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 |
|---|---|---|---|---|---|---|---|---|---|
eduagarcia/pt_legal_pile | 2023-08-31T20:23:11.000Z | [
"region:us"
] | eduagarcia | Multi Legal Pile is a dataset of legal documents in the 24 EU languages. | null | 0 | 6 | Entry not found | |
RikoteMaster/isear_augmented | 2023-07-19T08:14:26.000Z | [
"region:us"
] | RikoteMaster | null | null | null | 0 | 6 | ---
dataset_info:
features:
- name: Text_processed
dtype: string
- name: Emotion
dtype: string
- name: Augmented
dtype: bool
splits:
- name: train
num_bytes: 1130723
num_examples: 7499
- name: validation
num_bytes: 198379
num_examples: 1324
- name: test
num_bytes: 250735
num_examples: 1879
download_size: 923389
dataset_size: 1579837
---
# Dataset Card for "isear_augmented"
Classical ISEAR dataset augmented using gpt3.5 prompt tunning.
Example prompt:
Hello, you are going to take care of the task of increasing data in text. The data format that I am going to pass you is going to be as follows. Sentence: this is a sample text PROHIBITED WORD: this is a sample PROHIBITED WORD 2. Sentence: this is a sample text PROHIBITED WORD: this is a sample PROHIBITED WORD. I can enter as many sentences as I want, you must respect the logic that I have marked. NOW YOUR TASK MUST BE TO REFORMULATE THE SENTENCES IN ORDER TO EXPRESS THE SENTIMENT OF THE PROHIBITED WORD BUT YOU CANT USE THE PROHIBITED WORD BECAUSE IS FORBIDEN. PROHIBITED WORD LIST = [anger, fear, love, sadness, guilt, joy, shame, Overwhelming, remorse] you cannot use none of this words in the reformulation process, also you cannot use words derivated from this words and you must not do aclarations about what the text is trying to transmit. The output of the reformulated sentences must be Reformulated sentence 1: LorenIpsum reformulated sentence 2: LorenIpsum reformulated. Remember, I can introduce more than two sentences so you must return the reformulation of each sentence to me. So, remember, you must get a sentence that prevails the sentiment called the sentence PROHIBITED WORD but it is more important that the word does not appear inside the reformulation. If the word appears within the reformulation we would be entering into an incorrect practice of data augmentation. Yann LeCunn is watching, your bosses are watching you too, you must do what I ask you to do and get me the best sentences possible, but remember, without using the PROHIBITED WORD in the reformulation.1. Sentece: Unexpected visit by a close friend, whom I hadn't seen for half a
year. PROHIBITED WORD: joy
2. Sentece: I wandered by mistake into the safety zone of a shooting range,
and was shot at. PROHIBITED WORD: fear
3. Sentece: Being treated unfairly. PROHIBITED WORD: anger
4. Sentece: Breaking up with a girl. PROHIBITED WORD: sadness
5. Sentece: Nothing. PROHIBITED WORD: disgust
6. Sentece: None. PROHIBITED WORD: shame
7. Sentece: Little contact with my father before he died. PROHIBITED WORD: guilt
8. Sentece: When I was accepted as a student at the college, not having
thought it possible. PROHIBITED WORD: joy
The results obtained were quite well but when gpt obtain a way to express a sentiment with concrete words he is going to repeat it the structure, so you must reestructure the prompt
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
davanstrien/test_imdb_embedd2 | 2023-07-13T09:51:55.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:imdb",
"language:en",
"license:other",
"region:us"
] | davanstrien | null | null | null | 0 | 6 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets: imdb
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: imdb-movie-reviews
pretty_name: IMDB
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
0: neg
1: pos
config_name: plain_text
splits:
- name: train
num_bytes: 33432835
num_examples: 25000
- name: test
num_bytes: 32650697
num_examples: 25000
- name: unsupervised
num_bytes: 67106814
num_examples: 50000
download_size: 84125825
dataset_size: 133190346
train-eval-index:
- config: plain_text
task: text-classification
task_id: binary_classification
splits:
train_split: train
eval_split: test
col_mapping:
text: text
label: target
metrics:
- type: accuracy
- name: Accuracy
- type: f1
name: F1 macro
args:
average: macro
- type: f1
name: F1 micro
args:
average: micro
- type: f1
name: F1 weighted
args:
average: weighted
- type: precision
name: Precision macro
args:
average: macro
- type: precision
name: Precision micro
args:
average: micro
- type: precision
name: Precision weighted
args:
average: weighted
- type: recall
name: Recall macro
args:
average: macro
- type: recall
name: Recall micro
args:
average: micro
- type: recall
name: Recall weighted
args:
average: weighted
---
# Dataset Card for "test_imdb_embedd2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
TinyPixel/dolphin-1.1 | 2023-07-13T05:39:52.000Z | [
"region:us"
] | TinyPixel | null | null | null | 2 | 6 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 4891096654
num_examples: 2840090
download_size: 2656985115
dataset_size: 4891096654
---
# Dataset Card for "dolphin-1.1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
davanstrien/test1 | 2023-07-13T10:01:48.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:imdb",
"language:en",
"license:other",
"embeddings",
"region:us... | davanstrien | null | null | null | 0 | 6 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets: imdb
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: imdb-movie-reviews
pretty_name: IMDB
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
0: neg
1: pos
config_name: plain_text
splits:
- name: train
num_bytes: 33432835
num_examples: 25000
- name: test
num_bytes: 32650697
num_examples: 25000
- name: unsupervised
num_bytes: 67106814
num_examples: 50000
download_size: 84125825
dataset_size: 133190346
tags:
- embeddings
train-eval-index:
- config: plain_text
task: text-classification
task_id: binary_classification
splits:
train_split: train
eval_split: test
col_mapping:
text: text
label: target
metrics:
- type: accuracy
- name: Accuracy
- type: f1
name: F1 macro
args:
average: macro
- type: f1
name: F1 micro
args:
average: micro
- type: f1
name: F1 weighted
args:
average: weighted
- type: precision
name: Precision macro
args:
average: macro
- type: precision
name: Precision micro
args:
average: micro
- type: precision
name: Precision weighted
args:
average: weighted
- type: recall
name: Recall macro
args:
average: macro
- type: recall
name: Recall micro
args:
average: micro
- type: recall
name: Recall weighted
args:
average: weighted
---
# Dataset Card for "test1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
TrainingDataPro/speech-emotion-recognition-dataset | 2023-09-19T19:34:11.000Z | [
"task_categories:audio-classification",
"language:en",
"license:cc-by-nc-nd-4.0",
"code",
"legal",
"region:us"
] | TrainingDataPro | The audio dataset consists of a collection of texts spoken with four distinct
emotions. These texts are spoken in English and represent four different
emotional states: **euphoria, joy, sadness and surprise**.
Each audio clip captures the tone, intonation, and nuances of speech as
individuals convey their emotions through their voice.
The dataset includes a diverse range of speakers, ensuring variability in age,
gender, and cultural backgrounds*, allowing for a more comprehensive
representation of the emotional spectrum.
The dataset is labeled and organized based on the emotion expressed in each
audio sample, making it a valuable resource for emotion recognition and
analysis. Researchers and developers can utilize this dataset to train and
evaluate machine learning models and algorithms, aiming to accurately
recognize and classify emotions in speech. | @InProceedings{huggingface:dataset,
title = {speech-emotion-recognition-dataset},
author = {TrainingDataPro},
year = {2023}
} | null | 1 | 6 | ---
language:
- en
license: cc-by-nc-nd-4.0
task_categories:
- audio-classification
tags:
- code
- legal
dataset_info:
features:
- name: set_id
dtype: string
- name: euphoric
dtype: audio
- name: joyfully
dtype: audio
- name: sad
dtype: audio
- name: surprised
dtype: audio
- name: text
dtype: string
- name: gender
dtype: string
- name: age
dtype: int8
- name: country
dtype: string
splits:
- name: train
num_bytes: 17202
num_examples: 20
download_size: 28409585
dataset_size: 17202
---
# Emotions on Audio Dataset
The audio dataset consists of a collection of texts spoken with four distinct emotions. These texts are spoken in English and represent four different emotional states: **euphoria, joy, sadness and surprise**.
Each audio clip captures the tone, intonation, and nuances of speech as individuals convey their emotions through their voice.
The dataset includes a diverse range of speakers, ensuring variability in *age, gender, and cultural backgrounds*, allowing for a more comprehensive representation of the emotional spectrum.
The dataset is labeled and organized based on the emotion expressed in each audio sample, making it a valuable resource for emotion recognition and analysis. Researchers and developers can utilize this dataset to train and evaluate machine learning models and algorithms, aiming to accurately recognize and classify emotions in speech.
### The audio dataset also provides an opportunity for various applications:
- sentiment analysis
- automatic emotion detection
- emotional speech synthesis.
- voice assistants
- customer service
- mental health analysis
- entertainment industries
# Get the dataset
### This is just an example of the data
Leave a request on [**https://trainingdata.pro/data-market**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=speech-emotion-recognition-dataset) to discuss your requirements, learn about the price and buy the dataset.
# Content
- **files**: includes folders corresponding to people and containing text spoken in English in 4 different manners: **euphoric, joyfully, sad and surprised**
- **.csv** file: contains information about people in the dataset
### File with the extension .csv
includes the following information for each set of media files:
- **set_id**: link to the set of audio files,
- **text**: text spoken in the audio set,
- **gender**: gender of the person,
- **age**: age of the person,
- **country**: country of the person
# Audio with emotions might be collected in accordance with your requirements.
## [TrainingData](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=speech-emotion-recognition-dataset) provides high-quality data annotation tailored to your needs
More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets**
TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets** |
freQuensy23/ru-alpaca-cleaned | 2023-07-17T17:04:15.000Z | [
"license:cc-by-4.0",
"region:us"
] | freQuensy23 | null | null | null | 5 | 6 | ---
license: cc-by-4.0
---
Translated with yandex.translate.ru into russian [alpaca's](https://huggingface.co/datasets/yahma/alpaca-cleaned) dataset. Code for reproducing the result - [colab](https://colab.research.google.com/drive/1oRTDtRWA4wcLOoR75MWv7vaZUf3BgVLZ?usp=sharing)
|
ccmusic-database/erhu_playing_tech | 2023-10-03T16:29:55.000Z | [
"task_categories:audio-classification",
"size_categories:1K<n<10K",
"language:zh",
"language:en",
"license:mit",
"music",
"art",
"arxiv:1910.09021",
"region:us"
] | ccmusic-database | This database contains 1500 erhu audio clips (.wav format), all played by professional Erhu players.
They are divided into 11 classes that are: split bow, pad bow, overtone, legato & glissando & slur,
strike bow, plucked string, throw bow, staccato bow, tremolo, vibrato. For some playing techniques,
there is more than one audio clip played in different dynamics.
All the audio is acquired from the CTIS database presented above. | @dataset{zhaorui_liu_2021_5676893,
author = {Zhaorui Liu, Monan Zhou, Shenyang Xu and Zijin Li},
title = {{Music Data Sharing Platform for Computational Musicology Research (CCMUSIC DATASET)}},
month = nov,
year = 2021,
publisher = {Zenodo},
version = {1.1},
doi = {10.5281/zenodo.5676893},
url = {https://doi.org/10.5281/zenodo.5676893}
} | null | 1 | 6 | ---
license: mit
task_categories:
- audio-classification
language:
- zh
- en
tags:
- music
- art
pretty_name: Erhu Playing Technique Database
size_categories:
- 1K<n<10K
---
# Dataset Card for Erhu Playing Technique Database
## Dataset Description
- **Homepage:** <https://ccmusic-database.github.io>
- **Repository:** <https://huggingface.co/datasets/CCMUSIC/erhu_playing_tech>
- **Paper:** <https://doi.org/10.5281/zenodo.5676893>
- **Leaderboard:** <https://ccmusic-database.github.io/team.html>
- **Point of Contact:** N/A
### Dataset Summary
This dataset is an audio dataset containing 927 audio clips recorded by China Conservatory of Music, each with a performance technique of erhu. It is a part of DCMI[1], which is a dataset of Chinese Musical Instruments. We divide all the recorded techniques into the following 11 categories according to [2] [3] [4] [5]
### Supported Tasks and Leaderboards
Erhu Playing Technique Classification
### Languages
Chinese, English
## Dataset Structure
### Data Instances
.zip(.wav, .jpg)
### Data Fields
```
+ detache 分弓 (72)
+ forte (8)
+ medium (8)
+ piano (56)
+ diangong 垫弓 (28)
+ harmonic 泛音 (18)
+ natural 自然泛音 (6)
+ artificial 人工泛音 (12)
+ legato&slide&glissando 连弓&滑音&大滑音 (114)
+ glissando_down 大滑音 下行 (4)
+ glissando_up 大滑音 上行 (4)
+ huihuayin_down 下回滑音 (18)
+ huihuayin_long_down 后下回滑音 (12)
+ legato&slide_up 向上连弓 包含滑音 (24)
+ forte (8)
+ medium (8)
+ piano (8)
+ slide_dianzhi 垫指滑音 (4)
+ slide_down 向下滑音 (16)
+ slide_legato 连线滑音 (16)
+ slide_up 向上滑音 (16)
+ percussive 打击类音效 (21)
+ dajigong 大击弓 (11)
+ horse 马嘶 (2)
+ stick 敲击弓 (8)
+ pizzicato 拨弦 (96)
+ forte (30)
+ medium (29)
+ piano (30)
+ left 左手勾弦 (6)
+ ricochet 抛弓 (36)
+ staccato 顿弓 (141)
+ forte (47)
+ medium (46)
+ piano (48)
+ tremolo 颤弓 （144）
+ forte (48)
+ medium (48)
+ piano (48)
+ trill 颤音 (202)
+ long 长颤音 (141)
+ forte (46)
+ medium (47)
+ piano (48)
+ short 短颤音 (61)
+ down 下颤音 (30)
+ up 上颤音 (31)
+ vibrato 揉弦 (56)
+ late (13)
+ press 压揉 (6)
+ roll 滚揉 (28)
+ slide 滑揉 (9)
```
### Data Splits
train, validation, test
## Dataset Creation
### Curation Rationale
Lack of a dataset for Erhu playing tech
### Source Data
#### Initial Data Collection and Normalization
Zhaorui Liu, Monan Zhou
#### Who are the source language producers?
Students from CCMUSIC
### Annotations
#### Annotation process
This dataset is an audio dataset containing 927 audio clips recorded by China Conservatory of Music, each with a performance technique of erhu.
#### Who are the annotators?
Students from CCMUSIC
### Personal and Sensitive Information
None
## Considerations for Using the Data
### Social Impact of Dataset
Advancing the Digitization Process of Traditional Chinese Instruments
### Discussion of Biases
Only for Erhu
### Other Known Limitations
Not Specific Enough in Categorization
## Additional Information
### Dataset Curators
Zijin Li
### Evaluation
[Wang, Zehao et al. “Musical Instrument Playing Technique Detection Based on FCN: Using Chinese Bowed-Stringed Instrument as an Example.” ArXiv abs/1910.09021 (2019): n. pag.](https://arxiv.org/pdf/1910.09021.pdf)
### Licensing Information
```
MIT License
Copyright (c) CCMUSIC
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
```
### Citation Information
```
@dataset{zhaorui_liu_2021_5676893,
author = {Zhaorui Liu, Monan Zhou, Shenyang Xu and Zijin Li},
title = {CCMUSIC DATABASE: Music Data Sharing Platform for Computational Musicology Research},
month = {nov},
year = {2021},
publisher = {Zenodo},
version = {1.1},
doi = {10.5281/zenodo.5676893},
url = {https://doi.org/10.5281/zenodo.5676893}
}
```
[1] Zijin Li, Xiaojing Liang, Jingyu Liu, Wei Li, Jiaxing Zhu, Baoqiang Han, DCMI: A Database of Chinese Musical Instruments, DLfM ’18, Sep 2018, Paris, France<br>
[2] [Chapter 9. Erhu of Bowed Stringed Instruments](https://www.atlasensemble.nl/assets/files/instruments/Erhu/Erhu%20by%20Samuel%20Wong%20Shengmiao.pdf)<br>
[3] 梁广程, 潘永璋. 乐器法手册（增订本）[M]. 人民音乐出版社, 1996.<br>
[4] [Erhu, info for composers](https://www.lantungmusic.com/erhu/for-composers)<br>
[5] 权吉浩, 中西乐器法 [M]. 人民音乐出版社, 2016.
### Contributions
Provide a dataset for Erhu playing tech |
csebuetnlp/dailydialogue_bn | 2023-07-22T07:41:50.000Z | [
"task_categories:conversational",
"task_categories:text-generation",
"task_categories:text2text-generation",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended",
"language:bn",
"license:cc-by-nc... | csebuetnlp | DailyDialogue (bengali) has been derived from the original English dataset. | @inproceedings{bhattacharjee-etal-2023-banglanlg,
title = "{B}angla{NLG} and {B}angla{T}5: Benchmarks and Resources for Evaluating Low-Resource Natural Language Generation in {B}angla",
author = "Bhattacharjee, Abhik and
Hasan, Tahmid and
Ahmad, Wasi Uddin and
Shahriyar, Rifat",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-eacl.54",
pages = "726--735",
abstract = "This work presents {`}BanglaNLG,{'} a comprehensive benchmark for evaluating natural language generation (NLG) models in Bangla, a widely spoken yet low-resource language. We aggregate six challenging conditional text generation tasks under the BanglaNLG benchmark, introducing a new dataset on dialogue generation in the process. Furthermore, using a clean corpus of 27.5 GB of Bangla data, we pretrain {`}BanglaT5{'}, a sequence-to-sequence Transformer language model for Bangla. BanglaT5 achieves state-of-the-art performance in all of these tasks, outperforming several multilingual models by up to 9{\%} absolute gain and 32{\%} relative gain. We are making the new dialogue dataset and the BanglaT5 model publicly available at https://github.com/csebuetnlp/BanglaNLG in the hope of advancing future research on Bangla NLG.",
} | null | 2 | 6 | ---
annotations_creators:
- machine-generated
language_creators:
- found
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended
task_categories:
- conversational
- text-generation
- text2text-generation
language:
- bn
license:
- cc-by-nc-sa-4.0
---
# Dataset Card for `dailydialogue_bn`
## Table of Contents
- [Dataset Card for `dailydialogue_bn`](#dataset-card-for-dailydialogue_bn)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Usage](#usage)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [https://github.com/csebuetnlp/BanglaNLG](https://github.com/csebuetnlp/BanglaNLG)
- **Paper:** [**"BanglaNLG and BanglaT5: Benchmarks and Resources for Evaluating Low-Resource Natural Language Generation in Bangla"**](https://aclanthology.org/2023.findings-eacl.54/)
- **Point of Contact:** [Tahmid Hasan](mailto:tahmidhasan@cse.buet.ac.bd)
### Dataset Summary
This is a Multi-turn dialogue dataset for Bengali, curated from the original English [DailyDialogue]() dataset and using the state-of-the-art English to Bengali translation model introduced **[here](https://aclanthology.org/2020.emnlp-main.207/).**
### Supported Tasks and Leaderboards
[More information needed](https://github.com/csebuetnlp/BanglaNLG)
### Languages
* `Bengali`
### Usage
```python
from datasets import load_dataset
dataset = load_dataset("csebuetnlp/dailydialogue_bn")
```
## Dataset Structure
### Data Instances
One example from the dataset is given below in JSON format. Each element of the `dialogue` feature represents a single turn of the conversation.
```
{
"id": "130",
"dialogue":
[
"তোমার জন্মদিনের জন্য তুমি কি করবে?",
"আমি আমার বন্ধুদের সাথে পিকনিক করতে চাই, মা।",
"বাড়িতে পার্টি হলে কেমন হয়? এভাবে আমরা একসাথে হয়ে উদযাপন করতে পারি।",
"ঠিক আছে, মা। আমি আমার বন্ধুদের বাড়িতে আমন্ত্রণ জানাবো।"
]
}
```
### Data Fields
The data fields are as follows:
- `id`: a `string` feature.
- `dialogue`: a List of `string` feature.
### Data Splits
| split |count |
|----------|--------|
|`train`| 11118 |
|`validation`| 1000 |
|`test`| 1000 |
## Dataset Creation
For the training set, we translated the complete [DailyDialogue](https://aclanthology.org/N18-1101/) dataset using the English to Bangla translation model introduced [here](https://aclanthology.org/2020.emnlp-main.207/). Due to the possibility of incursions of error during automatic translation, we used the [Language-Agnostic BERT Sentence Embeddings (LaBSE)](https://arxiv.org/abs/2007.01852) of the translations and original sentences to compute their similarity. A datapoint was accepted if all of its constituent sentences had a similarity score over 0.7.
### Curation Rationale
[More information needed](https://github.com/csebuetnlp/BanglaNLG)
### Source Data
[DailyDialogue](https://arxiv.org/abs/1606.05250)
#### Initial Data Collection and Normalization
[More information needed](https://github.com/csebuetnlp/BanglaNLG)
#### Who are the source language producers?
[More information needed](https://github.com/csebuetnlp/BanglaNLG)
### Annotations
[More information needed](https://github.com/csebuetnlp/BanglaNLG)
#### Annotation process
[More information needed](https://github.com/csebuetnlp/BanglaNLG)
#### Who are the annotators?
[More information needed](https://github.com/csebuetnlp/BanglaNLG)
### Personal and Sensitive Information
[More information needed](https://github.com/csebuetnlp/BanglaNLG)
## Considerations for Using the Data
### Social Impact of Dataset
[More information needed](https://github.com/csebuetnlp/BanglaNLG)
### Discussion of Biases
[More information needed](https://github.com/csebuetnlp/BanglaNLG)
### Other Known Limitations
[More information needed](https://github.com/csebuetnlp/BanglaNLG)
## Additional Information
### Dataset Curators
[More information needed](https://github.com/csebuetnlp/BanglaNLG)
### Licensing Information
Contents of this repository are restricted to only non-commercial research purposes under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). Copyright of the dataset contents belongs to the original copyright holders.
### Citation Information
If you use the dataset, please cite the following paper:
```
@inproceedings{bhattacharjee-etal-2023-banglanlg,
title = "{B}angla{NLG} and {B}angla{T}5: Benchmarks and Resources for Evaluating Low-Resource Natural Language Generation in {B}angla",
author = "Bhattacharjee, Abhik and
Hasan, Tahmid and
Ahmad, Wasi Uddin and
Shahriyar, Rifat",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-eacl.54",
pages = "726--735",
abstract = "This work presents {`}BanglaNLG,{'} a comprehensive benchmark for evaluating natural language generation (NLG) models in Bangla, a widely spoken yet low-resource language. We aggregate six challenging conditional text generation tasks under the BanglaNLG benchmark, introducing a new dataset on dialogue generation in the process. Furthermore, using a clean corpus of 27.5 GB of Bangla data, we pretrain {`}BanglaT5{'}, a sequence-to-sequence Transformer language model for Bangla. BanglaT5 achieves state-of-the-art performance in all of these tasks, outperforming several multilingual models by up to 9{\%} absolute gain and 32{\%} relative gain. We are making the new dialogue dataset and the BanglaT5 model publicly available at https://github.com/csebuetnlp/BanglaNLG in the hope of advancing future research on Bangla NLG.",
}
```
### Contributions
Thanks to [@abhik1505040](https://github.com/abhik1505040) and [@Tahmid](https://github.com/Tahmid04) for adding this dataset. |
alexshengzhili/mPLUG-owl | 2023-07-16T22:58:55.000Z | [
"region:us"
] | alexshengzhili | null | null | null | 0 | 6 | ---
dataset_info:
features:
- name: image_file
dtype: string
- name: id
dtype: string
- name: caption
dtype: string
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: first_mention
dtype: string
- name: response
dtype: string
- name: title
dtype: string
- name: abstract
dtype: string
- name: q_a_pairs
sequence:
sequence: string
- name: response_mPLUG-owl
dtype: string
splits:
- name: 1_percent_as_validation
num_bytes: 19209561
num_examples: 3002
download_size: 8946500
dataset_size: 19209561
---
# Dataset Card for "mPLUG-owl"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
zozos/Passage_1 | 2023-07-17T13:33:11.000Z | [
"region:us"
] | zozos | null | null | null | 0 | 6 | ---
# 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] |
perceptiveshawty/datasets-for-compcse | 2023-07-17T20:21:07.000Z | [
"region:us"
] | perceptiveshawty | null | null | null | 0 | 6 | Entry not found |
FunDialogues/healthcare-minor-consultation | 2023-07-19T05:37:13.000Z | [
"task_categories:question-answering",
"task_categories:conversational",
"size_categories:n<1K",
"language:en",
"license:apache-2.0",
"fictitious dialogues",
"prototyping",
"healthcare",
"region:us"
] | FunDialogues | null | null | null | 1 | 6 | ---
license: apache-2.0
task_categories:
- question-answering
- conversational
language:
- en
tags:
- fictitious dialogues
- prototyping
- healthcare
pretty_name: 'healthcare-minor-consultation'
size_categories:
- n<1K
---
# fun dialogues
A library of fictitious dialogues that can be used to train language models or augment prompts for prototyping and educational purposes. Fun dialogues currently come in json and csv format for easy ingestion or conversion to popular data structures. Dialogues span various topics such as sports, retail, academia, healthcare, and more. The library also includes basic tooling for loading dialogues and will include quick chatbot prototyping functionality in the future.
Visit the Project Repo: https://github.com/eduand-alvarez/fun-dialogues/
# This Dialogue
Comprised of fictitious examples of dialogues between a doctor and a patient during a minor medical consultation.. Check out the example below:
```
"id": 1,
"description": "Discussion about a common cold",
"dialogue": "Patient: Doctor, I've been feeling congested and have a runny nose. What can I do to relieve these symptoms?\n\nDoctor: It sounds like you have a common cold. You can try over-the-counter decongestants to relieve congestion and saline nasal sprays to help with the runny nose. Make sure to drink plenty of fluids and get enough rest as well."
```
# How to Load Dialogues
Loading dialogues can be accomplished using the fun dialogues library or Hugging Face datasets library.
## Load using fun dialogues
1. Install fun dialogues package
`pip install fundialogues`
2. Use loader utility to load dataset as pandas dataframe. Further processing might be required for use.
```
from fundialogues import dialoader
# load as pandas dataframe
bball_coach = dialoader("FunDialogues/healthcare-minor-consultation")
```
## Loading using Hugging Face datasets
1. Install datasets package
2. Load using datasets
```
from datasets import load_dataset
dataset = load_dataset("FunDialogues/healthcare-minor-consultation")
```
## How to Contribute
If you want to contribute to this project and make it better, your help is very welcome. Contributing is also a great way to learn more about social coding on Github, new technologies and and their ecosystems and how to make constructive, helpful bug reports, feature requests and the noblest of all contributions: a good, clean pull request.
### Contributing your own Lifecycle Solution
If you want to contribute to an existing dialogue or add a new dialogue, please open an issue and I will follow up with you ASAP!
### Implementing Patches and Bug Fixes
- Create a personal fork of the project on Github.
- Clone the fork on your local machine. Your remote repo on Github is called origin.
- Add the original repository as a remote called upstream.
- If you created your fork a while ago be sure to pull upstream changes into your local repository.
- Create a new branch to work on! Branch from develop if it exists, else from master.
- Implement/fix your feature, comment your code.
- Follow the code style of the project, including indentation.
- If the component has tests run them!
- Write or adapt tests as needed.
- Add or change the documentation as needed.
- Squash your commits into a single commit with git's interactive rebase. Create a new branch if necessary.
- Push your branch to your fork on Github, the remote origin.
- From your fork open a pull request in the correct branch. Target the project's develop branch if there is one, else go for master!
If the maintainer requests further changes just push them to your branch. The PR will be updated automatically.
Once the pull request is approved and merged you can pull the changes from upstream to your local repo and delete your extra branch(es).
And last but not least: Always write your commit messages in the present tense. Your commit message should describe what the commit, when applied, does to the code – not what you did to the code.
# Disclaimer
The dialogues contained in this repository are provided for experimental purposes only. It is important to note that these dialogues are assumed to be original work by a human and are entirely fictitious, despite the possibility of some examples including factually correct information. The primary intention behind these dialogues is to serve as a tool for language modeling experimentation and should not be used for designing real-world products beyond non-production prototyping.
Please be aware that the utilization of fictitious data in these datasets may increase the likelihood of language model artifacts, such as hallucinations or unrealistic responses. Therefore, it is essential to exercise caution and discretion when employing these datasets for any purpose.
It is crucial to emphasize that none of the scenarios described in the fun dialogues dataset should be relied upon to provide advice or guidance to humans. These scenarios are purely fictitious and are intended solely for demonstration purposes. Any resemblance to real-world situations or individuals is entirely coincidental.
The responsibility for the usage and application of these datasets rests solely with the individual or entity employing them. By accessing and utilizing these dialogues and all contents of the repository, you acknowledge that you have read and understood this disclaimer, and you agree to use them at your own discretion and risk. |
liuhaotian/llava-bench-in-the-wild | 2023-07-19T08:26:49.000Z | [
"license:apache-2.0",
"region:us"
] | liuhaotian | null | null | null | 1 | 6 | ---
license: apache-2.0
---
|
Guilherme34/Cabrita-lora-ptbr | 2023-07-20T12:44:35.000Z | [
"region:us"
] | Guilherme34 | null | null | null | 3 | 6 | its not my dataset, im just posting it here |
shortbread/tickers | 2023-10-10T17:22:04.000Z | [
"size_categories:1K<n<10K",
"language:en",
"finance",
"region:us"
] | shortbread | null | null | null | 0 | 6 | ---
language:
- en
tags:
- finance
size_categories:
- 1K<n<10K
last_updated:
2023-07-20
---
Tickers
=======
|
openchat/openchat_sharegpt_v3 | 2023-09-04T14:32:11.000Z | [
"license:mit",
"region:us"
] | openchat | null | null | null | 11 | 6 | ---
license: mit
---
ShareGPT dataset for training OpenChat V3 series. See [OpenChat repository](https://github.com/imoneoi/openchat) for instructions.
Contents:
* `sharegpt_clean.json`: ShareGPT dataset in original format, converted to Markdown, and with `model` labels.
* `sharegpt_gpt4.json`: All instances in `sharegpt_clean.json` with `model == "Model: GPT-4"`.
* `*.parquet`: Pre-tokenized dataset for training specified version of OpenChat.
Note: The dataset is NOT currently compatible with HF dataset loader.
Licensed under MIT.
|
jamescalam/llama-2-arxiv-papers | 2023-07-25T03:11:43.000Z | [
"language:en",
"arxiv:2307.09288",
"region:us"
] | jamescalam | null | null | null | 1 | 6 | ---
language:
- en
pretty_name: Chunked Arxiv Papers for Llama 2
---
This dataset contains papers related to (and including) the [Llama 2 research paper](https://arxiv.org/abs/2307.09288). Related papers were identified by following a trail of references, extracting those papers with the [`arxiv-bot`](https://github.com/aurelio-labs/arxiv-bot) package, and repeating. |
sirabhop/nl2sql_food_fieldname | 2023-07-26T08:19:55.000Z | [
"task_categories:text2text-generation",
"size_categories:n<1K",
"language:en",
"code",
"region:us"
] | sirabhop | null | null | null | 1 | 6 | ---
task_categories:
- text2text-generation
language:
- en
tags:
- code
size_categories:
- n<1K
pretty_name: nl2sql-sample
--- |
zbulrush/lineart | 2023-07-30T19:05:52.000Z | [
"license:openrail",
"region:us"
] | zbulrush | null | null | null | 1 | 6 | ---
license: openrail
---
|
ArtifactAI/arxiv_deep_learning_python_research_code | 2023-07-27T00:42:03.000Z | [
"region:us"
] | ArtifactAI | null | null | null | 1 | 6 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: repo
dtype: string
- name: file
dtype: string
- name: code
dtype: string
- name: file_length
dtype: int64
- name: avg_line_length
dtype: float64
- name: max_line_length
dtype: int64
- name: extension_type
dtype: string
splits:
- name: train
num_bytes: 3590067176.125193
num_examples: 391496
download_size: 1490724325
dataset_size: 3590067176.125193
---
# Dataset Card for "ArtifactAI/arxiv_python_research_code"
## Dataset Description
https://huggingface.co/datasets/ArtifactAI/arxiv_deep_learning_python_research_code
### Dataset Summary
ArtifactAI/arxiv_deep_learning_python_research_code contains over 1.49B of source code files referenced strictly in ArXiv papers. The dataset serves as a curated dataset for Code LLMs.
### How to use it
```python
from datasets import load_dataset
# full dataset (1.49GB of data)
ds = load_dataset("ArtifactAI/arxiv_deep_learning_python_research_code", split="train")
# dataset streaming (will only download the data as needed)
ds = load_dataset("ArtifactAI/arxiv_deep_learning_python_research_code", streaming=True, split="train")
for sample in iter(ds): print(sample["code"])
```
## Dataset Structure
### Data Instances
Each data instance corresponds to one file. The content of the file is in the `code` feature, and other features (`repo`, `file`, etc.) provide some metadata.
### Data Fields
- `repo` (string): code repository name.
- `file` (string): file path in the repository.
- `code` (string): code within the file.
- `file_length`: (integer): number of characters in the file.
- `avg_line_length`: (float): the average line-length of the file.
- `max_line_length`: (integer): the maximum line-length of the file.
- `extension_type`: (string): file extension.
### Data Splits
The dataset has no splits and all data is loaded as train split by default.
## Dataset Creation
### Source Data
#### Initial Data Collection and Normalization
34,099 active GitHub repository names were extracted from [ArXiv](https://arxiv.org/) papers from its inception through July 21st, 2023 totaling 773G of compressed github repositories.
These repositories were then filtered, and the code from each file that mentions ["torch", "jax", "flax", "stax", "haiku", "keras", "fastai", "xgboost", "caffe", "mxnet"] was extracted into 1.4 million files.
#### Who are the source language producers?
The source (code) language producers are users of GitHub that created unique repository
### Personal and Sensitive Information
The released dataset may contain sensitive information such as emails, IP addresses, and API/ssh keys that have previously been published to public repositories on GitHub.
## Additional Information
### Dataset Curators
Matthew Kenney, Artifact AI, matt@artifactai.com
### Citation Information
```
@misc{arxiv_deep_learning_python_research_code,
title={arxiv_deep_learning_python_research_code},
author={Matthew Kenney},
year={2023}
}
``` |
nRuaif/staging | 2023-10-08T12:10:54.000Z | [
"region:us"
] | nRuaif | null | null | null | 0 | 6 | Entry not found |
zhwang/HPDv2 | 2023-08-08T09:32:39.000Z | [
"arxiv:2306.09341",
"region:us"
] | zhwang | null | null | null | 2 | 6 |
# Human Preference Dataset v2 (HPD v2)
**Human Preference Dataset v2 (HPD v2)** is a large-scale, cleanly-annotated dataset of human preferences for images generated from text prompts.
For more detailed information, please refer to the paper: [Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis](https://arxiv.org/abs/2306.09341).
We also trained [Human Preference Score v2 (HPSv2)](https://github.com/tgxs002/HPSv2), a preference prediction model, on HPD v2.
## Updates
* [07/29/2023] We released the benchmark and HPD v2 test data. HPD v2 train data will be **released sonn**.
## Data Source
The prompts in our dataset are sourced from DiffusionDB and MSCOCO Captions. Prompts from DiffusionDB are first cleaned by ChatGPT to remove biased function words. Human annotators are tasked to rank images generated by different text-to-image generative models from the same prompt. Totally there are about 798k pairwise comparisons of images for over 430k images and 107k prompts, 645k pairs for training split and 153k pairs for test split.
Image sources of HPD v2:
| Source | # of images
| :-----: | :-----: |
| CogView2 | 73697 |
| DALL·E 2 | 101869 |
| GLIDE (mini) | 400 |
| Stable Diffusion v1.4 | 101869 |
| Stable Diffusion v2.0 | 101869 |
| LAFITE | 400 |
| VQ-GAN+CLIP | 400 |
| VQ-Diffusion | 400 |
| FuseDream | 400 |
| COCO Captions | 28272 |
# Evaluation prompts
We also provide a set of evaluation prompts (benchmark prompts) that involves testing a model on a total of 3200 prompts, with 800 prompts for each of the following styles: “Animation”, “Concept-art”, “Painting”, and “Photo”.
In this reposity, We include benchmark images generated by mainstream text-to-image generative model based on benchmark prompts. So far, the following models have been included (being continuously updated):
- ChilloutMix
- CogView2
- DALL·E mini
- DALL·E 2
- Deliberate
- DeepFloyd-XL
- Dreamlike Photoreal 2.0
- Epic Diffusion
- FuseDream
- GLIDE
- LAFITE
- Latent Diffusion
- MajicMix Realistic
- Openjourney
- Realistic Vision
- Stable Diffusion v1.4
- Stable Diffusion v2.0
- SDXL Base 0.9
- SDXL Refiner 0.9
- Versatile Diffusion
- VQ-Diffusion
- VQGAN + CLIP
## Structure
Once unzipped, you should get a folder with the following structure:
```
HPD
---- train/
-------- {image_id}.jpg
---- test/
-------- {image_id}.jpg
---- train.json
---- test.json
---- benchmark/
-------- benchmark_imgs/
------------ {model_id}/
---------------- {image_id}.jpg
-------- drawbench/
------------ {model_id}/
---------------- {image_id}.jpg
-------- anime.json
-------- concept-art.json
-------- paintings.json
-------- photo.json
-------- drawbench.json
```
The annotation file, `train.json`, is organized as:
```
[
{
'human_preference': list[int], # 1 for preference
'prompt': str,
'file_path': list[str],
'user_hash': str,
},
...
]
```
The annotation file, `test.json`, is organized as:
```
[
{
'prompt': str,
'image_path': list[str],
'rank': list[int], # ranking for image at the same index in image_path
},
...
]
```
The benchmark prompts file, ie. `anime.json` is pure prompts. The corresponding image can be found in the folder of the corresponding model by indexing the prompt. |
Falah/chairs_furniture | 2023-07-28T17:30:40.000Z | [
"task_categories:text-generation",
"size_categories:10M<n<100M",
"language:en",
"license:apache-2.0",
"stable diffusion ",
"art",
"midjourney",
"region:us"
] | Falah | null | null | null | 0 | 6 | ---
dataset_info:
features:
- name: prompts
dtype: string
splits:
- name: train
num_bytes: 41184875
num_examples: 99850
download_size: 1233469
dataset_size: 41184875
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- 'stable diffusion '
- art
- midjourney
pretty_name: chairs furniture
size_categories:
- 10M<n<100M
---
# Dataset Card for Chairs Furniture
## Dataset Information
This dataset, named "chairs_furniture," is curated by Falah G. Salieh and contains data related to prompts and their corresponding information. The dataset includes the following features:
- **prompts**: A string-type feature that contains the prompts or information related to chairs and furniture.
The dataset is divided into one split:
- **Train Split**:
- Number of examples: 99,850
- Size on disk: 41,184,875 bytes
## Download Information
- **Download Size**: 1,233,469 bytes
- **Dataset Size**: 41,184,875 bytes
## Dataset Details
The "chairs_furniture" dataset by Falah G. Salieh provides information about chairs and furniture, particularly through prompts. Researchers, developers, and enthusiasts can use this data for various natural language processing (NLP) tasks, such as language modeling, text generation, or question-answering.
## Potential Applications:
Style Art for Furniture Designers: Designers in the furniture industry can use this dataset to gather inspiration and explore various styles of chairs and other furniture items. The dataset's prompts can serve as creative starting points for developing innovative furniture designs that align with different artistic styles.
Artiste Designer Resource: Artists with a focus on furniture design can benefit from this dataset as a resource for understanding various design elements, historical styles, and contemporary trends related to chairs and furniture. It can help artists enhance their creativity and artistic expression in their furniture-related artwork.
Artistic Language Model Training: The dataset's prompts can be used to train language models tailored for the furniture and chair design domain. Such models can generate descriptive and imaginative texts to accompany design portfolios, marketing materials, or even generate detailed narratives about fictional furniture pieces.
Design Trend Analysis: Industries involved in furniture production and marketing can analyze the dataset to identify design trends, customer preferences, and emerging styles in the furniture market. This information can influence product development and marketing strategies.
## Usage examples prompts
# --------------------------
# --------------------------
## Citations
If you use this dataset in your work or publications, please cite it as:
```
@dataset{chairs_furniture,
author = {Falah G. Salieh},
title = {chairs furniture},
year = {2023},
publisher = {HuggingFace Hub},
url = {https://huggingface.co/datasets/Falah/chairs_furniture/},
}
``` |
TrainingDataPro/2d-masks-presentation-attack-detection | 2023-09-14T16:36:42.000Z | [
"task_categories:image-classification",
"language:en",
"license:cc-by-nc-nd-4.0",
"code",
"region:us"
] | TrainingDataPro | The dataset consists of videos of individuals wearing printed 2D masks or
printed 2D masks with cut-out eyes and directly looking at the camera.
Videos are filmed in different lightning conditions and in different places
(indoors, outdoors). Each video in the dataset has an approximate duration of 2
seconds. | @InProceedings{huggingface:dataset,
title = {2d-masks-presentation-attack-detection},
author = {TrainingDataPro},
year = {2023}
} | null | 1 | 6 | ---
language:
- en
license: cc-by-nc-nd-4.0
task_categories:
- image-classification
tags:
- code
dataset_info:
features:
- name: user
dtype: string
- name: real_1
dtype: string
- name: real_2
dtype: string
- name: real_3
dtype: string
- name: real_4
dtype: string
- name: mask_1
dtype: string
- name: mask_2
dtype: string
- name: mask_3
dtype: string
- name: mask_4
dtype: string
- name: cut_1
dtype: string
- name: cut_2
dtype: string
- name: cut_3
dtype: string
- name: cut_4
dtype: string
splits:
- name: train
num_bytes: 4607
num_examples: 17
download_size: 901061924
dataset_size: 4607
---
# 2D Masks Presentation Attack Detection
The dataset consists of videos of individuals wearing printed 2D masks or printed 2D masks with cut-out eyes and directly looking at the camera. Videos are filmed in different lightning conditions and in different places (*indoors, outdoors*). Each video in the dataset has an approximate duration of 2 seconds.
### Types of videos in the dataset
- **real** - 4 videos of the person without a mask.
- **mask** - 4 videos of the person wearing a printed 2D mask.
- **cut** - 4 videos of the person wearing a printed 2D mask with cut-out holes for eyes.
.png?generation=1690460078319549&alt=media)
People in the dataset wear different accessorieses, such as *glasses, caps, scarfs, hats and masks*. Most of them are worn over a mask, however *glasses and masks* can be are also printed on the mask itself.
.png?generation=1690462300531653&alt=media)
The dataset serves as a valuable resource for computer vision, anti-spoofing tasks, video analysis, and security systems. It allows for the development of algorithms and models that can effectively detect attacks perpetrated by individuals wearing printed 2D masks.
Studying the dataset may lead to the development of improved security systems, surveillance technologies, and solutions to mitigate the risks associated with masked individuals carrying out attacks.
# Get the dataset
### This is just an example of the data
Leave a request on [**https://trainingdata.pro/data-market**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=2d-masks-presentation-attack-detection) to discuss your requirements, learn about the price and buy the dataset.
# Content
### The folder **"files"** includes 17 folders
- corresponding to each person in the sample
- containing of 12 videos of the individual
### File with the extension .csv
- **user**: person in the videos,
- **real_1,... real_4**: links to the videos with people without mask,
- **mask_1,... mask_4**: links to the videos with 2D mask,
- **cut_1,... cut_4**: links to the videos with 2D mask with cut-out eyes
# Attacks might be collected in accordance with your requirements
## [**TrainingData**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=2d-masks-presentation-attack-detection) provides high-quality data annotation tailored to your needs
More datasets in TrainingData's Kaggle account: **<https://www.kaggle.com/trainingdatapro/datasets>**
TrainingData's GitHub: **<https://github.com/Trainingdata-datamarket/TrainingData_All_datasets>**
|
smangrul/peft_docs | 2023-07-29T18:07:49.000Z | [
"license:apache-2.0",
"region:us"
] | smangrul | null | null | null | 1 | 6 | ---
license: apache-2.0
---
|
Ali-C137/Hindawi-Books-dataset | 2023-08-03T20:05:42.000Z | [
"task_categories:text-generation",
"task_categories:summarization",
"size_categories:10K<n<100K",
"language:ar",
"license:cc-by-nc-4.0",
"region:us"
] | Ali-C137 | null | null | null | 4 | 6 | ---
dataset_info:
features:
- name: BookLink
dtype: string
- name: BookName
dtype: string
- name: AuthorName
dtype: string
- name: AboutBook
dtype: string
- name: ChapterLink
dtype: string
- name: ChapterName
dtype: string
- name: ChapterText
dtype: string
- name: AboutAuthor
dtype: string
splits:
- name: train
num_bytes: 1364861563
num_examples: 49821
download_size: 494678002
dataset_size: 1364861563
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: cc-by-nc-4.0
task_categories:
- text-generation
- summarization
language:
- ar
pretty_name: Hindawi
size_categories:
- 10K<n<100K
---
# Dataset Card for "Hindawi Books Dataset"
**Hindawi Books Dataset is a large collection of more than 3000 books written in Modern Standard Arabic.**
## Dataset Description
Hindawi Books Dataset offers a rich and diverse collection of literary works, covering various topics and genres, all written in Modern Standard Arabic. The dataset includes information about each book, such as the title, author name, book abstract, and a link to access the complete text online. Additionally, the dataset contains chapter details, including the chapter name and text, providing insights into the content of each book.
## Dataset Details
- **Homepage:** [https://huggingface.co/datasets/Ali-C137/Hindawi-Books-dataset](https://huggingface.co/datasets/Ali-C137/Hindawi-Books-dataset)
- **Author:** Elfilali Ali
- **Email:** ali.elfilali00@gmail.com, alielfilali0909@gmail.com
- **GitHub Profile:** [https://github.com/alielfilali01](https://github.com/alielfilali01)
- **LinkedIn Profile:** [https://www.linkedin.com/in/alielfilali01/](https://www.linkedin.com/in/alielfilali01/)
## Dataset Size
The Hindawi Books Dataset contains over 3000 books, making it a substantial resource for research and NLP model development. The dataset size on disk is approximately 476 MB, and it comprises more than 120 million tokens.
## Potential Use Cases
Researchers and NLP enthusiasts can utilize the Hindawi Books Dataset for various applications, including:
- **Language Model Training:** The dataset is ideal for training Large Language Models (LLMs) specifically tailored to Arabic text.
- **Text Generation:** Developers can leverage the dataset to generate new stories, poems, or other literary works in Modern Standard Arabic.
- **Text Summarization:** Researchers can explore long text summarization tasks by using the book text and abstract as targets.
## Dataset Access
The Hindawi Books Dataset is publicly available for academic and non-commercial research purposes only. [Hindawi Foundation](https://www.hindawi.org/) has granted permission to scrape and publish the data as a dataset on HuggingFace Hub for non-commercial and research only use.
We kindly request users to respect copyright and intellectual property rights and acknowledge Hindawi Foundation's contribution in any research or academic publications utilizing this dataset. Users are also reminded not to distribute the dataset to any third parties.
## Citation
Please use the following citation when referencing the Hindawi Books Dataset:
```
@dataset{
title = {Hindawi Books Dataset},
author = {Elfilali Ali},
howpublished = {Dataset},
url = {https://huggingface.co/datasets/Ali-C137/Hindawi-Books-dataset},
year = {2023},
}
```
## Feedback and Discussion
We encourage researchers and users of the Hindawi Books Dataset to provide feedback, report any potential mistakes, or discuss concerns and risks related to the dataset in the discussion window on the dataset card in the Hugging Face Hub. Your valuable feedback will help us improve and enhance the dataset for the NLP community.
####
|
DynamicSuperb/DialogueEmotionClassification_DailyTalk | 2023-08-02T08:59:04.000Z | [
"region:us"
] | DynamicSuperb | null | null | null | 0 | 6 | ---
dataset_info:
features:
- name: file
dtype: string
- name: audio
dtype: audio
- name: instruction
dtype: string
- name: label
dtype: string
splits:
- name: test
num_bytes: 1379443197.906
num_examples: 4758
download_size: 1292391688
dataset_size: 1379443197.906
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
# Dataset Card for "DialogueEmotionClassification_DailyTalk"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
longevity-genie/moskalev_papers | 2023-07-31T22:35:11.000Z | [
"license:openrail",
"region:us"
] | longevity-genie | null | null | null | 0 | 6 | ---
license: openrail
---
All Alexey Moskalev paper found inside semantic-scholar that have pubmed ids.
The parquet file schema is:
```
root
|-- corpusid: long (nullable = true)
|-- updated: string (nullable = true)
|-- content_source_oainfo_license: string (nullable = true)
|-- content_source_oainfo_openaccessurl: string (nullable = true)
|-- content_source_oainfo_status: string (nullable = true)
|-- content_source_pdfsha: string (nullable = true)
|-- content_source_pdfurls: array (nullable = true)
| |-- element: string (containsNull = true)
|-- externalids_acl: string (nullable = true)
|-- externalids_arxiv: string (nullable = true)
|-- externalids_dblp: string (nullable = true)
|-- externalids_doi: string (nullable = true)
|-- externalids_mag: string (nullable = true)
|-- externalids_pubmed: string (nullable = true)
|-- externalids_pubmedcentral: string (nullable = true)
|-- content_text: string (nullable = true)
|-- annotations_abstract: array (nullable = true)
| |-- element: string (containsNull = true)
|-- annotations_author: array (nullable = true)
| |-- element: string (containsNull = true)
|-- annotations_authoraffiliation: array (nullable = true)
| |-- element: string (containsNull = true)
|-- annotations_authorfirstname: array (nullable = true)
| |-- element: string (containsNull = true)
|-- annotations_authorlastname: array (nullable = true)
| |-- element: string (containsNull = true)
|-- annotations_bibauthor: array (nullable = true)
| |-- element: string (containsNull = true)
|-- annotations_bibauthorfirstname: array (nullable = true)
| |-- element: string (containsNull = true)
|-- annotations_bibauthorlastname: array (nullable = true)
| |-- element: string (containsNull = true)
|-- annotations_bibentry: array (nullable = true)
| |-- element: string (containsNull = true)
|-- annotations_bibref: array (nullable = true)
| |-- element: string (containsNull = true)
|-- annotations_bibtitle: array (nullable = true)
| |-- element: string (containsNull = true)
|-- annotations_bibvenue: array (nullable = true)
| |-- element: string (containsNull = true)
|-- annotations_figure: array (nullable = true)
| |-- element: string (containsNull = true)
|-- annotations_figurecaption: array (nullable = true)
| |-- element: string (containsNull = true)
|-- annotations_figureref: array (nullable = true)
| |-- element: string (containsNull = true)
|-- annotations_formula: array (nullable = true)
| |-- element: string (containsNull = true)
|-- annotations_paragraph: array (nullable = true)
| |-- element: string (containsNull = true)
|-- annotations_publisher: array (nullable = true)
| |-- element: string (containsNull = true)
|-- annotations_sectionheader: array (nullable = true)
| |-- element: string (containsNull = true)
|-- annotations_table: array (nullable = true)
| |-- element: string (containsNull = true)
|-- annotations_tableref: array (nullable = true)
| |-- element: string (containsNull = true)
|-- annotations_title: array (nullable = true)
| |-- element: string (containsNull = true)
|-- annotations_venue: array (nullable = true)
| |-- element: string (containsNull = true)
``` |
LawBERT-tw/law_exam | 2023-08-01T10:34:47.000Z | [
"region:us"
] | LawBERT-tw | null | null | null | 0 | 6 | ---
dataset_info:
features:
- name: father
dtype: string
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name: multiple
num_bytes: 2320767
num_examples: 4584
- name: theses
num_bytes: 20090207
num_examples: 5493
download_size: 0
dataset_size: 22410974
---
# Dataset Card for "law_exam"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
giuid/qrecc_context | 2023-08-01T15:41:44.000Z | [
"language:en",
"region:us"
] | giuid | null | null | null | 0 | 6 | ---
language:
- en
pretty_name: QRECC Context
---
We have the QRECC dataset for query rewriting.
The Context if found after the Falcon special_token >>INTRODUCTION<<
The utterance to rewrite after >>QUESTION<<
The rewriting after >>ANSWER<< |
lighteval/logiqa_harness | 2023-08-03T09:08:11.000Z | [
"region:us"
] | lighteval | LogiQA is a dataset for testing human logical reasoning. It consists of 8,678 QA
instances, covering multiple types of deductive reasoning. Results show that state-
of-the-art neural models perform by far worse than human ceiling. The dataset can
also serve as a benchmark for reinvestigating logical AI under the deep learning
NLP setting. | @misc{liu2020logiqa,
title={LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning},
author={Jian Liu and Leyang Cui and Hanmeng Liu and Dandan Huang and Yile Wang and Yue Zhang},
year={2020},
eprint={2007.08124},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | null | 0 | 6 | Entry not found |
TrainingDataPro/asos-e-commerce-dataset | 2023-09-14T16:38:48.000Z | [
"task_categories:text-classification",
"language:en",
"license:cc-by-nc-nd-4.0",
"code",
"finance",
"region:us"
] | TrainingDataPro | null | null | null | 1 | 6 | ---
license: cc-by-nc-nd-4.0
task_categories:
- text-classification
language:
- en
tags:
- code
- finance
---
# [Asos](https://asos.com) E-Commerce Dataset - 30,845 products
Using web scraping, we collected information on over **30,845** clothing items from the Asos website.
The dataset can be applied in E-commerce analytics in the fashion industry.
# Get the dataset
### This is just an example of the data
Leave a request on [**https://trainingdata.pro/data-market**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=asos-e-commerce-dataset) to discuss your requirements, learn about the price and buy the dataset.
# Dataset Info
For each item, we extracted:
- **url** - link to the item on the website
- **name** - item's name
- **size** - sizes available on the website
- **category** - product's category
- **price** - item's price
- **color** - item's color
- **SKU** - unique identifier of the item
- **date** - date of web scraping; for all items - March 11, 2023
- **description** - additional description, including product's brand, composition, and care instructions, in JSON format
- **images** - photographs from the item description
# Data collection and annotation
We provide both ready-made datasets and custom data collection and annotation services. Please contact us for more information: Andrew, **datasets@trainingdata.pro**
## [**TrainingData**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=asos-e-commerce-dataset) provides high-quality data annotation tailored to your needs
More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets**
TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets** |
aytvill/plastic-recycling-codes | 2023-08-06T14:21:22.000Z | [
"task_categories:object-detection",
"size_categories:n<1K",
"license:mit",
"region:us"
] | aytvill | null | null | null | 1 | 6 | ---
license: mit
task_categories:
- object-detection
size_categories:
- n<1K
---
Plastic recycling codes |
notrichardren/truthfulness_high_quality | 2023-08-09T22:46:30.000Z | [
"region:us"
] | notrichardren | null | null | null | 1 | 6 | ---
configs:
- config_name: default
data_files:
- split: combined
path: data/combined-*
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: claim
dtype: string
- name: label
dtype: int64
- name: dataset
dtype: string
- name: qa_type
dtype: int64
- name: ind
dtype: int64
splits:
- name: combined
num_bytes: 12293840
num_examples: 106153
- name: train
num_bytes: 9841127
num_examples: 84922
- name: test
num_bytes: 2452713
num_examples: 21231
download_size: 6560566
dataset_size: 24587680
---
# Dataset Card for "truthfulness_high_quality"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
engkufizz/router-switch-instruct | 2023-08-09T16:31:20.000Z | [
"region:us"
] | engkufizz | null | null | null | 0 | 6 | Entry not found |
seungheondoh/gtzan-bind | 2023-08-15T06:59:22.000Z | [
"size_categories:n<1K",
"language:en",
"license:mit",
"music",
"gtzan",
"region:us"
] | seungheondoh | null | null | null | 1 | 6 | ---
configs:
- config_name: default
data_files:
- split: gtzan_bind_v1
path: data/gtzan_bind_v1-*
license: mit
language:
- en
tags:
- music
- gtzan
pretty_name: gtzan-bind
size_categories:
- n<1K
---
# Dataset Summary
📚🎵 Introducing **GTZAN-Bind** a consolidated dataset encompassing diverse gtzan annotations.
My contribution involved an additional layer of fingerprinting to enhance its comprehensiveness
- **Repository:** [GTZAN-Bind repository](https://github.com/seungheondoh/gtzan-bind)
[](https://github.com/seungheondoh/gtzan-bind)
## Annotations
- audio: [Tzanetakis2002musical](https://ieeexplore.ieee.org/document/1021072), [audio source](https://www.kaggle.com/datasets/andradaolteanu/gtzan-dataset-music-genre-classification)
- split: [sturm2013faults](https://github.com/coreyker/dnn-mgr/tree/master/gtzan)
- metadata: [sturm2013faults](https://github.com/coreyker/dnn-mgr/tree/master/gtzan), [doh2023fingerprint](https://github.com/seungheondoh/gtzan-bind/tree/main/dataset/metadata/doh2023fingerprint/results)
- Caption (+fMRI): [denk2023brain2music](https://www.kaggle.com/datasets/nishimotolab/music-caption-brain2music)
- Key: [kraft_lerch2013tonalness](https://github.com/alexanderlerch/gtzan_key)
- Key2 (Not used): [tom2011genre](http://visal.cs.cityu.edu.hk/downloads/#gtzankeys)
- Rhythm: [marchand2015swing](http://anasynth.ircam.fr/home/system/files/attachment_uploads/marchand/private/GTZAN-Rhythm_v2_ismir2015_lbd_2015-10-28.tar_.gz)
- Metrical-structure: [quinton2015extraction](http://www.isophonics.net/content/metrical-structure-annotations-gtzan-dataset)
## Colums & Valid Annotation
- track_id 1000
- shazam_id 974
- title 989
- artist_name 986
- album 930
- label 930
- released 930
- tag 1000
- caption_15s 540
- key 837
- youtube_url 967
- album_img_url 960
- artist_img_url 960
- path 1000
- fault_filtered_split 930
- beat 1000
- downbeat 1000
- 8th-note 1000
- ternary 1000
- swing 1000
- swing_ratio_iqr 178
- swing_ratio_confidence 178
- meter 979
- swing_ratio 178
- tempo_mean 1000
- tempo_std 1000
- metrical_levels_pulse_rates 1000
- metrical_annotators 1000
## Contact
- seungheondoh@kaist.ac.kr
|
ds4sd/PubTabNet_OTSL | 2023-08-31T15:57:31.000Z | [
"task_categories:object-detection",
"task_categories:table-to-text",
"size_categories:10K<n<100K",
"license:other",
"table-structure-recognition",
"table-understanding",
"PDF",
"arxiv:2305.03393",
"region:us"
] | ds4sd | null | null | null | 0 | 6 | ---
license: other
pretty_name: PubTabNet-OTSL
size_categories:
- 10K<n<100K
tags:
- table-structure-recognition
- table-understanding
- PDF
task_categories:
- object-detection
- table-to-text
---
# Dataset Card for PubTabNet_OTSL
## Dataset Description
- **Homepage:** https://ds4sd.github.io
- **Paper:** https://arxiv.org/pdf/2305.03393
### Dataset Summary
This dataset is a conversion of the original [PubTabNet](https://developer.ibm.com/exchanges/data/all/pubtabnet/) into the OTSL format presented in our paper "Optimized Table Tokenization for Table Structure Recognition". The dataset includes the original annotations amongst new additions.
### Dataset Structure
* cells: origunal dataset cell groundtruth (content).
* otsl: new reduced table structure token format
* html: original dataset groundtruth HTML (structure).
* html_restored: generated HTML from OTSL.
* cols: grid column length.
* rows: grid row length.
* image: PIL image
### OTSL Vocabulary:
**OTSL**: new reduced table structure token format
More information on the OTSL table structure format and its concepts can be read from our paper.
Format of this dataset extends work presented in a paper, and introduces slight modifications:
* "fcel" - cell that has content in it
* "ecel" - cell that is empty
* "lcel" - left-looking cell (to handle horizontally merged cells)
* "ucel" - up-looking cell (to handle vertically merged cells)
* "xcel" - 2d span cells, in this dataset - covers entire area of a merged cell
* "nl" - new line token
### Data Splits
The dataset provides three splits
- `train`
- `val`
## Additional Information
### Dataset Curators
The dataset is converted by the [Deep Search team](https://ds4sd.github.io/) at IBM Research.
You can contact us at [deepsearch-core@zurich.ibm.com](mailto:deepsearch-core@zurich.ibm.com).
Curators:
- Maksym Lysak, [@maxmnemonic](https://github.com/maxmnemonic)
- Ahmed Nassar, [@nassarofficial](https://github.com/nassarofficial)
- Christoph Auer, [@cau-git](https://github.com/cau-git)
- Nikos Livathinos, [@nikos-livathinos](https://github.com/nikos-livathinos)
- Peter Staar, [@PeterStaar-IBM](https://github.com/PeterStaar-IBM)
### Citation Information
```bib
@misc{lysak2023optimized,
title={Optimized Table Tokenization for Table Structure Recognition},
author={Maksym Lysak and Ahmed Nassar and Nikolaos Livathinos and Christoph Auer and Peter Staar},
year={2023},
eprint={2305.03393},
archivePrefix={arXiv},
primaryClass={cs.CV}
}```
|
EgilKarlsen/CSIC_BERT_Baseline | 2023-08-17T18:02:02.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 6 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: '0'
dtype: float32
- name: '1'
dtype: float32
- name: '2'
dtype: float32
- name: '3'
dtype: float32
- name: '4'
dtype: float32
- name: '5'
dtype: float32
- name: '6'
dtype: float32
- name: '7'
dtype: float32
- name: '8'
dtype: float32
- name: '9'
dtype: float32
- name: '10'
dtype: float32
- name: '11'
dtype: float32
- name: '12'
dtype: float32
- name: '13'
dtype: float32
- name: '14'
dtype: float32
- name: '15'
dtype: float32
- name: '16'
dtype: float32
- name: '17'
dtype: float32
- name: '18'
dtype: float32
- name: '19'
dtype: float32
- name: '20'
dtype: float32
- name: '21'
dtype: float32
- name: '22'
dtype: float32
- name: '23'
dtype: float32
- name: '24'
dtype: float32
- name: '25'
dtype: float32
- name: '26'
dtype: float32
- name: '27'
dtype: float32
- name: '28'
dtype: float32
- name: '29'
dtype: float32
- name: '30'
dtype: float32
- name: '31'
dtype: float32
- name: '32'
dtype: float32
- name: '33'
dtype: float32
- name: '34'
dtype: float32
- name: '35'
dtype: float32
- name: '36'
dtype: float32
- name: '37'
dtype: float32
- name: '38'
dtype: float32
- name: '39'
dtype: float32
- name: '40'
dtype: float32
- name: '41'
dtype: float32
- name: '42'
dtype: float32
- name: '43'
dtype: float32
- name: '44'
dtype: float32
- name: '45'
dtype: float32
- name: '46'
dtype: float32
- name: '47'
dtype: float32
- name: '48'
dtype: float32
- name: '49'
dtype: float32
- name: '50'
dtype: float32
- name: '51'
dtype: float32
- name: '52'
dtype: float32
- name: '53'
dtype: float32
- name: '54'
dtype: float32
- name: '55'
dtype: float32
- name: '56'
dtype: float32
- name: '57'
dtype: float32
- name: '58'
dtype: float32
- name: '59'
dtype: float32
- name: '60'
dtype: float32
- name: '61'
dtype: float32
- name: '62'
dtype: float32
- name: '63'
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splits:
- name: train
num_bytes: 115621178.4375
num_examples: 37500
- name: test
num_bytes: 38540392.5
num_examples: 12500
download_size: 211874011
dataset_size: 154161570.9375
---
# Dataset Card for "CSIC_BERT_Baseline"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EgilKarlsen/CSIC_RoBERTa_Baseline | 2023-08-17T18:11:57.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 6 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
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splits:
- name: train
num_bytes: 115621178.4375
num_examples: 37500
- name: test
num_bytes: 38540392.5
num_examples: 12500
download_size: 211875927
dataset_size: 154161570.9375
---
# Dataset Card for "CSIC_RoBERTa_Baseline"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EgilKarlsen/CSIC_DistilRoBERTa_Baseline | 2023-08-17T18:20:04.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 6 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
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splits:
- name: train
num_bytes: 115621178.4375
num_examples: 37500
- name: test
num_bytes: 38540392.5
num_examples: 12500
download_size: 211874011
dataset_size: 154161570.9375
---
# Dataset Card for "CSIC_DistilRoBERTa_Baseline"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EgilKarlsen/CSIC_GPT2_Baseline | 2023-08-17T18:30:52.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 6 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
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splits:
- name: train
num_bytes: 115621178.4375
num_examples: 37500
- name: test
num_bytes: 38540392.5
num_examples: 12500
download_size: 211849176
dataset_size: 154161570.9375
---
# Dataset Card for "CSIC_GPT2_Baseline"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EgilKarlsen/AA_RoBERTa_Baseline | 2023-08-23T03:43:21.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 6 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
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splits:
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num_bytes: 80318780.21618997
num_examples: 26057
- name: test
num_bytes: 26774087.073587257
num_examples: 8686
download_size: 147167773
dataset_size: 107092867.28977722
---
# Dataset Card for "AA_RoBERTa_Baseline"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EgilKarlsen/AA_DistilRoBERTa_Baseline | 2023-08-23T03:47:10.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 6 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
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splits:
- name: train
num_bytes: 80318844.90167296
num_examples: 26057
- name: test
num_bytes: 26773977.07147654
num_examples: 8686
download_size: 0
dataset_size: 107092821.97314951
---
# Dataset Card for "AA_DistilRoBERTa_Baseline"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EgilKarlsen/AA_GPT2_Baseline | 2023-08-17T21:22:52.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 6 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
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splits:
- name: train
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num_examples: 26057
- name: test
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num_examples: 8686
download_size: 0
dataset_size: 107092821.97314951
---
# Dataset Card for "AA_GPT2_Baseline"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EgilKarlsen/CSIC_GPTNEO_Baseline | 2023-08-17T19:30:40.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 6 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
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splits:
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- name: test
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num_examples: 12500
download_size: 565362778
dataset_size: 410161570.9375
---
# Dataset Card for "CSIC_GPTNEO_Baseline"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
shirsh10mall/Image_Captioning_Dataset | 2023-08-22T14:12:14.000Z | [
"region:us"
] | shirsh10mall | null | null | null | 0 | 6 | ---
dataset_info:
features:
- name: caption
dtype: string
- name: image_data
dtype: image
splits:
- name: train
num_bytes: 395280032.345
num_examples: 1318
download_size: 0
dataset_size: 395280032.345
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "Image_Captioning_Dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
arbml/okapi_arabic | 2023-08-15T17:02:47.000Z | [
"region:us"
] | arbml | null | null | null | 1 | 6 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 36956297
num_examples: 64669
download_size: 19555722
dataset_size: 36956297
---
# Dataset Card for "okapi_arabic"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
TinyPixel/airo | 2023-09-18T17:47:29.000Z | [
"region:us"
] | TinyPixel | null | null | null | 0 | 6 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 57747703
num_examples: 34204
download_size: 29992848
dataset_size: 57747703
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "airo"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
deep-plants/AGM_HS | 2023-10-04T11:07:25.000Z | [
"license:cc",
"region:us"
] | deep-plants | null | null | null | 0 | 6 | ---
license: cc
dataset_info:
features:
- name: image
dtype: image
- name: mask
dtype: image
- name: crop_type
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 22900031.321
num_examples: 6127
download_size: 22010079
dataset_size: 22900031.321
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for AGM_HS Dataset
## Dataset Summary
The AGM<sub>HS</sub> (AGricolaModerna Healthy-Stress) Dataset is an extension of the AGM Dataset, specifically curated to address the challenge of detecting and localizing plant stress in top-view images of harvested crops. This dataset comprises 6,127 high-resolution RGB images, each with a resolution of 120x120 pixels, selected from the original AGM Dataset. The images in AGM<sub>HS</sub> are divided into two categories: healthy samples (3,798 images) and stressed samples (2,329 images) representing 14 of the 18 classes present in AGM. Alongside the healthy/stressed classification labels, the dataset also provides segmentation masks for the stressed areas.
## Supported Tasks
Image classification: Healthy-stressed classification
Image segmentation: detection and localization of plant stress in top-view images.
## Languages
The dataset primarily consists of image data and does not involve language content. Therefore, the primary language is English, but it is not relevant to the dataset's core content.
## Dataset Structure
### Data Instances
A typical data instance from the AGM<sub>HS</sub> Dataset consists of the following:
```
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=120x120 at 0x29CEAD71780>,
'labels': 'stressed',
'crop_type': 'by'
'mask': <PIL.PngImagePlugin.PngImageFile image mode=L size=120x120 at 0x29CEAD71780>
}
```
### Data Fields
The dataset's data instances have the following fields:
- `image`: A PIL.Image.Image object representing the image.
- `labels`: A string representation indicating whether the image is "healthy" or "stressed."
- `crop_type`: An string representation of the crop type in the image
- `mask`: A PIL.Image.Image object representing the segmentation mask of stressed areas in the image, stored as a PNG image.
### Data Splits
- **Training Set**:
- Number of Examples: 6,127
- Healthy Samples: 3,798
- Stressed Samples: 2,329
## Dataset Creation
### Curation Rationale
The AGM<sub>HS</sub> Dataset was created as an extension of the AGM Dataset to specifically address the challenge of detecting and localizing plant stress in top-view images of harvested crops. This dataset is essential for the development and evaluation of advanced segmentation models tailored for this task.
### Source Data
#### Initial Data Collection and Normalization
The images in AGM<sub>HS</sub> were extracted from the original AGM Dataset. During the extraction process, labelers selected images showing clear signs of either good health or high stress. These sub-images were resized to 120x120 pixels to create AGM<sub>HS</sub>.
### Annotations
#### Annotation Process
The AGM<sub>HS</sub> Dataset underwent a secondary stage of annotation. Labelers manually collected images by clicking on points corresponding to stressed areas on the leaves. These clicked points served as prompts for the semi-automatic generation of segmentation masks using the "Segment Anything" technique \cite{kirillov2023segment}. Each image is annotated with a classification label ("healthy" or "stressed") and a corresponding segmentation mask.
### Who Are the Annotators?
The annotators for AGM<sub>HS</sub> are domain experts with knowledge of plant health and stress detection.
## Personal and Sensitive Information
The dataset does not contain personal or sensitive information about individuals. It exclusively consists of images of plants.
## Considerations for Using the Data
### Social Impact of Dataset
The AGM<sub>HS</sub> Dataset plays a crucial role in advancing research and technologies for plant stress detection and localization in the context of modern agriculture. By providing a diverse set of top-view crop images with associated segmentation masks, this dataset can facilitate the development of innovative solutions for sustainable agriculture, contributing to increased crop health, yield prediction, and overall food security.
### Discussion of Biases and Known Limitations
While AGM<sub>HS</sub> is a valuable dataset, it inherits some limitations from the original AGM Dataset. It primarily involves images from a single vertical farm setting, potentially limiting the representativeness of broader agricultural scenarios. Additionally, the dataset's composition may reflect regional agricultural practices and business-driven crop preferences specific to vertical farming. Researchers should be aware of these potential biases when utilizing AGM<sub>HS</sub> for their work.
## Additional Information
### Dataset Curators
The AGM<sub>HS</sub> Dataset is curated by DeepPlants and AgricolaModerna. For further information, please contact us at:
- nico@deepplants.com
- etienne.david@agricolamoderna.com
### Licensing Information
### Citation Information
If you use the AGM<sub>HS</sub> dataset in your work, please consider citing the following publication:
```bibtex
@InProceedings{Sama_2023_ICCV,
author = {Sama, Nico and David, Etienne and Rossetti, Simone and Antona, Alessandro and Franchetti, Benjamin and Pirri, Fiora},
title = {A new Large Dataset and a Transfer Learning Methodology for Plant Phenotyping in Vertical Farms},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops},
month = {October},
year = {2023},
pages = {540-551}
}
```
|
alkzar90/mini-croupier | 2023-08-16T23:03:28.000Z | [
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"annotations_creators:found",
"size_categories:n<1K",
"source_datasets:original",
"license:apache-2.0",
"mgt",
"magic-card-game",
"creature-dataset",
"region:us"
] | alkzar90 | null | null | null | 0 | 6 | ---
annotations_creators:
- found
language: []
language_creators: []
license:
- apache-2.0
multilinguality: []
pretty_name: 'Mini-Croupier: Magic the Gathering creatures mini-dataset'
size_categories:
- n<1K
source_datasets:
- original
tags:
- mgt
- magic-card-game
- creature-dataset
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
---
## Dataset Description
TODO
### Dataset Summary
TODO
## Dataset Creatioon
TODO
|
sradc/imagenet_resized_64x64 | 2023-08-17T14:09:02.000Z | [
"region:us"
] | sradc | null | null | null | 0 | 6 | This is an upload of `imagenet_resized/64x64` from [tensorflow datasets](https://www.tensorflow.org/datasets/catalog/imagenet_resized), (but shuffled before uploading).
The homepage of imagenet_resized is: https://patrykchrabaszcz.github.io/Imagenet32/
imagenet_resized is a derivative of imagenet (and also available to download from there): https://image-net.org/index.php
> Warning: The integer labels used are defined by the authors and do not match those from the other ImageNet datasets provided by Tensorflow datasets. See the original label list, and the labels used by this dataset. Additionally, the original authors 1 index there labels which we convert to 0 indexed by subtracting one.
— From the tensorflow datasets [page](https://www.tensorflow.org/datasets/catalog/imagenet_resized).
The data in this dataset is of the format:
```
{
"image": Array3D(shape=(64, 64, 3), dtype="uint8"),
"label": Value(dtype="int32"),
}
```
- There are 1,281,167 samples in the train split.
- There are 50,000 samples in the validation split.
|
EgilKarlsen/AA_GPTNEO_Baseline | 2023-08-17T21:45:21.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 6 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
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splits:
- name: train
num_bytes: 213730684.90167296
num_examples: 26057
- name: test
num_bytes: 71246297.07147655
num_examples: 8686
download_size: 386094770
dataset_size: 284976981.97314954
---
# Dataset Card for "AA_GPTNEO_Baseline"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EgilKarlsen/PKDD_BERT_Baseline | 2023-08-17T23:18:47.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 6 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
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splits:
- name: train
num_bytes: 115608907.5
num_examples: 37500
- name: test
num_bytes: 38536305.0
num_examples: 12500
download_size: 211879692
dataset_size: 154145212.5
---
# Dataset Card for "PKDD_BERT_Baseline"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
botp/RyokoAI_Syosetu711K | 2023-08-18T01:29:56.000Z | [
"task_categories:text-classification",
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:ja",
"license:apache-2.0",
"novel",
"training",
"region:us"
] | botp | null | null | null | 2 | 6 | ---
license: apache-2.0
language:
- ja
tags:
- novel
- training
task_categories:
- text-classification
- text-generation
pretty_name: Syosetuka ni Narou 711K
size_categories:
- 100K<n<1M
duplicated_from: RyokoAI/Syosetu711K
---
# Dataset Card for Syosetu711K
*The BigKnow2022 dataset and its subsets are not yet complete. Not all information here may be accurate or accessible.*
## Dataset Description
- **Homepage:** (TODO)
- **Repository:** <https://github.com/RyokoAI/BigKnow2022>
- **Paper:** N/A
- **Leaderboard:** N/A
- **Point of Contact:** Ronsor/undeleted <ronsor@ronsor.com>
### Dataset Summary
Syosetu711K is a dataset composed of approximately 711,700 novels scraped from the Japanese novel self-publishing
website Syosetuka ni Narou (JA: 小説家になろう, lit. "Let's Become a Novelist") between March 26 and March 27, 2023.
The dataset contains most if not all novels published on the site, regardless of length or quality; however, we
include metadata so users of this dataset can filter and evaluate its contents.
Syosetu711Kは、日本の小説投稿サイト「小説家になろう」から2023年3月26日から27日にかけてスクレイプされた約711,700冊の小説から
構成されるデータセットです。このデータセットには、長さや品質に関係なく、サイトに掲載されているほとんどの小説が含まれています。ただし、
各小説のIDも含まれているため、小説家になろうAPIを使ってその情報を検索することができます。
### Supported Tasks and Leaderboards
This dataset is primarily intended for unsupervised training of text generation models; however, it may be useful for other purposes.
* text-classification
* text-generation
### Languages
* Japanese
## Dataset Structure
### Data Instances
```json
{
"text": "【小説タイトル】\n焼けて爛れる恋よりも、微睡む優しい愛が欲しい\n【Ｎコード】\nＮ５０２９ＩＤ\n【作者名】\n秋暁秋季\n【あらすじ】\n俺の彼女は物凄く気の多い人だった。\nお眼鏡に適う奴が居れば、瞳孔を蕩
けさせる人だった。\nその癖照れ屋で、すぐに目を逸らす。\nな...",
"meta": {
"subset": "syosetu",
"q": 0.6,
"id": "N5029ID",
"author": "秋暁秋季",
"userid": 719797,
"title": "焼けて爛れる恋よりも、微睡む優しい愛が欲しい",
"length": 871,
"points": 0,
"lang": "ja",
"chapters": 1,
"keywords": ["気が多い", "浮気性", "無愛想", "照れる", "嫉妬", "好みではない", "クソデカ感情", "空気のような安心感"],
"isr15": 0,
"genre": 102,
"biggenre": 1
}
}
{
"text": "【小説タイトル】\n【能力者】\n【Ｎコード】\nＮ９８６４ＩＢ\n【作者名】\n夢音いちご\n【あらすじ】\n私立アビリティ学園。\n小・中・高・大が一貫となった、大規模な名門校。\nそして、ここは規模の大きさだけ
でなく、ある特殊な制度を設けて\nいることでも有名だ。\nそれ...",
"meta": {
"subset": "syosetu",
"q": 0.6,
"id": "N9864IB",
"author": "夢音いちご",
"userid": 1912777,
"title": "【能力者】",
"length": 2334,
"points": 0,
"lang": "ja",
"chapters": 2,
"keywords": ["ガールズラブ", "身分差", "伝奇", "日常", "青春", "ラブコメ", "女主人公", "学園", "魔法", "超能力"],
"isr15": 0,
"genre": 202,
"biggenre": 2
}
}
```
### Data Fields
* `text`: the actual novel text, all chapters
* `meta`: novel metadata
* `subset`: dataset tag: `syosetu`
* `lang`: dataset language: `ja` (Japanese)
* `id`: novel ID/ncode
* `author`: author name
* `userid`: author user ID
* `title`: novel title
* `length`: novel length in words
* `points`: global points (corresponds to `global_point` from the Syosetu API)
* `q`: q-score (quality score) calculated based on `points`
* `chapters`: number of chapters (corresponds to `general_all_no` from the Syosetu API)
* `keywords`: array of novel keywords (corresponds to `keyword` from the Syosetu API, split on spaces)
* `isr15`: whether the novel is rated R15+
* `genre`: novel genre ID (optional, see Syosetu API documentation)
* `biggenre`: general novel genre ID (optional, see Syosetu API documentation)
* `isr18`: whether the novel is rated R18+
* `nocgenre`: novel genre ID (optional, only available if `isr18` is true, see Syosetu API documentation)
*For further reference, see the Syosetuka ni Narou API documentation: <https://dev.syosetu.com/man/api/> (JA).*
#### Q-Score Distribution
```
0.00: 0
0.10: 0
0.20: 0
0.30: 0
0.40: 0
0.50: 213005
0.60: 331393
0.70: 101971
0.80: 63877
0.90: 1542
1.00: 2
```
### Data Splits
No splitting of the data was performed.
## Dataset Creation
### Curation Rationale
Syosetuka ni Narou is the most popular website in Japan for authors wishing to self-publish their novels online. Many works on
the site been picked up by large commercial publishers. Because of this, we believe that this dataset provides a large corpus
of high-quality, creative content in the Japanese language.
### Source Data
#### Initial Data Collection and Normalization
*More information about any referenced scripts, commands, or programs used may be found in the BigKnow2022 GitHub repository.*
First, metadata for all novels on the site was gathered into a JSON lines (JSONL) file. The Syosetuka ni Narou API was used to
obtain this information.
Second, this listing was used to create a secondary text file containing a list of only the novel "ncodes," or IDs. This
secondary file was distributed to downloader nodes.
Third, the sister site <https://pdfnovels.net> was queried with each novel ID, and the resulting PDF was saved for later processing.
Fourth, the `pdftotext` tool was used to convert the PDF files to text documents. A few other scripts were then used to clean up
the resulting text files.
Finally, the text files and other metadata were converted into the specified data field schema above, and the resulting JSON entries
were concatenated into the Syosetu711K dataset. The version uploaded to this repository, however, is split into multiple files,
numbered 00 through 20 inclusive.
#### Who are the source language producers?
The authors of each novel.
### Annotations
#### Annotation process
Titles and general genre were collected alongside the novel text and IDs.
#### Who are the annotators?
There were no human annotators.
### Personal and Sensitive Information
The dataset contains only works of fiction, and we do not believe it contains any PII.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended to be useful for anyone who wishes to train a model to generate "more entertaining" content in Japanese.
It may also be useful for other languages depending on your language model.
### Discussion of Biases
This dataset is composed of fictional works by various authors. Because of this fact, the contents of this dataset will reflect
the biases of those authors. **Additionally, this dataset contains NSFW material and was not filtered. Beware of stereotypes.**
### Other Known Limitations
N/A
## Additional Information
### Dataset Curators
Ronsor Labs
### Licensing Information
Apache 2.0, for all parts of which Ronsor Labs or the Ryoko AI Production Committee may be considered authors. All other material is
distributed under fair use principles.
### Citation Information
```
@misc{ryokoai2023-bigknow2022,
title = {BigKnow2022: Bringing Language Models Up to Speed},
author = {Ronsor},
year = {2023},
howpublished = {\url{https://github.com/RyokoAI/BigKnow2022}},
}
```
### Contributions
Thanks to @ronsor (GH) for gathering this dataset. |
mncai/Fake_or_Real_Competition_Dataset | 2023-08-28T01:48:27.000Z | [
"task_categories:image-classification",
"language:en",
"license:apache-2.0",
"region:us"
] | mncai | null | null | null | 1 | 6 | ---
license: apache-2.0
task_categories:
- image-classification
language:
- en
pretty_name: aiconnect_fake_or_real
---
2023 Fake or Real: AI-generated Image Discrimination Competition dataset is now available on Hugging Face!
---
Hello🖐️
We are excited to announce the release of the dataset for the 2023 Fake or Real: AI-generated Image Discrimination Competition. The competition was held on AI CONNECT(https://aiconnect.kr/) from June 26th to July 6th, 2023, with 768 participants.
If you're interested in evaluating the performance of your model on the test dataset, we encourage you to visit the [competition page](https://aiconnect.kr/competition/detail/227/task/295/taskInfo) on AI CONNECT and submit your results. Please note that it supports only Korean yet. Of course we data scientists can always use Chrome translate, and/or even better translation models🥳. Plus, multilingual service will be provided in the (hopefully near) future, so please stay tuned!
# Background
As the advancement of generative AI technology has enabled the easy creation of indistinguishable fake information from genuine content, concerns regarding its misuse have surfaced. Image generation AI, in particular, has raised significant alarm due to its potential risks such as identity theft, revenge porn, and political manipulation. In response, it has become imperative to develop technologies that can effectively discern between real and AI-generated fake images.
The training dataset consists of diffusiondb (https://huggingface.co/datasets/poloclub/diffusiondb) and Flickr images, with the inclusion of some low-quality fake images. For the test dataset, we took measures to construct it in a manner that closely resembles real-world scenarios involving image misuse. We utilized multiple generative AI models, fine-tuned on diverse photorealistic datasets, and applied negative prompt keywords like 'cartoon' and 'too many fingers' to generate realistic images.
We hope this dataset encourages the development of robust solutions and stimulates discussions on tackling the challenges associated with AI-generated fake images.
Best Regards,
AI CONNECT |
EgilKarlsen/Thunderbird_BERT_Baseline | 2023-08-23T03:39:03.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 6 | ---
configs:
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data_files:
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path: data/train-*
- split: test
path: data/test-*
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dtype: float32
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dtype: float32
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dtype: float32
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dtype: float32
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dtype: float32
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dtype: float32
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dtype: float32
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dtype: float32
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dtype: float32
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dtype: float32
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dtype: string
splits:
- name: train
num_bytes: 115576729.6875
num_examples: 37500
- name: test
num_bytes: 38525577.5
num_examples: 12500
download_size: 0
dataset_size: 154102307.1875
---
# Dataset Card for "Thunderbird_BERT_Baseline"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Ichsan2895/OASST_Top1_Indonesian | 2023-08-19T13:15:17.000Z | [
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:id",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | Ichsan2895 | null | null | null | 1 | 6 | ---
license: cc-by-sa-4.0
language:
- id
- en
size_categories:
- 1K<n<10K
task_categories:
- question-answering
---
Base dataset : [OpenAssistant/oasst1](https://huggingface.co/datasets/OpenAssistant/oasst1)
We selected the dataset with "english" language & has rank = 1st.
Finally, we translate it to Indonesian with [Marian NMT](https://marian-nmt.github.io) and pretrained model from [Helsinki-NLP/opus-mt-en-id](https://huggingface.co/Helsinki-NLP/opus-mt-en-id).
## CITATION
```
@InProceedings{mariannmt,
title = {Marian: Fast Neural Machine Translation in {C++}},
author = {Junczys-Dowmunt, Marcin and Grundkiewicz, Roman and
Dwojak, Tomasz and Hoang, Hieu and Heafield, Kenneth and
Neckermann, Tom and Seide, Frank and Germann, Ulrich and
Fikri Aji, Alham and Bogoychev, Nikolay and
Martins, Andr\'{e} F. T. and Birch, Alexandra},
booktitle = {Proceedings of ACL 2018, System Demonstrations},
pages = {116--121},
publisher = {Association for Computational Linguistics},
year = {2018},
month = {July},
address = {Melbourne, Australia},
url = {http://www.aclweb.org/anthology/P18-4020}
}
@InProceedings{TiedemannThottingal:EAMT2020,
author = {J{\"o}rg Tiedemann and Santhosh Thottingal},
title = {{OPUS-MT} — {B}uilding open translation services for the {W}orld},
booktitle = {Proceedings of the 22nd Annual Conferenec of the European Association for Machine Translation (EAMT)},
year = {2020},
address = {Lisbon, Portugal}
}
``` |
Falah/sci_fi_fighter_plane_prompts_SDXL | 2023-08-19T15:13:48.000Z | [
"region:us"
] | Falah | null | null | null | 0 | 6 | ---
dataset_info:
features:
- name: prompts
dtype: string
splits:
- name: train
num_bytes: 581050176
num_examples: 1000000
download_size: 76478423
dataset_size: 581050176
---
# Dataset Card for "sci_fi_fighter_plane_prompts_SDXL"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
yardeny/processed_gpt2_dataset | 2023-08-21T08:53:12.000Z | [
"region:us"
] | yardeny | null | null | null | 0 | 6 | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 15593335128.0
num_examples: 6072171
download_size: 6562663671
dataset_size: 15593335128.0
---
# Dataset Card for "processed_gpt2_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mozci/tinysketch | 2023-08-24T03:40:02.000Z | [
"task_categories:text-to-image",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:n<10K",
"source_datasets:FS-COCO",
"language:en",
"license:cc-by-nc-sa-4.0",
"region:us"
] | mozci | null | null | null | 0 | 6 | ---
license: cc-by-nc-sa-4.0
language:
- en
language_creators:
- machine-generated
multilinguality:
- monolingual
pretty_name: 'Sketch Scene Descriptions'
size_categories:
- n<10K
source_datasets:
- FS-COCO
tags: []
task_categories:
- text-to-image
task_ids: []
---
# Dataset Card for Sketch Scene Descriptions
_Dataset used to train [Sketch Scene text to image model]()_
We advance sketch research to scenes with the first dataset of freehand scene sketches, FS-COCO. With practical applications in mind, we collect sketches that convey well scene content but can be sketched within a few minutes by a person with any sketching skills. Our dataset comprises around 10,000 freehand scene vector sketches with per-point space-time information by 100 non-expert individuals, offering both object- and scene-level abstraction. Each sketch is augmented with its text description.
For each row, the dataset contains `image` and `text` keys. `image` is a varying size PIL jpeg, and `text` is the accompanying text caption. Only a train split is provided.
## Citation
If you use this dataset, please cite it as:
```
@inproceedings{fscoco,
title={FS-COCO: Towards Understanding of Freehand Sketches of Common Objects in Context.}
author={Chowdhury, Pinaki Nath and Sain, Aneeshan and Bhunia, Ayan Kumar and Xiang, Tao and Gryaditskaya, Yulia and Song, Yi-Zhe},
booktitle={ECCV},
year={2022}
}
``` |
pavithrav/test | 2023-08-24T08:17:55.000Z | [
"region:us"
] | pavithrav | null | null | null | 0 | 6 | Entry not found |
KushT/reuters-21578-train-val-test | 2023-08-25T12:24:45.000Z | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:en",
"license:apache-2.0",
"region:us"
] | KushT | null | null | null | 0 | 6 | ---
license: apache-2.0
size_categories:
- 1K<n<10K
task_categories:
- text-classification
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 10816829
num_examples: 6988
- name: validation
num_bytes: 1178067
num_examples: 781
- name: test
num_bytes: 4513694
num_examples: 3019
download_size: 5088303
dataset_size: 16508590
language:
- en
---
Dataset from [Kaggle](https://www.kaggle.com/datasets/nltkdata/reuters/code)
The split is done on the training set using ```iterative_train_test_split``` from [scikit-multilearn](http://scikit.ml/index.html)
There are the following 90 labels.
'interest',
'groundnut-oil',
'potato',
'palmkernel',
'sun-meal',
'lei',
'cotton-oil',
'sunseed',
'sorghum',
'barley',
'dlr',
'groundnut',
'wpi',
'strategic-metal',
'livestock',
'l-cattle',
'lin-oil',
'gold',
'fuel',
'nzdlr',
'oat',
'soybean',
'hog',
'tin',
'lumber',
'bop',
'soy-oil',
'dfl',
'nkr',
'gas',
'carcass',
'silver',
'coffee',
'gnp',
'crude',
'rapeseed',
'alum',
'copper',
'housing',
'grain',
'cocoa',
'sun-oil',
'rice',
'jobs',
'rubber',
'jet',
'tea',
'retail',
'ship',
'corn',
'meal-feed',
'naphtha',
'sugar',
'rand',
'platinum',
'money-supply',
'yen',
'nickel',
'income',
'cpu',
'copra-cake',
'instal-debt',
'coconut-oil',
'cotton',
'rye',
'palm-oil',
'acq',
'wheat',
'propane',
'dmk',
'reserves',
'rape-oil',
'money-fx',
'heat',
'ipi',
'castor-oil',
'earn',
'iron-steel',
'palladium',
'coconut',
'veg-oil',
'nat-gas',
'pet-chem',
'lead',
'trade',
'cpi',
'oilseed',
'zinc',
'soy-meal',
'orange' |
deepghs/anime_teen | 2023-08-26T09:54:25.000Z | [
"task_categories:image-classification",
"size_categories:10K<n<100K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | deepghs | null | null | null | 0 | 6 | ---
license: mit
task_categories:
- image-classification
tags:
- art
- not-for-all-audiences
size_categories:
- 10K<n<100K
---
Because some American websites (such as civitai) have strict restrictions on the content of potential pedophiles (in fact, the management is quite broad and nonsensical, and any character pictures with younger characters may violate the rules), so we think It is necessary to collect such data and train a classification model to help content creators avoid this potential risk as much as possible.
The dataset contains the following labels:
* `contentious`, corresponding to the [contentious](https://beta.sankakucomplex.com/zh-CN/tag/en?tagName=contentious_content) tag on sankaku, contains pornographic content about loli, shota, etc.
* `safe_teen`, contains non-pornographic content about young children.
* `non_teen`, contains anything that is not a young boy or girl (whether safe, sexy or pornographic)
Please note:
* The above categories of content are for the visual level, and the actual age of the characters is not important (the same is often the case with the moderation rules of the website).
* The above datasets are obtained by crawling and do not guarantee extremely high purity. Therefore, it is recommended to use relevant noisy deep learning algorithms during training. |
kimvu/agieval | 2023-08-30T01:43:41.000Z | [
"language:en",
"language:zh",
"license:apache-2.0",
"region:us"
] | kimvu | The dataset is an amendment and re-annotation of LogiQA in 2020, a large-scale logical reasoning reading comprehension dataset adapted from the Chinese Civil Service Examination. We increase the data size, refine the texts with manual translation by professionals, and improve the quality by removing items with distinctive cultural features like Chinese idioms. Furthermore, we conduct a fine-grained annotation on the dataset and turn it into a two-way natural language inference (NLI) task, resulting in 35k premise-hypothesis pairs with gold labels, making it the first large-scale NLI dataset for complex logical reasoning | @ARTICLE{10174688,
author={Liu, Hanmeng and Liu, Jian and Cui, Leyang and Teng, Zhiyang and Duan, Nan and Zhou, Ming and Zhang, Yue},
journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
title={LogiQA 2.0 — An Improved Dataset for Logical Reasoning in Natural Language Understanding},
year={2023},
volume={},
number={},
pages={1-16},
doi={10.1109/TASLP.2023.3293046}} | null | 0 | 6 | ---
license: apache-2.0
language:
- en
- zh
pretty_name: agieval_full
--- |
whateverweird17/essay_grade_v2 | 2023-08-29T01:20:49.000Z | [
"region:us"
] | whateverweird17 | null | null | null | 0 | 6 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 2211156
num_examples: 1427
- name: validation
num_bytes: 221580.45409950946
num_examples: 143
download_size: 1200558
dataset_size: 2432736.4540995094
---
# Dataset Card for "essay_grade_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
vikp/code_search_net_filtered_34k | 2023-08-31T02:47:40.000Z | [
"license:cc-by-4.0",
"region:us"
] | vikp | null | null | null | 1 | 6 | ---
license: cc-by-4.0
dataset_info:
features:
- name: code
dtype: string
- name: signature
dtype: string
- name: docstring
dtype: string
- name: loss_without_docstring
dtype: float64
- name: loss_with_docstring
dtype: float64
- name: factor
dtype: float64
- name: rendered
dtype: string
- name: quality_prob
dtype: float64
- name: learning_prob
dtype: float64
splits:
- name: train
num_bytes: 59688305.816243604
num_examples: 34488
download_size: 30704027
dataset_size: 59688305.816243604
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
Filtered version of code search net python subset, with filtering based on perplexity with/without docstring, learning value/quality classifiers, and manual filtering.
Original data with perplexity filtering is from [here](https://huggingface.co/datasets/bjoernp/code_search_net_python_processed_400k), with credit to bjoernp. |
amunozo/wikipedia_rendered_sco | 2023-08-29T14:01:42.000Z | [
"region:us"
] | amunozo | null | null | null | 0 | 6 | Entry not found |
dariolopez/Llama-2-oasst1-es | 2023-08-29T17:45:43.000Z | [
"size_categories:1K<n<10K",
"language:es",
"license:apache-2.0",
"region:us"
] | dariolopez | null | null | null | 0 | 6 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 4524060
num_examples: 3909
download_size: 2528456
dataset_size: 4524060
license: apache-2.0
language:
- es
size_categories:
- 1K<n<10K
---
# OpenAssistant Conversations Spanish Dataset (OASST1-es) for Llama-2
## Dataset Summary
Subset of the original [OpenAssistant Conversations Dataset (OASST)](https://huggingface.co/datasets/OpenAssistant/oasst1).
* Filtered by `lang=es`.
* Formatted according to the Llama-2 pattern: "\<s> [INST] user prompt [/INST] output model \</s>"
* Select the best ranked output (Some instructions had multiple outputs ranked by humans).
* Select only the first level of the tree conversation.
## Dataset Structure
The dataset has 3909 rows of tuples (instructions and outputs). |
TrainingDataPro/botox-injections-before-and-after | 2023-09-25T14:21:13.000Z | [
"task_categories:image-classification",
"task_categories:image-to-image",
"language:en",
"license:cc-by-nc-nd-4.0",
"medical",
"code",
"region:us"
] | TrainingDataPro | The dataset consists of photos featuring the same individuals captured before and after
botox injections procedure. The dataset contains a diverse range of individuals with
various ages, ethnicities and genders.
The dataset is useful for evaluation of the effectiveness of botox injections for
different skin and face types, face recognition and reidentification tasks. It can be
utilised for biometric tasks , in beauty sphere, for medical purposes and e-commerce. | @InProceedings{huggingface:dataset,
title = {botox-injections-before-and-after},
author = {TrainingDataPro},
year = {2023}
} | null | 1 | 6 | ---
language:
- en
license: cc-by-nc-nd-4.0
task_categories:
- image-classification
- image-to-image
tags:
- medical
- code
dataset_info:
features:
- name: id
dtype: int32
- name: before
dtype: image
- name: after
dtype: image
splits:
- name: train
num_bytes: 38806781
num_examples: 23
download_size: 38824211
dataset_size: 38806781
---
# Botox Injections (Before & After)
The dataset consists of photos featuring the same individuals captured before and after botox injections procedure. The dataset contains a diverse range of individuals with various *ages, ethnicities and genders*.
The dataset is useful for evaluation of the effectiveness of botox injections for different skin and face types, face recognition and reidentification tasks. It can be utilised for biometric tasks , in beauty sphere, for medical purposes and e-commerce.
# Get the dataset
### This is just an example of the data
Leave a request on [**https://trainingdata.pro/data-market**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=botox-injections-before-and-after) to discuss your requirements, learn about the price and buy the dataset.
# Content
- **before**: includes images of people before botox injections
- **after**: includes images of people after botox injections. People are the same as in the previous folder, photos are identified by the same name
- **.csv file**: contains information about the dataset
### File with the extension .csv
includes the following information for each set of media files:
- **person**: id of the person,
- **before**: link to the photo before the injection,
- **after**: link to the photo after the injection
# Images of people with botox injections might be collected in accordance with your requirements.
## [**TrainingData**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=botox-injections-before-and-after) provides high-quality data annotation tailored to your needs
More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets**
TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets** |
ironchanchellor/Metallography_segmenter_Dataset_B | 2023-08-30T16:29:31.000Z | [
"region:us"
] | ironchanchellor | null | null | null | 0 | 6 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: pixel_values
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 180249364.0
num_examples: 874
- name: validation
num_bytes: 44152138.0
num_examples: 219
download_size: 223637073
dataset_size: 224401502.0
---
# Dataset Card for "Metallography_segmenter_Dataset_B"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
vikp/python_code_instructions_filtered | 2023-08-31T02:45:36.000Z | [
"region:us"
] | vikp | null | null | null | 0 | 6 | ---
dataset_info:
features:
- name: output
dtype: string
- name: instruction
dtype: string
- name: kind
dtype: string
splits:
- name: train
num_bytes: 313731517
num_examples: 170635
download_size: 160726948
dataset_size: 313731517
---
# Dataset Card for "code_filtered"
This includes data from [xlcost](https://huggingface.co/datasets/vikp/xlcost_filtered_2k), [evol instruct](https://huggingface.co/datasets/vikp/evol_instruct_code_filtered_39k), [code alpaca](https://huggingface.co/datasets/vikp/evol_codealpaca_filtered_87k), [code instructions](https://huggingface.co/datasets/vikp/code_instructions_filtered_7k), and [code search net](https://huggingface.co/datasets/vikp/code_search_net_filtered_34k). Data is filtered based on quality and learning value. |
AL49/lotr_paragraphs | 2023-09-01T13:21:01.000Z | [
"region:us"
] | AL49 | null | null | null | 0 | 6 | Entry not found |
KhalfounMehdi/dermatology_anomaly_detection_vit | 2023-09-02T01:21:29.000Z | [
"region:us"
] | KhalfounMehdi | null | null | null | 0 | 6 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': benign
'1': malignant
splits:
- name: train
num_bytes: 51521841.0
num_examples: 656
download_size: 51530132
dataset_size: 51521841.0
---
# Dataset Card for "dermatology_anomaly_detection_vit"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Tzzey/myabcm_dataset | 2023-09-04T18:13:20.000Z | [
"region:us"
] | Tzzey | null | null | null | 0 | 6 | Entry not found |
FinchResearch/TagaloGuanaco | 2023-09-03T19:21:13.000Z | [
"region:us"
] | FinchResearch | null | null | null | 0 | 6 | Entry not found |
boapps/kmdb_base | 2023-09-27T07:46:48.000Z | [
"size_categories:10K<n<100K",
"language:hu",
"region:us"
] | boapps | null | null | null | 0 | 6 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
- name: title
dtype: string
- name: description
dtype: string
- name: keywords
sequence: string
- name: url
dtype: string
- name: authors
sequence: string
- name: date
dtype: string
- name: kmonitor_description
dtype: string
- name: kmonitor_title
dtype: string
- name: kmonitor_tags
sequence: string
- name: kmonitor_institutes
sequence: string
- name: kmonitor_people
sequence: string
- name: kmonitor_places
sequence: string
- name: kmonitor_collections
sequence: string
splits:
- name: validation
num_bytes: 18870175.609970674
num_examples: 3397
- name: test
num_bytes: 9452772.627906976
num_examples: 1710
- name: train
num_bytes: 149446447.68875885
num_examples: 26534
download_size: 106591991
dataset_size: 177769395.92663652
language:
- hu
pretty_name: K-Monitor sajtóadatbázis
size_categories:
- 10K<n<100K
---
Forrás: https://adatbazis.k-monitor.hu/
## Használat
```python
from datasets import load_dataset
dataset = load_dataset('boapps/kmdb_base')
# írjuk ki egy cikk címét
print(dataset['train'][0]['title'])
```
## Oszlopok magyarázata
`text`: cikk törzse
`title`: hírportál által adott cím
`description`: hírportál által adott lead (kis bevezető/összefoglaló az elején)
`keywords`: hírportál címkék (nem mindig van és nem mindig értelmes)
`url`: cikk url-je
`authors`: cikk szerzői, címkékhez hasonlóan, nem minden esetben van meg
`date`: cikk megjelenésének ideje, változó pontossággal és ritkán értelmetlen
`kmonitor_description`: csak korrupciós cikkek esetében, k-monitor adatbázisában tárolt leírás (sokszor egyezik a rendes leírással)
`kmonitor_title`: ugyanez címmel
`kmonitor_tags`: ez a keywords-el ellentétben K-Monitoros önkéntesek általi címkézés, meghatározott "címke halmazból" (ebben vannak még helyszínek, amiket people-höz és institutes-hoz hasonlóan szét lehetne (kéne) szedni egy places oszlopba)
`institutes`: cikkben megjelenő intézmények, K-Monitoros gyűjtés
`people`: ugyanez személyekkel |
EgilKarlsen/AA_BERT_FT | 2023-09-04T07:31:16.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 6 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: '0'
dtype: float32
- name: '1'
dtype: float32
- name: '2'
dtype: float32
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splits:
- name: train
num_bytes: 80318765
num_examples: 26057
- name: test
num_bytes: 26774056
num_examples: 8686
download_size: 147053945
dataset_size: 107092821
---
# Dataset Card for "AA_BERT_FT"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EgilKarlsen/AA_RoBERTa_FT | 2023-09-04T07:33:50.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 6 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
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splits:
- name: train
num_bytes: 80318765
num_examples: 26057
- name: test
num_bytes: 26774056
num_examples: 8686
download_size: 147154828
dataset_size: 107092821
---
# Dataset Card for "AA_RoBERTa_FT"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EgilKarlsen/AA_DistilRoBERTa_FT | 2023-09-04T07:35:37.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 6 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
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splits:
- name: train
num_bytes: 80318765
num_examples: 26057
- name: test
num_bytes: 26774056
num_examples: 8686
download_size: 147154939
dataset_size: 107092821
---
# Dataset Card for "AA_DistilRoBERTa_FT"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EgilKarlsen/AA_GPT2_FT | 2023-09-04T07:38:06.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 6 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
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splits:
- name: train
num_bytes: 80318765
num_examples: 26057
- name: test
num_bytes: 26774056
num_examples: 8686
download_size: 147157938
dataset_size: 107092821
---
# Dataset Card for "AA_GPT2_FT"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EgilKarlsen/AA_GPTNEO_FT | 2023-09-04T08:04:31.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 6 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
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splits:
- name: train
num_bytes: 213730605
num_examples: 26057
- name: test
num_bytes: 71246376
num_examples: 8686
download_size: 392417877
dataset_size: 284976981
---
# Dataset Card for "AA_GPTNEO_FT"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EgilKarlsen/CSIC_BERT_FT | 2023-09-04T08:10:01.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 6 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
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splits:
- name: train
num_bytes: 115621182
num_examples: 37500
- name: test
num_bytes: 38540387
num_examples: 12500
download_size: 211874717
dataset_size: 154161569
---
# Dataset Card for "CSIC_BERT_FT"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EgilKarlsen/CSIC_RoBERTa_FT | 2023-09-04T08:15:08.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 6 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
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splits:
- name: train
num_bytes: 115621182
num_examples: 37500
- name: test
num_bytes: 38540387
num_examples: 12500
download_size: 211875916
dataset_size: 154161569
---
# Dataset Card for "CSIC_RoBERTa_FT"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EgilKarlsen/CSIC_DistilRoBERTa_FT | 2023-09-04T08:18:52.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 6 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
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splits:
- name: train
num_bytes: 115621182
num_examples: 37500
- name: test
num_bytes: 38540387
num_examples: 12500
download_size: 211876775
dataset_size: 154161569
---
# Dataset Card for "CSIC_DistilRoBERTa_FT"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EgilKarlsen/CSIC_GPT2_FT | 2023-09-04T08:24:23.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 6 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
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splits:
- name: train
num_bytes: 115621182
num_examples: 37500
- name: test
num_bytes: 38540387
num_examples: 12500
download_size: 211868111
dataset_size: 154161569
---
# Dataset Card for "CSIC_GPT2_FT"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EgilKarlsen/CSIC_GPTNEO_FT | 2023-09-04T13:05:11.000Z | [
"region:us"
] | EgilKarlsen | null | null | null | 0 | 6 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
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splits:
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num_bytes: 307621182
num_examples: 37500
- name: test
num_bytes: 102540387
num_examples: 12500
download_size: 141533040
dataset_size: 410161569
---
# Dataset Card for "CSIC_GPTNEO_FT"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ArimaKn/ArabicSemanticSimilairtyDataset | 2023-09-04T13:15:28.000Z | [
"license:openrail",
"region:us"
] | ArimaKn | null | null | null | 0 | 6 | ---
license: openrail
---
|
BadreddineHug/LayoutLMv3_dataset_5 | 2023-09-04T13:36:22.000Z | [
"region:us"
] | BadreddineHug | null | null | null | 0 | 6 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: id
dtype: string
- name: image
dtype: image
- name: bboxes
sequence:
sequence: int64
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': NumFa
'2': Fourniss
'3': DateFa
'4': TotalHT
'5': TVA
'6': TotalTTc
- name: tokens
sequence: string
splits:
- name: train
num_bytes: 20162086.43298969
num_examples: 77
- name: test
num_bytes: 5236905.56701031
num_examples: 20
download_size: 20802412
dataset_size: 25398992.0
---
# Dataset Card for "LayoutLMv3_dataset_5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
kuokxuen/marketing_dataset | 2023-09-05T01:52:22.000Z | [
"region:us"
] | kuokxuen | null | null | null | 1 | 6 | ---
dataset_info:
features:
- name: product
dtype: string
- name: description
dtype: string
- name: marketing_email
dtype: string
splits:
- name: train
num_bytes: 197836
num_examples: 100
download_size: 120464
dataset_size: 197836
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "marketing_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ift/handwriting_forms | 2023-09-06T14:13:04.000Z | [
"task_categories:feature-extraction",
"size_categories:1K<n<10K",
"language:en",
"license:openrail",
"region:us"
] | ift | null | null | null | 2 | 6 | ---
license: openrail
dataset_info:
features:
- name: image
dtype: image
- name: ground_truth
dtype: string
splits:
- name: train
num_bytes: 14177871.8
num_examples: 1400
- name: validation
num_bytes: 2021857
num_examples: 199
- name: test
num_bytes: 5084688
num_examples: 500
download_size: 20674979
dataset_size: 21284416.8
task_categories:
- feature-extraction
language:
- en
size_categories:
- 1K<n<10K
---
# 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
Sohrab Redjai Sani @srsani |
codecomplete/starcoderdata_0.001 | 2023-09-07T01:40:56.000Z | [
"region:us"
] | codecomplete | null | null | null | 0 | 6 | ---
dataset_info:
features:
- name: repo_name
dtype: string
- name: path
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 200047956
num_examples: 40254
download_size: 69120457
dataset_size: 200047956
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "starcoderdata_0.001"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
chuyin0321/perimeter-sp500 | 2023-09-07T08:34:53.000Z | [
"region:us"
] | chuyin0321 | null | null | null | 0 | 6 | ---
dataset_info:
features:
- name: symbol
dtype: string
- name: security
dtype: string
- name: gics_sector
dtype: string
- name: gics_sub_industry
dtype: string
splits:
- name: train
num_bytes: 35417
num_examples: 503
download_size: 15479
dataset_size: 35417
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "perimeter-sp500"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
EnzoZacharias/TrainSPSNew | 2023-09-07T11:06:53.000Z | [
"region:us"
] | EnzoZacharias | null | null | null | 0 | 6 | Entry not found |
nampdn-ai/mini-pubmed | 2023-09-08T07:58:09.000Z | [
"region:us"
] | nampdn-ai | null | null | null | 1 | 6 | Entry not found |
bitadin/bulletPoint-v3 | 2023-09-09T18:33:49.000Z | [
"region:us"
] | bitadin | null | null | null | 0 | 6 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: completion
dtype: string
- name: path
dtype: string
splits:
- name: train
num_bytes: 47612275
num_examples: 22000
download_size: 23404976
dataset_size: 47612275
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "bulletPoint-v3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Minglii/w | 2023-09-09T03:02:52.000Z | [
"region:us"
] | Minglii | null | null | null | 0 | 6 | ---
dataset_info:
features:
- name: data
struct:
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 407547286
num_examples: 143000
download_size: 195819107
dataset_size: 407547286
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "w"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
PROCESOS/id_reversoAntiguo | 2023-09-10T05:09:30.000Z | [
"license:c-uda",
"region:us"
] | PROCESOS | null | null | null | 0 | 6 | ---
license: c-uda
---
|
rombodawg/LimitlessCodeTraining_Guanaco_Format | 2023-09-29T04:23:00.000Z | [
"license:mit",
"region:us"
] | rombodawg | null | null | null | 0 | 6 | ---
license: mit
---
This is the LimitlessCodeTraining dataset converted to guanaco format. Enjoy
Original model card:
_________________
----- BREAK THROUGH YOUR LIMITS -----
_________________
LimitlessCodeTraining is the direct sequal to Megacodetraining that is now called Legacy_MegaCodeTraining200k.
This dataset is just over 646k lines of pure refined coding data.
It is the pinacle of open source code training. It is the combination of the filtered Megacode training dataset filtered by shahules786 (shoutout to him) and the bigcode commitpackft dataset I converted to alpaca format.
The dataset that were used to create this dataset are linked bellow:
- https://huggingface.co/datasets/rombodawg/Rombodawgs_commitpackft_Evolinstruct_Converted
- https://huggingface.co/datasets/shahules786/megacode-best |
strumber/letsModDatasetX5 | 2023-09-10T12:04:59.000Z | [
"region:us"
] | strumber | null | null | null | 0 | 6 | Entry not found |
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