- **Total amount of disk used:** 396.66 MB
An example from the dataset looks as follows.
```
{
'wikidata_id': 'Q17341862',
'text': "\n_START_ARTICLE_\nÆgyptiske tekstiler\n_START_PARAGRAPH_\nTekstiler havde mange (...)",
'version_id': '9018011197452276273'
}
```
### Data Fields
The data fields are the same among all splits.
- `wikidata_id`: a `string` feature.
- `text`: a `string` feature.
- `version_id`: a `string` feature.
### Dataset Statistics
There are 109,486 samples in the training split, 6,173 samples in the validation split and 6,219 in the test split.
#### Document Length Distribution
## Additional Information
### Dataset Curators
[Dan Saattrup Nielsen](https://saattrupdan.github.io/) from the [The Alexandra
Institute](https://alexandra.dk/) uploaded it to the Hugging Face Hub.
### Licensing Information
The dataset is licensed under the [CC-BY-SA
license](https://creativecommons.org/licenses/by-sa/4.0/).
|
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adithya7/xlel_wd
|
adithya7
| 2022-07-13T07:46:57Z | 103 | 1 |
[
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:af",
"language:ar",
"language:be",
"language:bg",
"language:bn",
"language:ca",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:en",
"language:es",
"language:fa",
"language:fi",
"language:fr",
"language:he",
"language:hi",
"language:hu",
"language:id",
"language:it",
"language:ja",
"language:ko",
"language:ml",
"language:mr",
"language:ms",
"language:nl",
"language:no",
"language:pl",
"language:pt",
"language:ro",
"language:ru",
"language:si",
"language:sk",
"language:sl",
"language:sr",
"language:sv",
"language:sw",
"language:ta",
"language:te",
"language:th",
"language:tr",
"language:uk",
"language:vi",
"language:zh",
"license:cc-by-4.0",
"arxiv:2204.06535",
"region:us"
] |
[] | 2022-04-22T02:50:11Z |
---
annotations_creators:
- found
language_creators:
- found
language:
- af
- ar
- be
- bg
- bn
- ca
- cs
- da
- de
- el
- en
- es
- fa
- fi
- fr
- he
- hi
- hu
- id
- it
- ja
- ko
- ml
- mr
- ms
- nl
- 'no'
- pl
- pt
- ro
- ru
- si
- sk
- sl
- sr
- sv
- sw
- ta
- te
- th
- tr
- uk
- vi
- zh
license:
- cc-by-4.0
multilinguality:
- multilingual
pretty_name: XLEL-WD is a multilingual event linking dataset. This dataset contains
mention references in multilingual Wikipedia/Wikinews articles to event items from
Wikidata. The descriptions for Wikidata event items are taken from the corresponding
Wikipedia articles.
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories: []
task_ids: []
---
# Dataset Card for XLEL-WD
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** <https://github.com/adithya7/xlel-wd>
- **Repository:** <https://github.com/adithya7/xlel-wd>
- **Paper:** <https://arxiv.org/abs/2204.06535>
- **Leaderboard:** N/A
- **Point of Contact:** Adithya Pratapa
### Dataset Summary
XLEL-WD is a multilingual event linking dataset. This dataset repo contains mention references in multilingual Wikipedia/Wikinews articles to event items from Wikidata.
The descriptions for Wikidata event items were collected from the corresponding Wikipedia articles. Download the event dictionary from [adithya7/xlel_wd_dictionary](https://huggingface.co/datasets/adithya7/xlel_wd_dictionary).
### Supported Tasks and Leaderboards
This dataset can be used for the task of event linking. There are two variants of the task, multilingual and crosslingual.
- Multilingual linking: mention and the event descriptions are in the same language.
- Crosslingual linking: the event descriptions are only available in English.
### Languages
This dataset contains text from 44 languages. The language names and their ISO 639-1 codes are listed below. For details on the dataset distribution for each language, refer to the original paper.
| Language | Code | Language | Code | Language | Code | Language | Code |
| -------- | ---- | -------- | ---- | -------- | ---- | -------- | ---- |
| Afrikaans | af | Arabic | ar | Belarusian | be | Bulgarian | bg |
| Bengali | bn | Catalan | ca | Czech | cs | Danish | da |
| German | de | Greek | el | English | en | Spanish | es |
| Persian | fa | Finnish | fi | French | fr | Hebrew | he |
| Hindi | hi | Hungarian | hu | Indonesian | id | Italian | it |
| Japanese | ja | Korean | ko | Malayalam | ml | Marathi | mr |
| Malay | ms | Dutch | nl | Norwegian | no | Polish | pl |
| Portuguese | pt | Romanian | ro | Russian | ru | Sinhala | si |
| Slovak | sk | Slovene | sl | Serbian | sr | Swedish | sv |
| Swahili | sw | Tamil | ta | Telugu | te | Thai | th |
| Turkish | tr | Ukrainian | uk | Vietnamese | vi | Chinese | zh |
## Dataset Structure
### Data Instances
Each instance in the `train.jsonl`, `dev.jsonl` and `test.jsonl` files follow the below template.
```json
{
"context_left": "Minibaev's first major international medal came in the men's synchronized 10 metre platform event at the ",
"mention": "2010 European Championships",
"context_right": ".",
"context_lang": "en",
"label_id": "830917",
}
```
### Data Fields
| Field | Meaning |
| ----- | ------- |
| `mention` | text span of the mention |
| `context_left` | left paragraph context from the document |
| `context_right` | right paragraph context from the document |
| `context_lang` | language of the context (and mention) |
| `context_title` | document title of the mention (only Wikinews subset) |
| `context_date` | document publication date of the mention (only Wikinews subset) |
| `label_id` | Wikidata label ID for the event. E.g. 830917 refers to Q830917 from Wikidata. |
### Data Splits
The Wikipedia-based corpus has three splits. This is a zero-shot evaluation setup.
| | Train | Dev | Test | Total |
| ---- | :-----: | :---: | :----: | :-----: |
| Events | 8653 | 1090 | 1204 | 10947 |
| Event Sequences | 6758 | 844 | 846 | 8448 |
| Mentions | 1.44M | 165K | 190K | 1.8M |
| Languages | 44 | 44 | 44 | 44 |
The Wikinews-based evaluation set has two variants, one for cross-domain evaluation and another for zero-shot evaluation.
| | (Cross-domain) Test | (Zero-shot) Test |
| --- | :------------------: | :-----: |
| Events | 802 | 149 |
| Mentions | 2562 | 437 |
| Languages | 27 | 21 |
## Dataset Creation
### Curation Rationale
This dataset helps address the task of event linking. KB linking is extensively studied for entities, but its unclear if the same methodologies can be extended for linking mentions to events from KB. We use Wikidata as our KB, as it allows for linking mentions from multilingual Wikipedia and Wikinews articles.
### Source Data
#### Initial Data Collection and Normalization
First, we utilize spatial & temporal properties from Wikidata to identify event items. Second, we identify corresponding multilingual Wikipedia pages for each Wikidata event item. Third, we pool hyperlinks from multilingual Wikipedia & Wikinews articles to these event items.
#### Who are the source language producers?
The documents in XLEL-WD are written by Wikipedia and Wikinews contributors in respective languages.
### Annotations
#### Annotation process
This dataset was originally collected automatically from Wikipedia, Wikinews and Wikidata. It was post-processed to improve data quality.
#### Who are the annotators?
The annotations in XLEL-WD (hyperlinks from Wikipedia/Wikinews to Wikidata) are added the original Wiki contributors.
### 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
XLEL-WD v1.0.0 mostly caters to eventive nouns from Wikidata. It does not include any links to other event items from Wikidata such as disease outbreak (Q3241045), military offensive (Q2001676) and war (Q198).
## Additional Information
### Dataset Curators
The dataset was curated by Adithya Pratapa, Rishubh Gupta and Teruko Mitamura. The code for collecting the dataset is available at [Github:xlel-wd](https://github.com/adithya7/xlel-wd).
### Licensing Information
XLEL-WD dataset is released under [CC-BY-4.0 license](https://creativecommons.org/licenses/by/4.0/).
### Citation Information
```bib
@article{pratapa-etal-2022-multilingual,
title = {Multilingual Event Linking to Wikidata},
author = {Pratapa, Adithya and Gupta, Rishubh and Mitamura, Teruko},
publisher = {arXiv},
year = {2022},
url = {https://arxiv.org/abs/2204.06535},
}
```
### Contributions
Thanks to [@adithya7](https://github.com/adithya7) for adding this dataset.
|
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Multimodal-Fatima/Caltech101_not_background_test_facebook_opt_350m_Visclues_ns_5647_random
|
Multimodal-Fatima
| 2023-02-04T01:39:49Z | 103 | 0 |
[
"region:us"
] | null | 2023-02-04T01:14:54Z |
---
dataset_info:
features:
- name: id
dtype: int64
- name: image
dtype: image
- name: prompt
dtype: string
- name: true_label
dtype: string
- name: prediction
dtype: string
- name: scores
sequence: float64
splits:
- name: fewshot_1_bs_16
num_bytes: 86816473.125
num_examples: 5647
- name: fewshot_3_bs_16
num_bytes: 90734475.125
num_examples: 5647
download_size: 169654260
dataset_size: 177550948.25
---
# Dataset Card for "Caltech101_not_background_test_facebook_opt_350m_Visclues_ns_5647_random"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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shunk031/CAMERA
|
shunk031
| 2023-03-17T14:49:35Z | 103 | 4 |
[
"task_categories:text-generation",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:ja",
"license:cc-by-nc-sa-4.0",
"region:us"
] |
[
"text-generation"
] | 2023-03-17T14:18:03Z |
---
annotations_creators:
- crowdsourced
language:
- ja
language_creators:
- found
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
pretty_name: CAMERA
size_categories: []
source_datasets:
- original
tags: []
task_categories:
- text-generation
task_ids: []
---
# Dataset Card for CAMERA 📷
[](https://github.com/shunk031/huggingface-datasets_CAMERA/actions/workflows/ci.yaml)
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/CyberAgentAILab/camera
- **Repository:** https://github.com/shunk031/huggingface-datasets_CAMERA
### Dataset Summary
From [the official README.md](https://github.com/CyberAgentAILab/camera#camera-dataset):
> CAMERA (CyberAgent Multimodal Evaluation for Ad Text GeneRAtion) is the Japanese ad text generation dataset. We hope that our dataset will be useful in research for realizing more advanced ad text generation models.
### Supported Tasks and Leaderboards
[More Information Needed]
#### Supported Tasks
[More Information Needed]
#### Leaderboard
[More Information Needed]
### Languages
The language data in CAMERA is in Japanese ([BCP-47 ja-JP](https://www.rfc-editor.org/info/bcp47)).
## Dataset Structure
### Data Instances
When loading a specific configuration, users has to append a version dependent suffix:
#### without-lp-images
```python
from datasets import load_dataset
dataset = load_dataset("shunk031/CAMERA", name="without-lp-images")
print(dataset)
# DatasetDict({
# train: Dataset({
# features: ['asset_id', 'kw', 'lp_meta_description', 'title_org', 'title_ne1', 'title_ne2', 'title_ne3', 'domain', 'parsed_full_text_annotation'],
# num_rows: 12395
# })
# validation: Dataset({
# features: ['asset_id', 'kw', 'lp_meta_description', 'title_org', 'title_ne1', 'title_ne2', 'title_ne3', 'domain', 'parsed_full_text_annotation'],
# num_rows: 3098
# })
# test: Dataset({
# features: ['asset_id', 'kw', 'lp_meta_description', 'title_org', 'title_ne1', 'title_ne2', 'title_ne3', 'domain', 'parsed_full_text_annotation'],
# num_rows: 872
# })
# })
```
An example of the CAMERA (w/o LP images) dataset looks as follows:
```json
{
"asset_id": 13861,
"kw": "仙台 ホテル",
"lp_meta_description": "仙台のホテルや旅館をお探しなら楽天トラベルへ！楽天ポイントが使えて、貯まって、とってもお得な宿泊予約サイトです。さらに割引クーポンも使える！国内ツアー・航空券・レンタカー・バス予約も！",
"title_org": "仙台市のホテル",
"title_ne1": "",
"title_ne2": "",
"title_ne3": "",
"domain": "",
"parsed_full_text_annotation": {
"text": [
"trivago",
"Oops...AccessDenied 可",
"Youarenotallowedtoviewthispage!Ifyouthinkthisisanerror,pleasecontacttrivago.",
"Errorcode:0.3c99e86e.1672026945.25ba640YourIP:240d:1a:4d8:2800:b9b0:ea86:2087:d141AffectedURL:https://www.trivago.jp/ja/odr/%E8%BB%92", "%E4%BB%99%E5%8F%B0-%E5%9B%BD%E5%86%85?search=20072325",
"Backtotrivago"
],
"xmax": [
653,
838,
765,
773,
815,
649
],
"xmin": [
547,
357,
433,
420,
378,
550
],
"ymax": [
47,
390,
475,
558,
598,
663
],
"ymin": [
18,
198,
439,
504,
566,
651
]
}
}
```
#### with-lp-images
```python
from datasets import load_dataset
dataset = load_dataset("shunk031/CAMERA", name="with-lp-images")
print(dataset)
# DatasetDict({
# train: Dataset({
# features: ['asset_id', 'kw', 'lp_meta_description', 'title_org', 'title_ne1', 'title_ne2', 'title_ne3', 'domain', 'parsed_full_text_annotation', 'lp_image'],
# num_rows: 12395
# })
# validation: Dataset({
# features: ['asset_id', 'kw', 'lp_meta_description', 'title_org', 'title_ne1', 'title_ne2', 'title_ne3', 'domain', 'parsed_full_text_annotation', 'lp_image'],
# num_rows: 3098
# })
# test: Dataset({
# features: ['asset_id', 'kw', 'lp_meta_description', 'title_org', 'title_ne1', 'title_ne2', 'title_ne3', 'domain', 'parsed_full_text_annotation', 'lp_image'],
# num_rows: 872
# })
# })
```
An example of the CAMERA (w/ LP images) dataset looks as follows:
```json
{
"asset_id": 13861,
"kw": "仙台 ホテル",
"lp_meta_description": "仙台のホテルや旅館をお探しなら楽天トラベルへ！楽天ポイントが使えて、貯まって、とってもお得な宿泊予約サイトです。さらに割引クーポンも使える！国内ツアー・航空券・レンタカー・バス予約も！",
"title_org": "仙台市のホテル",
"title_ne1": "",
"title_ne2": "",
"title_ne3": "",
"domain": "",
"parsed_full_text_annotation": {
"text": [
"trivago",
"Oops...AccessDenied 可",
"Youarenotallowedtoviewthispage!Ifyouthinkthisisanerror,pleasecontacttrivago.",
"Errorcode:0.3c99e86e.1672026945.25ba640YourIP:240d:1a:4d8:2800:b9b0:ea86:2087:d141AffectedURL:https://www.trivago.jp/ja/odr/%E8%BB%92", "%E4%BB%99%E5%8F%B0-%E5%9B%BD%E5%86%85?search=20072325",
"Backtotrivago"
],
"xmax": [
653,
838,
765,
773,
815,
649
],
"xmin": [
547,
357,
433,
420,
378,
550
],
"ymax": [
47,
390,
475,
558,
598,
663
],
"ymin": [
18,
198,
439,
504,
566,
651
]
},
"lp_image": <PIL.PngImagePlugin.PngImageFile image mode=RGBA size=1200x680 at 0x7F8513446B20>
}
```
### Data Fields
#### without-lp-images
- `asset_id`: ids (associated with LP images)
- `kw`: search keyword
- `lp_meta_description`: meta description extracted from LP (i.e., LP Text)
- `title_org`: ad text (original gold reference)
- `title_ne{1-3}`: ad text (additonal gold references for multi-reference evaluation)
- `domain`: industry domain (HR, EC, Fin, Edu) for industry-wise evaluation
- `parsed_full_text_annotation`: OCR results for LP images
#### with-lp-images
- `asset_id`: ids (associated with LP images)
- `kw`: search keyword
- `lp_meta_description`: meta description extracted from LP (i.e., LP Text)
- `title_org`: ad text (original gold reference)
- `title_ne{1-3}`: ad text (additional gold references for multi-reference evaluation)
- `domain`: industry domain (HR, EC, Fin, Edu) for industry-wise evaluation
- `parsed_full_text_annotation`: OCR results for LP images
- `lp_image`: Landing page (LP) image
### Data Splits
From [the official paper](https://www.anlp.jp/proceedings/annual_meeting/2023/pdf_dir/H11-4.pdf):
| Split | # of data | # of reference ad text | industry domain label |
|-------|----------:|-----------------------:|:---------------------:|
| Train | 12,395 | 1 | - |
| Valid | 3,098 | 1 | - |
| Test | 869 | 4 | ✔ |
## 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
[More Information Needed]
### Dataset Curators
[More Information Needed]
### Licensing Information
> This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
### Citation Information
```bibtex
@inproceedings{mita-et-al:nlp2023,
author = "三田 雅人 and 村上 聡一朗 and 張 培楠",
title = "広告文生成タスクの規定とベンチマーク構築",
booktitle = "言語処理学会 第 29 回年次大会",
year = 2023,
}
```
### Contributions
Thanks to [Masato Mita](https://github.com/chemicaltree), [Soichiro Murakami](https://github.com/ichiroex), and [Peinan Zhang](https://github.com/peinan) for creating this dataset.
|
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c-s-ale/alpaca-gpt4-data-zh
|
c-s-ale
| 2023-05-03T17:56:55Z | 103 | 22 |
[
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:zh",
"license:cc-by-4.0",
"gpt",
"alpaca",
"fine-tune",
"instruct-tune",
"instruction",
"arxiv:2304.03277",
"region:us"
] |
[
"text-generation"
] | 2023-04-07T19:22:10Z |
---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 32150579
num_examples: 48818
download_size: 35100559
dataset_size: 32150579
license: cc-by-4.0
language:
- zh
pretty_name: Instruction Tuning with GPT-4
size_categories:
- 10K<n<100K
task_categories:
- text-generation
tags:
- gpt
- alpaca
- fine-tune
- instruct-tune
- instruction
---
# Dataset Description
- **Project Page:** https://instruction-tuning-with-gpt-4.github.io
- **Repo:** https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM
- **Paper:** https://arxiv.org/abs/2304.03277
# Dataset Card for "alpaca-gpt4-data-zh"
All of the work is done by [this team](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM).
# Usage and License Notices
The data is intended and licensed for research use only. The dataset is CC BY NC 4.0 (allowing only non-commercial use) and models trained using the dataset should not be used outside of research purposes.
# English Dataset
[Found here](https://huggingface.co/datasets/c-s-ale/alpaca-gpt4-data)
# Citation
```
@article{peng2023gpt4llm,
title={Instruction Tuning with GPT-4},
author={Baolin Peng, Chunyuan Li, Pengcheng He, Michel Galley, Jianfeng Gao},
journal={arXiv preprint arXiv:2304.03277},
year={2023}
}
```
|
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] |
takaaki-inada/databricks-dolly-15k-ja-zundamon
|
takaaki-inada
| 2023-06-17T10:41:52Z | 103 | 3 |
[
"license:cc-by-sa-3.0",
"region:us"
] | null | 2023-06-17T10:35:48Z |
---
license: cc-by-sa-3.0
---
This dataset was based on "kunishou/databricks-dolly-15k-ja".
This dataset is licensed under CC BY SA 3.0
Last Update : 2023-05-11
databricks-dolly-15k-ja
https://github.com/kunishou/databricks-dolly-15k-ja
databricks-dolly-15k
https://github.com/databrickslabs/dolly/tree/master/data
|
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open-llm-leaderboard/details_meta-llama__Llama-2-13b-hf
|
open-llm-leaderboard
| 2023-12-02T13:12:01Z | 103 | 0 |
[
"region:us"
] | null | 2023-08-19T22:35:59Z |
---
pretty_name: Evaluation run of meta-llama/Llama-2-13b-hf
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 123 configuration, each one coresponding to one of\
\ the evaluated task.\n\nThe dataset has been created from 8 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_meta-llama__Llama-2-13b-hf\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T13:11:49.394544](https://huggingface.co/datasets/open-llm-leaderboard/details_meta-llama__Llama-2-13b-hf/blob/main/results_2023-12-02T13-11-49.394544.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\": 0.22820318423047764,\n\
\ \"acc_stderr\": 0.011559914877317397\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.22820318423047764,\n \"acc_stderr\": 0.011559914877317397\n\
\ }\n}\n```"
repo_url: https://huggingface.co/meta-llama/Llama-2-13b-hf
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_19T22_35_38.117975
path:
- '**/details_harness|arc:challenge|25_2023-08-19T22:35:38.117975.parquet'
- split: 2023_08_23T17_28_00.015478
path:
- '**/details_harness|arc:challenge|25_2023-08-23T17:28:00.015478.parquet'
- split: 2023_08_29T22_26_02.660247
path:
- '**/details_harness|arc:challenge|25_2023-08-29T22:26:02.660247.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-29T22:26:02.660247.parquet'
- config_name: harness_drop_0
data_files:
- split: 2023_09_15T14_07_08.353318
path:
- '**/details_harness|drop|0_2023-09-15T14-07-08.353318.parquet'
- split: latest
path:
- '**/details_harness|drop|0_2023-09-15T14-07-08.353318.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_08T14_32_14.957248
path:
- '**/details_harness|drop|3_2023-09-08T14-32-14.957248.parquet'
- split: 2023_10_14T23_00_26.644553
path:
- '**/details_harness|drop|3_2023-10-14T23-00-26.644553.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-14T23-00-26.644553.parquet'
- config_name: harness_gsm8k_5
data_files:
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path:
- '**/details_harness|gsm8k|5_2023-09-08T14-32-14.957248.parquet'
- split: 2023_10_14T23_00_26.644553
path:
- '**/details_harness|gsm8k|5_2023-10-14T23-00-26.644553.parquet'
- split: 2023_12_02T13_11_49.394544
path:
- '**/details_harness|gsm8k|5_2023-12-02T13-11-49.394544.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T13-11-49.394544.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_19T22_35_38.117975
path:
- '**/details_harness|hellaswag|10_2023-08-19T22:35:38.117975.parquet'
- split: 2023_08_23T17_28_00.015478
path:
- '**/details_harness|hellaswag|10_2023-08-23T17:28:00.015478.parquet'
- split: 2023_08_29T22_26_02.660247
path:
- '**/details_harness|hellaswag|10_2023-08-29T22:26:02.660247.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-29T22:26:02.660247.parquet'
- config_name: harness_hendrycksTest_5
data_files:
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path:
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- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-19T22:35:38.117975.parquet'
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- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-19T22:35:38.117975.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-19T22:35:38.117975.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-19T22:35:38.117975.parquet'
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- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-19T22:35:38.117975.parquet'
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- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-19T22:35:38.117975.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-19T22:35:38.117975.parquet'
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- '**/details_harness|hendrycksTest-virology|5_2023-08-19T22:35:38.117975.parquet'
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- split: 2023_08_23T17_28_00.015478
path:
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- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-23T17:28:00.015478.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-23T17:28:00.015478.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-23T17:28:00.015478.parquet'
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- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-23T17:28:00.015478.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-23T17:28:00.015478.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-23T17:28:00.015478.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-23T17:28:00.015478.parquet'
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- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-23T17:28:00.015478.parquet'
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- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-23T17:28:00.015478.parquet'
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- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-23T17:28:00.015478.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-23T17:28:00.015478.parquet'
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- '**/details_harness|hendrycksTest-management|5_2023-08-23T17:28:00.015478.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-23T17:28:00.015478.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-23T17:28:00.015478.parquet'
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- '**/details_harness|hendrycksTest-nutrition|5_2023-08-23T17:28:00.015478.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-23T17:28:00.015478.parquet'
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- '**/details_harness|hendrycksTest-security_studies|5_2023-08-23T17:28:00.015478.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-23T17:28:00.015478.parquet'
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- '**/details_harness|hendrycksTest-virology|5_2023-08-23T17:28:00.015478.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-23T17:28:00.015478.parquet'
- split: 2023_08_29T22_26_02.660247
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-29T22:26:02.660247.parquet'
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- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-29T22:26:02.660247.parquet'
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- '**/details_harness|hendrycksTest-computer_security|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-29T22:26:02.660247.parquet'
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- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-29T22:26:02.660247.parquet'
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- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-29T22:26:02.660247.parquet'
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- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-29T22:26:02.660247.parquet'
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- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-29T22:26:02.660247.parquet'
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- '**/details_harness|hendrycksTest-marketing|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-29T22:26:02.660247.parquet'
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- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-29T22:26:02.660247.parquet'
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- '**/details_harness|hendrycksTest-security_studies|5_2023-08-29T22:26:02.660247.parquet'
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- '**/details_harness|hendrycksTest-virology|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-29T22:26:02.660247.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-29T22:26:02.660247.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-29T22:26:02.660247.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
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path:
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- split: 2023_08_23T17_28_00.015478
path:
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- split: 2023_08_29T22_26_02.660247
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-29T22:26:02.660247.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-29T22:26:02.660247.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
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path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-19T22:35:38.117975.parquet'
- split: 2023_08_23T17_28_00.015478
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-23T17:28:00.015478.parquet'
- split: 2023_08_29T22_26_02.660247
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-29T22:26:02.660247.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-29T22:26:02.660247.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
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path:
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- split: 2023_08_23T17_28_00.015478
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-23T17:28:00.015478.parquet'
- split: 2023_08_29T22_26_02.660247
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-29T22:26:02.660247.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-29T22:26:02.660247.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
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path:
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path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-23T17:28:00.015478.parquet'
- split: 2023_08_29T22_26_02.660247
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-29T22:26:02.660247.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-29T22:26:02.660247.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
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path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-19T22:35:38.117975.parquet'
- split: 2023_08_23T17_28_00.015478
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-23T17:28:00.015478.parquet'
- split: 2023_08_29T22_26_02.660247
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-29T22:26:02.660247.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-29T22:26:02.660247.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-19T22:35:38.117975.parquet'
- split: 2023_08_23T17_28_00.015478
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-23T17:28:00.015478.parquet'
- split: 2023_08_29T22_26_02.660247
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-29T22:26:02.660247.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-29T22:26:02.660247.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-19T22:35:38.117975.parquet'
- split: 2023_08_23T17_28_00.015478
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-23T17:28:00.015478.parquet'
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path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-29T22:26:02.660247.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-29T22:26:02.660247.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-19T22:35:38.117975.parquet'
- split: 2023_08_23T17_28_00.015478
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-23T17:28:00.015478.parquet'
- split: 2023_08_29T22_26_02.660247
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-29T22:26:02.660247.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-29T22:26:02.660247.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-19T22:35:38.117975.parquet'
- split: 2023_08_23T17_28_00.015478
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-23T17:28:00.015478.parquet'
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path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-29T22:26:02.660247.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-29T22:26:02.660247.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-19T22:35:38.117975.parquet'
- split: 2023_08_23T17_28_00.015478
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-23T17:28:00.015478.parquet'
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path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-29T22:26:02.660247.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-29T22:26:02.660247.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-19T22:35:38.117975.parquet'
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path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-23T17:28:00.015478.parquet'
- split: 2023_08_29T22_26_02.660247
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-29T22:26:02.660247.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-29T22:26:02.660247.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
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path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-19T22:35:38.117975.parquet'
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path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-23T17:28:00.015478.parquet'
- split: 2023_08_29T22_26_02.660247
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-29T22:26:02.660247.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-29T22:26:02.660247.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-19T22:35:38.117975.parquet'
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path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-23T17:28:00.015478.parquet'
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path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-29T22:26:02.660247.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-29T22:26:02.660247.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-19T22:35:38.117975.parquet'
- split: 2023_08_23T17_28_00.015478
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-23T17:28:00.015478.parquet'
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path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-29T22:26:02.660247.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-29T22:26:02.660247.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
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path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-19T22:35:38.117975.parquet'
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path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-23T17:28:00.015478.parquet'
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path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-29T22:26:02.660247.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-29T22:26:02.660247.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-19T22:35:38.117975.parquet'
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path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-23T17:28:00.015478.parquet'
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path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-29T22:26:02.660247.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-29T22:26:02.660247.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
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path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-19T22:35:38.117975.parquet'
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path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-23T17:28:00.015478.parquet'
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path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-29T22:26:02.660247.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-29T22:26:02.660247.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
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path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-19T22:35:38.117975.parquet'
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path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-23T17:28:00.015478.parquet'
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path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-29T22:26:02.660247.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-29T22:26:02.660247.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-19T22:35:38.117975.parquet'
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path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-23T17:28:00.015478.parquet'
- split: 2023_08_29T22_26_02.660247
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-29T22:26:02.660247.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-29T22:26:02.660247.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-19T22:35:38.117975.parquet'
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path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-23T17:28:00.015478.parquet'
- split: 2023_08_29T22_26_02.660247
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-29T22:26:02.660247.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-29T22:26:02.660247.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
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path:
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- split: 2023_08_23T17_28_00.015478
path:
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- split: 2023_08_29T22_26_02.660247
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-29T22:26:02.660247.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_19T22_35_38.117975
path:
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- split: 2023_08_23T17_28_00.015478
path:
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- split: 2023_08_29T22_26_02.660247
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-29T22:26:02.660247.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-29T22:26:02.660247.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
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path:
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- split: 2023_08_23T17_28_00.015478
path:
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- split: 2023_08_29T22_26_02.660247
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-29T22:26:02.660247.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_19T22_35_38.117975
path:
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- split: 2023_08_23T17_28_00.015478
path:
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- split: 2023_08_29T22_26_02.660247
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-29T22:26:02.660247.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
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path:
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- split: 2023_08_23T17_28_00.015478
path:
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- split: 2023_08_29T22_26_02.660247
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-29T22:26:02.660247.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-29T22:26:02.660247.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
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path:
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- split: 2023_08_23T17_28_00.015478
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-23T17:28:00.015478.parquet'
- split: 2023_08_29T22_26_02.660247
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-29T22:26:02.660247.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-29T22:26:02.660247.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
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path:
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- split: 2023_08_23T17_28_00.015478
path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
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path:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
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path:
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- split: 2023_08_23T17_28_00.015478
path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
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path:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
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path:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_human_aging_5
data_files:
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path:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
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path:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_international_law_5
data_files:
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path:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
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path:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
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path:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_machine_learning_5
data_files:
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path:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_management_5
data_files:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_marketing_5
data_files:
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path:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_nutrition_5
data_files:
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path:
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path:
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path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-29T22:26:02.660247.parquet'
- split: latest
path:
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- config_name: harness_hendrycksTest_philosophy_5
data_files:
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path:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_prehistory_5
data_files:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
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path:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_professional_law_5
data_files:
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path:
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path:
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path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-29T22:26:02.660247.parquet'
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path:
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- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
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path:
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path:
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- split: 2023_08_29T22_26_02.660247
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-29T22:26:02.660247.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-29T22:26:02.660247.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_19T22_35_38.117975
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-19T22:35:38.117975.parquet'
- split: 2023_08_23T17_28_00.015478
path:
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- split: 2023_08_29T22_26_02.660247
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-29T22:26:02.660247.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
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path:
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- split: 2023_08_23T17_28_00.015478
path:
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- split: 2023_08_29T22_26_02.660247
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-29T22:26:02.660247.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-29T22:26:02.660247.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
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path:
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- split: 2023_08_23T17_28_00.015478
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-23T17:28:00.015478.parquet'
- split: 2023_08_29T22_26_02.660247
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-29T22:26:02.660247.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-19T22:35:38.117975.parquet'
- split: 2023_08_23T17_28_00.015478
path:
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path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-29T22:26:02.660247.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-29T22:26:02.660247.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
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path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-19T22:35:38.117975.parquet'
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path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-23T17:28:00.015478.parquet'
- split: 2023_08_29T22_26_02.660247
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-29T22:26:02.660247.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_19T22_35_38.117975
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-19T22:35:38.117975.parquet'
- split: 2023_08_23T17_28_00.015478
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-23T17:28:00.015478.parquet'
- split: 2023_08_29T22_26_02.660247
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-29T22:26:02.660247.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-29T22:26:02.660247.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
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path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-19T22:35:38.117975.parquet'
- split: 2023_08_23T17_28_00.015478
path:
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- split: 2023_08_29T22_26_02.660247
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-29T22:26:02.660247.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
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path:
- '**/details_harness|truthfulqa:mc|0_2023-08-19T22:35:38.117975.parquet'
- split: 2023_08_23T17_28_00.015478
path:
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- split: 2023_08_29T22_26_02.660247
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-29T22:26:02.660247.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-29T22:26:02.660247.parquet'
- config_name: harness_winogrande_5
data_files:
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path:
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- split: 2023_10_14T23_00_26.644553
path:
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- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-14T23-00-26.644553.parquet'
- config_name: original_mmlu_5
data_files:
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path:
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path:
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data_files:
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path:
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path:
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data_files:
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path:
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path:
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data_files:
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path:
- '**/details_original|mmlu:astronomy|5_2023-08-28T19:56:56.621542.parquet'
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path:
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- config_name: original_mmlu_business_ethics_5
data_files:
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path:
- '**/details_original|mmlu:business_ethics|5_2023-08-28T19:56:56.621542.parquet'
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path:
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- config_name: original_mmlu_clinical_knowledge_5
data_files:
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path:
- '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T19:56:56.621542.parquet'
- split: latest
path:
- '**/details_original|mmlu:clinical_knowledge|5_2023-08-28T19:56:56.621542.parquet'
- config_name: original_mmlu_college_biology_5
data_files:
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path:
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- split: latest
path:
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- config_name: original_mmlu_college_chemistry_5
data_files:
- split: 2023_08_28T19_56_56.621542
path:
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- split: latest
path:
- '**/details_original|mmlu:college_chemistry|5_2023-08-28T19:56:56.621542.parquet'
- config_name: original_mmlu_college_computer_science_5
data_files:
- split: 2023_08_28T19_56_56.621542
path:
- '**/details_original|mmlu:college_computer_science|5_2023-08-28T19:56:56.621542.parquet'
- split: latest
path:
- '**/details_original|mmlu:college_computer_science|5_2023-08-28T19:56:56.621542.parquet'
- config_name: original_mmlu_college_mathematics_5
data_files:
- split: 2023_08_28T19_56_56.621542
path:
- '**/details_original|mmlu:college_mathematics|5_2023-08-28T19:56:56.621542.parquet'
- split: latest
path:
- '**/details_original|mmlu:college_mathematics|5_2023-08-28T19:56:56.621542.parquet'
- config_name: original_mmlu_college_medicine_5
data_files:
- split: 2023_08_28T19_56_56.621542
path:
- '**/details_original|mmlu:college_medicine|5_2023-08-28T19:56:56.621542.parquet'
- split: latest
path:
- '**/details_original|mmlu:college_medicine|5_2023-08-28T19:56:56.621542.parquet'
- config_name: original_mmlu_college_physics_5
data_files:
- split: 2023_08_28T19_56_56.621542
path:
- '**/details_original|mmlu:college_physics|5_2023-08-28T19:56:56.621542.parquet'
- split: latest
path:
- '**/details_original|mmlu:college_physics|5_2023-08-28T19:56:56.621542.parquet'
- config_name: original_mmlu_computer_security_5
data_files:
- split: 2023_08_28T19_56_56.621542
path:
- '**/details_original|mmlu:computer_security|5_2023-08-28T19:56:56.621542.parquet'
- split: latest
path:
- '**/details_original|mmlu:computer_security|5_2023-08-28T19:56:56.621542.parquet'
- config_name: original_mmlu_conceptual_physics_5
data_files:
- split: 2023_08_28T19_56_56.621542
path:
- '**/details_original|mmlu:conceptual_physics|5_2023-08-28T19:56:56.621542.parquet'
- split: latest
path:
- '**/details_original|mmlu:conceptual_physics|5_2023-08-28T19:56:56.621542.parquet'
- config_name: original_mmlu_econometrics_5
data_files:
- split: 2023_08_28T19_56_56.621542
path:
- '**/details_original|mmlu:econometrics|5_2023-08-28T19:56:56.621542.parquet'
- split: latest
path:
- '**/details_original|mmlu:econometrics|5_2023-08-28T19:56:56.621542.parquet'
- config_name: original_mmlu_electrical_engineering_5
data_files:
- split: 2023_08_28T19_56_56.621542
path:
- '**/details_original|mmlu:electrical_engineering|5_2023-08-28T19:56:56.621542.parquet'
- split: latest
path:
- '**/details_original|mmlu:electrical_engineering|5_2023-08-28T19:56:56.621542.parquet'
- config_name: original_mmlu_elementary_mathematics_5
data_files:
- split: 2023_08_28T19_56_56.621542
path:
- '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T19:56:56.621542.parquet'
- split: latest
path:
- '**/details_original|mmlu:elementary_mathematics|5_2023-08-28T19:56:56.621542.parquet'
- config_name: original_mmlu_formal_logic_5
data_files:
- split: 2023_08_28T19_56_56.621542
path:
- '**/details_original|mmlu:formal_logic|5_2023-08-28T19:56:56.621542.parquet'
- split: latest
path:
- '**/details_original|mmlu:formal_logic|5_2023-08-28T19:56:56.621542.parquet'
- config_name: original_mmlu_global_facts_5
data_files:
- split: 2023_08_28T19_56_56.621542
path:
- '**/details_original|mmlu:global_facts|5_2023-08-28T19:56:56.621542.parquet'
- split: latest
path:
- '**/details_original|mmlu:global_facts|5_2023-08-28T19:56:56.621542.parquet'
- config_name: original_mmlu_high_school_biology_5
data_files:
- split: 2023_08_28T19_56_56.621542
path:
- '**/details_original|mmlu:high_school_biology|5_2023-08-28T19:56:56.621542.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_biology|5_2023-08-28T19:56:56.621542.parquet'
- config_name: original_mmlu_high_school_chemistry_5
data_files:
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path:
- '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T19:56:56.621542.parquet'
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path:
- '**/details_original|mmlu:high_school_chemistry|5_2023-08-28T19:56:56.621542.parquet'
- config_name: original_mmlu_high_school_computer_science_5
data_files:
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path:
- '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T19:56:56.621542.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_computer_science|5_2023-08-28T19:56:56.621542.parquet'
- config_name: original_mmlu_high_school_european_history_5
data_files:
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path:
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- split: latest
path:
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- config_name: original_mmlu_high_school_geography_5
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path:
- '**/details_original|mmlu:high_school_geography|5_2023-08-28T19:56:56.621542.parquet'
- config_name: original_mmlu_high_school_government_and_politics_5
data_files:
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- split: latest
path:
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- config_name: original_mmlu_high_school_macroeconomics_5
data_files:
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path:
- '**/details_original|mmlu:high_school_macroeconomics|5_2023-08-28T19:56:56.621542.parquet'
- config_name: original_mmlu_high_school_mathematics_5
data_files:
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path:
- '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T19:56:56.621542.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_mathematics|5_2023-08-28T19:56:56.621542.parquet'
- config_name: original_mmlu_high_school_microeconomics_5
data_files:
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path:
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path:
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- config_name: original_mmlu_high_school_physics_5
data_files:
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path:
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- split: latest
path:
- '**/details_original|mmlu:high_school_physics|5_2023-08-28T19:56:56.621542.parquet'
- config_name: original_mmlu_high_school_psychology_5
data_files:
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path:
- '**/details_original|mmlu:high_school_psychology|5_2023-08-28T19:56:56.621542.parquet'
- split: latest
path:
- '**/details_original|mmlu:high_school_psychology|5_2023-08-28T19:56:56.621542.parquet'
- config_name: original_mmlu_high_school_statistics_5
data_files:
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- split: latest
path:
- '**/details_original|mmlu:high_school_statistics|5_2023-08-28T19:56:56.621542.parquet'
- config_name: original_mmlu_high_school_us_history_5
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- split: latest
path:
- '**/details_original|mmlu:high_school_us_history|5_2023-08-28T19:56:56.621542.parquet'
- config_name: original_mmlu_high_school_world_history_5
data_files:
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- config_name: original_mmlu_human_aging_5
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path:
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- config_name: original_mmlu_human_sexuality_5
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- '**/details_original|mmlu:human_sexuality|5_2023-08-28T19:56:56.621542.parquet'
- split: latest
path:
- '**/details_original|mmlu:human_sexuality|5_2023-08-28T19:56:56.621542.parquet'
- config_name: original_mmlu_international_law_5
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- '**/details_original|mmlu:international_law|5_2023-08-28T19:56:56.621542.parquet'
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path:
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- config_name: original_mmlu_jurisprudence_5
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path:
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- config_name: original_mmlu_logical_fallacies_5
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- '**/details_original|mmlu:logical_fallacies|5_2023-08-28T19:56:56.621542.parquet'
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path:
- '**/details_original|mmlu:logical_fallacies|5_2023-08-28T19:56:56.621542.parquet'
- config_name: original_mmlu_machine_learning_5
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path:
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path:
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path:
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- config_name: original_mmlu_marketing_5
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- split: latest
path:
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- config_name: original_mmlu_medical_genetics_5
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- split: latest
path:
- '**/details_original|mmlu:medical_genetics|5_2023-08-28T19:56:56.621542.parquet'
- config_name: original_mmlu_miscellaneous_5
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path:
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- config_name: original_mmlu_moral_disputes_5
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path:
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- split: latest
path:
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- config_name: original_mmlu_philosophy_5
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path:
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- split: latest
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- split: latest
path:
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- config_name: original_mmlu_professional_accounting_5
data_files:
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- split: latest
path:
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- config_name: original_mmlu_professional_law_5
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- '**/details_original|mmlu:professional_law|5_2023-08-28T19:56:56.621542.parquet'
- split: latest
path:
- '**/details_original|mmlu:professional_law|5_2023-08-28T19:56:56.621542.parquet'
- config_name: original_mmlu_professional_medicine_5
data_files:
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path:
- '**/details_original|mmlu:professional_medicine|5_2023-08-28T19:56:56.621542.parquet'
- split: latest
path:
- '**/details_original|mmlu:professional_medicine|5_2023-08-28T19:56:56.621542.parquet'
- config_name: original_mmlu_professional_psychology_5
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path:
- '**/details_original|mmlu:professional_psychology|5_2023-08-28T19:56:56.621542.parquet'
- split: latest
path:
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- config_name: original_mmlu_public_relations_5
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- split: latest
path:
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- config_name: original_mmlu_security_studies_5
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path:
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- split: latest
path:
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- config_name: original_mmlu_sociology_5
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path:
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- config_name: original_mmlu_us_foreign_policy_5
data_files:
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path:
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data_files:
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path:
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path:
- '**/details_original|mmlu:virology|5_2023-08-28T19:56:56.621542.parquet'
- config_name: original_mmlu_world_religions_5
data_files:
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path:
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- split: latest
path:
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- config_name: results
data_files:
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- split: 2023_08_29T22_26_02.660247
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- results_2023-08-29T22:26:02.660247.parquet
- split: 2023_09_08T14_32_14.957248
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- split: 2023_09_15T14_07_08.353318
path:
- results_2023-09-15T14-07-08.353318.parquet
- split: 2023_10_14T23_00_26.644553
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- results_2023-10-14T23-00-26.644553.parquet
- split: 2023_12_02T13_11_49.394544
path:
- results_2023-12-02T13-11-49.394544.parquet
- split: latest
path:
- results_2023-12-02T13-11-49.394544.parquet
---
# Dataset Card for Evaluation run of meta-llama/Llama-2-13b-hf
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/meta-llama/Llama-2-13b-hf
- **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 [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 123 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 8 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_meta-llama__Llama-2-13b-hf",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T13:11:49.394544](https://huggingface.co/datasets/open-llm-leaderboard/details_meta-llama__Llama-2-13b-hf/blob/main/results_2023-12-02T13-11-49.394544.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": 0.22820318423047764,
"acc_stderr": 0.011559914877317397
},
"harness|gsm8k|5": {
"acc": 0.22820318423047764,
"acc_stderr": 0.011559914877317397
}
}
```
### 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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MemGPT/MSC-Self-Instruct
|
MemGPT
| 2023-11-02T07:40:08Z | 103 | 8 |
[
"license:apache-2.0",
"arxiv:2310.08560",
"region:us"
] | null | 2023-10-11T02:51:50Z |
---
license: apache-2.0
---
MemGPT
===
This is the self-instruct dataset of MSC conversations used for MemGPT paper. For more information please refer to memgpt.ai
The [MSC dataset](https://parl.ai/projects/msc/) is a multi-round human conversations. In this dataset, our goal is to come up with a conversation opener, that is personalized to the user by referencing topics from the previous conversations.
These were generated while evaluating [MemGPT](https://arxiv.org/abs/2310.08560).
|
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] |
sunjun/medqa_sj
|
sunjun
| 2023-10-24T12:49:39Z | 103 | 0 |
[
"region:us"
] | null | 2023-10-24T12:49:24Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
- name: options
struct:
- name: A
dtype: string
- name: B
dtype: string
- name: C
dtype: string
- name: D
dtype: string
- name: meta_info
dtype: string
- name: answer_idx
dtype: string
- name: metamap_phrases
sequence: string
- name: choices
sequence: string
- name: answer_index
dtype: int64
splits:
- name: train
num_bytes: 16555933
num_examples: 10178
- name: test
num_bytes: 2118034
num_examples: 1273
download_size: 9784195
dataset_size: 18673967
---
# Dataset Card for "medqa_sj"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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haryoaw/stif-indonesia
|
haryoaw
| 2023-10-30T10:19:03Z | 103 | 1 |
[
"task_categories:translation",
"task_categories:text2text-generation",
"size_categories:1K<n<10K",
"language:id",
"license:mit",
"arxiv:2011.03286",
"region:us"
] |
[
"translation",
"text2text-generation"
] | 2023-10-30T10:10:47Z |
---
configs:
- config_name: default
data_files:
- split: train
path: train.csv
- split: dev
path: dev.csv
- split: test
path: test.csv
dataset_info:
features:
- name: informal
dtype: string
- name: formal
dtype: string
splits:
- name: train
num_bytes: 344179
num_examples: 1922
- name: dev
num_bytes: 37065
num_examples: 214
- name: test
num_bytes: 66682
num_examples: 363
download_size: 276834
dataset_size: 447926
license: mit
task_categories:
- translation
- text2text-generation
language:
- id
size_categories:
- 1K<n<10K
---
# Dataset Card for "stif-indonesia"
# STIF-Indonesia
A dataset of ["Semi-Supervised Low-Resource Style Transfer of Indonesian Informal to Formal Language with Iterative Forward-Translation"](https://arxiv.org/abs/2011.03286v1).
You can also find Indonesian informal-formal parallel corpus in this repository.
## Description
We were researching transforming a sentence from informal to its formal form. Our work addresses a style-transfer from informal to formal Indonesian as a low-resource **machine translation** problem. We benchmark several strategies to perform the style transfer.
In this repository, we provide the Phrase-Based Statistical Machine Translation, which has the highest result in our experiment. Note that, our data is extremely low-resource and domain-specific (Customer Service domain). Therefore, the system might not be robust towards out-of-domain input. Our future work includes exploring more robust style transfer. Stay tuned!
## Paper
You can access our paper below:
[Semi-Supervised Low-Resource Style Transfer of Indonesian Informal to Formal Language with Iterative Forward-Translation (IALP 2020)](https://arxiv.org/abs/2011.03286v1)
## Team
1. Haryo Akbarianto Wibowo @ Kata.ai
2. Tatag Aziz Prawiro @ Universitas Indonesia
3. Muhammad Ihsan @ Bina Nusantara
4. Alham Fikri Aji @ Kata.ai
5. Radityo Eko Prasojo @ Kata.ai
6. Rahmad Mahendra @ Universitas Indonesia
|
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] |
coastalcph/fm-updates-llama-7b
|
coastalcph
| 2023-11-21T16:56:04Z | 103 | 0 |
[
"region:us"
] | null | 2023-11-13T11:04:59Z |
---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: query
struct:
- name: label
dtype: string
- name: objects
list:
- name: aliases
sequence: string
- name: label
dtype: string
- name: qid
dtype: string
- name: qid
dtype: string
- name: rel_id
dtype: string
- name: relation
dtype: string
- name: prediction
struct:
- name: predictions
list:
- name: answer
dtype: string
- name: first_token_probability
dtype: float64
- name: per_token_probability
sequence: float64
- name: perplexity
dtype: float64
- name: query
dtype: string
- name: f1
dtype: float64
- name: relation
dtype: string
- name: type
dtype: string
- name: original_answer
dtype: string
- name: updates
sequence: string
splits:
- name: test
num_bytes: 440727.6757775683
num_examples: 480
- name: validation
num_bytes: 46827.315551366635
num_examples: 51
download_size: 380771
dataset_size: 487554.99132893496
---
# Dataset Card for "fm-updates-llama-7b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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Jzuluaga/atcosim_corpus
|
Jzuluaga
| 2022-12-05T11:14:57Z | 102 | 0 |
[
"task_categories:automatic-speech-recognition",
"multilinguality:monolingual",
"language:en",
"audio",
"automatic-speech-recognition",
"en-atc",
"en",
"robust-speech-recognition",
"noisy-speech-recognition",
"speech-recognition",
"arxiv:2203.16822",
"region:us"
] |
[
"automatic-speech-recognition"
] | 2022-11-16T09:04:42Z |
---
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: 471628915.76
num_examples: 1901
- name: train
num_bytes: 1934757106.88
num_examples: 7638
download_size: 0
dataset_size: 2406386022.6400003
tags:
- audio
- automatic-speech-recognition
- en-atc
- en
- robust-speech-recognition
- noisy-speech-recognition
- speech-recognition
task_categories:
- automatic-speech-recognition
language:
- en
multilinguality:
- monolingual
---
# Dataset Card for ATCOSIM 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:** [ATCOSIM homepage](https://www.spsc.tugraz.at/databases-and-tools/atcosim-air-traffic-control-simulation-speech-corpus.html)
- **Repository:** [GitHub repository (used in research)](https://github.com/idiap/w2v2-air-traffic)
- **Paper:** [The ATCOSIM Corpus of Non-Prompted Clean Air Traffic Control Speech](https://aclanthology.org/L08-1507/)
- **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 ATCOSIM Air Traffic Control Simulation Speech corpus is a speech database of air traffic control (ATC) operator speech, provided by Graz University of Technology (TUG) and Eurocontrol Experimental Centre (EEC). It consists of ten hours of speech data, which were recorded during ATC real-time simulations using a close-talk headset microphone. The utterances are in English language and pronounced by ten non-native speakers. The database includes orthographic transcriptions and additional information on speakers and recording sessions. It was recorded and annotated by Konrad Hofbauer ([description here](https://www.spsc.tugraz.at/databases-and-tools/atcosim-air-traffic-control-simulation-speech-corpus.html)).
### 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 participating controllers were all actively employed air traffic controllers and possessed professional experience in the simulated sectors. The six male and four female controllers were of either German or Swiss nationality and had German, Swiss German or Swiss French native tongue. The controllers had agreed to the recording of their voice for the purpose of language analysis as well as for research and development in speech technologies, and were asked to show their normal working behaviour.
## 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 [ATCOSIM corpus](https://www.spsc.tugraz.at/databases-and-tools/atcosim-air-traffic-control-simulation-speech-corpus.html) creators.
### 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:
```
@inproceedings{hofbauer-etal-2008-atcosim,
title = "The {ATCOSIM} Corpus of Non-Prompted Clean Air Traffic Control Speech",
author = "Hofbauer, Konrad and
Petrik, Stefan and
Hering, Horst",
booktitle = "Proceedings of the Sixth International Conference on Language Resources and Evaluation ({LREC}'08)",
month = may,
year = "2008",
address = "Marrakech, Morocco",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2008/pdf/545_paper.pdf",
}
```
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irds/trec-cast_v1
|
irds
| 2023-01-05T04:03:19Z | 102 | 1 |
[
"task_categories:text-retrieval",
"region:us"
] |
[
"text-retrieval"
] | 2023-01-05T04:03:14Z |
---
pretty_name: '`trec-cast/v1`'
viewer: false
source_datasets: []
task_categories:
- text-retrieval
---
# Dataset Card for `trec-cast/v1`
The `trec-cast/v1` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/trec-cast#trec-cast/v1).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=38,622,444
This dataset is used by: [`trec-cast_v1_2020`](https://huggingface.co/datasets/irds/trec-cast_v1_2020), [`trec-cast_v1_2020_judged`](https://huggingface.co/datasets/irds/trec-cast_v1_2020_judged)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/trec-cast_v1', 'docs')
for record in docs:
record # {'doc_id': ..., 'text': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@inproceedings{Dalton2019Cast,
title={CAsT 2019: The Conversational Assistance Track Overview},
author={Jeffrey Dalton and Chenyan Xiong and Jamie Callan},
booktitle={TREC},
year={2019}
}
```
|
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] |
kuroneko5943/stock11
|
kuroneko5943
| 2023-01-16T04:11:18Z | 102 | 6 |
[
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:zh",
"license:apache-2.0",
"stock",
"region:us"
] |
[
"text-classification"
] | 2023-01-10T12:13:05Z |
---
annotations_creators:
- machine-generated
language:
- zh
language_creators:
- crowdsourced
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: stock11
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- stock
task_categories:
- text-classification
task_ids:
- sentiment-classification
---
|
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Thaweewat/alpaca-cleaned-52k-th
|
Thaweewat
| 2023-05-09T16:18:02Z | 102 | 3 |
[
"task_categories:question-answering",
"task_categories:summarization",
"size_categories:10K<n<100K",
"language:th",
"license:cc-by-sa-3.0",
"instruction-finetuning",
"region:us"
] |
[
"question-answering",
"summarization"
] | 2023-05-09T15:45:46Z |
---
license: cc-by-sa-3.0
task_categories:
- question-answering
- summarization
tags:
- instruction-finetuning
language:
- th
size_categories:
- 10K<n<100K
---
# Summary
This is a Thai 🇹🇭-instructed dataset translated from cleaned version of the original Alpaca Dataset released by Stanford using Google Cloud Translation, contain 52,000 instructions and demonstrations generated by OpenAI's `text-davinci-003` engine.
This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better.
The following issues have been identified in the original release and fixed in this dataset:
1. **Hallucinations:** Many instructions in the original dataset had instructions referencing data on the internet, which just caused GPT3 to hallucinate an answer.
2. **Merged Instructions:** There were many instructions that were merged together in the original dataset for some reason.
3. **Empty outputs:** Some entries in the original dataset had empty outputs.
4. **Empty code examples:** Some descriptions in the original dataset were missing code examples, making it difficult to understand the intended behavior of the code.
5. **Instructions to generate images:** Some descriptions in the original dataset included instructions to generate images, something obviously not possible.
6. **N/A outputs:** Some code snippets in the original dataset had N/A outputs.
7. **Inconsistent input field:** The original dataset had inconsistent usage of the input field when it was supposed to be empty.
8. **Wrong answers:** Some instructions/questions in the original dataset had incorrect answers. About 80% of the math problems are estimated to have incorrect answers.
9. **Non-Sensical/Unclear instructions:** Many instructions are unclear, we try to clarify (or re-write) if instructions are non-sensical. Instructions that are slightly unclear, but where one could deduce the meaning are not altered.
10. **Extraneous escape and control characters:** The original dataset had several entries with extraneous escape and control characters.
### Original Alpaca Dataset Summary
Alpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's `text-davinci-003` engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better.
The authors built on the data generation pipeline from [Self-Instruct framework](https://github.com/yizhongw/self-instruct) and made the following modifications:
- The `text-davinci-003` engine to generate the instruction data instead of `davinci`.
- A [new prompt](https://github.com/tatsu-lab/stanford_alpaca/blob/main/prompt.txt) was written that explicitly gave the requirement of instruction generation to `text-davinci-003`.
- Much more aggressive batch decoding was used, i.e., generating 20 instructions at once, which significantly reduced the cost of data generation.
- The data generation pipeline was simplified by discarding the difference between classification and non-classification instructions.
- Only a single instance was generated for each instruction, instead of 2 to 3 instances as in Self-Instruct.
The authors built on the data generation pipeline from [Self-Instruct framework](https://github.com/yizhongw/self-instruct) and made the following modifications:
- The `text-davinci-003` engine to generate the instruction data instead of `davinci`.
- A [new prompt](https://github.com/tatsu-lab/stanford_alpaca/blob/main/prompt.txt) was written that explicitly gave the requirement of instruction generation to `text-davinci-003`.
- Much more aggressive batch decoding was used, i.e., generating 20 instructions at once, which significantly reduced the cost of data generation.
- The data generation pipeline was simplified by discarding the difference between classification and non-classification instructions.
- Only a single instance was generated for each instruction, instead of 2 to 3 instances as in Self-Instruct.
This produced an instruction-following dataset with 52K examples obtained at a much lower cost (less than $500).
In a preliminary study, the authors also found that the 52K generated data to be much more diverse than the data released by [Self-Instruct](https://github.com/yizhongw/self-instruct/blob/main/data/seed_tasks.jsonl).
Supported Tasks:
- Training LLMs
- Synthetic Data Generation
- Data Augmentation
Languages: Thai
Version: 1.0
---
|
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