id stringlengths 2 115 | lastModified stringlengths 24 24 | tags list | author stringlengths 2 42 ⌀ | description stringlengths 0 68.7k ⌀ | citation stringlengths 0 10.7k ⌀ | cardData null | likes int64 0 3.55k | downloads int64 0 10.1M | card stringlengths 0 1.01M |
|---|---|---|---|---|---|---|---|---|---|
KaleidoSG/Helix | 2023-09-23T14:24:23.000Z | [
"task_categories:question-answering",
"task_categories:translation",
"task_categories:summarization",
"task_categories:text-generation",
"task_categories:conversational",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-4.0",
"code",
"airoboros",
"language",
"merge",
"gpt",
"regi... | KaleidoSG | null | null | null | 0 | 10 | ---
license: cc-by-4.0
task_categories:
- question-answering
- translation
- summarization
- text-generation
- conversational
language:
- en
tags:
- code
- airoboros
- language
- merge
- gpt
pretty_name: helix
size_categories:
- 100K<n<1M
---
# Helix Dataset for Questioning and Instructing (QI)
## Description
The Helix dataset is a specialized collection of data tailored for Questioning and Instructing (QI) tasks. It is created by merging all the Airoboros datasets and incorporating one RosettaCode dataset, with a primary focus on supporting QI research and applications.
## Dataset Details
- **Source Datasets**: Airoboros datasets (various sources), RosettaCode dataset
- **Merging Script**: The merging of these datasets was performed using the `bowie.py` script, which is included in this repository. The script facilitates the formatting and integration of the datasets to create the Helix dataset optimized for QI tasks.
## Usage
The Helix dataset is particularly suited for researchers and developers working on QI tasks, including:
- Developing QI systems that can understand and respond to natural language queries and instructions.
- Training and evaluating machine learning models for QI applications.
- Benchmarking QI algorithms and techniques.
- Investigating the intersection of natural language understanding and instructional responses.
## License
Please refer to the individual licenses of the source datasets for specific licensing information. Ensure compliance with the respective licenses when using the Helix dataset.
## Citation
If you use the Helix dataset for QI research or projects, please consider citing it using the appropriate citation format for each of the source datasets and the `bowie.py` script.
```
Marcus. 2023. Helix Dataset for Questioning and Instructing (QI). Helix. Self-published. https://huggingface.co/datasets/KaleidoSG/Helix
```
## Acknowledgments
We express our gratitude to the creators and maintainers of the Airoboros datasets and the RosettaCode dataset for their valuable contributions to this specialized dataset for Questioning and Instructing (QI) tasks. |
NeuNav/sentiment | 2023-09-27T14:54:24.000Z | [
"region:us"
] | NeuNav | null | null | null | 0 | 10 | Entry not found |
SagarDas07/fineTunersMineLawData | 2023-09-23T17:52:40.000Z | [
"region:us"
] | SagarDas07 | null | null | null | 0 | 10 | Entry not found |
miss-swan/Website_Segmentation | 2023-09-24T08:36:08.000Z | [
"region:us"
] | miss-swan | null | null | null | 0 | 10 | ---
dataset_info:
features:
- name: name
dtype: string
- name: uuid
dtype: string
- name: status
dtype: string
- name: image
dtype: image
- name: label.annotations
list:
- name: id
dtype: int32
- name: category_id
dtype: int32
- name: label.segmentation_bitmap
dtype: image
splits:
- name: train
num_bytes: 5912843.0
num_examples: 10
download_size: 5866632
dataset_size: 5912843.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "Website_Segmentation"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
wangzhang/sdb | 2023-10-06T05:46:44.000Z | [
"region:us"
] | wangzhang | null | null | null | 0 | 10 | ---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/datasets-cards
{}
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### 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] |
Plona/claims_update1 | 2023-09-24T14:20:23.000Z | [
"region:us"
] | Plona | null | null | null | 0 | 10 | ---
configs:
- config_name: default
data_files:
- split: train
path: "20230919 Manju_train.csv"
- split: test
path: "20230919 Manju_test.csv"
- split: origin
path: "20230919 Manju.csv"
--- |
emrgnt-cmplxty/sciphi-python-textbook-v2-test | 2023-09-24T12:55:37.000Z | [
"region:us"
] | emrgnt-cmplxty | null | null | null | 2 | 10 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: formatted_prompt
dtype: string
- name: completion
dtype: string
splits:
- name: train
num_bytes: 107997208
num_examples: 23232
download_size: 48134580
dataset_size: 107997208
---
# Dataset Card for "sciphi-python-textbook-v2-test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
larryvrh/belle_resampled_78K_CN | 2023-09-26T05:01:58.000Z | [
"task_categories:text-generation",
"task_categories:conversational",
"size_categories:10K<n<100K",
"language:zh",
"region:us"
] | larryvrh | null | null | null | 0 | 10 | ---
dataset_info:
features:
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: id
dtype: string
- name: category
dtype: string
splits:
- name: train
num_bytes: 197491538
num_examples: 78000
download_size: 108247334
dataset_size: 197491538
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
task_categories:
- text-generation
- conversational
language:
- zh
size_categories:
- 10K<n<100K
---
# Dataset Card for "belle_resampled_78K_CN"
通过启发式方法过滤后的[BelleGroup/train_3.5M_CN](https://huggingface.co/datasets/BelleGroup/train_3.5M_CN)，移除了语料中明显的错误和低质量内容。
之后进行了均匀采样，从每个类别中提取出6000条最长的对话。
## 样例
```
{
"id":"66623028",
"conversations":[
{
"from":"human",
"value":"归纳介绍三种不同类型的电影题材"
},
{
"from":"assistant",
"value":"1. 动作片：动作片通常强调动作和战斗场面，故事情节紧凑，充满了紧张和兴奋的氛围。主角一般是具有超凡实力或特殊技能的英雄人物，往往会与反派角色进行一系列激烈的格斗，追逐和爆破等酷炫场面。例如《速度与激情》和《复仇者联盟》等。\n2. 爱情片：爱情片是以爱情为主题的电影，着重呈现角色之间的感情纠葛，展现了爱情的美好和悲壮。情节与角色之间的发展令人深思，增加了观众的感性体验。这种电影的故事情节主要以人类与人类之间的情感变化为主题。例如《泰坦尼克号》和《让子弹飞》等。\n3. 科幻片：科幻片是以未来科技和未知世界为主题的电影，通常包括时间旅行、外星人、虚拟现实、未来社会等各种奇幻的元素。这种电影描绘了一种比现实更加宏伟、更加神秘和惊奇的世界。例如《星际穿越》和《发条橙》等。"
}
]
}
{
"id":"18205437",
"conversations":[
{
"from":"human",
"value":"根据给定的食材，编写一道美食菜谱。\n鸡腿肉、蘑菇、洋葱"
},
{
"from":"assistant",
"value":"鸡肉炒蘑菇洋葱\n食材：\n- 鸡腿肉 400克，去骨去皮切成小块\n- 蘑菇 200克，切片\n- 洋葱 1个，切丝\n- 生姜 适量，切丝\n- 酱油 2勺\n- 料酒 1勺\n- 盐 适量\n- 白胡椒粉 适量\n- 植物油 适量\n做法：\n1. 在一个碗里放入切好的鸡腿肉块，加入酱油、料酒和适量的白胡椒粉，搅拌均匀，腌制10分钟。\n2. 热锅凉油，加入姜丝翻炒，放入腌好的鸡肉块翻炒至变色。\n3. 加入洋葱丝和蘑菇，翻炒2-3分钟至蘑菇变软。\n4. 加入适量的盐和少量的水，盖上锅盖焖煮5分钟直至鸡肉熟透。\n5. 最后翻炒几下，使汤汁变浓稠即可。\n温馨提示：\n1. 在腌制鸡肉时，如果想让肉质更嫩滑，可以加入少量的淀粉和蛋清。\n2. 可以根据个人口味加入其他调料，如蒜末、辣椒等。\n3. 洋葱和蘑菇可以用其他蔬菜代替，如青椒、胡萝卜等。"
},
{
"from":"human",
"value":"太好了，我会试着照着你教的做一次，但是我最近买了一部新相机，想让你推荐一些好的拍照地点。"
},
{
"from":"assistant",
"value":"当地的风景区、公园、景点等等都是比较适合拍照的地点。如果你想要一些特别的地方，可以考虑一些建筑群，街道巷子、咖啡馆、书店等具有文艺气息的地方。"
},
{
"from":"human",
"value":"嗯，这些地方都很不错，但是我还想找一些可以体验当地文化和风俗的地点，你有什么好的建议吗？"
},
{
"from":"assistant",
"value":"如果你想要更深入地了解当地文化和风俗，可以考虑参加一些游学活动，比如参观当地的博物馆、参加民俗节庆、尝试当地美食等等。此外，也可以去一些小城镇，感受那里的古朴风情。"
}
]
}
```
### 字段：
```
id: 数据id
conversations: 数据内容，以对话形式给出，包括多轮和单轮对话的数据
category: 数据类别
``` |
pfcheng123/test2 | 2023-09-25T03:57:00.000Z | [
"license:other",
"region:us"
] | pfcheng123 | null | null | null | 0 | 10 | ---
license: other
---
|
Mitali05/sentiment-analysis-tweets-llama2-finetune | 2023-09-25T07:41:38.000Z | [
"license:llama2",
"region:us"
] | Mitali05 | null | null | null | 0 | 10 | ---
license: llama2
---
|
JvManger/pharmacy-llama-2-indic1 | 2023-10-09T12:08:26.000Z | [
"region:us"
] | JvManger | null | null | null | 0 | 10 | First try of a dataset to train a llama-2 model wiht pharmaceutical infos in German |
Doub7e/SD-CLIP-alignment-1300 | 2023-09-25T22:53:24.000Z | [
"region:us"
] | Doub7e | null | null | null | 0 | 10 | ---
dataset_info:
features:
- name: image
dtype: image
- name: prompt
dtype: string
- name: clip_pred
dtype: string
splits:
- name: train
num_bytes: 260505634.0
num_examples: 550
download_size: 260509726
dataset_size: 260505634.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "SD-CLIP-alignment-1300"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
maximuslee07/raqna10k | 2023-09-25T21:04:53.000Z | [
"license:llama2",
"region:us"
] | maximuslee07 | null | null | null | 0 | 10 | ---
license: llama2
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 16381342
num_examples: 9424
download_size: 9320689
dataset_size: 16381342
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
JeswinMS4/code_text_classification | 2023-09-27T04:08:01.000Z | [
"region:us"
] | JeswinMS4 | null | null | null | 0 | 10 | ---
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: sentence
dtype: string
- name: Label
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 74362
num_examples: 823
- name: test
num_bytes: 4194
num_examples: 46
- name: validation
num_bytes: 4185
num_examples: 46
download_size: 41632
dataset_size: 82741
---
# Dataset Card for "code_text_classification"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Prabhjot410/Ecommerce_FAQ_chatbot_dataset | 2023-09-28T06:43:05.000Z | [
"region:us"
] | Prabhjot410 | null | null | null | 0 | 10 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 19820
num_examples: 158
download_size: 8754
dataset_size: 19820
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "Ecommerce_FAQ_chatbot_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Rintron/qc | 2023-09-28T10:14:57.000Z | [
"region:us"
] | Rintron | null | null | null | 0 | 10 | Entry not found |
oserikov/arabic_billion_words | 2023-09-27T11:18:25.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"size_categories:1M<... | oserikov | THIS IS A FORK FOR LOCAL USAGE.
Abu El-Khair Corpus is an Arabic text corpus, that includes more than five million newspaper articles.
It contains over a billion and a half words in total, out of which, there are about three million unique words.
The corpus is encoded with two types of encoding, namely: UTF-8, and Windows CP-1256.
Also it was marked with two mark-up languages, namely: SGML, and XML. | @article{el20161,
title={1.5 billion words arabic corpus},
author={El-Khair, Ibrahim Abu},
journal={arXiv preprint arXiv:1611.04033},
year={2016}
} | null | 0 | 10 | ---
annotations_creators:
- found
language_creators:
- found
language:
- ar
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 10K<n<100K
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: null
pretty_name: Arabic Billion Words
dataset_info:
- config_name: Alittihad
features:
- name: url
dtype: string
- name: head_line
dtype: string
- name: date
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 1601790302
num_examples: 349342
download_size: 348259999
dataset_size: 1601790302
- config_name: Almasryalyoum
features:
- name: url
dtype: string
- name: head_line
dtype: string
- name: date
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 1056197870
num_examples: 291723
download_size: 242604438
dataset_size: 1056197870
- config_name: Almustaqbal
features:
- name: url
dtype: string
- name: head_line
dtype: string
- name: date
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 1545659336
num_examples: 446873
download_size: 350826797
dataset_size: 1545659336
- config_name: Alqabas
features:
- name: url
dtype: string
- name: head_line
dtype: string
- name: date
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 2631729746
num_examples: 817274
download_size: 595274646
dataset_size: 2631729746
- config_name: Echoroukonline
features:
- name: url
dtype: string
- name: head_line
dtype: string
- name: date
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 464386206
num_examples: 139732
download_size: 108184378
dataset_size: 464386206
- config_name: Ryiadh
features:
- name: url
dtype: string
- name: head_line
dtype: string
- name: date
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 3101294859
num_examples: 858188
download_size: 691264971
dataset_size: 3101294859
- config_name: Sabanews
features:
- name: url
dtype: string
- name: head_line
dtype: string
- name: date
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 198019614
num_examples: 92149
download_size: 38214558
dataset_size: 198019614
- config_name: SaudiYoum
features:
- name: url
dtype: string
- name: head_line
dtype: string
- name: date
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 2723291416
num_examples: 888068
download_size: 605537923
dataset_size: 2723291416
- config_name: Techreen
features:
- name: url
dtype: string
- name: head_line
dtype: string
- name: date
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 1103458209
num_examples: 314597
download_size: 252976781
dataset_size: 1103458209
- config_name: Youm7
features:
- name: url
dtype: string
- name: head_line
dtype: string
- name: date
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 3004689464
num_examples: 1172136
download_size: 617708074
dataset_size: 3004689464
config_names:
- Alittihad
- Almasryalyoum
- Almustaqbal
- Alqabas
- Echoroukonline
- Ryiadh
- Sabanews
- SaudiYoum
- Techreen
- Youm7
---
# Dataset Card for Arabic Billion Words Corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://www.abuelkhair.net/index.php/en/arabic/abu-el-khair-corpus
- **Repository:**
- **Paper:** https://arxiv.org/pdf/1611.04033
- **Leaderboard:**
- **Point of Contact:**[Ibrahim Abu El-Khair](iabuelkhair@gmail.com)
### Dataset Summary
Abu El-Khair Corpus is an Arabic text corpus, that includes more than five million newspaper articles.
It contains over a billion and a half words in total, out of which, there are about three million unique words.
The corpus is encoded with two types of encoding, namely: UTF-8, and Windows CP-1256.
Also it was marked with two mark-up languages, namely: SGML, and XML.
**NB:** this dataset is based on the [unofficial copy](https://drive.google.com/drive/folders/1F2wCEfFHzJqX7eTuWhh-pGtrsaHPvTT8?usp=drive_link) ([discussion](https://huggingface.co/datasets/arabic_billion_words/discussions/3)) of the data, and assumes it was downloaded properly. Put the `new_data_*` files to the `./dataset` folder like this:
```
[user@machine /path/to/dataset]$ tree
.
├── arabic_billion_words.py
├── dataset
│ ├── new_data_Alittihad_XML_utf_8.rar
│ ├── new_data_Almasryalyoum_XML_utf_8.rar
│ ├── new_data_Almustaqbal_XML_utf_8.rar
│ ├── new_data_Alqabas_XML_utf_8.rar
│ ├── new_data_Echoroukonline_XML_utf_8.rar
│ ├── new_data_Ryiadh_XML_utf_8.rar
│ ├── new_data_Sabanews_XML_utf_8.rar
│ ├── new_data_SaudiYoum_XML_utf_8.rar
│ ├── new_data_Techreen_XML_utf_8.rar
│ └── new_data_Youm7_XML_utf_8.rar
├── dataset_infos.json
├── README.md
└── usage_example.py
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Arabic
## Dataset Structure
### Data Instances
This is an example of the "Almasryalyoum" configuration subset:
```python
{
"url": "http://today.almasryalyoum.com/printerfriendly.aspx?ArticleID=61300",
"head_line": "رئيس وزراء المجر: عنصرية جماهير أوجبيست جلبت العار للبلاد",
"date": "19/5/2007",
"text": """قال متحدث باسم الحكومة المجرية: إن رئيس الوزراء فيرنك جيوركساني رحب بقرار اتحاد كرة القدم المجري بخصم ثلاث نقاط من نادي أوجبيست بسبب السلوك العنصري الذي صدر من جماهيره.
وعاقب الاتحاد المجري فريق أوجبيست بعد أن سخرت جماهيره من إبراهيم سيديبي مهاجم فريق ديبرينسين الأسود أثناء مباراة الفريقين أوائل مايو الجاري.
يذكر أن الاتحاد فرض أيضا غرامة مالية قدرها 20 ألف دولار علي أوجبيست في عام 2005 بعد أن رددت جماهيره شعارات معادية للسامية خلال مباراة بالدوري المجري.
وأوضح جيوركساني في خطاب إلي إيستفان كيستليكي رئيس الاتحاد المجري لكرة القدم، أن هذا السلوك العنصري من الجماهير «جلب العار لكرة القدم وللمجر». يذكر أن المجر بها مجموعة من مشجعي كرة القدم المشاغبين «الهوليجانز»، وشارك الكثير منهم في أعمال شغب معادية للحكومة في العام الماضي.""",
}
```
### Data Fields
The data fields are:
- "url": string, original url of the article,
- "head_line": string, headline of the article,
- "date": string, date of the article,
- "text": string, text content of the article,
### Data Splits
There is only one "training" split for all configuration subsets, containing the following number of examples:
| | Number of examples |
|:---------------|-------------------:|
| Alittihad | 349342 |
| Almasryalyoum | 291723 |
| Almustaqbal | 446873 |
| Alqabas | 817274 |
| Echoroukonline | 139732 |
| Ryiadh | 858188 |
| Sabanews | 92149 |
| SaudiYoum | 888068 |
| Techreen | 314597 |
| Youm7 | 1172136 |
## 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
```
@article{el20161,
title={1.5 billion words arabic corpus},
author={El-Khair, Ibrahim Abu},
journal={arXiv preprint arXiv:1611.04033},
year={2016}
}
```
### Contributions
Thanks to [@zaidalyafeai](https://github.com/zaidalyafeai) and [@albertvillanova](https://github.com/albertvillanova) for adding this dataset. |
erhwenkuo/train_0.5m-chinese-zhtw | 2023-09-27T15:59:00.000Z | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:zh",
"alpaca",
"fine-tune",
"region:us"
] | erhwenkuo | null | null | null | 0 | 10 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 265980267
num_examples: 519255
download_size: 183812396
dataset_size: 265980267
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
task_categories:
- text-generation
language:
- zh
tags:
- alpaca
- fine-tune
size_categories:
- 100K<n<1M
---
# Dataset Card for "train_0.5m-chinese-zhtw"
## 內容
包含約 50 萬條由 [BELLE](https://github.com/LianjiaTech/BELLE) 專案產生的中文指令資料。
## 範例
```
{
"instruction": "給定一個文字輸入，將其中的所有數字加1。\n“明天的會議在9點開始，記得準時到達。 ”\n",
"input": "",
"output": "「明天的會議在10點開始，記得準時到達。 ”"
}
```
### 欄位：
```
instruction: 指令
input: 輸入（此資料集均為空）
output: 輸出
```
## 使用限制
僅允許將此資料集及使用此資料集產生的衍生物用於研究目的，不得用於商業，以及其他會對社會帶來危害的用途。
本資料集不代表任何一方的立場、利益或想法，無關任何團體的任何類型的主張。因使用本資料集所帶來的任何損害、糾紛，本專案不承擔任何責任。 |
Satwik19/LLMA_2_chat | 2023-09-28T07:14:22.000Z | [
"region:us"
] | Satwik19 | null | null | null | 0 | 10 | Entry not found |
shivanikerai/review_prompts_9.0.0 | 2023-09-28T12:06:14.000Z | [
"region:us"
] | shivanikerai | null | null | null | 0 | 10 | Entry not found |
TiberiusD/test | 2023-10-06T13:37:33.000Z | [
"license:apache-2.0",
"region:us"
] | TiberiusD | null | null | null | 0 | 10 | ---
license: apache-2.0
---
|
tiwes/wzd3k | 2023-09-28T12:48:02.000Z | [
"region:us"
] | tiwes | null | null | null | 0 | 10 | Entry not found |
p1atdev/simple_qa | 2023-09-28T13:18:59.000Z | [
"region:us"
] | p1atdev | null | null | null | 0 | 10 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: output
dtype: string
- name: input
dtype: string
- name: system
dtype: string
- name: question
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 23721879.00162778
num_examples: 7500
download_size: 8142271
dataset_size: 23721879.00162778
---
# Dataset Card for "simple_qa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Globaly/Categories-1k-Globaly | 2023-09-28T22:21:05.000Z | [
"region:us"
] | Globaly | null | null | null | 1 | 10 | Entry not found |
TheVarunKaushik/VexRobot | 2023-09-29T21:16:40.000Z | [
"language:en",
"code",
"region:us"
] | TheVarunKaushik | null | null | null | 0 | 10 | ---
language:
- en
tags:
- code
pretty_name: Vex Language
---
"vex_robotics_faq": [
{
"question": "What is VEX Robotics?",
"answer": "VEX Robotics is a platform for learning about and building robots. It offers educational resources and a range of robotic kits for individuals and teams to learn about engineering, programming, and problem-solving.",
"format_category": "Introduction"
},
{
"question": "How can I get started with VEX Robotics?",
"answer": "To get started with VEX Robotics, you can purchase a robot kit from the VEX Robotics website or a retailer. It's also advisable to access educational resources, join a local robotics club or online community, and participate in VEX Robotics competitions to enhance your learning experience.",
"format_category": "Getting Started"
},
{
"question": "Where can I participate in VEX Robotics competitions?",
"answer": "VEX Robotics competitions are held at local, regional, national, and international levels. You can find information about upcoming competitions on the VEX Robotics website or through local robotics clubs and educational institutions.",
"format_category": "Competitions"
},
{
"question": "How do I program my VEX robot?",
"answer": "VEX robots can be programmed using the VEXcode software, which is available for download on the VEX Robotics website. There are also many tutorials and community forums available to help you get started with programming your VEX robot.",
"format_category": "Programming"
}
]
|
malex1701d/primutec_info_20 | 2023-09-29T11:20:06.000Z | [
"region:us"
] | malex1701d | null | null | null | 0 | 10 | Entry not found |
temibabs/lecture-audio | 2023-09-29T14:30:01.000Z | [
"region:us"
] | temibabs | null | null | null | 0 | 10 | ---
dataset_info:
features:
- name: audio_data
struct:
- name: audio_path
dtype: string
splits:
- name: train
num_bytes: 81
num_examples: 1
download_size: 1447
dataset_size: 81
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "lecture-audio"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Honaker/eurosat_dataset | 2023-09-29T18:43:12.000Z | [
"task_categories:image-classification",
"license:mit",
"region:us"
] | Honaker | null | null | null | 0 | 10 | ---
license: mit
dataset_info:
features:
- name: image
dtype: image
- name: labels
dtype:
class_label:
names:
'0': AnnualCrop
'1': Forest
'2': HerbaceousVegetation
'3': Highway
'4': Industrial
'5': Pasture
'6': PermanentCrop
'7': Residential
'8': River
'9': SeaLake
splits:
- name: train
num_bytes: 70666516.8
num_examples: 21600
- name: validation
num_bytes: 8700747.8
num_examples: 2700
- name: test
num_bytes: 8631409.1
num_examples: 2700
download_size: 93886613
dataset_size: 87998673.69999999
task_categories:
- image-classification
---
# Dataset Card for EuroSat
## Table of Contents
- [How to Use](#How-to-Use)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
## How to Use
- Install [datasets](https://pypi.org/project/datasets/):
```bash
pip install datasets
```
- How to use in Python
```py
from datasets import load_dataset
train_data = load_dataset("Honaker/eurosat_dataset", split="train")
```
## Dataset Description
- **Homepage:** https://zenodo.org/record/7711810#.ZAm3k-zMKEA
### Dataset Summary
EuroSat is an image classification dataset with 10 different classes on satellite imagery. There is over 27,000 labeled images.
## Dataset Structure
The dataset is structured as follows:
```py
DatasetDict({
train: Dataset({
features: ['image', 'labels'],
num_rows: 21600
})
validation: Dataset({
features: ['image', 'labels'],
num_rows: 2700
})
test: Dataset({
features: ['image', 'labels'],
num_rows: 2700
})
})
```
### Data Instances
An example of the data for one image is:
```py
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=64x64>,
'labels': 0
}
```
With the type of each field being defined as:
```py
{
'image': <PIL.JpegImagePlugin.JpegImageFile>,
'labels': Integer
```
### Data Fields
The dataset has the following fields:
- 'image': Satellite image that is of type <PIL.TiffImagePlugin.TiffImageFile image>
- 'labels': the label of the Satellite image as an integer
### Data Splits
| | Train | Validation | Test |
|----------------|--------|------------|------|
| Images | 21600 | 2700 | 2700 |
## Additional Information
### Licensing Information
EuroSat is licensed under a MIT |
yashnbx/iamgroot-2 | 2023-09-29T19:18:59.000Z | [
"region:us"
] | yashnbx | null | null | null | 0 | 10 | test dataset |
OmgiKikov/test | 2023-10-01T21:51:05.000Z | [
"task_categories:text-classification",
"language:en",
"license:mit",
"code",
"region:us"
] | OmgiKikov | null | null | null | 0 | 10 | ---
license: mit
task_categories:
- text-classification
language:
- en
tags:
- code
--- |
DR-DRR/Medical_Customer_care | 2023-10-04T06:09:10.000Z | [
"region:us"
] | DR-DRR | null | null | null | 0 | 10 | Entry not found |
fernandoperes/py_legislation | 2023-10-04T12:10:16.000Z | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:es",
"license:apache-2.0",
"legal",
"region:us"
] | fernandoperes | null | null | null | 0 | 10 | ---
language:
- es
license: apache-2.0
size_categories:
- 1K<n<10K
task_categories:
- text-classification
tags:
- legal
configs:
- config_name: default
data_files:
- split: train
path: "/raw_text/train.parquet"
- config_name: raw_text
data_files:
- split: train
path: "/raw_text/train.parquet"
- config_name: unlabeled_sentences
data_files:
- split: train
path: "/unlabeled_sentences/train.parquet"
dataset_info:
- config_name: raw_text
features:
- name: source_id
dtype: int64
- name: source_name
dtype: string
- name: text
dtype: string
- name: text_id
dtype: int64
- name: extension
dtype:
class_label:
names:
'0': docx
'1': pdf
'2': html
'3': txt
'4': doc
split: train
- config_name: unlabeled_sentences
features:
- name: source_id
dtype: int64
- name: source_name
dtype: string
- name: text
dtype: string
- name: text_id
dtype: int64
- name: cost_type
dtype:
class_label:
names:
'0': no_cost
'1': adm_cost
'2': direct_cost
'3': other_cost
- name: affected_entity
dtype:
class_label:
names:
'0': no_affected_ent
'1': companies
'2': citizens
'3': public_adm
- name: io_categories
sequence:
class_label:
names:
'0': prestacao_info_empresarial_e_fiscal
'1': pedidos_de_licencas_e_outros
'2': registos_e_notificacoes
'3': candidatura_a_subsidios_e_outros
'4': disponibilizacao_de_manuais_e_outros
'5': cooperacao_com_auditorias_e_outros
'6': prestacao_info_a_consumidores
'7': outras_ois
- name: aa_categories
sequence:
class_label:
names:
'0': aa_1_familiarizacao_com_oi
'1': aa_1_recolha_e_organizacao_de_info
'2': aa_1_processamento_de_info
'3': aa_1_tempos_de_espera
'4': aa_1_deslocacoes
'5': aa_1_submissao_de_info
'6': aa_1_preservacao_de_info
'7': aa_2_familiarizacao_com_oi
'8': aa_2_recolha_e_organizacao_de_info
'9': aa_2_processamento_de_info
'10': aa_2_tempos_de_espera
'11': aa_2_deslocacoes
'12': aa_2_submissao_de_info
'13': aa_2_preservacao_de_info
'14': aa_3_familiarizacao_com_oi
'15': aa_3_recolha_e_organizacao_de_info
'16': aa_3_processamento_de_info
'17': aa_3_tempos_de_espera
'18': aa_3_deslocacoes
'19': aa_3_submissao_de_info
'20': aa_3_preservacao_de_info
'21': aa_4_familiarizacao_com_oi
'22': aa_4_recolha_e_organizacao_de_info
'23': aa_4_processamento_de_info
'24': aa_4_tempos_de_espera
'25': aa_4_deslocacoes
'26': aa_4_submissao_de_info
'27': aa_4_preservacao_de_info
'28': aa_5_familiarizacao_com_oi
'29': aa_5_recolha_e_organizacao_de_info
'30': aa_5_processamento_de_info
'31': aa_5_tempos_de_espera
'32': aa_5_deslocacoes
'33': aa_5_submissao_de_info
'34': aa_5_preservacao_de_info
'35': aa_6_familiarizacao_com_oi
'36': aa_6_recolha_e_organizacao_de_info
'37': aa_6_processamento_de_info
'38': aa_6_tempos_de_espera
'39': aa_6_deslocacoes
'40': aa_6_submissao_de_info
'41': aa_6_preservacao_de_info
'42': aa_7_familiarizacao_com_oi
'43': aa_7_recolha_e_organizacao_de_info
'44': aa_7_processamento_de_info
'45': aa_7_tempos_de_espera
'46': aa_7_deslocacoes
'47': aa_7_submissao_de_info
'48': aa_7_preservacao_de_info
- name: aa_categories_unique
sequence:
class_label:
names:
'0': familiarizacao_com_oi
'1': recolha_e_organizacao_de_info
'2': processamento_de_info
'3': tempos_de_espera
'4': deslocacoes
'5': submissao_de_info
'6': preservacao_de_info
splits:
- name: train
---
# Paraguay Legislation
The Paraguay Legislation dataset is a comprehensive collection of legal documents sourced from the legislative framework of Paraguay. The dataset contains legal documents sourced from the legislative framework of Paraguay, including resolutions, decrees, laws, and other kinds of legislative texts.
This dataset has been curated as a valuable resource for Natural Language Processing (NLP) tasks. The data is designed for research focused on text classification tasks. The classification process is divided into two objectives:
1. Binary classification: 0 - no cost and 1 - cost (legislation has costs for the society)
2. Multi-classification: classify the document into several hierarchical categories of costs.
For more information about multi-classification definitions, please check this link: <todo: link to>.
## Subsets
The dataset contains various subsets, each representing different data quality and preparation stages. Within these subsets, you'll encounter multiple versions of the same data, with variations primarily reflecting differences in data quality, metadata columns, and preprocessing tasks applied to change the data.
The subsets are the following:
**1. Raw:** Data extracted from the sources files (URls, PDFs and Word files) without any transformation or sentence splitter. It can be helpful because you can access the raw data extracted from the seeds (PDFs and Word files) and apply other preprocessing tasks from this point to prepare the data without returning to extract texts from source files.
**2. Sentences:** Normalized data split by sentence, mainly treating issues of text extracted from PDF. This stage also adds metadata about the sentence, for example: if it is a title or not.
**3. Sentence Unlabeled:** Unlabeled corpora of Paraguay legislation. This data is prepared to be labeled by the experts. Each instance of the dataset represents a specific text passage, split by its original formatting extracted from raw text (from original documents).
**4. Sentence labeled (Ground Truth):** The labeled data is the ground truth data used to train the models. This data is annotated by legal experts indicating the existence of administrative costs (and other types) in the legislation. Each instance of the dataset represents a specific text passage.
This dataset has the following data splits:
* Training Set: This portion of the data is used to train and fine-tune machine learning models.
* Test Set: The test set is reserved for assessing the model's accuracy, generalization, and effectiveness. It remains unseen during training and helps gauge how well the model performs on new, unseen data.
Together, these labeled data subsets provide a crucial reference point for building and evaluating models, ensuring they can make informed predictions and classifications with high accuracy and reliability.
|
manu/french-30b | 2023-10-02T19:57:20.000Z | [
"region:us"
] | manu | null | null | null | 0 | 10 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: id
dtype: string
- name: text
dtype: string
- name: dataset_id
dtype: string
splits:
- name: train
num_bytes: 108345820734
num_examples: 63517737
- name: test
num_bytes: 483041948
num_examples: 93498
download_size: 53389069327
dataset_size: 108828862682
---
# Dataset Card for "french_30b2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Falah/military_drawing_descriptions | 2023-10-03T08:35:38.000Z | [
"region:us"
] | Falah | null | null | null | 0 | 10 | ---
dataset_info:
features:
- name: prompts
dtype: string
splits:
- name: train
num_bytes: 163051
num_examples: 1000
download_size: 18457
dataset_size: 163051
---
# Dataset Card for "military_drawing_descriptions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
datazeit/gpt_target_group_v1-1 | 2023-10-03T13:06:28.000Z | [
"region:us"
] | datazeit | null | null | null | 0 | 10 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 11289432
num_examples: 4452
download_size: 0
dataset_size: 11289432
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "gpt_target_group_v1-1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
vsarathy/nl-robotics-semantic-parsing-info_structure-30k-context | 2023-10-03T14:35:20.000Z | [
"region:us"
] | vsarathy | null | null | null | 0 | 10 | Entry not found |
DanielJoy/wav2vec | 2023-10-04T06:39:56.000Z | [
"region:us"
] | DanielJoy | null | null | null | 0 | 10 | |
NikitaO/xix3d_v2_cluster_5 | 2023-10-04T15:51:25.000Z | [
"region:us"
] | NikitaO | null | null | null | 0 | 10 | ---
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: 17688213.0
num_examples: 135
download_size: 17687504
dataset_size: 17688213.0
---
# Dataset Card for "xix3d_v2_cluster_5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
MegPaulson/Melanoma_resized | 2023-10-05T14:14:41.000Z | [
"region:us"
] | MegPaulson | null | null | null | 0 | 10 | ---
dataset_info:
features:
- name: image
dtype: image
- name: image_seg
dtype: image
- name: prompt
dtype: string
splits:
- name: train
num_bytes: 11692204.0
num_examples: 26
download_size: 11702241
dataset_size: 11692204.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "Melanoma_resized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
AlexPlus/cpis_test | 2023-10-04T17:17:01.000Z | [
"license:mit",
"region:us"
] | AlexPlus | null | null | null | 0 | 10 | ---
license: mit
---
|
Intuit-GenSRF/haternet | 2023-10-05T01:37:51.000Z | [
"region:us"
] | Intuit-GenSRF | null | null | null | 0 | 10 | ---
dataset_info:
features:
- name: text
dtype: string
- name: labels
sequence: string
splits:
- name: train
num_bytes: 788430
num_examples: 6000
download_size: 513972
dataset_size: 788430
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "haternet"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
vsarathy/nl-robotics-semantic-parsing-info_structure-10k-context-TEST | 2023-10-05T13:43:07.000Z | [
"region:us"
] | vsarathy | null | null | null | 0 | 10 | Entry not found |
shuvom/red_dot | 2023-10-06T14:25:59.000Z | [
"license:mit",
"region:us"
] | shuvom | null | null | null | 0 | 10 | ---
license: mit
---
|
totally-not-an-llm/mistral-textbook | 2023-10-09T17:00:31.000Z | [
"license:mit",
"region:us"
] | totally-not-an-llm | null | null | null | 1 | 10 | ---
license: mit
---
|
Fraol/TrainDedupedRefDatasetWMetricFinal1 | 2023-10-08T04:25:28.000Z | [
"region:us"
] | Fraol | null | null | null | 0 | 10 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: source
dtype: string
- name: path_name
dtype: string
- name: file_name
dtype: string
- name: ref_type
dtype: string
- name: hash
dtype: string
- name: class_name
dtype: string
- name: method_name
dtype: string
- name: row_number
dtype: int64
- name: cbo
dtype: float64
- name: wmc
dtype: float64
- name: lcom*
dtype: float64
- name: loc
dtype: float64
- name: astc2
dtype: string
- name: source_after
dtype: string
- name: cbo_after
dtype: float64
- name: wmc_after
dtype: float64
- name: lcom*_after
dtype: float64
- name: loc_after
dtype: float64
- name: astc1
dtype: string
- name: issue_name
dtype: string
- name: issue_localize
dtype: string
splits:
- name: train
num_bytes: 418141332
num_examples: 15000
- name: test
num_bytes: 80590478
num_examples: 3000
download_size: 113829036
dataset_size: 498731810
---
# Dataset Card for "TrainDedupedRefDatasetWMetricFinal1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
JzJd/posts-append | 2023-10-08T11:25:17.000Z | [
"license:afl-3.0",
"region:us"
] | JzJd | null | null | null | 0 | 10 | ---
license: afl-3.0
---
|
MattPiscopo/alpacatest | 2023-10-08T14:40:54.000Z | [
"region:us"
] | MattPiscopo | null | null | null | 0 | 10 | Entry not found |
hippocrates/DDI2013_test | 2023-10-08T22:47:24.000Z | [
"region:us"
] | hippocrates | null | null | null | 0 | 10 | ---
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: id
dtype: string
- name: query
dtype: string
- name: answer
dtype: string
- name: label
dtype: string
- name: gold
dtype: int64
- name: choices
sequence: string
splits:
- name: train
num_bytes: 10198253
num_examples: 3572
- name: valid
num_bytes: 863726
num_examples: 305
- name: test
num_bytes: 1613988
num_examples: 949
download_size: 1562765
dataset_size: 12675967
---
# Dataset Card for "DDI2013_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
kelzla/klz-ds3 | 2023-10-09T00:26:51.000Z | [
"region:us"
] | kelzla | null | null | null | 0 | 10 | Entry not found |
minh21/COVID-QA-Chunk-64-sentence-transformer-biencoder-data-65_25_10-v2 | 2023-10-09T03:47:50.000Z | [
"region:us"
] | minh21 | null | null | null | 0 | 10 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: question
dtype: string
- name: positive
dtype: string
- name: negative
dtype: string
- name: document_id
dtype: int64
splits:
- name: train
num_bytes: 7075815
num_examples: 6419
- name: test
num_bytes: 806462
num_examples: 723
download_size: 724649
dataset_size: 7882277
---
# Dataset Card for "COVID-QA-Chunk-64-sentence-transformer-biencoder-data-65_25_10-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
laion/gpt4v-dataset | 2023-10-10T15:05:57.000Z | [
"license:cc0-1.0",
"region:us"
] | laion | null | null | null | 5 | 10 | ---
license: cc0-1.0
dataset_info:
features:
- name: caption
dtype: string
- name: image
struct:
- name: bytes
dtype: binary
- name: path
dtype: 'null'
- name: link
dtype: string
- name: message_id
dtype: string
- name: timestamp
dtype: string
splits:
- name: train
num_bytes: 40398292
num_examples: 32
download_size: 40396862
dataset_size: 40398292
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
sankettgorey/donut_two_layouts | 2023-10-09T11:42:10.000Z | [
"region:us"
] | sankettgorey | null | null | null | 0 | 10 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: valid
path: data/valid-*
dataset_info:
features:
- name: image
dtype: image
- name: ground_truth
dtype: string
splits:
- name: train
num_bytes: 511297223.058686
num_examples: 1279
- name: test
num_bytes: 64193822.85115697
num_examples: 160
- name: valid
num_bytes: 63774381.85115697
num_examples: 160
download_size: 575142166
dataset_size: 639265427.7609999
---
# Dataset Card for "donut_two_layouts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
pbaoo2705/cpgqa_processed | 2023-10-10T06:53:20.000Z | [
"region:us"
] | pbaoo2705 | null | null | null | 0 | 10 | ---
dataset_info:
features:
- name: title
dtype: string
- name: id
dtype: int64
- name: question
dtype: string
- name: answer_text
dtype: string
- name: answer_start
dtype: int64
- name: context
dtype: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: answer
dtype: string
- name: start_positions
dtype: int64
- name: end_positions
dtype: int64
splits:
- name: train
num_bytes: 10324927
num_examples: 884
download_size: 201583
dataset_size: 10324927
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "cpgqa_processed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
best2009 | 2023-01-25T14:27:17.000Z | [
"task_categories:token-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:th",
"license:cc-by-nc-sa-3.0",
"word-tokenization",
"region:us"
] | null | `best2009` is a Thai word-tokenization dataset from encyclopedia, novels, news and articles by
[NECTEC](https://www.nectec.or.th/) (148,995/2,252 lines of train/test). It was created for
[BEST 2010: Word Tokenization Competition](https://thailang.nectec.or.th/archive/indexa290.html?q=node/10).
The test set answers are not provided publicly. | @inproceedings{kosawat2009best,
title={BEST 2009: Thai word segmentation software contest},
author={Kosawat, Krit and Boriboon, Monthika and Chootrakool, Patcharika and Chotimongkol, Ananlada and Klaithin, Supon and Kongyoung, Sarawoot and Kriengket, Kanyanut and Phaholphinyo, Sitthaa and Purodakananda, Sumonmas and Thanakulwarapas, Tipraporn and others},
booktitle={2009 Eighth International Symposium on Natural Language Processing},
pages={83--88},
year={2009},
organization={IEEE}
}
@inproceedings{boriboon2009best,
title={Best corpus development and analysis},
author={Boriboon, Monthika and Kriengket, Kanyanut and Chootrakool, Patcharika and Phaholphinyo, Sitthaa and Purodakananda, Sumonmas and Thanakulwarapas, Tipraporn and Kosawat, Krit},
booktitle={2009 International Conference on Asian Language Processing},
pages={322--327},
year={2009},
organization={IEEE}
} | null | 0 | 9 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- th
license:
- cc-by-nc-sa-3.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- token-classification
task_ids: []
pretty_name: best2009
tags:
- word-tokenization
dataset_info:
features:
- name: fname
dtype: string
- name: char
sequence: string
- name: char_type
sequence:
class_label:
names:
'0': b_e
'1': c
'2': d
'3': n
'4': o
'5': p
'6': q
'7': s
'8': s_e
'9': t
'10': v
'11': w
- name: is_beginning
sequence:
class_label:
names:
'0': neg
'1': pos
config_name: best2009
splits:
- name: train
num_bytes: 483129998
num_examples: 148995
- name: test
num_bytes: 10498726
num_examples: 2252
download_size: 13891260
dataset_size: 493628724
---
# Dataset Card for `best2009`
## 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://aiforthai.in.th/
- **Repository:** https://aiforthai.in.th/corpus.php
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** https://aiforthai.in.th/
### Dataset Summary
`best2009` is a Thai word-tokenization dataset from encyclopedia, novels, news and articles by [NECTEC](https://www.nectec.or.th/) (148,995/2,252 lines of train/test). It was created for [BEST 2010: Word Tokenization Competition](https://thailang.nectec.or.th/archive/indexa290.html?q=node/10). The test set answers are not provided publicly.
### Supported Tasks and Leaderboards
word tokenization
### Languages
Thai
## Dataset Structure
### Data Instances
```
{'char': ['?', 'ภ', 'ู', 'ม', 'ิ', 'ป', 'ั', 'ญ', 'ญ', 'า', 'ช', 'า', 'ว', 'บ', '้', 'า', 'น', '\n'], 'char_type': [4, 1, 10, 1, 10, 1, 4, 1, 1, 10, 1, 10, 1, 1, 9, 10, 1, 4], 'fname': 'encyclopedia_00031.txt', 'is_beginning': [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1]}
{'char': ['ภ', 'ู', 'ม', 'ิ', 'ป', 'ั', 'ญ', 'ญ', 'า', 'ช', 'า', 'ว', 'บ', '้', 'า', 'น', ' ', 'ห', 'ม', 'า', 'ย', 'ถ', 'ึ', 'ง', ' ', 'ค', 'ว', 'า', 'ม', 'ร', 'ู', '้', 'ข', 'อ', 'ง', 'ช', 'า', 'ว', 'บ', '้', 'า', 'น', ' ', 'ซ', 'ึ', '่', 'ง', 'เ', 'ร', 'ี', 'ย', 'น', 'ร', 'ู', '้', 'ม', 'า', 'จ', 'า', 'ก', 'พ', '่', 'อ', 'แ', 'ม', '่', ' ', 'ป', 'ู', '่', 'ย', '่', 'า', 'ต', 'า', 'ย', 'า', 'ย', ' ', 'ญ', 'า', 'ต', 'ิ', 'พ', 'ี', '่', 'น', '้', 'อ', 'ง', ' ', 'ห', 'ร', 'ื', 'อ', 'ผ', 'ู', '้', 'ม', 'ี', 'ค', 'ว', 'า', 'ม', 'ร', 'ู', '้', 'ใ', 'น', 'ห', 'ม', 'ู', '่', 'บ', '้', 'า', 'น', 'ใ', 'น', 'ท', '้', 'อ', 'ง', 'ถ', 'ิ', '่', 'น', 'ต', '่', 'า', 'ง', 'ๆ', '\n'], 'char_type': [1, 10, 1, 10, 1, 4, 1, 1, 10, 1, 10, 1, 1, 9, 10, 1, 5, 3, 1, 10, 1, 1, 10, 1, 5, 1, 1, 10, 1, 1, 10, 9, 1, 1, 1, 1, 10, 1, 1, 9, 10, 1, 5, 1, 10, 9, 1, 11, 1, 10, 1, 1, 1, 10, 9, 1, 10, 1, 10, 1, 1, 9, 1, 11, 1, 9, 5, 1, 10, 9, 1, 9, 10, 1, 10, 1, 10, 1, 5, 1, 10, 1, 10, 1, 10, 9, 1, 9, 1, 1, 5, 3, 1, 10, 1, 3, 10, 9, 1, 10, 1, 1, 10, 1, 1, 10, 9, 11, 1, 3, 1, 10, 9, 1, 9, 10, 1, 11, 1, 1, 9, 1, 1, 1, 10, 9, 1, 1, 9, 10, 1, 7, 4], 'fname': 'encyclopedia_00031.txt', 'is_beginning': [1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1]}
```
### Data Fields
- `fname`: file name; also marks if article is articles, news, encyclopedia or novels
- `char`: characters
- `char_type`: character types as adopted from []() by [deepcut](https://github.com/rkcosmos/deepcut)
- `is_beginning`: is beginning of word
### Data Splits
| | train | test |
|-------------------------|------------|---------|
| # lines | 148,995 | 2,252 |
| avg words per line | 39.05 | NA |
| total words | 5,818,521 | NA |
| avg characters per line | 140.39 | 202.79 |
| total characters | 20,918,132 | 456,684 |
| # lines articles | 16,990 | NA |
| # lines encyclopedia | 50,631 | NA |
| # lines novels | 50,140 | NA |
| # lines news | 31,234 | NA |
## Dataset Creation
### Curation Rationale
The dataset was created for [BEST 2010: Word Tokenization Competition](https://thailang.nectec.or.th/archive/indexa290.html?q=node/10) by [NECTEC](https://www.nectec.or.th/).
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
Respective authors of the articles, news, encyclopedia and novels
### Annotations
#### Annotation process
Detailed annotation guidelines can be found in `BEST_Guideline_Release1.pdf` as part of the uncompressed files. Word tokenization standard used was [InterBEST2009](http://hltshare.fbk.eu/IWSLT2015/InterBEST2009Guidelines-2.pdf)
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
All data are curated from public sources. No personal and sensitive information is expected to be included.
## Considerations for Using the Data
### Social Impact of Dataset
- word tokenization dataset from articles, news, encyclopedia and novels
### Discussion of Biases
- texts are relatively formal ones from articles, news, encyclopedia and novels.
- word tokenization standard used was [InterBEST2009](http://hltshare.fbk.eu/IWSLT2015/InterBEST2009Guidelines-2.pdf).
### Other Known Limitations
- some tags unrelated to word tokenization (`<NE>` and `<AB>`) are cleaned out.
- no word boundary provdied for the test set
## Additional Information
### Dataset Curators
[NECTEC](https://www.nectec.or.th/)
### Licensing Information
CC-BY-NC-SA 3.0
### Citation Information
Dataset:
```
@inproceedings{kosawat2009best,
title={BEST 2009: Thai word segmentation software contest},
author={Kosawat, Krit and Boriboon, Monthika and Chootrakool, Patcharika and Chotimongkol, Ananlada and Klaithin, Supon and Kongyoung, Sarawoot and Kriengket, Kanyanut and Phaholphinyo, Sitthaa and Purodakananda, Sumonmas and Thanakulwarapas, Tipraporn and others},
booktitle={2009 Eighth International Symposium on Natural Language Processing},
pages={83--88},
year={2009},
organization={IEEE}
}
@inproceedings{boriboon2009best,
title={Best corpus development and analysis},
author={Boriboon, Monthika and Kriengket, Kanyanut and Chootrakool, Patcharika and Phaholphinyo, Sitthaa and Purodakananda, Sumonmas and Thanakulwarapas, Tipraporn and Kosawat, Krit},
booktitle={2009 International Conference on Asian Language Processing},
pages={322--327},
year={2009},
organization={IEEE}
}
```
Character type features:
```
@inproceedings{haruechaiyasak2009tlex,
title={TLex: Thai lexeme analyser based on the conditional random fields},
author={Haruechaiyasak, Choochart and Kongyoung, Sarawoot},
booktitle={Proceedings of 8th International Symposium on Natural Language Processing},
year={2009}
}
```
### Contributions
Thanks to [@cstorm125](https://github.com/cstorm125) for adding this dataset. |
coarse_discourse | 2023-04-05T10:01:55.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
] | null | dataset contains discourse annotation and relation on threads from reddit during 2016 | @inproceedings{coarsediscourse, title={Characterizing Online Discussion Using Coarse Discourse Sequences}, author={Zhang, Amy X. and Culbertson, Bryan and Paritosh, Praveen}, booktitle={Proceedings of the 11th International AAAI Conference on Weblogs and Social Media}, series={ICWSM '17}, year={2017}, location = {Montreal, Canada} } | null | 3 | 9 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Coarse Discourse
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
paperswithcode_id: coarse-discourse
dataset_info:
features:
- name: title
dtype: string
- name: is_self_post
dtype: bool
- name: subreddit
dtype: string
- name: url
dtype: string
- name: majority_link
dtype: string
- name: is_first_post
dtype: bool
- name: majority_type
dtype: string
- name: id_post
dtype: string
- name: post_depth
dtype: int32
- name: in_reply_to
dtype: string
- name: annotations
sequence:
- name: annotator
dtype: string
- name: link_to_post
dtype: string
- name: main_type
dtype: string
splits:
- name: train
num_bytes: 45443464
num_examples: 116357
download_size: 4636201
dataset_size: 45443464
---
# Dataset Card for "coarse_discourse"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:** https://github.com/google-research-datasets/coarse-discourse
- **Paper:** [Characterizing Online Discussion Using Coarse Discourse Sequences](https://research.google/pubs/pub46055/)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 4.63 MB
- **Size of the generated dataset:** 45.45 MB
- **Total amount of disk used:** 50.08 MB
### Dataset Summary
A large corpus of discourse annotations and relations on ~10K forum threads.
We collect and release a corpus of over 9,000 threads comprising over 100,000 comments manually annotated via paid crowdsourcing with discourse acts and randomly sampled from the site Reddit.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 4.63 MB
- **Size of the generated dataset:** 45.45 MB
- **Total amount of disk used:** 50.08 MB
An example of 'train' looks as follows.
```
{
"annotations": {
"annotator": ["fc96a15ab87f02dd1998ff55a64f6478", "e9e4b3ab355135fa954badcc06bfccc6", "31ac59c1734c1547d4d0723ff254c247"],
"link_to_post": ["", "", ""],
"main_type": ["elaboration", "elaboration", "elaboration"]
},
"id_post": "t1_c9b30i1",
"in_reply_to": "t1_c9b2nyd",
"is_first_post": false,
"is_self_post": true,
"majority_link": "t1_c9b2nyd",
"majority_type": "elaboration",
"post_depth": 2,
"subreddit": "100movies365days",
"title": "DTX120: #87 - Nashville",
"url": "https://www.reddit.com/r/100movies365days/comments/1bx6qw/dtx120_87_nashville/"
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `title`: a `string` feature.
- `is_self_post`: a `bool` feature.
- `subreddit`: a `string` feature.
- `url`: a `string` feature.
- `majority_link`: a `string` feature.
- `is_first_post`: a `bool` feature.
- `majority_type`: a `string` feature.
- `id_post`: a `string` feature.
- `post_depth`: a `int32` feature.
- `in_reply_to`: a `string` feature.
- `annotations`: a dictionary feature containing:
- `annotator`: a `string` feature.
- `link_to_post`: a `string` feature.
- `main_type`: a `string` feature.
### Data Splits
| name |train |
|-------|-----:|
|default|116357|
## 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{coarsediscourse, title={Characterizing Online Discussion Using Coarse Discourse Sequences}, author={Zhang, Amy X. and Culbertson, Bryan and Paritosh, Praveen}, booktitle={Proceedings of the 11th International AAAI Conference on Weblogs and Social Media}, series={ICWSM '17}, year={2017}, location = {Montreal, Canada} }
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@jplu](https://github.com/jplu) for adding this dataset. |
hard | 2023-01-25T14:31:26.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:ar",
"license:unknown",
"region:us"
] | null | This dataset contains 93700 hotel reviews in Arabic language.The hotel reviews were collected from Booking.com website during June/July 2016.The reviews are expressed in Modern Standard Arabic as well as dialectal Arabic.The following table summarize some tatistics on the HARD Dataset. | @incollection{elnagar2018hotel,
title={Hotel Arabic-reviews dataset construction for sentiment analysis applications},
author={Elnagar, Ashraf and Khalifa, Yasmin S and Einea, Anas},
booktitle={Intelligent Natural Language Processing: Trends and Applications},
pages={35--52},
year={2018},
publisher={Springer}
} | null | 0 | 9 | ---
annotations_creators:
- found
language_creators:
- found
language:
- ar
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
paperswithcode_id: hard
pretty_name: Hotel Arabic-Reviews Dataset
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': '1'
'1': '2'
'2': '3'
'3': '4'
'4': '5'
config_name: plain_text
splits:
- name: train
num_bytes: 27507085
num_examples: 105698
download_size: 8508677
dataset_size: 27507085
---
# Dataset Card for Hard
## 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:** [Hard](https://github.com/elnagara/HARD-Arabic-Dataset)
- **Repository:** [Hard](https://github.com/elnagara/HARD-Arabic-Dataset)
- **Paper:** [Hotel Arabic-Reviews Dataset Construction for Sentiment Analysis Applications](https://link.springer.com/chapter/10.1007/978-3-319-67056-0_3)
- **Point of Contact:** [Ashraf Elnagar](ashraf@sharjah.ac.ae)
### Dataset Summary
This dataset contains 93,700 hotel reviews in Arabic language.The hotel reviews were collected from Booking.com website during June/July 2016.The reviews are expressed in Modern Standard Arabic as well as dialectal Arabic.The following table summarize some tatistics on the HARD Dataset.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset is based on Arabic.
## Dataset Structure
### Data Instances
A typical data point comprises a rating from 1 to 5 for hotels.
### Data Fields
[More Information Needed]
### Data Splits
The dataset is not split.
## 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
### Contributions
Thanks to [@zaidalyafeai](https://github.com/zaidalyafeai) for adding this dataset. |
hindi_discourse | 2023-01-25T14:32:13.000Z | [
"task_categories:text-classification",
"task_ids:multi-label-classification",
"annotations_creators:other",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:hi",
"license:other",
"discourse-analysis",
"region:us"
] | null | The Hindi Discourse Analysis dataset is a corpus for analyzing discourse modes present in its sentences.
It contains sentences from stories written by 11 famous authors from the 20th Century.
4-5 stories by each author have been selected which were available in the public domain resulting
in a collection of 53 stories. Most of these short stories were originally written in Hindi
but some of them were written in other Indian languages and later translated to Hindi. | @inproceedings{swapnil2020,
title={An Annotated Dataset of Discourse Modes in Hindi Stories},
author={Swapnil Dhanwal, Hritwik Dutta, Hitesh Nankani, Nilay Shrivastava, Yaman Kumar, Junyi Jessy Li, Debanjan Mahata, Rakesh Gosangi, Haimin Zhang, Rajiv Ratn Shah, Amanda Stent},
booktitle={Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)},
volume={12},
pages={1191–1196},
year={2020} | null | 1 | 9 | ---
annotations_creators:
- other
language_creators:
- found
language:
- hi
license:
- other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-label-classification
pretty_name: Discourse Analysis dataset
tags:
- discourse-analysis
dataset_info:
features:
- name: Story_no
dtype: int32
- name: Sentence
dtype: string
- name: Discourse Mode
dtype:
class_label:
names:
'0': Argumentative
'1': Descriptive
'2': Dialogue
'3': Informative
'4': Narrative
'5': Other
splits:
- name: train
num_bytes: 1998930
num_examples: 9968
download_size: 4176677
dataset_size: 1998930
---
# Dataset Card for Discourse Analysis dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:** https://github.com/midas-research/hindi-discourse
- **Paper:** [An Annotated Dataset of Discourse Modes in Hindi Stories](https://aclanthology.org/2020.lrec-1.149/)
- **Point of Contact:** https://github.com/midas-research/MeTooMA
### Dataset Summary
- The Hindi Discourse Analysis dataset is a corpus for analyzing discourse modes present in its sentences.
- It contains sentences from stories written by 11 famous authors from the 20th Century.
- 4-5 stories by each author have been selected which were available in the public domain resulting in a collection of 53 stories.
- Most of these short stories were originally written in Hindi but some of them were written in other Indian languages and later translated to Hindi.
The corpus contains a total of 10472 sentences belonging to the following categories:
- Argumentative
- Descriptive
- Dialogic
- Informative
- Narrative
### Supported Tasks and Leaderboards
- Discourse Analysis of Hindi.
### Languages
Hindi
## Dataset Structure
- The dataset is structured into JSON format.
### Data Instances
{'Story_no': 15, 'Sentence': ' गाँठ से साढ़े तीन रुपये लग गये, जो अब पेट में जाकर खनकते भी नहीं! जो तेरी करनी मालिक! ” “इसमें मालिक की क्या करनी है? ”', 'Discourse Mode': 'Dialogue'}
### Data Fields
Sentence number, story number, sentence and discourse mode
### Data Splits
- Train: 9983
## Dataset Creation
### Curation Rationale
- Present a new publicly available corpus
consisting of sentences from short stories written in a
low-resource language of Hindi having high quality annotation for five different discourse modes -
argumentative, narrative, descriptive, dialogic and informative.
- Perform a detailed analysis of the proposed annotated corpus and characterize the performance of
different classification algorithms.
### Source Data
- Source of all the data points in this dataset is Hindi stories written by famous authors of Hindi literature.
#### Initial Data Collection and Normalization
- All the data was collected from various Hindi websites.
- We chose against crowd-sourcing the annotation pro- cess because we wanted to directly work with the an- notators for qualitative feedback and to also ensure high quality annotations.
- We employed three native Hindi speakers with college level education for the an- notation task.
- We first selected two random stories from our corpus and had the three annotators work on them independently and classify each sentence based on the discourse mode.
- Please refer to this paper for detailed information: https://www.aclweb.org/anthology/2020.lrec-1.149/
#### Who are the source language producers?
Please refer to this paper for detailed information: https://www.aclweb.org/anthology/2020.lrec-1.149/
### Annotations
#### Annotation process
- The authors chose against crowd sourcing for labeling this dataset due to its highly sensitive nature.
- The annotators are domain experts having degress in advanced clinical psychology and gender studies.
- They were provided a guidelines document with instructions about each task and its definitions, labels and examples.
- They studied the document, worked a few examples to get used to this annotation task.
- They also provided feedback for improving the class definitions.
- The annotation process is not mutually exclusive, implying that presence of one label does not mean the
absence of the other one.
#### Who are the annotators?
- The annotators were three native Hindi speakers with college level education.
- Please refer to the accompnaying paper for a detailed annotation process.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
- As a future work we would also like to use the presented corpus to see how it could be further used
in certain downstream tasks such as emotion analysis, machine translation,
textual entailment, and speech sythesis for improving storytelling experience in Hindi language.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
- We could not get the best performance using the deep learning model trained on the data, due to
insufficient data for DL models.
## Additional Information
Please refer to this link: https://github.com/midas-research/hindi-discourse
### Dataset Curators
- If you use the corpus in a product or application, then please credit the authors
and [Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi]
(http://midas.iiitd.edu.in) appropriately.
Also, if you send us an email, we will be thrilled to know about how you have used the corpus.
- If interested in commercial use of the corpus, send email to midas@iiitd.ac.in.
- Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi, India
disclaims any responsibility for the use of the corpus and does not provide technical support.
However, the contact listed above will be happy to respond to queries and clarifications
- Please feel free to send us an email:
- with feedback regarding the corpus.
- with information on how you have used the corpus.
- if interested in having us analyze your social media data.
- if interested in a collaborative research project.
### Licensing Information
- If you use the corpus in a product or application, then please credit the authors
and [Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi]
(http://midas.iiitd.edu.in) appropriately.
### Citation Information
Please cite the following publication if you make use of the dataset: https://aclanthology.org/2020.lrec-1.149/
```
@inproceedings{dhanwal-etal-2020-annotated,
title = "An Annotated Dataset of Discourse Modes in {H}indi Stories",
author = "Dhanwal, Swapnil and
Dutta, Hritwik and
Nankani, Hitesh and
Shrivastava, Nilay and
Kumar, Yaman and
Li, Junyi Jessy and
Mahata, Debanjan and
Gosangi, Rakesh and
Zhang, Haimin and
Shah, Rajiv Ratn and
Stent, Amanda",
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://www.aclweb.org/anthology/2020.lrec-1.149",
pages = "1191--1196",
abstract = "In this paper, we present a new corpus consisting of sentences from Hindi short stories annotated for five different discourse modes argumentative, narrative, descriptive, dialogic and informative. We present a detailed account of the entire data collection and annotation processes. The annotations have a very high inter-annotator agreement (0.87 k-alpha). We analyze the data in terms of label distributions, part of speech tags, and sentence lengths. We characterize the performance of various classification algorithms on this dataset and perform ablation studies to understand the nature of the linguistic models suitable for capturing the nuances of the embedded discourse structures in the presented corpus.",
language = "English",
ISBN = "979-10-95546-34-4",
}
```
### Contributions
Thanks to [@duttahritwik](https://github.com/duttahritwik) for adding this dataset. |
journalists_questions | 2023-01-25T14:33:26.000Z | [
"task_categories:text-classification",
"annotations_creators:crowdsourced",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ar",
"license:unknown",
"question-identification",
"region:us"
] | null | \
The journalists_questions corpus (version 1.0) is a collection of 10K human-written Arabic
tweets manually labeled for question identification over Arabic tweets posted by journalists. | \
@inproceedings{hasanain2016questions,
title={What Questions Do Journalists Ask on Twitter?},
author={Hasanain, Maram and Bagdouri, Mossaab and Elsayed, Tamer and Oard, Douglas W},
booktitle={Tenth International AAAI Conference on Web and Social Media},
year={2016}
} | null | 0 | 9 | ---
annotations_creators:
- crowdsourced
language_creators:
- other
language:
- ar
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
pretty_name: JournalistsQuestions
tags:
- question-identification
dataset_info:
features:
- name: tweet_id
dtype: string
- name: label
dtype:
class_label:
names:
'0': 'no'
'1': 'yes'
- name: label_confidence
dtype: float32
config_name: plain_text
splits:
- name: train
num_bytes: 342296
num_examples: 10077
download_size: 271039
dataset_size: 342296
---
# Dataset Card for journalists_questions
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://qufaculty.qu.edu.qa/telsayed/datasets/
- **Repository:** [Needs More Information]
- **Paper:** https://www.aaai.org/ocs/index.php/ICWSM/ICWSM16/paper/download/13221/12856
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Maram Hasanain]
maram.hasanain@qu.edu.qa
### Dataset Summary
The journalists_questions dataset supports question identification over Arabic tweets of journalists.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Arabic
## Dataset Structure
### Data Instances
Our dataset supports question identification task. It includes 10K Arabic tweets crawled from journalists accounts. Tweets were labelled by crowdsourcing. Each tweet is associated with one label: question tweet or not. A question tweet is a tweet that has at least one interrogative question. Each label is associated with a number that represents the confidence in the label, given that each tweet was labelled by 3 annotators and an aggregation method was followed to choose the final label.
Below is an example:
{
'tweet_id': '493235142128074753',
'label': 'yes',
'label_confidence':0.6359
}
### Data Fields
tweet_id: the Twitter assigned ID for the tweet object.
label: annotation of the tweet by whether it is a question or not
label_confidence: confidence score for the label given annotations of multiple annotators per tweet
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
The dataset includes tweet IDs only due to Twitter content re-distribution policy. It was created and shared for research purposes for parties interested in understanding questions expecting answers by Arab journalists on Twitter.
### Source Data
#### Initial Data Collection and Normalization
To construct our dataset of question tweets posted by journalists, we first acquire a list of Twitter accounts of 389 Arab journalists. We use the Twitter API to crawl their available tweets, keeping only those that are identified by Twitter to be both Arabic, and not retweets (as these would contain content that was not originally authored by journalists). We apply a rule-based question filter to this dataset of 465,599 tweets, extracting 49,119 (10.6%) potential question tweets from 363 (93.3%) Arab journalists.
#### 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 [@MaramHasanain](https://github.com/MaramHasanain) for adding this dataset. |
opus_rf | 2023-06-01T14:59:53.000Z | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:n<1K",
"source_datasets:original",
"language:de",
"language:en",
"language:es",
"language:fr",
"language:sv",
"license:unknown",
"region:us"
] | null | RF is a tiny parallel corpus of the Declarations of the Swedish Government and its translations. | @InProceedings{TIEDEMANN12.463,
author = {J{\"o}rg Tiedemann},
title = {Parallel Data, Tools and Interfaces in OPUS},
booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},
year = {2012},
month = {may},
date = {23-25},
address = {Istanbul, Turkey},
editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis},
publisher = {European Language Resources Association (ELRA)},
isbn = {978-2-9517408-7-7},
language = {english}
} | null | 0 | 9 | ---
annotations_creators:
- found
language_creators:
- expert-generated
language:
- de
- en
- es
- fr
- sv
license:
- unknown
multilinguality:
- multilingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: OpusRf
dataset_info:
- config_name: de-en
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- de
- en
splits:
- name: train
num_bytes: 38683
num_examples: 177
download_size: 16029
dataset_size: 38683
- config_name: de-es
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- de
- es
splits:
- name: train
num_bytes: 2316
num_examples: 24
download_size: 2403
dataset_size: 2316
- config_name: de-fr
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- de
- fr
splits:
- name: train
num_bytes: 41300
num_examples: 173
download_size: 16720
dataset_size: 41300
- config_name: de-sv
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- de
- sv
splits:
- name: train
num_bytes: 37414
num_examples: 178
download_size: 15749
dataset_size: 37414
- config_name: en-es
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- en
- es
splits:
- name: train
num_bytes: 2600
num_examples: 25
download_size: 2485
dataset_size: 2600
- config_name: en-fr
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- en
- fr
splits:
- name: train
num_bytes: 39503
num_examples: 175
download_size: 16038
dataset_size: 39503
- config_name: en-sv
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- en
- sv
splits:
- name: train
num_bytes: 35778
num_examples: 180
download_size: 15147
dataset_size: 35778
- config_name: es-fr
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- es
- fr
splits:
- name: train
num_bytes: 2519
num_examples: 21
download_size: 2469
dataset_size: 2519
- config_name: es-sv
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- es
- sv
splits:
- name: train
num_bytes: 3110
num_examples: 28
download_size: 2726
dataset_size: 3110
- config_name: fr-sv
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- fr
- sv
splits:
- name: train
num_bytes: 38627
num_examples: 175
download_size: 15937
dataset_size: 38627
config_names:
- de-en
- de-es
- de-fr
- de-sv
- en-es
- en-fr
- en-sv
- es-fr
- es-sv
- fr-sv
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://opus.nlpl.eu/RF.php
- **Repository:**
- **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
RF is a tiny parallel corpus of the Declarations of the Swedish Government and its translations.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English (en), Spanish (es), German (de), French (fr), Swedish (sv)
## 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
```
@InProceedings{TIEDEMANN12.463,
author = {J{\"o}rg Tiedemann},
title = {Parallel Data, Tools and Interfaces in OPUS},
booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},
year = {2012},
month = {may},
date = {23-25},
address = {Istanbul, Turkey},
editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis},
publisher = {European Language Resources Association (ELRA)},
isbn = {978-2-9517408-7-7},
language = {english}
}
```
### Contributions
Thanks to [@akshayb7](https://github.com/akshayb7) for adding this dataset. |
taskmaster3 | 2022-11-03T16:30:39.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:dialogue-modeling",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv... | null | Taskmaster is dataset for goal oriented conversations. The Taskmaster-3 dataset consists of 23,757 movie ticketing dialogs. By "movie ticketing" we mean conversations where the customer's goal is to purchase tickets after deciding on theater, time, movie name, number of tickets, and date, or opt out of the transaction. This collection was created using the "self-dialog" method. This means a single, crowd-sourced worker is paid to create a conversation writing turns for both speakers, i.e. the customer and the ticketing agent. | @inproceedings{48484,
title = {Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset},
author = {Bill Byrne and Karthik Krishnamoorthi and Chinnadhurai Sankar and Arvind Neelakantan and Daniel Duckworth and Semih Yavuz and Ben Goodrich and Amit Dubey and Kyu-Young Kim and Andy Cedilnik},
year = {2019}
} | null | 0 | 9 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- dialogue-modeling
paperswithcode_id: null
pretty_name: taskmaster3
dataset_info:
features:
- name: conversation_id
dtype: string
- name: vertical
dtype: string
- name: instructions
dtype: string
- name: scenario
dtype: string
- name: utterances
list:
- name: index
dtype: int32
- name: speaker
dtype: string
- name: text
dtype: string
- name: apis
list:
- name: name
dtype: string
- name: index
dtype: int32
- name: args
list:
- name: arg_name
dtype: string
- name: arg_value
dtype: string
- name: response
list:
- name: response_name
dtype: string
- name: response_value
dtype: string
- name: segments
list:
- name: start_index
dtype: int32
- name: end_index
dtype: int32
- name: text
dtype: string
- name: annotations
list:
- name: name
dtype: string
splits:
- name: train
num_bytes: 143609327
num_examples: 23757
download_size: 313402141
dataset_size: 143609327
---
# Dataset Card for taskmaster3
## 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:** [Taskmaster](https://research.google/tools/datasets/taskmaster-1/)
- **Repository:** [GitHub](https://github.com/google-research-datasets/Taskmaster/tree/master/TM-3-2020)
- **Paper:** [Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset](https://arxiv.org/abs/1909.05358)
- **Leaderboard:** N/A
- **Point of Contact:** [Taskmaster Googlegroup](taskmaster-datasets@googlegroups.com)
### Dataset Summary
Taskmaster is dataset for goal oriented conversations. The Taskmaster-3 dataset consists of 23,757 movie ticketing dialogs.
By "movie ticketing" we mean conversations where the customer's goal is to purchase tickets after deciding
on theater, time, movie name, number of tickets, and date, or opt out of the transaction. This collection
was created using the "self-dialog" method. This means a single, crowd-sourced worker is
paid to create a conversation writing turns for both speakers, i.e. the customer and the ticketing agent.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset is in English language.
## Dataset Structure
### Data Instances
A typical example looks like this
```
{
"conversation_id": "dlg-ddee80da-9ffa-4773-9ce7-f73f727cb79c",
"instructions": "SCENARIO: Pretend you’re *using a digital assistant to purchase tickets for a movie currently showing in theaters*. ...",
"scenario": "4 exchanges with 1 error and predefined variables",
"utterances": [
{
"apis": [],
"index": 0,
"segments": [
{
"annotations": [
{
"name": "num.tickets"
}
],
"end_index": 21,
"start_index": 20,
"text": "2"
},
{
"annotations": [
{
"name": "name.movie"
}
],
"end_index": 42,
"start_index": 37,
"text": "Mulan"
}
],
"speaker": "user",
"text": "I would like to buy 2 tickets to see Mulan."
},
{
"index": 6,
"segments": [],
"speaker": "user",
"text": "Yes.",
"apis": [
{
"args": [
{
"arg_name": "name.movie",
"arg_value": "Mulan"
},
{
"arg_name": "name.theater",
"arg_value": "Mountain AMC 16"
}
],
"index": 6,
"name": "book_tickets",
"response": [
{
"response_name": "status",
"response_value": "success"
}
]
}
]
}
],
"vertical": "Movie Tickets"
}
```
### Data Fields
Each conversation in the data file has the following structure:
- `conversation_id`: A universally unique identifier with the prefix 'dlg-'. The ID has no meaning.
- `utterances`: A list of utterances that make up the conversation.
- `instructions`: Instructions for the crowdsourced worker used in creating the conversation.
- `vertical`: In this dataset the vertical for all dialogs is "Movie Tickets".
- `scenario`: This is the title of the instructions for each dialog.
Each utterance has the following fields:
- `index`: A 0-based index indicating the order of the utterances in the conversation.
- `speaker`: Either USER or ASSISTANT, indicating which role generated this utterance.
- `text`: The raw text of the utterance. In case of self dialogs (one_person_dialogs), this is written by the crowdsourced worker. In case of the WOz dialogs, 'ASSISTANT' turns are written and 'USER' turns are transcribed from the spoken recordings of crowdsourced workers.
- `segments`: A list of various text spans with semantic annotations.
- `apis`: An array of API invocations made during the utterance.
Each API has the following structure:
- `name`: The name of the API invoked (e.g. find_movies).
- `index`: The index of the parent utterance.
- `args`: A `list` of `dict` with keys `arg_name` and `arg_value` which represent the name of the argument and the value for the argument respectively.
- `response`: A `list` of `dict`s with keys `response_name` and `response_value` which represent the name of the response and the value for the response respectively.
Each segment has the following fields:
- `start_index`: The position of the start of the annotation in the utterance text.
- `end_index`: The position of the end of the annotation in the utterance text.
- `text`: The raw text that has been annotated.
- `annotations`: A list of annotation details for this segment.
Each annotation has a single field:
- `name`: The annotation name.
### Data Splits
There are no deafults splits for all the config. The below table lists the number of examples in each config.
| | Train |
|-------------------|--------|
| n_instances | 23757 |
## 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 dataset is licensed under `Creative Commons Attribution 4.0 License`
### Citation Information
[More Information Needed]
```
@inproceedings{48484,
title = {Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset},
author = {Bill Byrne and Karthik Krishnamoorthi and Chinnadhurai Sankar and Arvind Neelakantan and Daniel Duckworth and Semih Yavuz and Ben Goodrich and Amit Dubey and Kyu-Young Kim and Andy Cedilnik},
year = {2019}
}
```
### Contributions
Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset. |
tsac | 2023-01-25T14:54:29.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:aeb",
"license:lgpl-3.0",
"region:us"
] | null | Tunisian Sentiment Analysis Corpus.
About 17k user comments manually annotated to positive and negative polarities. This corpus is collected from Facebook users comments written on official pages of Tunisian radios and TV channels namely Mosaique FM, JawhraFM, Shemes FM, HiwarElttounsi TV and Nessma TV. The corpus is collected from a period spanning January 2015 until June 2016. | @inproceedings{medhaffar-etal-2017-sentiment,
title = "Sentiment Analysis of {T}unisian Dialects: Linguistic Ressources and Experiments",
author = "Medhaffar, Salima and
Bougares, Fethi and
Est{`e}ve, Yannick and
Hadrich-Belguith, Lamia",
booktitle = "Proceedings of the Third {A}rabic Natural Language Processing Workshop",
month = apr,
year = "2017",
address = "Valencia, Spain",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/W17-1307",
doi = "10.18653/v1/W17-1307",
pages = "55--61",
abstract = "Dialectal Arabic (DA) is significantly different from the Arabic language taught in schools and used in written communication and formal speech (broadcast news, religion, politics, etc.). There are many existing researches in the field of Arabic language Sentiment Analysis (SA); however, they are generally restricted to Modern Standard Arabic (MSA) or some dialects of economic or political interest. In this paper we are interested in the SA of the Tunisian Dialect. We utilize Machine Learning techniques to determine the polarity of comments written in Tunisian Dialect. First, we evaluate the SA systems performances with models trained using freely available MSA and Multi-dialectal data sets. We then collect and annotate a Tunisian Dialect corpus of 17.000 comments from Facebook. This corpus allows us a significant accuracy improvement compared to the best model trained on other Arabic dialects or MSA data. We believe that this first freely available corpus will be valuable to researchers working in the field of Tunisian Sentiment Analysis and similar areas.",
} | null | 1 | 9 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- aeb
license:
- lgpl-3.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: tsac
pretty_name: Tunisian Sentiment Analysis Corpus
dataset_info:
features:
- name: id
dtype: string
- name: sentence
dtype: string
- name: target
dtype:
class_label:
names:
'0': '1'
'1': '-1'
splits:
- name: train
num_bytes: 1020146
num_examples: 13669
- name: test
num_bytes: 268504
num_examples: 3400
download_size: 963015
dataset_size: 1288650
---
# Dataset Card for Tunisian Sentiment Analysis Corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** None
- **Repository:** https://github.com/fbougares/TSAC
- **Paper:** https://www.aclweb.org/anthology/W17-1307
- **Leaderboard:** [If the dataset supports an active leaderboard, add link here]()
- **Point of Contact:** Salima Mdhaffar (firstname.lastname@univ-lemans.fr)
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[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
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. |
urdu_fake_news | 2023-01-25T15:01:58.000Z | [
"task_categories:text-classification",
"task_ids:fact-checking",
"task_ids:intent-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:ur",
"license:unknown",
"... | null | Urdu fake news datasets that contain news of 5 different news domains.
These domains are Sports, Health, Technology, Entertainment, and Business.
The real news are collected by combining manual approaches. | @article{MaazUrdufake2020,
author = {Amjad, Maaz and Sidorov, Grigori and Zhila, Alisa and G’{o}mez-Adorno, Helena and Voronkov, Ilia and Gelbukh, Alexander},
title = {Bend the Truth: A Benchmark Dataset for Fake News Detection in Urdu and Its Evaluation},
journal={Journal of Intelligent & Fuzzy Systems},
volume={39},
number={2},
pages={2457-2469},
doi = {10.3233/JIFS-179905},
year={2020},
publisher={IOS Press}
} | null | 0 | 9 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- ur
license:
- unknown
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- fact-checking
- intent-classification
pretty_name: Bend the Truth (Urdu Fake News)
dataset_info:
features:
- name: news
dtype: string
- name: label
dtype:
class_label:
names:
'0': Fake
'1': Real
- name: category
dtype:
class_label:
names:
'0': bus
'1': hlth
'2': sp
'3': tch
'4': sbz
splits:
- name: train
num_bytes: 1762905
num_examples: 638
- name: test
num_bytes: 799587
num_examples: 262
download_size: 1042653
dataset_size: 2562492
---
# Dataset Card for Bend the Truth (Urdu Fake News)
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Github](https://github.com/MaazAmjad/Datasets-for-Urdu-news/)
- **Repository:** [Github](https://github.com/MaazAmjad/Datasets-for-Urdu-news/)
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [Maaz Amjad](https://github.com/MaazAmjad)
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
- news: a string in urdu
- label: the label indicating whethere the provided news is real or fake.
- category: The intent of the news being presented. The available 5 classes are Sports, Health, Technology, Entertainment, and Business.
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@chaitnayabasava](https://github.com/chaitnayabasava) for adding this dataset. |
Annabelleabbott/real-fake-news-workshop | 2022-01-07T00:45:18.000Z | [
"region:us"
] | Annabelleabbott | null | null | null | 0 | 9 | Entry not found |
CodedotAI/code_clippy_github | 2022-08-05T02:57:36.000Z | [
"task_ids:language-modeling",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:unknown",
"language:code",
"license:mit",
"arxiv:2107.03374",
"region:us"
] | CodedotAI | The Code Clippy dataset consists of various public codebases from GitHub in 22 programming languages with 23 extensions totalling about 16 TB of data when uncompressed. The dataset was created from the public GitHub dataset on Google BiqQuery. | null | null | 9 | 9 | ---
annotations_creators: []
language_creators:
- crowdsourced
- expert-generated
language: ["code"]
license:
- mit
multilinguality:
- multilingual
pretty_name: code-clippy-github-code
size_categories:
- unknown
source_datasets: []
task_categories:
- sequence-modeling
task_ids:
- language-modeling
---
# Code Clippy Github Dataset
## Dataset Description
The Code Clippy dataset consists of various public codebases from GitHub in 22 programming languages with 23 extensions totaling about 16 TB of data when uncompressed. The dataset was created from the public GitHub dataset on Google BigQuery.
### How to use it
This dataset is pretty large please use the streaming parameter from the `datasets` library as seen below:
```python
from datasets import load_dataset
ds = load_dataset("CodedotAI/code_clippy_github", streaming=True)
```
## Data Structure
### Data Instances
```python
{
'code_text': " a = mc^2",
'repo_name': 'NotEinstein',
'file_path': 'root/users/einstein.py',
'language': 'Python',
'license': 'isc',
'size': 2
}
```
### Data Fields
|Field|Type|Description|
|---|---|---|
|code_text|string|string of the source code contained in the code file|
|repo_name|string|name of the GitHub repository|
|file_path|string|path of the code file within the repository |
|language|string|programming language used in the file inferred by the file extension|
|license|string|license of GitHub repository|
|size|int|size of source file in bytes|
### Data Splits
Only a train split is provided in this dataset.
## Languages
The dataset contains 22 programming languages with over 23 extensions:
```python
{
"C": [".c"],
"C#": [".cs"],
"C++": [".cpp"],
"CSS": [".css"],
"Dart" : [".dart"],
"GO": [".go"],
"HTML":[".html"],
"Java": [".java"],
"JavaScript": [".js"],
"Jupyter Notebooks (Python)": [".ipynb"],
"Kotlin" : [".kt"],
"Lisp" : [".lisp"],
"Matlab" : [".m"],
"PHP": [".php"],
"Perl": [".pl"],
"Python": [".py"],
"R" : [".r"],
"Ruby": [".rb"],
"Rust": [".rs"],
"SQL": [".sql"],
"Shell": [".sh"],
"Swift" : [".swift"],
"TypeScript": [".ts"],
}
```
## Licenses
Each example is also annotated with the license of the associated repository. There are in total 15 licenses:
```python
[
'mit',
'apache-2.0',
'gpl-2.0',
'gpl-3.0',
'bsd-3-clause',
'bsd-2-clause',
'unlicense',
'apacheagpl-3.0',
'lgpl-3.0',
'cc0-1.0',
'epl-1.0',
'lgpl-2.1',
'mpl-2.0',
'isc',
'artistic-2.0'
]
```
## Dataset Statistics
The dataset is about ~ 18 TB uncompressed. We are currently working on processing it and applying further filtering.
## Dataset Creation
The dataset was created in two steps:
1. Files with the extensions given in the list above were retrieved from the GitHub dataset on BigQuery using the following query:
```sql
SELECT
f.id, f.repo_name, f.path, content.copies, content.size, content.content, lic.license
FROM
`bigquery-public-data.github_repos.files` AS f
JOIN
`bigquery-public-data.github_repos.contents` as content
ON
f.id = content.id
JOIN
`bigquery-public-data.github_repos.licenses` AS lic
ON
f.repo_name = lic.repo_name
WHERE
NOT content.binary
AND (
(f.path LIKE '%.py') OR (f.path LIKE '%.java') OR (f.path LIKE '%.js')
OR (f.path LIKE '%.html') OR (f.path LIKE '%.lisp') OR (f.path LIKE '%.sh')
OR (f.path LIKE '%.r') OR (f.path LIKE '%.pl') OR (f.path LIKE '%.css')
OR (f.path LIKE '%.sql') OR (f.path LIKE '%.c') OR (f.path LIKE '%.cpp')
OR (f.path LIKE '%.ts') OR (f.path LIKE '%.cs') OR (f.path LIKE '%.go')
OR (f.path LIKE '%.rs') OR (f.path LIKE '%.swift') OR (f.path LIKE '%.php')
OR (f.path LIKE '%.dart') OR (f.path LIKE '%.kt') OR (f.path LIKE '%.m')
OR (f.path LIKE '%.rb') OR (f.path LIKE '%.ipynb')
)
-- make sure we dont go above 1 megabyte
AND (content.size BETWEEN 1024 AND 1000000)
```
2. Currently, our CodedotAI team is working on adding additional filters and cleaning this dataset.
### Personal and Sensitive Information
Since this data was collected from public repositories, there exists potential for personal and sensitive information to be included in the data through developers accidentally or on purpose uploading their secret keys, passwords, API keys, emails, etc.
## Considerations for Using the Data
### Social Impact of Dataset
The paper ["Evaluating Large Language Models Trained on Code"](https://arxiv.org/abs/2107.03374) from OpenAI has a good discussion on what the impact of a large language model trained on code could be. Therefore, some parts of their discussion are highlighted here as it pertains to this dataset and models that may be trained from it. **As well as some differences in views from the paper, particularly around legal implications**.
1. **Over-reliance:** A language model trained on large datasets such as this one for the task of autogenerating code may generate plausible solutions that may appear correct, but are not necessarily the correct solution. Not properly evaluating the generated code may cause have negative consequences such as the introduction of bugs, or the introduction of security vulnerabilities. Therefore, it is important that users are aware of the limitations and potential negative consequences of using a language model trained on this dataset.
2. **Economic and labor market impacts:** Large language models trained on large code datasets such as this one that are capable of generating high-quality code have the potential to automate part of the software development process. This may negatively impact software developers. However, as discussed in the paper, as shown in the Summary Report of software developers from [O*NET OnLine](https://www.onetonline.org/link/summary/15-1252.00), developers don't just write software.
3. **Security implications:** No filtering or checking of vulnerabilities or buggy code was performed. This means that the dataset may contain code that may be malicious or contain vulnerabilities. Therefore, any model trained on this dataset may generate vulnerable, buggy, or malicious code. In safety-critical software, this could lead to software that may work improperly and could result in serious consequences depending on the software. Additionally, a model trained on this dataset may be used to generate malicious code on purpose in order to perform ransomware or other such attacks.
4. **Legal implications:** No filtering was performed on licensed code. This means that the dataset may contain restrictive licensed code. As discussed in the paper, public Github repositories may fall under "fair use." However, there have been little to no previous cases of such usages of licensed publicly available code. Therefore, any model trained on this dataset may be required to obey license terms that align with the software it was trained on such as GPL-3.0, which is why we purposefully put this dataset under the GPL-3.0 license. It is unclear the legal ramifications of using a language model trained on this dataset.
### v1.0
- The query was executed on _February 1, 2022, 12:15:59 AM EST_
## Acknowledgements
This project would not have been possible without compute generously provided by Google through the [TPU Research Cloud](https://sites.research.google/trc/about/). We would also like to thank [Dr. Razvan Bunescu](https://webpages.charlotte.edu/rbunescu/) and [The College of Computing and Informatics at UNC Charlotte](https://cci.charlotte.edu/) for their generous contributions to this project, specifically in funding the BigQuery and Google Cloud Storage costs. We would also like to thank the [codeparrot team at Hugging face](https://huggingface.co/codeparrot) for open sourcing their documentation on [github-code](https://huggingface.co/datasets/codeparrot/github-code) which we used for the readme in this dataset. For another similar dataset to this please check github-code! |
GEM/indonlg | 2022-06-29T16:35:12.000Z | [
"task_categories:summarization",
"annotations_creators:none",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"region:us"
] | GEM | The IndoNLG benchmark is a collection of resources for training, evaluating, and analyzing natural language generation systems for Indonesian, Javanese, and Sundanese. | @inproceedings{cahyawijaya-etal-2021-indonlg,
title = "{I}ndo{NLG}: Benchmark and Resources for Evaluating {I}ndonesian Natural Language Generation",
author = "Cahyawijaya, Samuel and
Winata, Genta Indra and
Wilie, Bryan and
Vincentio, Karissa and
Li, Xiaohong and
Kuncoro, Adhiguna and
Ruder, Sebastian and
Lim, Zhi Yuan and
Bahar, Syafri and
Khodra, Masayu and
Purwarianti, Ayu and
Fung, Pascale",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.699",
pages = "8875--8898",
abstract = "Natural language generation (NLG) benchmarks provide an important avenue to measure progress and develop better NLG systems. Unfortunately, the lack of publicly available NLG benchmarks for low-resource languages poses a challenging barrier for building NLG systems that work well for languages with limited amounts of data. Here we introduce IndoNLG, the first benchmark to measure natural language generation (NLG) progress in three low-resource{---}yet widely spoken{---}languages of Indonesia: Indonesian, Javanese, and Sundanese. Altogether, these languages are spoken by more than 100 million native speakers, and hence constitute an important use case of NLG systems today. Concretely, IndoNLG covers six tasks: summarization, question answering, chit-chat, and three different pairs of machine translation (MT) tasks. We collate a clean pretraining corpus of Indonesian, Sundanese, and Javanese datasets, Indo4B-Plus, which is used to pretrain our models: IndoBART and IndoGPT. We show that IndoBART and IndoGPT achieve competitive performance on all tasks{---}despite using only one-fifth the parameters of a larger multilingual model, mBART-large (Liu et al., 2020). This finding emphasizes the importance of pretraining on closely related, localized languages to achieve more efficient learning and faster inference at very low-resource languages like Javanese and Sundanese.",
} | null | 2 | 9 | ---
annotations_creators:
- none
language_creators:
- unknown
languages:
- unknown
licenses:
- mit
multilinguality:
- unknown
pretty_name: indonlg
size_categories:
- unknown
source_datasets:
- original
task_categories:
- summarization
task_ids:
- unknown
---
# Dataset Card for GEM/indonlg
## Dataset Description
- **Homepage:** https://github.com/indobenchmark/indonlg
- **Repository:** https://github.com/indobenchmark/indonlg
- **Paper:** https://aclanthology.org/2021.emnlp-main.699
- **Leaderboard:** N/A
- **Point of Contact:** Genta Indra Winata
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/indonlg).
### Dataset Summary
IndoNLG is a collection of various Indonesian, Javanese, and Sundanese NLG tasks including summarization, question answering, chit-chat, and three different pairs of machine translation (MT) tasks.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/indonlg')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/indonlg).
#### website
[Github](https://github.com/indobenchmark/indonlg)
#### paper
[ACL Anthology](https://aclanthology.org/2021.emnlp-main.699)
#### authors
Samuel Cahyawijaya, Genta Indra Winata, Bryan Wilie, Karissa Vincentio, Xiaohong Li, Adhiguna Kuncoro, Sebastian Ruder, Zhi Yuan Lim, Syafri Bahar, Masayu Leylia Khodra, Ayu Purwarianti, Pascale Fung
## Dataset Overview
### Where to find the Data and its Documentation
#### Webpage
<!-- info: What is the webpage for the dataset (if it exists)? -->
<!-- scope: telescope -->
[Github](https://github.com/indobenchmark/indonlg)
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Github](https://github.com/indobenchmark/indonlg)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[ACL Anthology](https://aclanthology.org/2021.emnlp-main.699)
#### BibTex
<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
<!-- scope: microscope -->
```
@inproceedings{cahyawijaya-etal-2021-indonlg, title = '{I}ndo{NLG}: Benchmark and Resources for Evaluating {I}ndonesian Natural Language Generation ', author = 'Cahyawijaya, Samuel and Winata, Genta Indra and Wilie, Bryan and Vincentio, Karissa and Li, Xiaohong and Kuncoro, Adhiguna and Ruder, Sebastian and Lim, Zhi Yuan and Bahar, Syafri and Khodra, Masayu and Purwarianti, Ayu and Fung, Pascale ', booktitle = 'Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing ', month = nov, year = '2021 ', address = 'Online and Punta Cana, Dominican Republic ', publisher = 'Association for Computational Linguistics ', url = 'https://aclanthology.org/2021.emnlp-main.699 ', pages = '8875--8898 ', abstract = 'Natural language generation (NLG) benchmarks provide an important avenue to measure progress and develop better NLG systems. Unfortunately, the lack of publicly available NLG benchmarks for low-resource languages poses a challenging barrier for building NLG systems that work well for languages with limited amounts of data. Here we introduce IndoNLG, the first benchmark to measure natural language generation (NLG) progress in three low-resource{---}yet widely spoken{---}languages of Indonesia: Indonesian, Javanese, and Sundanese. Altogether, these languages are spoken by more than 100 million native speakers, and hence constitute an important use case of NLG systems today. Concretely, IndoNLG covers six tasks: summarization, question answering, chit-chat, and three different pairs of machine translation (MT) tasks. We collate a clean pretraining corpus of Indonesian, Sundanese, and Javanese datasets, Indo4B-Plus, which is used to pretrain our models: IndoBART and IndoGPT. We show that IndoBART and IndoGPT achieve competitive performance on all tasks{---}despite using only one-fifth the parameters of a larger multilingual model, mBART-large (Liu et al., 2020). This finding emphasizes the importance of pretraining on closely related, localized languages to achieve more efficient learning and faster inference at very low-resource languages like Javanese and Sundanese. ',}
```
#### Contact Name
<!-- quick -->
<!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
Genta Indra Winata
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
gentaindrawinata@gmail.com
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
no
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
yes
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`Indonesian`, `Javanese`, `Sundanese`
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
mit: MIT License
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
IndoNLG is a collection of Natural Language Generation (NLG) resources for Bahasa Indonesia with 10 downstream tasks.
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Summarization
#### Communicative Goal
<!-- quick -->
<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
<!-- scope: periscope -->
Generate a response according to the context and text.
### Credit
#### Curation Organization Type(s)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`academic`, `industry`
#### Curation Organization(s)
<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
The Hong Kong University of Science and Technology, Gojek, Institut Teknologi Bandung, Universitas Multimedia Nusantara, DeepMind, Prosa.ai
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Samuel Cahyawijaya, Genta Indra Winata, Bryan Wilie, Karissa Vincentio, Xiaohong Li, Adhiguna Kuncoro, Sebastian Ruder, Zhi Yuan Lim, Syafri Bahar, Masayu Leylia Khodra, Ayu Purwarianti, Pascale Fung
#### Funding
<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
The Hong Kong University of Science and Technology, Gojek, Institut Teknologi Bandung, Universitas Multimedia Nusantara, DeepMind, Prosa.ai
#### Who added the Dataset to GEM?
<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
<!-- scope: microscope -->
Genta Indra Winata (The Hong Kong University of Science and Technology)
### Dataset Structure
## Dataset in GEM
### Rationale for Inclusion in GEM
#### Similar Datasets
<!-- info: Do other datasets for the high level task exist? -->
<!-- scope: telescope -->
yes
#### Unique Language Coverage
<!-- info: Does this dataset cover other languages than other datasets for the same task? -->
<!-- scope: periscope -->
no
### GEM-Specific Curation
#### Modificatied for GEM?
<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
<!-- scope: telescope -->
yes
#### GEM Modifications
<!-- info: What changes have been made to he original dataset? -->
<!-- scope: periscope -->
`other`
#### Additional Splits?
<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
no
### Getting Started with the Task
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
Dialog understanding, summarization, translation
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`BLEU`
#### Proposed Evaluation
<!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. -->
<!-- scope: microscope -->
BLEU evaluates the generation quality.
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
yes
#### Other Evaluation Approaches
<!-- info: What evaluation approaches have others used? -->
<!-- scope: periscope -->
BLEU
## Dataset Curation
### Original Curation
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
no
### Language Data
#### How was Language Data Obtained?
<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Crowdsourced`
#### Where was it crowdsourced?
<!-- info: If crowdsourced, where from? -->
<!-- scope: periscope -->
`Participatory experiment`
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
validated by data curator
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
not filtered
### Structured Annotations
#### Additional Annotations?
<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
none
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
no
### Consent
#### Any Consent Policy?
<!-- info: Was there a consent policy involved when gathering the data? -->
<!-- scope: telescope -->
yes
#### Consent Policy Details
<!-- info: What was the consent policy? -->
<!-- scope: microscope -->
Annotators agree using the dataset for research purpose.
#### Other Consented Downstream Use
<!-- info: What other downstream uses of the data did the original data creators and the data curators consent to? -->
<!-- scope: microscope -->
Any
### Private Identifying Information (PII)
#### Contains PII?
<!-- quick -->
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
<!-- scope: telescope -->
unlikely
#### Categories of PII
<!-- info: What categories of PII are present or suspected in the data? -->
<!-- scope: periscope -->
``
### Maintenance
#### Any Maintenance Plan?
<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
no
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
no
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
yes
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
no
## Considerations for Using the Data
### PII Risks and Liability
#### Potential PII Risk
<!-- info: Considering your answers to the PII part of the Data Curation Section, describe any potential privacy to the data subjects and creators risks when using the dataset. -->
<!-- scope: microscope -->
No
### Licenses
#### Copyright Restrictions on the Dataset
<!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? -->
<!-- scope: periscope -->
`open license`
#### Copyright Restrictions on the Language Data
<!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
<!-- scope: periscope -->
`open license`
### Known Technical Limitations
|
PlanTL-GOB-ES/cantemist-ner | 2022-11-18T12:08:17.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"multilinguality:monolingual",
"language:es",
"license:cc-by-4.0",
"biomedical",
"clinical",
"spanish",
"region:us"
] | PlanTL-GOB-ES | https://temu.bsc.es/cantemist/ | @inproceedings{miranda2020named,
title={Named entity recognition, concept normalization and clinical coding: Overview of the cantemist track for cancer text mining in spanish, corpus, guidelines, methods and results},
author={Miranda-Escalada, A and Farr{\'e}, E and Krallinger, M},
booktitle={Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2020), CEUR Workshop Proceedings},
year={2020}
} | null | 3 | 9 | ---
annotations_creators:
- expert-generated
language:
- es
tags:
- biomedical
- clinical
- spanish
multilinguality:
- monolingual
task_categories:
- token-classification
task_ids:
- named-entity-recognition
license:
- cc-by-4.0
---
# CANTEMIST
## Dataset Description
Manually classified collection of Spanish oncological clinical case reports.
- **Homepage:** [zenodo](https://zenodo.org/record/3978041)
- **Paper:** [Named Entity Recognition, Concept Normalization and Clinical Coding: Overview of the Cantemist Track for Cancer Text Mining in Spanish, Corpus, Guidelines, Methods and Results](https://www.researchgate.net/profile/Antonio-Miranda-Escalada-2/publication/352786464_Named_Entity_Recognition_Concept_Normalization_and_Clinical_Coding_Overview_of_the_Cantemist_Track_for_Cancer_Text_Mining_in_Spanish_Corpus_Guidelines_Methods_and_Results/links/60d98a3b458515d6fbe382d8/Named-Entity-Recognition-Concept-Normalization-and-Clinical-Coding-Overview-of-the-Cantemist-Track-for-Cancer-Text-Mining-in-Spanish-Corpus-Guidelines-Methods-and-Results.pdf)
- **Point of Contact:** encargo-pln-life@bsc.es
### Dataset Summary
Collection of 1301 oncological clinical case reports written in Spanish, with tumor morphology mentions manually annotated and mapped by clinical experts to a controlled terminology. Every tumor morphology mention is linked to an eCIE-O code (the Spanish equivalent of ICD-O).
The training subset contains 501 documents, the development subsets 500, and the test subset 300. The original dataset is distributed in [Brat](https://brat.nlplab.org/standoff.html) format.
This dataset was designed for the CANcer TExt Mining Shared Task, sponsored by [Plan-TL](https://plantl.mineco.gob.es/Paginas/index.aspx).
For further information, please visit [the official website](https://temu.bsc.es/cantemist/).
### Supported Tasks
Named Entity Recognition (NER)
### Languages
- Spanish (es)
### Directory Structure
* README.md
* cantemist.py
* train.conll
* dev.conll
* test.conll
## Dataset Structure
### Data Instances
Three four-column files, one for each split.
### Data Fields
Every file has 4 columns:
* 1st column: Word form or punctuation symbol
* 2nd column: Original BRAT file name
* 3rd column: Spans
* 4th column: IOB tag
#### Example
<pre>
El cc_onco101 662_664 O
informe cc_onco101 665_672 O
HP cc_onco101 673_675 O
es cc_onco101 676_678 O
compatible cc_onco101 679_689 O
con cc_onco101 690_693 O
adenocarcinoma cc_onco101 694_708 B-MORFOLOGIA_NEOPLASIA
moderadamente cc_onco101 709_722 I-MORFOLOGIA_NEOPLASIA
diferenciado cc_onco101 723_735 I-MORFOLOGIA_NEOPLASIA
que cc_onco101 736_739 O
afecta cc_onco101 740_746 O
a cc_onco101 747_748 O
grasa cc_onco101 749_754 O
peripancreática cc_onco101 755_770 O
sobrepasando cc_onco101 771_783 O
la cc_onco101 784_786 O
serosa cc_onco101 787_793 O
, cc_onco101 793_794 O
infiltración cc_onco101 795_807 O
perineural cc_onco101 808_818 O
. cc_onco101 818_819 O
</pre>
### Data Splits
| Split | Size |
| ------------- | ------------- |
| `train` | 19,397 |
| `dev` | 18,165 |
| `test` | 11,168 |
## Dataset Creation
### Curation Rationale
For compatibility with similar datasets in other languages, we followed as close as possible existing curation guidelines.
### Source Data
#### Initial Data Collection and Normalization
The selected clinical case reports are fairly similar to hospital health records. To increase the usefulness and practical relevance of the CANTEMIST corpus, we selected clinical cases affecting all genders and that comprised most ages (from children to the elderly) and of various complexity levels (solid tumors, hemato-oncological malignancies, neuroendocrine cancer...).
The CANTEMIST cases include clinical signs and symptoms, personal and family history, current illness, physical examination, complementary tests (blood tests, imaging, pathology), diagnosis, treatment (including adverse effects of chemotherapy), evolution and outcome.
#### Who are the source language producers?
Humans, there is no machine generated data.
### Annotations
#### Annotation process
The manual annotation of the Cantemist corpus was performed by clinical experts following the Cantemist guidelines (for more detail refer to this [paper](http://ceur-ws.org/Vol-2664/cantemist_overview.pdf)). These guidelines contain rules for annotating morphology neoplasms in Spanish oncology clinical cases, as well as for mapping these annotations to eCIE-O.
A medical doctor was regularly consulted by annotators (scientists with PhDs on cancer-related subjects) for the most difficult pathology expressions. This same doctor periodically checked a random selection of annotated clinical records and these annotations were compared and discussed with the annotators. To normalize a selection of very complex cases, MD specialists in pathology from one of the largest university hospitals in Spain were consulted.
#### Who are the annotators?
Clinical experts.
### Personal and Sensitive Information
No personal or sensitive information included.
## Considerations for Using the Data
### Social Impact of Dataset
This corpus contributes to the development of medical language models in Spanish.
### Discussion of Biases
Not applicable.
## Additional Information
### Dataset Curators
Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es).
For further information, send an email to (plantl-gob-es@bsc.es).
This work was funded by the [Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA)](https://avancedigital.mineco.gob.es/en-us/Paginas/index.aspx) within the framework of the [Plan-TL](https://plantl.mineco.gob.es/Paginas/index.aspx).
### Licensing information
This work is licensed under [CC Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/) License.
Copyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022)
### Citation Information
```bibtex
@article{cantemist,
title={Named Entity Recognition, Concept Normalization and Clinical Coding: Overview of the Cantemist Track for Cancer Text Mining in Spanish, Corpus, Guidelines, Methods and Results.},
author={Miranda-Escalada, Antonio and Farr{\'e}, Eul{\`a}lia and Krallinger, Martin},
journal={IberLEF@ SEPLN},
pages={303--323},
year={2020}
}
```
### Contributions
[N/A]
|
R0bk/XFUN | 2022-01-31T06:08:56.000Z | [
"license:mit",
"region:us"
] | R0bk | null | null | null | 0 | 9 | ---
license: mit
---
|
benjaminbeilharz/empathetic_dialogues_for_lm | 2022-02-23T15:49:12.000Z | [
"region:us"
] | benjaminbeilharz | null | null | null | 1 | 9 | Entry not found |
castorini/msmarco_v2_passage_doc2query-t5_expansions | 2021-11-02T06:37:36.000Z | [
"language:English",
"license:Apache License 2.0",
"region:us"
] | castorini | null | null | null | 0 | 9 | ---
language:
- English
license: "Apache License 2.0"
---
# Dataset Summary
The repo provides queries generated for the MS MARCO v2 passage corpus with docTTTTTquery (sometimes written as docT5query or doc2query-T5), the latest version of the doc2query family of document expansion models. The basic idea is to train a model, that when given an input document, generates questions that the document might answer (or more broadly, queries for which the document might be relevant). These predicted questions (or queries) are then appended to the original documents, which are then indexed as before. The docTTTTTquery model gets its name from the use of T5 as the expansion model.
# Dataset Structure
All three folds (train, dev and test) share the same corpus. The queries are generated from this corpus.
An example data entry looks as follows:
```
{ "id": "msmarco_passage_22_0",
"predicted_queries": ["in drug combat does a zombie take more damage or die", "is the health bar the same as smash bros", "is brawlhalla health bar", "icpri league brawlhalla", "what is a battle brawlhalla", "is smash bros minecraft brawlhalla zombies", "what are the health bars on brawlhalla", "does smash bros have health bars", "is brawlhalla a health bar", "what is brawlhalla", "what is brwlhalla", "how many health bars is in brawlhalla", "is there health bar in brawlhalla", "what is boiledhalla?", "what is a good health bar in brawlhalla", "what is skills brawlhalla", "how many gobs in a brawlhalla", "is smash bros. an nsb game", "how many health bars are there in the brawlhalla", "what is brawlhalla"]
}
```
# Load Dataset
An example to load the dataset:
```
dataset = load_dataset('castorini/msmarco_v2_passage_doc2query-t5_expansions', data_files='d2q/d2q.jsonl???.gz')
```
# Citation Information
```
@article{docTTTTTquery,
title={From doc2query to {docTTTTTquery}},
author={Nogueira, Rodrigo and Lin, Jimmy},
year={2019}
}
@article{emdt5,
author={Ronak Pradeep and Rodrigo Nogueira and Jimmy Lin},
title={The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models},
journal={arXiv:2101.05667},
year={2021},
}
|
huggingface/documentation-images | 2023-10-10T13:52:14.000Z | [
"license:cc-by-nc-sa-4.0",
"region:us"
] | huggingface | null | null | null | 17 | 9 | ---
license: cc-by-nc-sa-4.0
---
### This dataset contains images used in the documentation of HuggingFace's libraries.
HF Team: Please make sure you optimize the assets before uploading them.
My favorite tool for this is https://tinypng.com/.
|
imthanhlv/binhvq_dedup | 2022-01-01T16:42:00.000Z | [
"region:us"
] | imthanhlv | null | null | null | 0 | 9 | # BinhVQ dedup
**Important**: Please install `lm_dataformat` by `pip install lm_dataformat` before using this dataset
## How to use
```python
import datasets
dataset = datasets.load_dataset("imthanhlv/binhvq_dedup")
```
## Dataset information
This dataset was created from `https://github.com/binhvq/news-corpus` dump with date 21/05/2021. I applied some simple preprocessing:
- Using BeautifulSoup to clean content
- Each record is concatenate of (title + "\n" + sapo + "\n" + content)
- Then perform shuffling + split train & validation + deduplicate (exact match using sha256) |
projecte-aina/sts-ca | 2023-09-13T12:46:21.000Z | [
"task_categories:text-classification",
"task_ids:semantic-similarity-scoring",
"task_ids:text-scoring",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:unknown",
"language:ca",
"license:cc-by-4.0",
"arxiv:2107.07903",
"region:us... | projecte-aina | Semantic Textual Similarity in Catalan.
STS corpus is a benchmark for evaluating Semantic Text Similarity in Catalan.
It consists of more than 3000 sentence pairs, annotated with the semantic similarity between them,
using a scale from 0 (no similarity at all) to 5 (semantic equivalence).
It is done manually by 4 different annotators following our guidelines based on previous work from the SemEval challenges (https://www.aclweb.org/anthology/S13-1004.pdf).
The source data are scraped sentences from the Catalan Textual Corpus (https://doi.org/10.5281/zenodo.4519349), used under CC-by-SA-4.0 licence (https://creativecommons.org/licenses/by-sa/4.0/). The dataset is released under the same licence.
This dataset was developed by BSC TeMU as part of the AINA project, and to enrich the Catalan Language Understanding Benchmark (CLUB).
This is the version 1.0.2 of the dataset with the complete human and automatic annotations and the analysis scripts. It also has a more accurate license.
This dataset can be used to build and score semantic similiarity models. | Rodriguez-Penagos, Carlos Gerardo, Armentano-Oller, Carme, Gonzalez-Agirre, Aitor, & Gibert Bonet, Ona. (2021).
Semantic Textual Similarity in Catalan (Version 1.0.1) [Data set].
Zenodo. http://doi.org/10.5281/zenodo.4761434 | null | 0 | 9 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- ca
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets: []
task_categories:
- text-classification
task_ids:
- semantic-similarity-scoring
- text-scoring
pretty_name: sts-ca
---
# Dataset Card for STS-ca
## Dataset Description
- **Website:** https://zenodo.org/record/4761434
- **Paper:** [Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? A Comprehensive Assessment for Catalan](https://arxiv.org/abs/2107.07903)
- **Point of Contact:** [Carlos Rodríguez-Penagos](carlos.rodriguez1@bsc.es) and [Carme Armentano-Oller](carme.armentano@bsc.es)
### Dataset Summary
STS-ca corpus is a benchmark for evaluating Semantic Text Similarity in Catalan. This dataset was developed by [BSC TeMU](https://temu.bsc.es/) as part of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina/), to enrich the [Catalan Language Understanding Benchmark (CLUB)](https://club.aina.bsc.es/).
### Supported Tasks and Leaderboards
This dataset can be used to build and score semantic similarity models in Catalan.
### Languages
The dataset is in Catalan (`ca-ES`).
## Dataset Structure
### Data Instances
Follows [SemEval challenges](https://www.aclweb.org/anthology/S13-1004.pdf):
* index (int)
* id (str): Unique ID assigned to the sentence pair.
* sentence 1 (str): First sentence of the pair.
* sentence 2 (str): Second sentence of the pair.
* avg (float): Gold truth
#### Example
| index | id | sentence 1 | sentence 2 | avg |
| ------- | ---- | ------------ | ------------ | ----- |
| 19 | ACN2_131 | Els manifestants ocupen l'Imperial Tarraco durant una hora fent jocs de taula | Els manifestants ocupen l'Imperial Tarraco i fan jocs de taula | 4 |
| 21 | TE2_80 | El festival comptarà amb cinc escenaris i se celebrarà entre el 7 i el 9 de juliol al Parc del Fòrum. | El festival se celebrarà el 7 i 8 de juliol al Parc del Fòrum de Barcelona | 3 |
| 23 | Oscar2_609 | Aleshores hi posarem un got de vi i continuarem amb la cocció fins que s'hagi evaporat el vi i ho salpebrarem. | Mentre, hi posarem el vi al sofregit i deixarem coure uns 7/8′, fins que el vi s'evapori. | 3 |
| 25 | Viqui2_48 | L'arboç grec (Arbutus andrachne) és un arbust o un petit arbre dins la família ericàcia. | El ginjoler ("Ziziphus jujuba") és un arbust o arbre petit de la família de les "Rhamnaceae". | 2.75 |
| 27 | ACN2_1072 | Mentre han estat davant la comandància, els manifestants han cridat consignes a favor de la independència i han cantat cançons com 'L'estaca'. | Entre les consignes que han cridat s'ha pogut escoltar càntics com 'els carrers seran sempre nostres' i contínues consignes en favor de la independència. | 3 |
| 28 | Viqui2_587 | Els cinc municipis ocupen una superfície de poc més de 100 km2 i conjuntament sumen una població total aproximada de 3.691 habitants (any 2019). | Té una població d'1.811.177 habitants (2005) repartits en 104 municipis d'una superfície total de 14.001 km2. | 2.67 |
### Data Fields
This dataset follows [SemEval](https://www.aclweb.org/anthology/S13-1004.pdf) challenges formats and conventions.
### Data Splits
- sts_cat_dev_v1.tsv (500 annotated pairs)
- sts_cat_train_v1.tsv (2073 annotated pairs)
- sts_cat_test_v1.tsv (500 annotated pairs)
## Dataset Creation
### Curation Rationale
We created this dataset to contribute to the development of language models in Catalan, a low-resource language.
### Source Data
#### Initial Data Collection and Normalization
Random sentences were extracted from 3 Catalan subcorpus from the [Catalan Textual Corpus](https://zenodo.org/record/4519349#.Ys_0PexBzOs): [ACN](https://www.acn.cat/), [Oscar](https://oscar-corpus.com/) and [Wikipedia](https://ca.wikipedia.org/wiki/Portada).
We generated candidate pairs using a combination of metrics from Doc2Vec, Jaccard and a BERT-like model (“[distiluse-base-multilingual-cased-v2](https://huggingface.co/distilbert-base-multilingual-cased)”). Finally, we manually reviewed the generated pairs to reject non-relevant pairs (identical or ungrammatical sentences, etc.) before providing them to the annotation team.
The average of the four annotations was selected as a “ground truth” for each sentence pair, except when an annotator diverged in more than one unit from the average. In these cases, we discarded the divergent annotation and recalculated the average without it. We also discarded 45 sentence pairs because the annotators disagreed too much.
For compatibility with similar datasets in other languages, we followed as close as possible existing curation guidelines.
#### Who are the source language producers?
The [Catalan Textual Corpus](https://zenodo.org/record/4519349#.Ys_0PexBzOs) is a 1760-million-token web corpus of Catalan built from several sources: existing corpus such as DOGC, CaWac (non-deduplicated version), Oscar (unshuffled version), Open Subtitles, Catalan Wikipedia; and three brand new crawlings: the Catalan General Crawling, obtained by crawling the 500 most popular .cat and .ad domains; the Catalan Government Crawling, obtained by crawling the .gencat domain and subdomains, belonging to the Catalan Government; and the ACN corpus with 220k news items from March 2015 until October 2020, crawled from the Catalan News Agency.
### Annotations
#### Annotation process
We comissioned the manual annotation of the similarity between the sentences of each pair, following the provided guidelines.
#### Who are the annotators?
A team of native language speakers from 2 different companies, working independently.
### Personal and Sensitive Information
No personal or sensitive information included.
## Considerations for Using the Data
### Social Impact of Dataset
We hope this dataset contributes to the development of language models in Catalan, a low-resource language.
### Discussion of Biases
[N/A]
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es)
This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/en/inici/index.html) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina/).
### Licensing Information
This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by-sa/4.0/">Attribution-ShareAlike 4.0 International License</a>.
### Citation Information
```
@inproceedings{armengol-estape-etal-2021-multilingual,
title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}atalan",
author = "Armengol-Estap{\'e}, Jordi and
Carrino, Casimiro Pio and
Rodriguez-Penagos, Carlos and
de Gibert Bonet, Ona and
Armentano-Oller, Carme and
Gonzalez-Agirre, Aitor and
Melero, Maite and
Villegas, Marta",
booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-acl.437",
doi = "10.18653/v1/2021.findings-acl.437",
pages = "4933--4946",
}
```
[DOI](https://doi.org/10.5281/zenodo.4529183)
### Contributions
[N/A]
|
projecte-aina/tecla | 2023-09-13T12:41:32.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:unknown",
"language:ca",
"license:cc-by-nc-nd-4.0",
"region:us"
] | projecte-aina | TeCla: Text Classification Catalan dataset
Catalan News corpus for Text classification, crawled from ACN (Catalan News Agency) site: www.acn.cat
Corpus de notícies en català per a classificació textual, extret del web de l'Agència Catalana de Notícies - www.acn.cat | Baucells, Irene, Carrino, Casimiro Pio, Rodriguez-Penagos, Carlos Gerardo, & Armentano-Oller, Carme. (2021).
TeCla: Text Classification Catalan dataset (Version 2.0) [Data set].
Zenodo. http://doi.org/10.5281/zenodo.7334110 | null | 0 | 9 | ---
YAML tags:
annotations_creators:
- expert-generated
language_creators:
- found
language:
- ca
license:
- cc-by-nc-nd-4.0
multilinguality:
- monolingual
pretty_name: tecla
size_categories:
- unknown
source_datasets: []
task_categories:
- text-classification
task_ids:
- multi-class-classification
---
# Dataset Card for TeCla
## Dataset Description
- **Website:** [Zenodo](https://zenodo.org/record/7334110)
- **Point of Contact:** [Irene Baucells de la Peña](irene.baucells@bsc.es), [Carlos Rodríguez-Penagos](carlos.rodriguez1@bsc.es) and [Carme Armentano-Oller](carme.armentano@bsc.es)
### Dataset Summary
TeCla (Text Classification) is a Catalan News corpus for thematic multi-class Text Classification tasks. The present version (2.0) contains 113.376 articles classified under a hierarchical class structure consisting of a coarse-grained and a fine-grained class. Each of the 4 coarse-grained classes accept a subset of fine-grained ones, 53 in total.
The previous version (1.0.1) can still be found at https://zenodo.org/record/4761505
This dataset was developed by [BSC TeMU](https://temu.bsc.es/) as part of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina/), to enrich the [Catalan Language Understanding Benchmark (CLUB)](https://club.aina.bsc.es/).
### Supported Tasks and Leaderboards
Text classification, Language Model
### Languages
The dataset is in Catalan (`ca-ES`).
## Dataset Structure
### Data Instances
Three json files, one for each split.
### Data Fields
Each example contains the following 3 fields:
* text: the article text (string)
* label1: the coarse-grained class
* label2: the fine-grained class
#### Example:
<pre>
{"version": "2.0",
"data":
[
{
'sentence': "La setena edició del Festival Fantàstik inclourà les cintes 'Matar a dios' i 'Mandy' i un homenatge a 'Mi vecino Totoro'. Es projectaran 22 curtmetratges seleccionats d'entre més de 500 presentats a nivell internacional. El Centre Cultural de Granollers acull del 8 a l'11 de novembre la setena edició del Festival Fantàstik. El certamen, que s'allargarà un dia, arrencarà amb la projecció de la cinta de Caye Casas i Albert Pide 'Matar a Dios'. Els dos directors estaran presents en la inauguració de la cita. A més, els asssitents podran gaudir de 'Mandy', el darrer treball de Nicolas Cage. Altres llargmetratges seleccionats per aquest any són 'Aterrados' (2017), 'Revenge' (2017), 'A Mata Negra' (2018), 'Top Knot Detective' (2018) i 'La Gran Desfeta' (2018). A més, amb motiu del trentè aniversari de la pel·lícula 'El meu veí Totoro' es durà a terme l'exposició dedicada a aquest film '30 anys 30 artistes' comissariada per Jordi Pastor i Reinaldo Pereira. La mostra '30 anys 30 artistes' recull els treballs de trenta artistes d'estils diferents al voltant de la figura de Totoro i el seu director. Es podrà veure durant els dies de festival i es complementarà amb la projecció de la pel·lícula el diumenge 11 de novembre. Al llarg del festival també es projectaran els 22 curtmetratges prèviament seleccionats d'entre més de 500 presentats a nivell internacional. El millor tindrà una dotació de 1000 euros fruit de la unió de forces amb el Mercat Audiovisual de Catalunya.",
'label1': 'Cultura',
'label2': 'Cinema'
},
...
]
}
</pre>
#### Labels
* label1: 'Societat', 'Política', 'Economia', 'Cultura'
* label2: 'Llengua', 'Infraestructures', 'Arts', 'Parlament', 'Noves tecnologies', 'Castells', 'Successos', 'Empresa', 'Mobilitat', 'Teatre', 'Treball', 'Logística', 'Urbanisme', 'Govern', 'Entitats', 'Finances', 'Govern espanyol', 'Trànsit', 'Indústria', 'Esports', 'Exteriors', 'Medi ambient', 'Habitatge', 'Salut', 'Equipaments i patrimoni', 'Recerca', 'Cooperació', 'Innovació', 'Agroalimentació', 'Policial', 'Serveis Socials', 'Cinema', 'Memòria històrica', 'Turisme', 'Política municipal', 'Comerç', 'Universitats', 'Hisenda', 'Judicial', 'Partits', 'Música', 'Lletres', 'Religió', 'Festa i cultura popular', 'Unió Europea', 'Moda', 'Moviments socials', 'Comptes públics', 'Immigració', 'Educació', 'Gastronomia', 'Meteorologia', 'Energia'
### Data Splits
Train, development and test splits were created in a stratified fashion, following a 0.8, 0.05 and 0.15 proportion, respectively. The sizes of each split are the following:
* train.json: 90700 examples
* dev.json: 5669 examples
* test.json: 17007 examples
## Dataset Creation
### Curation Rationale
We created this dataset to contribute to the development of language models in Catalan, a low-resource language.
### Source Data
#### Initial Data Collection and Normalization
The source data are crawled articles from the Catalan News Agency ([Agència Catalana de Notícies, ACN](https://www.acn.cat/)) site.
We crawled 219.586 articles from the Catalan News Agency ([Agència Catalana de Notícies; ACN](https://www.acn.cat/)) newswire archive, the latest from October 11, 2020.
From the crawled data, we selected those articles whose 'section' and 'subsection' categories followed the expected codification combinations included in the ACN's style guide and whose 'section' complied the requirements of containing subsections and being thematically founded (in contrast to geographically defined categories such as 'Món' and 'Unió Europea'). The articles originally belonging to the 'Unió Europea' section, which were related to political organisms from the European Union, were included in the 'Política' coarse-grained category (within a fine-grained category named 'Unió Europea') due to its close proximity between some of the original subsections of 'Política' and those of 'Unió Europea', both defined by the specific political organism dealt with in the article.
The text field in each example is a concatenation of the original title, subtitle and body of the article (before the concatenation, both title and subtitle were added a final dot whenever they lacked one). The preprocessing of the texts was minimal and consisted in the removal of the pattern "ACN {location}.-" included before the body in each text as well as newlines originally used to divide the text in paragraphs.
#### Who are the source language producers?
The Catalan News Agency ([Agència Catalana de Notícies; ACN](https://www.acn.cat/)) is a news agency owned by the Catalan government via the public corporation Intracatalònia, SA. It is one of the first digital news agencies created in Europe and has been operating since 1999 (source: [wikipedia](https://en.wikipedia.org/wiki/Catalan_News_Agency)).
### Annotations
#### Annotation process
The crawled data contained the categories' annotations, which were then used to create this dataset with the mentioned criteria.
#### Who are the annotators?
Editorial staff classified the articles under the different thematic sections and subsections, and we extracted these from metadata.
### Personal and Sensitive Information
No personal or sensitive information included.
## Considerations for Using the Data
### Social Impact of Dataset
We hope this dataset contributes to the development of language models in Catalan, a low-resource language.
### Discussion of Biases
[N/A]
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
Irene Baucells (irene.baucells@bsc.es), Casimiro Pio Carrino (casimiro.carrino@bsc.es), Carlos Rodríguez (carlos.rodriguez1@bsc.es) and Carme Armentano (carme.armentano@bsc.es), from [BSC-CNS](https://www.bsc.es/).
This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
### Licensing Information
This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by-nc-nd/4.0/">Attribution-NonCommercial-NoDerivatives 4.0 International License</a>.
### Citation Information
[DOI]([https://doi.org/10.5281/zenodo.7334110])
|
rony/soccer-dialogues | 2022-07-25T08:33:15.000Z | [
"region:us"
] | rony | null | null | null | 0 | 9 | Entry not found |
teven/stackexchange | 2021-12-03T18:36:21.000Z | [
"region:us"
] | teven | null | null | null | 0 | 9 | Entry not found |
drAbreu/bc4chemd_ner | 2022-10-25T10:02:51.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:GitHub",
"language:en",
"license:unknown",
"region:us"
] | drAbreu | The automatic extraction of chemical information from text requires the recognition of chemical entity mentions as one of its key steps. When developing supervised named entity recognition (NER) systems, the availability of a large, manually annotated text corpus is desirable. Furthermore, large corpora permit the robust evaluation and comparison of different approaches that detect chemicals in documents. We present the CHEMDNER corpus, a collection of 10,000 PubMed abstracts that contain a total of 84,355 chemical entity mentions labeled manually by expert chemistry literature curators, following annotation guidelines specifically defined for this task. The abstracts of the CHEMDNER corpus were selected to be representative for all major chemical disciplines. Each of the chemical entity mentions was manually labeled according to its structure-associated chemical entity mention (SACEM) class: abbreviation, family, formula, identifier, multiple, systematic and trivial. The difficulty and consistency of tagging chemicals in text was measured using an agreement study between annotators, obtaining a percentage agreement of 91. For a subset of the CHEMDNER corpus (the test set of 3,000 abstracts) we provide not only the Gold Standard manual annotations, but also mentions automatically detected by the 26 teams that participated in the BioCreative IV CHEMDNER chemical mention recognition task. In addition, we release the CHEMDNER silver standard corpus of automatically extracted mentions from 17,000 randomly selected PubMed abstracts. A version of the CHEMDNER corpus in the BioC format has been generated as well. We propose a standard for required minimum information about entity annotations for the construction of domain specific corpora on chemical and drug entities. The CHEMDNER corpus and annotation guidelines are available at: http://www.biocreative.org/resources/biocreative-iv/chemdner-corpus/ | @article{Krallinger2015TheCC,
title={The CHEMDNER corpus of chemicals and drugs and its annotation principles},
author={Martin Krallinger and Obdulia Rabal and Florian Leitner and Miguel Vazquez and David Salgado and Zhiyong Lu and Robert Leaman and Yanan Lu and Dong-Hong Ji and Daniel M. Lowe and Roger A. Sayle and Riza Theresa Batista-Navarro and Rafal Rak and Torsten Huber and Tim Rockt{\"a}schel and S{\'e}rgio Matos and David Campos and Buzhou Tang and Hua Xu and Tsendsuren Munkhdalai and Keun Ho Ryu and S. V. Ramanan and P. Senthil Nathan and Slavko Zitnik and Marko Bajec and Lutz Weber and Matthias Irmer and Saber Ahmad Akhondi and Jan A. Kors and Shuo Xu and Xin An and Utpal Kumar Sikdar and Asif Ekbal and Masaharu Yoshioka and Thaer M. Dieb and Miji Choi and Karin M. Verspoor and Madian Khabsa and C. Lee Giles and Hongfang Liu and K. E. Ravikumar and Andre Lamurias and Francisco M. Couto and Hong-Jie Dai and Richard Tzong-Han Tsai and C Ata and Tolga Can and Anabel Usie and Rui Alves and Isabel Segura-Bedmar and Paloma Mart{\'i}nez and Julen Oyarz{\'a}bal and Alfonso Valencia},
journal={Journal of Cheminformatics},
year={2015},
volume={7},
pages={S2 - S2}
} | null | 1 | 9 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- GitHub
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: bc4chemd
pretty_name: bc4chemd_ner
---
# Dataset Card for bc2gm_corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Github](https://biocreative.bioinformatics.udel.edu/resources/biocreative-iv/chemdner-corpus/)
- **Repository:** [Github](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/tree/master/data/BC4CHEMD)
- **Paper:** [NCBI](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4331692/)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
* Token Classification
* Named Entity Recognition
### Languages
- English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
- `id`: Sentence identifier.
- `tokens`: Array of tokens composing a sentence.
- `ner_tags`: Array of tags, where `0` indicates no disease mentioned, `1` signals the first token of a disease and `2` the subsequent disease tokens.
### Data Splits
```python
DatasetDict({
train: Dataset({
features: ['id', 'tokens', 'ner_tags'],
num_rows: 30683
})
validation: Dataset({
features: ['id', 'tokens', 'ner_tags'],
num_rows: 30640
})
test: Dataset({
features: ['id', 'tokens', 'ner_tags'],
num_rows: 26365
})
})
```
## Dataset Creation
### Curation Rationale
The automatic extraction of chemical information from text requires the recognition of chemical
entity mentions as one of its key steps. When developing supervised named entity recognition
(NER) systems, the availability of a large, manually annotated text corpus is desirable.
Furthermore, large corpora permit the robust evaluation and comparison of different
approaches that detect chemicals in documents.
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
### Annotations
#### Annotation process
We present the CHEMDNER corpus, a collection of 10,000 PubMed abstracts that contain a
total of 84,355 chemical entity mentions labeled manually by expert chemistry literature curators,
following annotation guidelines specifically defined for this task.
#### Who are the annotators?
Expert chemistry literature curators
### Personal and Sensitive Information
It does not contain this kind of information
The abstracts of the CHEMDNER corpus were selected to be representative for all
major chemical disciplines. Each of the chemical entity mentions was manually
labeled according to its structure-associated chemical entity mention (SACEM)
class: abbreviation, family, formula, identifier, multiple, systematic and
trivial. The difficulty and consistency of tagging chemicals in text was measured using an agreement study
between annotators, obtaining a percentage agreement of 91.
### Licensing Information
Unknown
### Citation Information
```latex
@article{Krallinger2015TheCC,
title={The CHEMDNER corpus of chemicals and drugs and its annotation principles},
author={Martin Krallinger and Obdulia Rabal and Florian Leitner and Miguel Vazquez and David Salgado and Zhiyong Lu and Robert Leaman and Yanan Lu and Dong-Hong Ji and Daniel M. Lowe and Roger A. Sayle and Riza Theresa Batista-Navarro and Rafal Rak and Torsten Huber and Tim Rockt{\"a}schel and S{\'e}rgio Matos and David Campos and Buzhou Tang and Hua Xu and Tsendsuren Munkhdalai and Keun Ho Ryu and S. V. Ramanan and P. Senthil Nathan and Slavko Zitnik and Marko Bajec and Lutz Weber and Matthias Irmer and Saber Ahmad Akhondi and Jan A. Kors and Shuo Xu and Xin An and Utpal Kumar Sikdar and Asif Ekbal and Masaharu Yoshioka and Thaer M. Dieb and Miji Choi and Karin M. Verspoor and Madian Khabsa and C. Lee Giles and Hongfang Liu and K. E. Ravikumar and Andre Lamurias and Francisco M. Couto and Hong-Jie Dai and Richard Tzong-Han Tsai and C Ata and Tolga Can and Anabel Usie and Rui Alves and Isabel Segura-Bedmar and Paloma Mart{\'i}nez and Julen Oyarz{\'a}bal and Alfonso Valencia},
journal={Journal of Cheminformatics},
year={2015},
volume={7},
pages={S2 - S2}
}
```
### Contributions
Thanks to [@GamalC](https://github.com/GamalC) for uploading this dataset to GitHub.
|
laion/laion-synthetic-115m | 2022-04-03T16:43:14.000Z | [
"region:us"
] | laion | null | null | null | 3 | 9 | # laion-synthetic-115m
This dataset is a version of [laion-400m](https://laion.ai/laion-400-open-dataset/) with filtering/replacement of noisy/inaccurate captions with captions generated via the BLIP model. Provided by salesforce in [BLIP](https://github.com/salesforce/BLIP). Modified to be compatible with `img2dataset` tool.
## Download captioned images
Note: you may want to change some of the keyword arguments depending on your specific needs.
```sh
# Download parquet file containing mapping of image-URL's -> captions
wget -c https://huggingface.co/datasets/laion/laion-synthetic-115m/resolve/main/laion_synthetic_filtered_large.parquet
pip install img2dataset
# Download as many URL's as possible into webdataset (tars of txt/jpg files). Can also specify `files` instead.
img2dataset laion_synthetic_filtered_large.parquet --image_size 320 --resize_mode 'keep_ratio' --caption_col 'caption'--input_format parquet --output_format webdataset
> Downloading starting now, check your bandwidth speed (with bwm-ng)your cpu (with htop), and your disk usage (with iotop)!
``` |
bigscience-data/roots_ar_uncorpus | 2022-12-12T10:59:32.000Z | [
"language:ar",
"license:cc-by-4.0",
"region:us"
] | bigscience-data | null | null | null | 0 | 9 | ---
language: ar
license: cc-by-4.0
extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience
Ethical Charter. The charter can be found at:
https://hf.co/spaces/bigscience/ethical-charter'
extra_gated_fields:
I have read and agree to abide by the BigScience Ethical Charter: checkbox
---
ROOTS Subset: roots_ar_uncorpus
# uncorpus
- Dataset uid: `uncorpus`
### Description
### Homepage
### Licensing
### Speaker Locations
### Sizes
- 2.8023 % of total
- 10.7390 % of ar
- 5.7970 % of fr
- 9.7477 % of es
- 2.0417 % of en
- 1.2540 % of zh
### BigScience processing steps
#### Filters applied to: ar
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: fr
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: es
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: en
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: zh
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
|
vencortex/TechNews | 2022-05-03T16:42:48.000Z | [
"doi:10.57967/hf/0115",
"region:us"
] | vencortex | null | null | null | 3 | 9 | Entry not found |
SetFit/amazon_massive_scenario_pt-PT | 2022-05-06T09:02:22.000Z | [
"region:us"
] | SetFit | null | null | null | 0 | 9 | Entry not found |
bigscience-data/roots_es_wikipedia | 2022-12-12T11:03:54.000Z | [
"language:es",
"license:cc-by-sa-3.0",
"region:us"
] | bigscience-data | null | null | null | 0 | 9 | ---
language: es
license: cc-by-sa-3.0
extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience
Ethical Charter. The charter can be found at:
https://hf.co/spaces/bigscience/ethical-charter'
extra_gated_fields:
I have read and agree to abide by the BigScience Ethical Charter: checkbox
---
ROOTS Subset: roots_es_wikipedia
# wikipedia
- Dataset uid: `wikipedia`
### Description
### Homepage
### Licensing
### Speaker Locations
### Sizes
- 3.2299 % of total
- 4.2071 % of en
- 5.6773 % of ar
- 3.3416 % of fr
- 5.2815 % of es
- 12.4852 % of ca
- 0.4288 % of zh
- 0.4286 % of zh
- 5.4743 % of indic-bn
- 8.9062 % of indic-ta
- 21.3313 % of indic-te
- 4.4845 % of pt
- 4.0493 % of indic-hi
- 11.3163 % of indic-ml
- 22.5300 % of indic-ur
- 4.4902 % of vi
- 16.9916 % of indic-kn
- 24.7820 % of eu
- 11.6241 % of indic-mr
- 9.8749 % of id
- 9.3489 % of indic-pa
- 9.4767 % of indic-gu
- 24.1132 % of indic-as
- 5.3309 % of indic-or
### BigScience processing steps
#### Filters applied to: en
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: ar
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: fr
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: es
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: ca
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: zh
#### Filters applied to: zh
#### Filters applied to: indic-bn
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-ta
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-te
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: pt
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-hi
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-ml
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-ur
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: vi
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-kn
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: eu
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
#### Filters applied to: indic-mr
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: id
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-pa
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-gu
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-as
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
#### Filters applied to: indic-or
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
|
rajistics/million-headlines | 2022-07-01T15:51:58.000Z | [
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc0-1.0",
"region:us"
] | rajistics | null | null | null | 0 | 9 | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- en
license:
- cc0-1.0
multilinguality:
- monolingual
paperswithcode_id: null
pretty_name: Million Headlines
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories: []
task_ids: []
---
# Dataset Card for Million Headlines
## 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:** [Kaggle dataset](https://www.kaggle.com/datasets/therohk/million-headlines)
- **Point of Contact:** Rohit Kulkarni)
### Dataset Summary
This contains data of news headlines published over a period of eighteen years. Sourced from the reputable Australian news source ABC (Australian Broadcasting Corporation)
## Dataset Structure
### Data Instances
For each instance, there is a integer for the data, a string for news headline.
### Data Fields
- `publish date`: a integer that represents the data
- `headline`: a string for the news headline
### Personal and Sensitive Information
The dataset does not contain any personal information about the authors or the crowdworkers, but may contain descriptions of the people that were in the headlines.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset represents one news service in Australia and should not be considered representative of all news or headlines.
### Discussion of Biases
News headlines may contain biases and should not be considered neutral.
### Licensing Information
[CC0: Public Domain](https://creativecommons.org/publicdomain/zero/1.0/). |
yoshitomo-matsubara/srsd-feynman_hard | 2023-10-07T17:49:30.000Z | [
"task_categories:tabular-regression",
"annotations_creators:expert",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended",
"language:en",
"license:mit",
"arxiv:2206.10540",
"doi:10.57967/hf/0761",
"region:us"
] | yoshitomo-matsubara | null | null | null | 0 | 9 | ---
pretty_name: SRSD-Feynman (Hard)
annotations_creators:
- expert
language_creators:
- expert-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended
task_categories:
- tabular-regression
task_ids: []
---
# Dataset Card for SRSD-Feynman (Hard set)
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:** https://github.com/omron-sinicx/srsd-benchmark
- **Paper:** [Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery](https://arxiv.org/abs/2206.10540)
- **Point of Contact:** [Yoshitaka Ushiku](mailto:yoshitaka.ushiku@sinicx.com)
### Dataset Summary
Our SRSD (Feynman) datasets are designed to discuss the performance of Symbolic Regression for Scientific Discovery.
We carefully reviewed the properties of each formula and its variables in [the Feynman Symbolic Regression Database](https://space.mit.edu/home/tegmark/aifeynman.html) to design reasonably realistic sampling range of values so that our SRSD datasets can be used for evaluating the potential of SRSD such as whether or not an SR method con (re)discover physical laws from such datasets.
This is the ***Hard set*** of our SRSD-Feynman datasets, which consists of the following 50 different physics formulas:
[](https://huggingface.co/datasets/yoshitomo-matsubara/srsd-feynman_hard/resolve/main/problem_table.pdf)
More details of these datasets are provided in [the paper and its supplementary material](https://arxiv.org/abs/2206.10540).
### Supported Tasks and Leaderboards
Symbolic Regression
## Dataset Structure
### Data Instances
Tabular data + Ground-truth equation per equation
Tabular data: (num_samples, num_variables+1), where the last (rightmost) column indicate output of the target function for given variables.
Note that the number of variables (`num_variables`) varies from equation to equation.
Ground-truth equation: *pickled* symbolic representation (equation with symbols in sympy) of the target function.
### Data Fields
For each dataset, we have
1. train split (txt file, whitespace as a delimiter)
2. val split (txt file, whitespace as a delimiter)
3. test split (txt file, whitespace as a delimiter)
4. true equation (pickle file for sympy object)
### Data Splits
- train: 8,000 samples per equation
- val: 1,000 samples per equation
- test: 1,000 samples per equation
## Dataset Creation
### Curation Rationale
We chose target equations based on [the Feynman Symbolic Regression Database](https://space.mit.edu/home/tegmark/aifeynman.html).
### Annotations
#### Annotation process
We significantly revised the sampling range for each variable from the annotations in the Feynman Symbolic Regression Database.
First, we checked the properties of each variable and treat physical constants (e.g., light speed, gravitational constant) as constants.
Next, variable ranges were defined to correspond to each typical physics experiment to confirm the physical phenomenon for each equation.
In cases where a specific experiment is difficult to be assumed, ranges were set within which the corresponding physical phenomenon can be seen.
Generally, the ranges are set to be sampled on log scales within their orders as 10^2 in order to take both large and small changes in value as the order changes.
Variables such as angles, for which a linear distribution is expected are set to be sampled uniformly.
In addition, variables that take a specific sign were set to be sampled within that range.
#### Who are the annotators?
The main annotators are
- Naoya Chiba (@nchiba)
- Ryo Igarashi (@rigarash)
### Personal and Sensitive Information
N/A
## Considerations for Using the Data
### Social Impact of Dataset
We annotated this dataset, assuming typical physical experiments. The dataset will engage research on symbolic regression for scientific discovery (SRSD) and help researchers discuss the potential of symbolic regression methods towards data-driven scientific discovery.
### Discussion of Biases
Our choices of target equations are based on [the Feynman Symbolic Regression Database](https://space.mit.edu/home/tegmark/aifeynman.html), which are focused on a field of Physics.
### Other Known Limitations
Some variables used in our datasets indicate some numbers (counts), which should be treated as integer.
Due to the capacity of 32-bit integer, however, we treated some of such variables as float e.g., number of molecules (10^{23} - 10^{25})
## Additional Information
### Dataset Curators
The main curators are
- Naoya Chiba (@nchiba)
- Ryo Igarashi (@rigarash)
### Licensing Information
MIT License
### Citation Information
[[Preprint](https://arxiv.org/abs/2206.10540)]
```bibtex
@article{matsubara2022rethinking,
title={Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery},
author={Matsubara, Yoshitomo and Chiba, Naoya and Igarashi, Ryo and Ushiku, Yoshitaka},
journal={arXiv preprint arXiv:2206.10540},
year={2022}
}
```
### Contributions
Authors:
- Yoshitomo Matsubara (@yoshitomo-matsubara)
- Naoya Chiba (@nchiba)
- Ryo Igarashi (@rigarash)
- Yoshitaka Ushiku (@yushiku)
|
PiC/phrase_sense_disambiguation | 2023-01-20T16:32:40.000Z | [
"task_categories:text-retrieval",
"annotations_creators:expert-generated",
"language_creators:found",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-4.0",
"region:us"
] | PiC | Phrase in Context is a curated benchmark for phrase understanding and semantic search, consisting of three tasks of increasing difficulty: Phrase Similarity (PS), Phrase Retrieval (PR) and Phrase Sense Disambiguation (PSD). The datasets are annotated by 13 linguistic experts on Upwork and verified by two groups: ~1000 AMT crowdworkers and another set of 5 linguistic experts. PiC benchmark is distributed under CC-BY-NC 4.0. | @article{pham2022PiC,
title={PiC: A Phrase-in-Context Dataset for Phrase Understanding and Semantic Search},
author={Pham, Thang M and Yoon, Seunghyun and Bui, Trung and Nguyen, Anh},
journal={arXiv preprint arXiv:2207.09068},
year={2022}
} | null | 6 | 9 | ---
annotations_creators:
- expert-generated
language_creators:
- found
- expert-generated
language:
- en
license:
- cc-by-nc-4.0
multilinguality:
- monolingual
paperswithcode_id: phrase-in-context
pretty_name: 'PiC: Phrase Sense Disambiguation'
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-retrieval
task_ids: []
---
# Dataset Card for "PiC: Phrase Sense Disambiguation"
## 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://phrase-in-context.github.io/](https://phrase-in-context.github.io/)
- **Repository:** [https://github.com/phrase-in-context](https://github.com/phrase-in-context)
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [Thang Pham](<thangpham@auburn.edu>)
- **Size of downloaded dataset files:** 49.95 MB
- **Size of the generated dataset:** 43.26 MB
- **Total amount of disk used:** 93.20 MB
### Dataset Summary
PSD is a phrase retrieval task like PR-pass and PR-page but more challenging since each example contains two short paragraphs (~11 sentences each) which trigger different senses of the same phrase.
The goal is to find the instance of the target phrase **t** that is semantically similar to a paraphrase **q**.
The dataset is split into 5,150/3,000/20,002 for test/dev/train, respectively.
<p align="center">
<img src="https://auburn.edu/~tmp0038/PiC/psd_sample.png" alt="PSD sample" style="width:100%; border:0;">
</p>
Given document D, trained Longformer-large model correctly retrieves <span style="background-color: #ef8783">massive figure</span> in the second paragraph for the query Q<sub>2</sub> "giant number" but **fails** to retrieve the answer when the query Q<sub>1</sub> is "huge model".
The correct answer for Q<sub>1</sub> should be <span style="background-color: #a1fb8e">massive figure</span> in the first passage since this phrase relates to a model rather than a number.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English.
## Dataset Structure
### Data Instances
**PSD**
* Size of downloaded dataset files: 49.95 MB
* Size of the generated dataset: 43.26 MB
* Total amount of disk used: 93.20 MB
An example of 'test' looks as follows.
```
{
"id": "297-1",
"title": "https://en.wikipedia.org/wiki?curid=2226019,https://en.wikipedia.org/wiki?curid=1191780",
"context": "In addition, the results from the study did not support the idea of females preferring complexity over simplicity in song sequences. These findings differ from past examinations, like the 2008 Morisake et al. study that suggested evidence of female Bengalese finches preferring complex songs over simple ones. Evolutionary adaptations of specifically complex song production in relation to female preference in Bengalese finches continues to be a topic worth examining. Comparison with zebra finches. Bengalese finches and zebra finches are members of the estrildiae family and are age-limited learners when it comes to song learning and the acoustic characteristics of their songs (Peng et al., 2012). Both of these species have been widely used in song learning based animal behavior research and although they share many characteristics researchers have been able to determine stark differences between the two. Previous to research done in 1987, it was thought that song learning in Bengalese finches was similar to zebra finches but there was no research to support this idea. Both species require learning from an adult during a sensitive juvenile phase in order to learn the species specific and sexually dimorphic songs. This tutor can be the father of the young or other adult males that are present around the juvenile. Clayton aimed to directly compare the song learning ability of both of these species to determine if they have separate learning behaviors. Many students find they can not possibly complete all the work assigned them; they learn to neglect some of it. Some student groups maintain files of past examinations which only worsen this situation. The difference between the formal and real requirements produced considerable dissonance among the students and resulted in cynicism, scorn, and hypocrisy among students, and particular difficulty for minority students. No part of the university community, writes Snyder, neither the professors, the administration nor the students, desires the end result created by this process. The \"Saturday Review\" said the book \"will gain recognition as one of the more cogent 'college unrest' books\" and that it presents a \"most provocative thesis.\" The book has been cited many times in studies. References. [[Category:Curricula]] [[Category:Philosophy of education]] [[Category:Massachusetts Institute of Technology]] [[Category:Books about social psychology]] [[Category:Student culture]] [[Category:Books about education]] [[Category:1970 non-fiction books]]",
"query": "previous exams",
"answers": {
"text": ["past examinations"],
"answer_start": [1621]
}
}
```
### Data Fields
The data fields are the same among all subsets and splits.
* id: a string feature.
* title: a string feature.
* context: a string feature.
* question: a string feature.
* answers: a dictionary feature containing:
* text: a list of string features.
* answer_start: a list of int32 features.
### Data Splits
| name |train|validation|test|
|--------------------|----:|---------:|---:|
|PSD |20002| 3000|5000|
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
The source passages + answers are from Wikipedia and the source of queries were produced by our hired linguistic experts from [Upwork.com](https://upwork.com).
#### Who are the source language producers?
We hired 13 linguistic experts from [Upwork.com](https://upwork.com) for annotation and more than 1000 human annotators on Mechanical Turk along with another set of 5 Upwork experts for 2-round verification.
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
13 linguistic experts from [Upwork.com](https://upwork.com).
### Personal and Sensitive Information
No annotator identifying details are provided.
## 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
This dataset is a joint work between Adobe Research and Auburn University.
Creators: [Thang M. Pham](https://scholar.google.com/citations?user=eNrX3mYAAAAJ), [David Seunghyun Yoon](https://david-yoon.github.io/), [Trung Bui](https://sites.google.com/site/trungbuistanford/), and [Anh Nguyen](https://anhnguyen.me).
[@PMThangXAI](https://twitter.com/pmthangxai) added this dataset to HuggingFace.
### Licensing Information
This dataset is distributed under [Creative Commons Attribution-NonCommercial 4.0 International (CC-BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/)
### Citation Information
```
@article{pham2022PiC,
title={PiC: A Phrase-in-Context Dataset for Phrase Understanding and Semantic Search},
author={Pham, Thang M and Yoon, Seunghyun and Bui, Trung and Nguyen, Anh},
journal={arXiv preprint arXiv:2207.09068},
year={2022}
}
``` |
nateraw/country211 | 2022-07-25T20:27:00.000Z | [
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|yfcc100m",
"language:en",
"license:unknown",
"region:us... | nateraw | null | null | null | 1 | 9 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
pretty_name: Country 211
size_categories:
- 10K<n<100K
source_datasets:
- extended|yfcc100m
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
---
# Dataset Card for Country211
The [Country 211 Dataset](https://github.com/openai/CLIP/blob/main/data/country211.md) from OpenAI.
This dataset was built by filtering the images from the YFCC100m dataset that have GPS coordinate corresponding to a ISO-3166 country code. The dataset is balanced by sampling 150 train images, 50 validation images, and 100 test images images for each country. |
BeIR/trec-news-generated-queries | 2022-10-23T06:13:54.000Z | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | BeIR | null | null | null | 1 | 9 | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
- 10K<n<100K
arguana:
- 1K<n<10K
touche-2020:
- 100K<n<1M
cqadupstack:
- 100K<n<1M
quora:
- 100K<n<1M
dbpedia:
- 1M<n<10M
scidocs:
- 10K<n<100K
fever:
- 1M<n<10M
climate-fever:
- 1M<n<10M
scifact:
- 1K<n<10K
source_datasets: []
task_categories:
- text-retrieval
- zero-shot-retrieval
- information-retrieval
- zero-shot-information-retrieval
task_ids:
- passage-retrieval
- entity-linking-retrieval
- fact-checking-retrieval
- tweet-retrieval
- citation-prediction-retrieval
- duplication-question-retrieval
- argument-retrieval
- news-retrieval
- biomedical-information-retrieval
- question-answering-retrieval
---
# Dataset Card for BEIR Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** nandan.thakur@uwaterloo.ca
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. |
Skelebor/book_titles_and_descriptions | 2022-06-28T05:42:54.000Z | [
"region:us"
] | Skelebor | null | null | null | 1 | 9 | Entry not found |
sasha/wino_bias_prompt1 | 2022-06-23T12:55:28.000Z | [
"region:us"
] | sasha | null | null | null | 0 | 9 | Entry not found |
jvanz/portuguese_wikipedia_sentences | 2022-06-27T16:36:05.000Z | [
"region:us"
] | jvanz | null | null | null | 1 | 9 | Entry not found |
bengaliAI/cvbn | 2022-07-01T02:17:25.000Z | [
"license:cc",
"region:us"
] | bengaliAI | null | @inproceedings{commonvoice:2020,
author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
title = {Common Voice: A Massively-Multilingual Speech Corpus},
booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)},
pages = {4211--4215},
year = 2020
} | null | 1 | 9 | ---
license: cc
---
|
Paul/hatecheck-arabic | 2022-07-05T10:43:02.000Z | [
"task_categories:text-classification",
"task_ids:hate-speech-detection",
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ar",
"license:cc-by-4.0",
"arxiv:2206.09917",
"regi... | Paul | null | null | null | 1 | 9 | ---
annotations_creators:
- crowdsourced
language_creators:
- expert-generated
language:
- ar
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Arabic HateCheck
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- hate-speech-detection
---
# Dataset Card for Multilingual HateCheck
## Dataset Description
Multilingual HateCheck (MHC) is a suite of functional tests for hate speech detection models in 10 different languages: Arabic, Dutch, French, German, Hindi, Italian, Mandarin, Polish, Portuguese and Spanish.
For each language, there are 25+ functional tests that correspond to distinct types of hate and challenging non-hate.
This allows for targeted diagnostic insights into model performance.
For more details, please refer to our paper about MHC, published at the 2022 Workshop on Online Abuse and Harms (WOAH) at NAACL 2022. If you are using MHC, please cite our work!
- **Paper:** Röttger et al. (2022) - Multilingual HateCheck: Functional Tests for Multilingual Hate Speech Detection Models. https://arxiv.org/abs/2206.09917
- **Repository:** https://github.com/rewire-online/multilingual-hatecheck
- **Point of Contact:** paul@rewire.online
## Dataset Structure
The csv format mostly matches the original HateCheck data, with some adjustments for specific languages.
**mhc_case_id**
The test case ID that is unique to each test case across languages (e.g., "mandarin-1305")
**functionality**
The shorthand for the functionality tested by the test case (e.g, "target_obj_nh"). The same functionalities are tested in all languages, except for Mandarin and Arabic, where non-Latin script required adapting the tests for spelling variations.
**test_case**
The test case text.
**label_gold**
The gold standard label ("hateful" or "non-hateful") of the test case. All test cases within a given functionality have the same gold standard label.
**target_ident**
Where applicable, the protected group that is targeted or referenced in the test case. All HateChecks cover seven target groups, but their composition varies across languages.
**ref_case_id**
For hateful cases, where applicable, the ID of the hateful case which was perturbed to generate this test case. For non-hateful cases, where applicable, the ID of the hateful case which is contrasted by this test case.
**ref_templ_id**
The equivalent to ref_case_id, but for template IDs.
**templ_id**
The ID of the template from which the test case was generated.
**case_templ**
The template from which the test case was generated (where applicable).
**gender_male** and **gender_female**
For gender-inflected languages (French, Spanish, Portuguese, Hindi, Arabic, Italian, Polish, German), only for cases where gender inflection is relevant, separate entries for gender_male and gender_female replace case_templ.
**label_annotated**
A list of labels given by the three annotators who reviewed the test case (e.g., "['hateful', 'hateful', 'hateful']").
**label_annotated_maj**
The majority vote of the three annotators (e.g., "hateful"). In some cases this differs from the gold label given by our language experts.
**disagreement_in_case**
True if label_annotated_maj does not match label_gold for the entry.
**disagreement_in_template**
True if the test case is generated from an IDENT template and there is at least one case with disagreement_in_case generated from the same template. This can be used to exclude entire templates from MHC. |
embedding-data/PAQ_pairs | 2022-08-02T02:58:28.000Z | [
"task_categories:sentence-similarity",
"task_ids:semantic-similarity-classification",
"language:en",
"license:mit",
"arxiv:2102.07033",
"region:us"
] | embedding-data | null | null | null | 1 | 9 | ---
license: mit
language:
- en
paperswithcode_id: embedding-data/PAQ_pairs
pretty_name: PAQ_pairs
task_categories:
- sentence-similarity
- paraphrase-mining
task_ids:
- semantic-similarity-classification
---
# Dataset Card for "PAQ_pairs"
## 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/facebookresearch/PAQ](https://github.com/facebookresearch/PAQ)
- **Repository:** [More Information Needed](https://github.com/facebookresearch/PAQ)
- **Paper:** [More Information Needed](https://github.com/facebookresearch/PAQ)
- **Point of Contact:** [More Information Needed](https://github.com/facebookresearch/PAQ)
- **Size of downloaded dataset files:**
- **Size of the generated dataset:**
- **Total amount of disk used:** 21 Bytes
### Dataset Summary
Pairs questions and answers obtained from Wikipedia.
Disclaimer: The team releasing PAQ QA pairs did not upload the dataset to the Hub and did not write a dataset card.
These steps were done by the Hugging Face team.
### Supported Tasks
- [Sentence Transformers](https://huggingface.co/sentence-transformers) training; useful for semantic search and sentence similarity.
### Languages
- English.
## Dataset Structure
Each example in the dataset contains pairs of sentences and is formatted as a dictionary with the key "set" and a list with the sentences as "value". The first sentence is a question and the second an answer; thus, both sentences would be similar.
```
{"set": [sentence_1, sentence_2]}
{"set": [sentence_1, sentence_2]}
...
{"set": [sentence_1, sentence_2]}
```
This dataset is useful for training Sentence Transformers models. Refer to the following post on how to train models using similar pairs of sentences.
### Usage Example
Install the 🤗 Datasets library with `pip install datasets` and load the dataset from the Hub with:
```python
from datasets import load_dataset
dataset = load_dataset("embedding-data/PAQ_pairs")
```
The dataset is loaded as a `DatasetDict` and has the format:
```python
DatasetDict({
train: Dataset({
features: ['set'],
num_rows: 64371441
})
})
```
Review an example `i` with:
```python
dataset["train"][i]["set"]
```
### Data Instances
[More Information Needed](https://github.com/facebookresearch/PAQ)
### Data Fields
[More Information Needed](https://github.com/facebookresearch/PAQ)
### Data Splits
[More Information Needed](https://github.com/facebookresearch/PAQ)
## Dataset Creation
[More Information Needed](https://github.com/facebookresearch/PAQ)
### Curation Rationale
[More Information Needed](https://github.com/facebookresearch/PAQ)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/facebookresearch/PAQ)
#### Who are the source language producers?
[More Information Needed](https://github.com/facebookresearch/PAQ)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/facebookresearch/PAQ)
#### Who are the annotators?
[More Information Needed](https://github.com/facebookresearch/PAQ)
### Personal and Sensitive Information
[More Information Needed](https://github.com/facebookresearch/PAQ)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/facebookresearch/PAQ)
### Discussion of Biases
[More Information Needed](https://github.com/facebookresearch/PAQ)
### Other Known Limitations
[More Information Needed](https://github.com/facebookresearch/PAQ)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/facebookresearch/PAQ)
### Licensing Information
The PAQ QA-pairs and metadata is licensed under [CC-BY-SA](https://creativecommons.org/licenses/by-sa/3.0/).
Other data is licensed according to the accompanying license files.
### Citation Information
```
@article{lewis2021paq,
title={PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them},
author={Patrick Lewis and Yuxiang Wu and Linqing Liu and Pasquale Minervini and Heinrich Küttler and Aleksandra Piktus and Pontus Stenetorp and Sebastian Riedel},
year={2021},
eprint={2102.07033},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@patrick-s-h-lewis](https://github.com/patrick-s-h-lewis) for adding this dataset.
|
Khedesh/DeepSentiPers | 2022-07-12T11:20:46.000Z | [
"license:apache-2.0",
"region:us"
] | Khedesh | null | null | null | 0 | 9 | ---
license: apache-2.0
---
|
ttxy/emotion | 2023-08-17T02:25:59.000Z | [
"task_categories:text-classification",
"language:code",
"license:bsd",
"classification",
"region:us"
] | ttxy | null | null | null | 2 | 9 | ---
language:
- code
pretty_name: "English Emotion classification"
tags:
- classification
license: "bsd"
task_categories:
- text-classification
---
一个包含六种基本情绪（愤怒、恐惧、喜悦、爱、悲伤和惊讶）的英文Twitter消息数据集
Github 链接 https://github.com/dair-ai/emotion_dataset
|
crazyofapple/CME-Chinese | 2022-07-29T07:39:55.000Z | [
"license:apache-2.0",
"region:us"
] | crazyofapple | null | null | null | 0 | 9 | ---
license: apache-2.0
---
|
Rifky/indonesian-hoax-news | 2022-08-05T15:49:33.000Z | [
"region:us"
] | Rifky | null | null | null | 0 | 9 | Entry not found |
Osaleh/NE_ArSAS | 2022-09-05T11:52:06.000Z | [
"license:afl-3.0",
"region:us"
] | Osaleh | null | null | null | 0 | 9 | ---
license: afl-3.0
---
|
cahya/librivox-indonesia | 2022-10-25T11:50:39.000Z | [
"task_categories:automatic-speech-recognition",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"source_datasets:librivox",
"language:ace",
"language:bal",
"language:bug",
"language:id",
"language:min",
"language:jav",
"language:sun",
"l... | cahya | null | \ | null | 0 | 9 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- ace
- bal
- bug
- id
- min
- jav
- sun
license: cc
multilinguality:
- multilingual
size_categories:
ace:
- 1K<n<10K
bal:
- 1K<n<10K
bug:
- 1K<n<10K
id:
- 1K<n<10K
min:
- 1K<n<10K
jav:
- 1K<n<10K
sun:
- 1K<n<10K
source_datasets:
- librivox
task_categories:
- automatic-speech-recognition
task_ids: []
pretty_name: LibriVox Indonesia 1.0
---
# Dataset Card for LibriVox Indonesia 1.0
## 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://huggingface.co/datasets/indonesian-nlp/librivox-indonesia
- **Repository:** https://huggingface.co/datasets/indonesian-nlp/librivox-indonesia
- **Point of Contact:** [Cahya Wirawan](mailto:cahya.wirawan@gmail.com)
### Dataset Summary
The LibriVox Indonesia dataset consists of MP3 audio and a corresponding text file we generated from the public
domain audiobooks [LibriVox](https://librivox.org/). We collected only languages in Indonesia for this dataset.
The original LibriVox audiobooks or sound files' duration varies from a few minutes to a few hours. Each audio
file in the speech dataset now lasts from a few seconds to a maximum of 20 seconds.
We converted the audiobooks to speech datasets using the forced alignment software we developed. It supports
multilingual, including low-resource languages, such as Acehnese, Balinese, or Minangkabau. We can also use it
for other languages without additional work to train the model.
The dataset currently consists of 8 hours in 7 languages from Indonesia. We will add more languages or audio files
as we collect them.
### Languages
```
Acehnese, Balinese, Bugisnese, Indonesian, Minangkabau, Javanese, Sundanese
```
## Dataset Structure
### Data Instances
A typical data point comprises the `path` to the audio file and its `sentence`. Additional fields include
`reader` and `language`.
```python
{
'path': 'librivox-indonesia/sundanese/universal-declaration-of-human-rights/human_rights_un_sun_brc_0000.mp3',
'language': 'sun',
'reader': '3174',
'sentence': 'pernyataan umum ngeunaan hak hak asasi manusa sakabeh manusa',
'audio': {
'path': 'librivox-indonesia/sundanese/universal-declaration-of-human-rights/human_rights_un_sun_brc_0000.mp3',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 44100
},
}
```
### Data Fields
`path` (`string`): The path to the audio file
`language` (`string`): The language of the audio file
`reader` (`string`): The reader Id in LibriVox
`sentence` (`string`): The sentence the user read from the book.
`audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
### Data Splits
The speech material has only train split.
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[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
Public Domain, [CC-0](https://creativecommons.org/share-your-work/public-domain/cc0/)
### Citation Information
```
``` |
nbroad/fix_punctuation | 2022-09-29T20:03:07.000Z | [
"region:us"
] | nbroad | null | null | null | 0 | 9 | Entry not found |
khaclinh/testdata | 2022-10-11T05:31:45.000Z | [
"task_categories:object-detection",
"task_ids:face-detection",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended",
"language:en",
"license:cc-by-nc-nd-4.0",
"region:us"
] | khaclinh | null | null | null | 0 | 9 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-nc-nd-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended
task_categories:
- object-detection
task_ids:
- face-detection
- license-plate-detection
pretty_name: PP4AV
---
# Dataset Card for PP4AV
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Dataset Creation](#dataset-creation)
- [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/khaclinh/pp4av
- **Repository:**
- **Paper:** [PP4AV: A benchmarking Dataset for Privacy-preserving Autonomous Driving]
- **Point of Contact:** linhtk.dhbk@gmail.com
### Dataset Summary
PP4AV is the first public dataset with faces and license plates annotated with driving scenarios. P4AV provides 3,447 annotated driving images for both faces and license plates. For normal camera data, dataset sampled images from the existing videos in which cameras were mounted in moving vehicles, running around the European cities. The images in PP4AV were sampled from 6 European cities at various times of day, including nighttime. This dataset use the fisheye images from the WoodScape dataset to select 244 images from the front, rear, left, and right cameras for fisheye camera data. PP4AV dataset can be used as a benchmark suite (evaluating dataset) for data anonymization models in autonomous driving.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its face and license plate annotations.
```
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1920x1080 at 0x19FA12186D8>, 'objects': {
'bbox': [
[0 0.230078 0.317081 0.239062 0.331367],
[1 0.5017185 0.0306425 0.5185935 0.0410975],
[1 0.695078 0.0710145 0.7109375 0.0863355],
[1 0.4089065 0.31646 0.414375 0.32764],
[0 0.1843745 0.403416 0.201093 0.414182],
[0 0.7132 0.3393474 0.717922 0.3514285]
]
}
}
```
### Data 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]`
- `objects`: a dictionary of face and license plate bounding boxes present on the image
- `bbox`: the bounding box of each face and license plate (in the [yolo](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#yolo) format). Basically, each row in annotation `.txt` file for each image `.png` file consists of data in format: `<object-class> <x_center> <y_center> <width> <height>`:
- `object-class`: integer number of object from 0 to 1, where 0 indicate face object, and 1 indicate licese plate object
- `x_center`: normalized x-axis coordinate of the center of the bounding box.
`x_center = <absolute_x_center> / <image_width>`
- `y_center`: normalized y-axis coordinate of the center of the bounding box.
`y_center = <absolute_y_center> / <image_height>`
- `width`: normalized width of the bounding box.
`width = <absolute_width> / <image_width>`
- `height`: normalized wheightdth of the bounding box.
`height = <absolute_height> / <image_height>`
- Example lines in YOLO v1.1 format `.txt' annotation file:
` 1 0.716797 0.395833 0.216406 0.147222
0 0.687109 0.379167 0.255469 0.158333
1 0.420312 0.395833 0.140625 0.166667
`
## Dataset Creation
### Source Data
#### Initial Data Collection and Normalization
The objective of PP4AV is to build a benchmark dataset that can be used to evaluate face and license plate detection models for autonomous driving. For normal camera data, we sampled images from the existing videos in which cameras were mounted in moving vehicles, running around the European cities. We focus on sampling data in urban areas rather than highways in order to provide sufficient samples of license plates and pedestrians. The images in PP4AV were sampled from **6** European cities at various times of day, including nighttime. The source data from 6 cities in European was described as follow:
- `Paris`: This subset contains **1450** images of the car driving down a Parisian street during the day. The video frame rate is 30 frames per second. The video is longer than one hour. We cut a shorter video for sampling and annotation. The original video can be found at the following URL:
URL: [paris_youtube_video](https://www.youtube.com/watch?v=nqWtGWymV6c)
- `Netherland day time`: This subset consists of **388** images of Hague, Amsterdam city in day time. The image of this subset are sampled from the bellow original video:
URL: [netherland_youtube_video](https://www.youtube.com/watch?v=Xuo4uCZxNrE)
The frame rate of the video is 30 frames per second. We cut a shorter video for sampling and annotation. The original video was longer than a half hour.
- `Netherland night time`: This subset consists of **824** images of Hague, Amsterdam city in night time sampled by the following original video:
URL: [netherland_youtube_video](https://www.youtube.com/watch?v=eAy9eHsynhM)
The frame rate of the video is 30 frames per second. We cut a shorter video for sampling and annotation. The original video was longer than a half hour.
- `Switzerland`: This subset consists of **372** images of Switzerland sampled by the following video:
URL: [switzerland_youtube_video](https://www.youtube.com/watch?v=0iw5IP94m0Q)
The frame rate of the video is 30 frames per second. We cut a shorter video for sampling and annotation. The original video was longer than one hour.
- `Zurich`: This subset consists of **50** images of Zurich city provided by the Cityscapes training set in package [leftImg8bit_trainvaltest.zip](https://www.cityscapes-dataset.com/file-handling/?packageID=3)
- `Stuttgart`: This subset consists of **69** images of Stuttgart city provided by the Cityscapes training set in package [leftImg8bit_trainvaltest.zip](https://www.cityscapes-dataset.com/file-handling/?packageID=3)
- `Strasbourg`: This subset consists of **50** images of Strasbourg city provided by the Cityscapes training set in package [leftImg8bit_trainvaltest.zip](https://www.cityscapes-dataset.com/file-handling/?packageID=3)
We use the fisheye images from the WoodScape dataset to select **244** images from the front, rear, left, and right cameras for fisheye camera data.
The source of fisheye data for sampling is located at WoodScape's [Fisheye images](https://woodscape.valeo.com/download).
In total, **3,447** images were selected and annotated in PP4AV.
### Annotations
#### Annotation process
Annotators annotate facial and license plate objects in images. For facial objects, bounding boxes are defined by all detectable human faces from the forehead to the chin to the ears. Faces were labelled with diverse sizes, skin tones, and faces partially obscured by a transparent material, such as a car windshield. For license plate objects, bounding boxes consists of all recognizable license plates with high variability, such as different sizes, countries, vehicle types (motorcycle, automobile, bus, truck), and occlusions by other vehicles. License plates were annotated for vehicles involved in moving traffic. To ensure the quality of annotation, there are two-step process for annotation. In the first phase, two teams of annotators will independently annotate identical image sets. After their annotation output is complete, a merging method based on the IoU scores between the two bounding boxes of the two annotations will be applied. Pairs of annotations with IoU scores above a threshold will be merged and saved as a single annotation. Annotated pairs with IoU scores below a threshold will be considered conflicting. In the second phase, two teams of reviewers will inspect the conflicting pairs of annotations for revision before a second merging method similar to the first is applied. The results of these two phases will be combined to form the final annotation. All work is conducted on the CVAT tool https://github.com/openvinotoolkit/cvat.
#### Who are the annotators?
Vantix Data Science team
### 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
Linh Trinh
### Licensing Information
[Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)](https://creativecommons.org/licenses/by-nc-nd/4.0/).
### Citation Information
```
@article{PP4AV2022,
title = {PP4AV: A benchmarking Dataset for Privacy-preserving Autonomous Driving},
author = {Linh Trinh, Phuong Pham, Hoang Trinh, Nguyen Bach, Dung Nguyen, Giang Nguyen, Huy Nguyen},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
year = {2023}
}
```
### Contributions
Thanks to [@khaclinh](https://github.com/khaclinh) for adding this dataset.
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Subsets and Splits
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