id stringlengths 2 115 | lastModified stringlengths 24 24 | tags list | author stringlengths 2 42 ⌀ | description stringlengths 0 6.67k ⌀ | citation stringlengths 0 10.7k ⌀ | likes int64 0 3.66k | downloads int64 0 8.89M | created timestamp[us] | card stringlengths 11 977k | card_len int64 11 977k | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|
bgglue/bgglue | 2023-08-06T15:22:26.000Z | [
"task_categories:text-classification",
"task_categories:token-classification",
"task_categories:question-answering",
"task_categories:multiple-choice",
"task_ids:multiple-choice-qa",
"task_ids:named-entity-recognition",
"task_ids:natural-language-inference",
"task_ids:part-of-speech",
"task_ids:sent... | bgglue | The Bulgarian General Language Understanding Evaluation (bgGLUE) benchmark is a collection of resources for
training, evaluating, and analyzing natural language understanding systems in Bulgarian. | @inproceedings{hardalov-etal-2023-bgglue,
title = "{bgGLUE}: A Bulgarian General Language Understanding Evaluation Benchmark",
author = "Hardalov, Momchil and
Atanasova, Pepa and
Mihaylov, Todor and
Angelova, Galia and
Simov, Kiril and
Osenova, Petya and
Stoyanov, Ves and
Koychev, Ivan and
Nakov, Preslav and
Radev, Dragomir",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = july,
year = "2023",
address = "Online",
publisher = "Association for Computational Linguistics",
address = "Toronto, Canada",
url = "https://arxiv.org/abs/2306.02349"
} | 0 | 84 | 2023-07-08T10:43:00 | ---
task_categories:
- text-classification
- token-classification
- question-answering
- multiple-choice
language:
- bg
pretty_name: Bulgarian GLUE
size_categories:
- n<1K
- 1K<n<10K
- 10K<n<100K
- 100K<n<1M
license:
- mit
- cc-by-3.0
- cc-by-sa-4.0
- other
- cc-by-nc-4.0
- cc-by-nc-3.0
task_ids:
- multiple-choice-qa
- named-entity-recognition
- natural-language-inference
- part-of-speech
- sentiment-analysis
source_datasets:
- bsnlp
- wikiann
- exams
- ct21.t1
- fakenews
- crediblenews
- universal_dependencies
tags:
- check-worthiness-estimation
- fake-new-detection
- humor-detection
- regression
- ranking
---
# Dataset Card for "bgGLUE: A Bulgarian General Language Understanding Evaluation Benchmark"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://bgglue.github.io/](https://bgglue.github.io/)
- **Repository:** [https://github.com/bgGLUE](https://github.com/bgGLUE)
- **Paper:** [bgGLUE: A Bulgarian General Language Understanding Evaluation Benchmark](https://arxiv.org/abs/2306.02349)
- **Point of Contact:** [bulgarianglue [at] gmail [dot] com](mailto:bulgarianglue@gmail.com)
### Dataset Summary
bgGLUE (Bulgarian General Language Understanding Evaluation) is a benchmark for evaluating language models on Natural Language Understanding (NLU) tasks in Bulgarian. The benchmark includes NLU tasks targeting a variety of NLP problems (e.g., natural language inference, fact-checking, named entity recognition, sentiment analysis, question answering, etc.) and machine learning tasks (sequence labeling, document-level classification, and regression).
### Supported Tasks and Leaderboards
List of supported tasks: [Tasks](https://bgglue.github.io/tasks/).
Leaderboard: [bgGLUE Leaderboard](https://bgglue.github.io/leaderboard/).
### Languages
Bulgarian
## Dataset Structure
### Data Instances
<div id="container">
<table id="table-tasks" class="table table-striped table-bordered">
<thead>
<tr>
<th scope="col">Name</th>
<th scope="col">Task type</th>
<th scope="col">Identifier</th>
<th scope="col" data-toggle="tooltip" data-placement="top" title="Tooltip on right">Download</th>
<th scope="col">More Info</th>
<th scope="col">Metrics</th>
<th scope="col">Train / Val / Test</th>
</tr>
</thead>
<tbody>
<tr>
<td>Balto-Slavic NLP Shared Task</td>
<td>Named Entity Recognition</td>
<td>BSNLP</td>
<td class="text-center"><a href="https://github.com/bgGLUE/bgglue/raw/main/data/bsnlp.tar.gz" target="_blank" rel="noopener">URL</a> </td>
<td class="text-center"><a href="https://bgglue.github.io/tasks/task_info/bsnlp/">Info</a> </td>
<td>F1</td>
<td>724 / 182 / 301</td>
</tr>
<tr>
<td>CheckThat! (2021), Task 1A </td>
<td>Check-Worthiness Estimation</td>
<td>CT21.T1</td>
<td class="text-center"><a href="https://gitlab.com/checkthat_lab/clef2021-checkthat-lab/-/tree/master/task1" target="_blank" rel="noopener">URL</a> </td>
<td class="text-center"><a href="https://bgglue.github.io/tasks/task_info/ct21-t1/">Info</a> </td>
<td>Average Precision</td>
<td>2,995 / 350 / 357</td>
</tr>
<tr>
<td>Cinexio Movie Reviews</td>
<td>Sentiment Analysis</td>
<td>Cinexio</td>
<td class="text-center"><a href="https://github.com/bgGLUE/bgglue/raw/main/data/cinexio.tar.gz" target="_blank" rel="noopener">URL</a> </td>
<td class="text-center"><a href="https://bgglue.github.io/tasks/task_info/cinexio/">Info</a> </td>
<td>Pearson-Spearman Corr</td>
<td>8,155 / 811 / 861</td>
</tr>
<tr>
<td>Hack the News Datathon (2019)</td>
<td>Fake News Detection</td>
<td>Fake-N</td>
<td class="text-center"><a href="https://github.com/bgGLUE/bgglue/raw/main/data/fakenews.tar.gz" target="_blank" rel="noopener">URL</a> </td>
<td class="text-center"><a href="https://bgglue.github.io/tasks/task_info/fakenews/">Info</a> </td>
<td>Binary F1</td>
<td>1,990 / 221 / 701</td>
</tr>
<tr>
<td>In Search of Credible News</td>
<td>Humor Detection</td>
<td>Cred.-N</td>
<td class="text-center"><a href="https://forms.gle/Z7PYHMAvFvFusWT37" target="_blank" rel="noopener">URL</a> </td>
<td class="text-center"><a href="https://bgglue.github.io/tasks/task_info/crediblenews/">Info</a> </td>
<td>Binary F1</td>
<td>19,227 / 5,949 / 17,887</td>
</tr>
<tr>
<td>Multi-Subject High School Examinations Dataset</td>
<td>Multiple-choice QA</td>
<td>EXAMS</td>
<td class="text-center"><a href="https://github.com/bgGLUE/bgglue/raw/main/data/exams.tar.gz" target="_blank" rel="noopener">URL</a> </td>
<td class="text-center"><a href="https://bgglue.github.io/tasks/task_info/exams/">Info</a> </td>
<td>Accuracy</td>
<td>1,512 / 365 / 1,472</td>
</tr>
<tr>
<td>Universal Dependencies</td>
<td>Part-of-Speech Tagging</td>
<td>U.Dep</td>
<td class="text-center"><a href="https://universaldependencies.org/#bulgarian-treebanks" target="_blank" rel="noopener">URL</a> </td>
<td class="text-center"><a href="https://bgglue.github.io/tasks/task_info/udep/">Info</a> </td>
<td>F1</td>
<td>8,907 / 1,115 / 1,116</td>
</tr>
<tr>
<td>Cross-lingual Natural Language Inference</td>
<td>Natural Language Inference</td>
<td>XNLI</td>
<td class="text-center"><a href="https://github.com/facebookresearch/XNLI#download" target="_blank" rel="noopener">URL</a> </td>
<td class="text-center"><a href="https://bgglue.github.io/tasks/task_info/xnli/">Info</a> </td>
<td>Accuracy</td>
<td>392,702 / 5,010 / 2,490</td>
</tr>
<tr>
<td>Cross-lingual Name Tagging and Linking (PAN-X / WikiAnn)</td>
<td>Named Entity Recognition</td>
<td>PAN-X</td>
<td class="text-center"><a href="https://github.com/bgGLUE/bgglue/raw/main/data/wikiann_bg.tar.gz">URL</a> </td>
<td class="text-center"><a href="https://bgglue.github.io/tasks/task_info/wikiann/">Info</a> </td>
<td>F1</td>
<td>16,237 / 7,029 / 7,263 </td>
</tr>
</tbody>
</table>
</div>
## Dataset Creation
### Source Data
#### Initial Data Collection and Normalization
Here, we describe the pre-processing steps we took to prepare the datasets before including them in the bgGLUE benchmark. Our main goal was to ensure that the setup evaluated the language understanding abilities of the models in a principled way and in a diverse set of domains. Since all of the datasets were publicly available, we preserved the original setup as much as possible. Nevertheless, we found that some datasets contained duplicate examples across their train/dev/test splits, or that all of the splits came from the same domain, which may overestimate the model's performance. Hereby, \textit{we removed data leaks} and proposed new topic-based or temporal-based (i.e., timestamp-based) data splits where needed. We deduplicated the examples based on a complete word overlap in two pairs of normalized texts, i.e., lowercased, and excluding all stop words.
## Considerations for Using the Data
### Discussion of Biases
The datasets included in bgGLUE were annotated by human annotators, who could be subject to potential biases in their annotation process. Hence, the datasets in \benchmarkName could potentially be misused to develop models that make predictions that are unfair to individuals or groups. Therefore, we ask users of bgGLUE to be aware of such potential biases and risks of misuse. We note that any biases that might exist in the original resources gathered in this benchmark are unintentional and do not aim to cause harm.
### Other Known Limitations
#### Tasks in bgGLUE
The bgGLUE benchmark is comprised of nine challenging NLU tasks, including three token classification tasks, one ranking task and five text classification tasks. While we cover three different types of tasks in the benchmark, we are restricted by the available resources for Bulgarian, and thus we could not include some other NLP tasks, such as language generation. We also consider only NLP tasks and we do not include tasks with other/multiple modalities. Finally, some of the tasks are of similar nature, e.g., we include two datasets for NER and two for credibility/fake news classification.
### Domains in bgGLUE
The tasks included in bgGLUE span over multiple domains such as social media posts, Wikipedia, and news articles and can test both for short and long document understanding. However, each task is limited to one domain and the topics within the domain do not necessarily have full coverage of all possible topics. Moreover, some of the tasks have overlapping domains, e.g., the documents in both Cred.-N and Fake-N are news articles.
## Additional Information
### Licensing Information
The primary bgGLUE tasks are built on and derived from existing datasets.
We refer users to the original licenses accompanying each dataset.
For each dataset the license is listed on its ["Tasks" page](https://bgglue.github.io/tasks/) on the bgGLUE website.
### Citation Information
```
@inproceedings{hardalov-etal-2023-bgglue,
title = "bg{GLUE}: A {B}ulgarian General Language Understanding Evaluation Benchmark",
author = "Hardalov, Momchil and
Atanasova, Pepa and
Mihaylov, Todor and
Angelova, Galia and
Simov, Kiril and
Osenova, Petya and
Stoyanov, Veselin and
Koychev, Ivan and
Nakov, Preslav and
Radev, Dragomir",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.487",
pages = "8733--8759",
}
```
### Contributions
[List of bgGLUE contributors](https://bgglue.github.io/people/) | 11,572 | [
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YaHi/chata_rl_dataset | 2023-09-22T20:13:04.000Z | [
"region:us"
] | YaHi | null | null | 0 | 84 | 2023-09-20T02:12:29 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
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dtype: string
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splits:
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num_bytes: 2113014
num_examples: 1549
- name: test
num_bytes: 151721
num_examples: 104
download_size: 801956
dataset_size: 2264735
---
# Dataset Card for "chata_rl_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 657 | [
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phongmt184172/python_data_27k | 2023-10-05T02:30:02.000Z | [
"region:us"
] | phongmt184172 | null | null | 0 | 84 | 2023-10-05T02:29:17 | ---
dataset_info:
features:
- name: id
dtype: int64
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dtype: string
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splits:
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num_bytes: 39244063.17425801
num_examples: 19056
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num_examples: 4084
- name: val
num_bytes: 8410618.912870996
num_examples: 4084
download_size: 23588770
dataset_size: 56065301.0
---
# Dataset Card for "python_data_27k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 577 | [
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derek-thomas/dataset-creator-reddit-bestofredditorupdates | 2023-11-02T05:00:10.000Z | [
"region:us"
] | derek-thomas | null | null | 0 | 84 | 2023-10-25T10:40:45 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: content
dtype: string
- name: score
dtype: int64
splits:
- name: train
num_bytes: 57893042
num_examples: 9932
download_size: 33939424
dataset_size: 57893042
---
--- Generated Part of README Below ---
## Dataset Overview
The goal is to have an open dataset of `bestofredditorupdates` submissions. Im leveraging PRAW and the reddit API to get downloads.
There is a limit of 1000 in an API call and limited search functionality, so this is run every day to get new submissions.
## Creation Details
THis was created by [derek-thomas/dataset-creator-reddit](https://huggingface.co/spaces/derek-thomas/dataset-creator-reddit)
## Update Frequency
The dataset is updated daily with the most recent day being `2023-11-02` where we added `46` new rows.
## Licensing
[Reddit Licensing terms](https://www.redditinc.com/policies/data-api-terms) as accessed on October 25:
> The Content created with or submitted to our Services by Users (“User Content”) is owned by Users and not by Reddit. Subject to your complete and ongoing compliance with the Data API Terms, Reddit grants you a non-exclusive, non-transferable, non-sublicensable, and revocable license to copy and display the User Content using the Data API solely as necessary to develop, deploy, distribute, and run your App to your App Users. You may not modify the User Content except to format it for such display. You will comply with any requirements or restrictions imposed on usage of User Content by their respective owners, which may include "all rights reserved" notices, Creative Commons licenses, or other terms and conditions that may be agreed upon between you and the owners. Except as expressly permitted by this section, no other rights or licenses are granted or implied, including any right to use User Content for other purposes, such as for training a machine learning or AI model, without the express permission of rightsholders in the applicable User Content
My take is that you can't use this data for *training* without getting permission.
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Chun/dataset | 2021-08-24T08:16:33.000Z | [
"region:us"
] | Chun | null | null | 0 | 83 | 2022-03-02T23:29:22 | A translation dataset between english and traditional chinese
train : 101497 rows
val : 1000 rows
test : 1000 rows
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DrishtiSharma/hi_opus100_processed | 2022-02-09T18:34:07.000Z | [
"region:us"
] | DrishtiSharma | null | null | 0 | 83 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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Fraser/dream-coder | 2022-04-25T10:49:02.000Z | [
"language:en",
"license:mit",
"program-synthesis",
"region:us"
] | Fraser | null | null | 2 | 83 | 2022-03-02T23:29:22 | ---
language:
- en
thumbnail: "https://huggingface.co/datasets/Fraser/dream-coder/resolve/main/img.png"
tags:
- program-synthesis
license: "mit"
datasets:
- program-synthesis
---
# Program Synthesis Data
Generated program synthesis datasets used to train [dreamcoder](https://github.com/ellisk42/ec).
Currently just supports text & list data.
| 427 | [
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GEM/ART | 2022-10-24T13:01:25.000Z | [
"task_categories:other",
"annotations_creators:automatically-created",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"reasoning",
"arxiv:1908.05739",
"arxiv:1906.05317",
"region:us"
] | GEM | the Abductive Natural Language Generation Dataset from AI2 | @InProceedings{anli,
author = {Chandra, Bhagavatula and Ronan, Le Bras and Chaitanya, Malaviya and Keisuke, Sakaguchi and Ari, Holtzman
and Hannah, Rashkin and Doug, Downey and Scott, Wen-tau Yih and Yejin, Choi},
title = {Abductive Commonsense Reasoning},
year = {2020}
} | 3 | 83 | 2022-03-02T23:29:22 | ---
annotations_creators:
- automatically-created
language_creators:
- unknown
language:
- en
license:
- apache-2.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- other
task_ids: []
pretty_name: ART
tags:
- reasoning
---
# Dataset Card for GEM/ART
## Dataset Description
- **Homepage:** http://abductivecommonsense.xyz/
- **Repository:** https://storage.googleapis.com/ai2-mosaic/public/abductive-commonsense-reasoning-iclr2020/anlg.zip
- **Paper:** https://openreview.net/pdf?id=Byg1v1HKDB
- **Leaderboard:** N/A
- **Point of Contact:** Chandra Bhagavatulla
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/ART).
### Dataset Summary
Abductive reasoning is inference to the most plausible explanation. For example, if Jenny finds her house in a mess when she returns from work, and remembers that she left a window open, she can hypothesize that a thief broke into her house and caused the mess, as the most plausible explanation.
This data loader focuses on abductive NLG: a conditional English generation task for explaining given observations in natural language.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/ART')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/ART).
#### website
[Website](http://abductivecommonsense.xyz/)
#### paper
[OpenReview](https://openreview.net/pdf?id=Byg1v1HKDB)
#### authors
Chandra Bhagavatula (AI2), Ronan Le Bras (AI2), Chaitanya Malaviya (AI2), Keisuke Sakaguchi (AI2), Ari Holtzman (AI2, UW), Hannah Rashkin (AI2, UW), Doug Downey (AI2), Wen-tau Yih (AI2), Yejin Choi (AI2, UW)
## Dataset Overview
### Where to find the Data and its Documentation
#### Webpage
<!-- info: What is the webpage for the dataset (if it exists)? -->
<!-- scope: telescope -->
[Website](http://abductivecommonsense.xyz/)
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Google Storage](https://storage.googleapis.com/ai2-mosaic/public/abductive-commonsense-reasoning-iclr2020/anlg.zip)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[OpenReview](https://openreview.net/pdf?id=Byg1v1HKDB)
#### BibTex
<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
<!-- scope: microscope -->
```
@inproceedings{
Bhagavatula2020Abductive,
title={Abductive Commonsense Reasoning},
author={Chandra Bhagavatula and Ronan Le Bras and Chaitanya Malaviya and Keisuke Sakaguchi and Ari Holtzman and Hannah Rashkin and Doug Downey and Wen-tau Yih and Yejin Choi},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=Byg1v1HKDB}
}
```
#### Contact Name
<!-- quick -->
<!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
Chandra Bhagavatulla
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
chandrab@allenai.org
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
no
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
no
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`English`
#### Whose Language?
<!-- info: Whose language is in the dataset? -->
<!-- scope: periscope -->
Crowdworkers on the Amazon Mechanical Turk platform based in the U.S, Canada, U.K and Australia.
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
apache-2.0: Apache License 2.0
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
To study the viability of language-based abductive reasoning. Training and evaluating models to generate a plausible hypothesis to explain two given observations.
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Reasoning
### Credit
#### Curation Organization Type(s)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`industry`
#### Curation Organization(s)
<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
Allen Institute for AI
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Chandra Bhagavatula (AI2), Ronan Le Bras (AI2), Chaitanya Malaviya (AI2), Keisuke Sakaguchi (AI2), Ari Holtzman (AI2, UW), Hannah Rashkin (AI2, UW), Doug Downey (AI2), Wen-tau Yih (AI2), Yejin Choi (AI2, UW)
#### Funding
<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
Allen Institute for AI
#### Who added the Dataset to GEM?
<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
<!-- scope: microscope -->
Chandra Bhagavatula (AI2), Ronan LeBras (AI2), Aman Madaan (CMU), Nico Daheim (RWTH Aachen University)
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
- `observation_1`: A string describing an observation / event.
- `observation_2`: A string describing an observation / event.
- `label`: A string that plausibly explains why observation_1 and observation_2 might have happened.
#### How were labels chosen?
<!-- info: How were the labels chosen? -->
<!-- scope: microscope -->
Explanations were authored by crowdworkers on the Amazon Mechanical Turk platform using a custom template designed by the creators of the dataset.
#### Example Instance
<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
<!-- scope: periscope -->
```
{
'gem_id': 'GEM-ART-validation-0',
'observation_1': 'Stephen was at a party.',
'observation_2': 'He checked it but it was completely broken.',
'label': 'Stephen knocked over a vase while drunk.'
}
```
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
- `train`: Consists of training instances.
- `dev`: Consists of dev instances.
- `test`: Consists of test instances.
## Dataset in GEM
### Rationale for Inclusion in GEM
#### Why is the Dataset in GEM?
<!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? -->
<!-- scope: microscope -->
Abductive reasoning is a crucial capability of humans and ART is the first dataset curated to study language-based abductive reasoning.
#### Similar Datasets
<!-- info: Do other datasets for the high level task exist? -->
<!-- scope: telescope -->
no
#### Ability that the Dataset measures
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: periscope -->
Whether models can reason abductively about a given pair of observations.
### GEM-Specific Curation
#### Modificatied for GEM?
<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
<!-- scope: telescope -->
no
#### Additional Splits?
<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
no
### Getting Started with the Task
#### Pointers to Resources
<!-- info: Getting started with in-depth research on the task. Add relevant pointers to resources that researchers can consult when they want to get started digging deeper into the task. -->
<!-- scope: microscope -->
- [Paper](https://arxiv.org/abs/1908.05739)
- [Code](https://github.com/allenai/abductive-commonsense-reasoning)
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
Whether models can reason abductively about a given pair of observations.
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`BLEU`, `BERT-Score`, `ROUGE`
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
no
## Dataset Curation
### Original Curation
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
no
### Language Data
#### How was Language Data Obtained?
<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Crowdsourced`
#### Where was it crowdsourced?
<!-- info: If crowdsourced, where from? -->
<!-- scope: periscope -->
`Amazon Mechanical Turk`
#### Language Producers
<!-- info: What further information do we have on the language producers? -->
<!-- scope: microscope -->
Language producers were English speakers in U.S., Canada, U.K and Australia.
#### Topics Covered
<!-- info: Does the language in the dataset focus on specific topics? How would you describe them? -->
<!-- scope: periscope -->
No
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
validated by crowdworker
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
algorithmically
#### Filter Criteria
<!-- info: What were the selection criteria? -->
<!-- scope: microscope -->
Adversarial filtering algorithm as described in the [paper](https://arxiv.org/abs/1908.05739)
### Structured Annotations
#### Additional Annotations?
<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
automatically created
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
no
#### Annotation Values
<!-- info: Purpose and values for each annotation -->
<!-- scope: microscope -->
Each observation is associated with a list of COMET (https://arxiv.org/abs/1906.05317) inferences.
#### Any Quality Control?
<!-- info: Quality control measures? -->
<!-- scope: telescope -->
none
### Consent
#### Any Consent Policy?
<!-- info: Was there a consent policy involved when gathering the data? -->
<!-- scope: telescope -->
no
### Private Identifying Information (PII)
#### Contains PII?
<!-- quick -->
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
<!-- scope: telescope -->
no PII
#### Justification for no PII
<!-- info: Provide a justification for selecting `no PII` above. -->
<!-- scope: periscope -->
The dataset contains day-to-day events. It does not contain names, emails, addresses etc.
### Maintenance
#### Any Maintenance Plan?
<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
no
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
no
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
no
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
no
## Considerations for Using the Data
### PII Risks and Liability
#### Potential PII Risk
<!-- info: Considering your answers to the PII part of the Data Curation Section, describe any potential privacy to the data subjects and creators risks when using the dataset. -->
<!-- scope: microscope -->
None
### Licenses
#### Copyright Restrictions on the Dataset
<!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? -->
<!-- scope: periscope -->
`public domain`
#### Copyright Restrictions on the Language Data
<!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
<!-- scope: periscope -->
`public domain`
### Known Technical Limitations
| 13,478 | [
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GroNLP/ik-nlp-22_transqe | 2022-10-21T08:06:50.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language_creators:machine-generated",
"multilinguality:translation",
"size_categories:unknown",
"source_datasets:extended|esnli",
"language:en... | GroNLP | The e-SNLI dataset extends the Stanford Natural Language Inference Dataset to
include human-annotated natural language explanations of the entailment
relations. This version includes an automatic translation to Dutch and two quality estimation annotations
for each translated field. | @incollection{NIPS2018_8163,
title = {e-SNLI: Natural Language Inference with Natural Language Explanations},
author = {Camburu, Oana-Maria and Rockt\"{a}schel, Tim and Lukasiewicz, Thomas and Blunsom, Phil},
booktitle = {Advances in Neural Information Processing Systems 31},
editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
pages = {9539--9549},
year = {2018},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/8163-e-snli-natural-language-inference-with-natural-language-explanations.pdf}
} | 0 | 83 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
- machine-generated
language:
- en
- nl
license:
- apache-2.0
multilinguality:
- translation
size_categories:
- unknown
source_datasets:
- extended|esnli
task_categories:
- text-classification
task_ids:
- natural-language-inference
pretty_name: iknlp22-transqe
tags:
- quality-estimation
---
# Dataset Card for IK-NLP-22 Project 3: Translation Quality-driven Data Selection for Natural Language Inference
## Table of Contents
- [Dataset Card for IK-NLP-22 Project 3: Translation Quality-driven Data Selection for Natural Language Inference](#dataset-card-for-ik-nlp-22-project-3-translation-quality-driven-data-selection-for-natural-language-inference)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Splits](#data-splits)
- [Data Example](#data-example)
- [Dataset Creation](#dataset-creation)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Source:** [Github](https://github.com/OanaMariaCamburu/e-SNLI)
- **Point of Contact:** [Gabriele Sarti](mailto:ik-nlp-course@rug.nl)
### Dataset Summary
This dataset contains the full [e-SNLI](https://huggingface.co/datasets/esnli) dataset, automatically translated to Dutch using the [Helsinki-NLP/opus-mt-en-nl](https://huggingface.co/Helsinki-NLP/opus-mt-en-nl) neural machine translation model. The translation of each field has been anotated with two quality estimation scores using the referenceless version of the [COMET](https://github.com/Unbabel/COMET/) metric by Unbabel.
The intended usage of this corpus is restricted to the scope of final project for the 2022 edition of the Natural Language Processing course at the Information Science Master's Degree (IK) at the University of Groningen, taught by [Arianna Bisazza](https://research.rug.nl/en/persons/arianna-bisazza) and [Gabriele Sarti](https://research.rug.nl/en/persons/gabriele-sarti), with the assistance of [Anjali Nair](https://nl.linkedin.com/in/anjalinair012).
*The e-SNLI corpus was made freely available by the authors on Github. The present dataset was created for educational purposes, and is based on the original e-SNLI dataset by Camburu et al..All rights of the present contents are attributed to the original authors.*
### Languages
The language data of this corpus is in English (BCP-47 `en`) and Dutch (BCP-47 `nl`).
## Dataset Structure
### Data Instances
The dataset contains a single condiguration by default, named `plain_text`, with the three original splits `train`, `validation` and `test`. Every split contains the following fields:
| **Field** | **Description** |
|------------|-----------------------------|
|`premise_en`| The original English premise.|
|`premise_nl`| The premise automatically translated to Dutch.|
|`hypothesis_en`| The original English hypothesis.|
|`hypothesis_nl`| The hypothesis automatically translated to Dutch.|
|`label`| The label of the data instance (0 for entailment, 1 for neutral, 2 for contradiction).|
|`explanation_1_en`| The first explanation for the assigned label in English.|
|`explanation_1_nl`| The first explanation automatically translated to Dutch.|
|`explanation_2_en`| The second explanation for the assigned label in English.|
|`explanation_2_nl`| The second explanation automatically translated to Dutch.|
|`explanation_3_en`| The third explanation for the assigned label in English.|
|`explanation_3_nl`| The third explanation automatically translated to Dutch.|
|`da_premise`| The quality estimation produced by the `wmt20-comet-qe-da` model for the premise translation.|
|`da_hypothesis`| The quality estimation produced by the `wmt20-comet-qe-da` model for the hypothesis translation.|
|`da_explanation_1`| The quality estimation produced by the `wmt20-comet-qe-da` model for the first explanation translation.|
|`da_explanation_2`| The quality estimation produced by the `wmt20-comet-qe-da` model for the second explanation translation.|
|`da_explanation_3`| The quality estimation produced by the `wmt20-comet-qe-da` model for the third explanation translation.|
|`mqm_premise`| The quality estimation produced by the `wmt21-comet-qe-mqm` model for the premise translation.|
|`mqm_hypothesis`| The quality estimation produced by the `wmt21-comet-qe-mqm` model for the hypothesis translation.|
|`mqm_explanation_1`| The quality estimation produced by the `wmt21-comet-qe-mqm` model for the first explanation translation.|
|`mqm_explanation_2`| The quality estimation produced by the `wmt21-comet-qe-mqm` model for the second explanation translation.|
|`mqm_explanation_3`| The quality estimation produced by the `wmt21-comet-qe-mqm` model for the third explanation translation.|
Explanation 2 and 3 and related quality estimation scores are only present in the `validation` and `test` splits.
### Data Splits
| config| train | validation | test |
|------------:|---------|------------|------|
|`plain_text` | 549'367 | 9842 | 9824 |
For your analyses, use the amount of data that is the most reasonable for your computational setup. The more, the better.
### Data Example
The following is an example of entry 2000 taken from the `test` split:
```json
{
"premise_en": "A young woman wearing a yellow sweater and black pants is ice skating outdoors.",
"premise_nl": "Een jonge vrouw met een gele trui en zwarte broek schaatst buiten.",
"hypothesis_en": "a woman is practicing for the olympics",
"hypothesis_nl": "een vrouw oefent voor de Olympische Spelen",
"label": 1,
"explanation_1_en": "You can not infer it's for the Olympics.",
"explanation_1_nl": "Het is niet voor de Olympische Spelen.",
"explanation_2_en": "Just because a girl is skating outdoors does not mean she is practicing for the Olympics.",
"explanation_2_nl": "Alleen omdat een meisje buiten schaatst betekent niet dat ze oefent voor de Olympische Spelen.",
"explanation_3_en": "Ice skating doesn't imply practicing for the olympics.",
"explanation_3_nl": "Schaatsen betekent niet oefenen voor de Olympische Spelen.",
"da_premise": "0.6099",
"mqm_premise": "0.1298",
"da_hypothesis": "0.8504",
"mqm_hypothesis": "0.1521",
"da_explanation_1": "0.0001",
"mqm_explanation_1": "0.1237",
"da_explanation_2": "0.4017",
"mqm_explanation_2": "0.1467",
"da_explanation_3": "0.6069",
"mqm_explanation_3": "0.1389"
}
```
### Dataset Creation
The dataset was created through the following steps:
- Translating every field of the original e-SNLI corpus to Dutch using the [Helsinki-NLP/opus-mt-en-nl](https://huggingface.co/Helsinki-NLP/opus-mt-en-nl) neural machine translation model.
- Annotating the quality estimation of the translations with two referenceless versions of the [COMET](https://github.com/Unbabel/COMET/) metric by Unbabel.
## Additional Information
### Dataset Curators
For problems on this 🤗 Datasets version, please contact us at [ik-nlp-course@rug.nl](mailto:ik-nlp-course@rug.nl).
### Licensing Information
The dataset is licensed under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0.html).
### Citation Information
Please cite the authors if you use these corpora in your work:
```bibtex
@incollection{NIPS2018_8163,
title = {e-SNLI: Natural Language Inference with Natural Language Explanations},
author = {Camburu, Oana-Maria and Rockt\"{a}schel, Tim and Lukasiewicz, Thomas and Blunsom, Phil},
booktitle = {Advances in Neural Information Processing Systems 31},
editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
pages = {9539--9549},
year = {2018},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/8163-e-snli-natural-language-inference-with-natural-language-explanations.pdf}
}
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HenryAI/KerasCodeExamples.txt | 2021-12-15T15:57:06.000Z | [
"region:us"
] | HenryAI | null | null | 0 | 83 | 2022-03-02T23:29:22 | Keras Code Examples from https://keras.io/examples/ <br />
organized as .txt file for input to this HF tutorial: <br />
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Jack0508/vi-ko-TED-txt | 2021-11-07T18:49:04.000Z | [
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Langame/waiting-messages | 2021-12-19T08:56:56.000Z | [
"region:us"
] | Langame | null | null | 0 | 83 | 2022-03-02T23:29:22 | # Langame/waiting-messages
Generated using OpenAI GPT-3 davinci-codex based on random initial samples written by a human.
⚠️ The dataset has not been de-duplicated, so there may be duplicates. ⚠️ | 196 | [
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NTUYG/RAGTest | 2021-05-02T15:31:15.000Z | [
"region:us"
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NahedAbdelgaber/evaluating-student-writing | 2021-12-30T05:48:09.000Z | [
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PaulLerner/triviaqa_splits_for_viquae | 2022-02-22T13:58:30.000Z | [
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---
license: cc-by-3.0
---
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PlanTL-GOB-ES/cantemist-ner | 2022-11-18T12:08:17.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"multilinguality:monolingual",
"language:es",
"license:cc-by-4.0",
"biomedical",
"clinical",
"spanish",
"region:us"
] | PlanTL-GOB-ES | https://temu.bsc.es/cantemist/ | @inproceedings{miranda2020named,
title={Named entity recognition, concept normalization and clinical coding: Overview of the cantemist track for cancer text mining in spanish, corpus, guidelines, methods and results},
author={Miranda-Escalada, A and Farr{\'e}, E and Krallinger, M},
booktitle={Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2020), CEUR Workshop Proceedings},
year={2020}
} | 3 | 83 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language:
- es
tags:
- biomedical
- clinical
- spanish
multilinguality:
- monolingual
task_categories:
- token-classification
task_ids:
- named-entity-recognition
license:
- cc-by-4.0
---
# CANTEMIST
## Dataset Description
Manually classified collection of Spanish oncological clinical case reports.
- **Homepage:** [zenodo](https://zenodo.org/record/3978041)
- **Paper:** [Named Entity Recognition, Concept Normalization and Clinical Coding: Overview of the Cantemist Track for Cancer Text Mining in Spanish, Corpus, Guidelines, Methods and Results](https://www.researchgate.net/profile/Antonio-Miranda-Escalada-2/publication/352786464_Named_Entity_Recognition_Concept_Normalization_and_Clinical_Coding_Overview_of_the_Cantemist_Track_for_Cancer_Text_Mining_in_Spanish_Corpus_Guidelines_Methods_and_Results/links/60d98a3b458515d6fbe382d8/Named-Entity-Recognition-Concept-Normalization-and-Clinical-Coding-Overview-of-the-Cantemist-Track-for-Cancer-Text-Mining-in-Spanish-Corpus-Guidelines-Methods-and-Results.pdf)
- **Point of Contact:** encargo-pln-life@bsc.es
### Dataset Summary
Collection of 1301 oncological clinical case reports written in Spanish, with tumor morphology mentions manually annotated and mapped by clinical experts to a controlled terminology. Every tumor morphology mention is linked to an eCIE-O code (the Spanish equivalent of ICD-O).
The training subset contains 501 documents, the development subsets 500, and the test subset 300. The original dataset is distributed in [Brat](https://brat.nlplab.org/standoff.html) format.
This dataset was designed for the CANcer TExt Mining Shared Task, sponsored by [Plan-TL](https://plantl.mineco.gob.es/Paginas/index.aspx).
For further information, please visit [the official website](https://temu.bsc.es/cantemist/).
### Supported Tasks
Named Entity Recognition (NER)
### Languages
- Spanish (es)
### Directory Structure
* README.md
* cantemist.py
* train.conll
* dev.conll
* test.conll
## Dataset Structure
### Data Instances
Three four-column files, one for each split.
### Data Fields
Every file has 4 columns:
* 1st column: Word form or punctuation symbol
* 2nd column: Original BRAT file name
* 3rd column: Spans
* 4th column: IOB tag
#### Example
<pre>
El cc_onco101 662_664 O
informe cc_onco101 665_672 O
HP cc_onco101 673_675 O
es cc_onco101 676_678 O
compatible cc_onco101 679_689 O
con cc_onco101 690_693 O
adenocarcinoma cc_onco101 694_708 B-MORFOLOGIA_NEOPLASIA
moderadamente cc_onco101 709_722 I-MORFOLOGIA_NEOPLASIA
diferenciado cc_onco101 723_735 I-MORFOLOGIA_NEOPLASIA
que cc_onco101 736_739 O
afecta cc_onco101 740_746 O
a cc_onco101 747_748 O
grasa cc_onco101 749_754 O
peripancreática cc_onco101 755_770 O
sobrepasando cc_onco101 771_783 O
la cc_onco101 784_786 O
serosa cc_onco101 787_793 O
, cc_onco101 793_794 O
infiltración cc_onco101 795_807 O
perineural cc_onco101 808_818 O
. cc_onco101 818_819 O
</pre>
### Data Splits
| Split | Size |
| ------------- | ------------- |
| `train` | 19,397 |
| `dev` | 18,165 |
| `test` | 11,168 |
## Dataset Creation
### Curation Rationale
For compatibility with similar datasets in other languages, we followed as close as possible existing curation guidelines.
### Source Data
#### Initial Data Collection and Normalization
The selected clinical case reports are fairly similar to hospital health records. To increase the usefulness and practical relevance of the CANTEMIST corpus, we selected clinical cases affecting all genders and that comprised most ages (from children to the elderly) and of various complexity levels (solid tumors, hemato-oncological malignancies, neuroendocrine cancer...).
The CANTEMIST cases include clinical signs and symptoms, personal and family history, current illness, physical examination, complementary tests (blood tests, imaging, pathology), diagnosis, treatment (including adverse effects of chemotherapy), evolution and outcome.
#### Who are the source language producers?
Humans, there is no machine generated data.
### Annotations
#### Annotation process
The manual annotation of the Cantemist corpus was performed by clinical experts following the Cantemist guidelines (for more detail refer to this [paper](http://ceur-ws.org/Vol-2664/cantemist_overview.pdf)). These guidelines contain rules for annotating morphology neoplasms in Spanish oncology clinical cases, as well as for mapping these annotations to eCIE-O.
A medical doctor was regularly consulted by annotators (scientists with PhDs on cancer-related subjects) for the most difficult pathology expressions. This same doctor periodically checked a random selection of annotated clinical records and these annotations were compared and discussed with the annotators. To normalize a selection of very complex cases, MD specialists in pathology from one of the largest university hospitals in Spain were consulted.
#### Who are the annotators?
Clinical experts.
### Personal and Sensitive Information
No personal or sensitive information included.
## Considerations for Using the Data
### Social Impact of Dataset
This corpus contributes to the development of medical language models in Spanish.
### Discussion of Biases
Not applicable.
## Additional Information
### Dataset Curators
Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es).
For further information, send an email to (plantl-gob-es@bsc.es).
This work was funded by the [Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA)](https://avancedigital.mineco.gob.es/en-us/Paginas/index.aspx) within the framework of the [Plan-TL](https://plantl.mineco.gob.es/Paginas/index.aspx).
### Licensing information
This work is licensed under [CC Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/) License.
Copyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022)
### Citation Information
```bibtex
@article{cantemist,
title={Named Entity Recognition, Concept Normalization and Clinical Coding: Overview of the Cantemist Track for Cancer Text Mining in Spanish, Corpus, Guidelines, Methods and Results.},
author={Miranda-Escalada, Antonio and Farr{\'e}, Eul{\`a}lia and Krallinger, Martin},
journal={IberLEF@ SEPLN},
pages={303--323},
year={2020}
}
```
### Contributions
[N/A]
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Serhii/Custom_SQuAD | 2021-09-21T10:47:39.000Z | [
"region:us"
] | Serhii | Shellcode_IA32 is a dataset for shellcode generation from English intents. The shellcodes are compilable on Intel Architecture 32-bits. | @inproceedings{liguori-etal-2021-shellcode,
title = "{S}hellcode{\_}{IA}32: A Dataset for Automatic Shellcode Generation",
author = "Liguori, Pietro and
Al-Hossami, Erfan and
Cotroneo, Domenico and
Natella, Roberto and
Cukic, Bojan and
Shaikh, Samira",
booktitle = "Proceedings of the 1st Workshop on Natural Language Processing for Programming (NLP4Prog 2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.nlp4prog-1.7",
doi = "10.18653/v1/2021.nlp4prog-1.7",
pages = "58--64",
abstract = "We take the first step to address the task of automatically generating shellcodes, i.e., small pieces of code used as a payload in the exploitation of a software vulnerability, starting from natural language comments. We assemble and release a novel dataset (Shellcode{\_}IA32), consisting of challenging but common assembly instructions with their natural language descriptions. We experiment with standard methods in neural machine translation (NMT) to establish baseline performance levels on this task.",
} | 1 | 83 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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SoLID/shellcode_i_a32 | 2022-11-17T19:53:43.000Z | [
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"task_ids:language-modeling",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language_creators:found",
"multilinguality:translation",
"size_categories:unknown",
"source_datasets:original",
"language:code",
"language:en",
"licens... | SoLID | Shellcode_IA32 is a dataset for shellcode generation from English intents. The shellcodes are compilable on Intel Architecture 32-bits. | @inproceedings{liguori-etal-2021-shellcode,
title = "{S}hellcode{\_}{IA}32: A Dataset for Automatic Shellcode Generation",
author = "Liguori, Pietro and
Al-Hossami, Erfan and
Cotroneo, Domenico and
Natella, Roberto and
Cukic, Bojan and
Shaikh, Samira",
booktitle = "Proceedings of the 1st Workshop on Natural Language Processing for Programming (NLP4Prog 2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.nlp4prog-1.7",
doi = "10.18653/v1/2021.nlp4prog-1.7",
pages = "58--64",
abstract = "We take the first step to address the task of automatically generating shellcodes, i.e., small pieces of code used as a payload in the exploitation of a software vulnerability, starting from natural language comments. We assemble and release a novel dataset (Shellcode{\_}IA32), consisting of challenging but common assembly instructions with their natural language descriptions. We experiment with standard methods in neural machine translation (NMT) to establish baseline performance levels on this task.",
} | 4 | 83 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
- found
language:
- code
- en
license:
- gpl-3.0
multilinguality:
- translation
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text-generation
task_ids:
- language-modeling
paperswithcode_id: shellcode-ia32
---
# Shellcode_IA32
___Shellcode_IA32___ is a dataset containing _20_ years of shellcodes from a variety of sources is the largest collection of shellcodes in assembly available to date.
This dataset consists of 3,200 examples of instructions in assembly language for _IA-32_ (the 32-bit version of the x86 Intel Architecture) from publicly available security exploits. We collected assembly programs used to generate shellcode from [exploit-db](https://www.exploit-db.com/shellcodes?platform=linux_x86) and from [shell-storm](http://shell-storm.org/shellcode/).
We enriched the dataset by adding examples of assembly programs for the _IA-32_ architecture from popular tutorials and books. This allowed us to understand how different authors and assembly experts comment and, thus, how to deal with the ambiguity of natural language in this specific context. Our dataset consists of 10% of instructions collected from books and guidelines, and the rest from real shellcodes.
Our focus is on Linux, the most common OS for security-critical network services. Accordingly, we added assembly instructions written with _Netwide Assembler_ (NASM) for Linux.
Each line of _Shellcode\_IA32_ dataset represents a snippet - intent pair. The _snippet_ is a line or a combination of multiple lines of assembly code, built by following the NASM syntax. The _intent_ is a comment in the English language.
Further statistics on the dataset and a set of preliminary experiments performed with a neural machine translation (NMT) model are described in the following paper: [Shellcode_IA32: A Dataset for Automatic Shellcode Generation](https://arxiv.org/abs/2104.13100).
**Note**: This work was done in collaboration with the [DESSERT Lab](http://www.dessert.unina.it/).
The dataset is also hosted on the [DESSERT Lab Github](https://github.com/dessertlab/Shellcode_IA32).
Please consider citing our work:
```
@inproceedings{liguori-etal-2021-shellcode,
title = "{S}hellcode{\_}{IA}32: A Dataset for Automatic Shellcode Generation",
author = "Liguori, Pietro and
Al-Hossami, Erfan and
Cotroneo, Domenico and
Natella, Roberto and
Cukic, Bojan and
Shaikh, Samira",
booktitle = "Proceedings of the 1st Workshop on Natural Language Processing for Programming (NLP4Prog 2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.nlp4prog-1.7",
doi = "10.18653/v1/2021.nlp4prog-1.7",
pages = "58--64",
abstract = "We take the first step to address the task of automatically generating shellcodes, i.e., small pieces of code used as a payload in the exploitation of a software vulnerability, starting from natural language comments. We assemble and release a novel dataset (Shellcode{\_}IA32), consisting of challenging but common assembly instructions with their natural language descriptions. We experiment with standard methods in neural machine translation (NMT) to establish baseline performance levels on this task.",
}
```
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SocialGrep/the-2022-trucker-strike-on-reddit | 2022-07-01T18:00:49.000Z | [
"annotations_creators:lexyr",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
] | SocialGrep | null | null | 1 | 83 | 2022-03-02T23:29:22 | ---
annotations_creators:
- lexyr
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
paperswithcode_id: null
---
# Dataset Card for the-2022-trucker-strike-on-reddit
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://socialgrep.com/datasets](https://socialgrep.com/datasets/the-2022-trucker-strike-on-reddit?utm_source=huggingface&utm_medium=link&utm_campaign=the2022truckerstrikeonreddit)
- **Point of Contact:** [Website](https://socialgrep.com/contact?utm_source=huggingface&utm_medium=link&utm_campaign=the2022truckerstrikeonreddit)
### Dataset Summary
This corpus contains all the comments under the /r/Ottawa convoy megathreads.
Comments are annotated with their score.
### Languages
Mainly English.
## Dataset Structure
### Data Instances
A data point is a Reddit comment.
### Data Fields
- 'type': the type of the data point. Can be 'post' or 'comment'.
- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.
- 'subreddit.id': the base-36 Reddit ID of the data point's host subreddit. Unique.
- 'subreddit.name': the human-readable name of the data point's host subreddit.
- 'subreddit.nsfw': a boolean marking the data point's host subreddit as NSFW or not.
- 'created_utc': a UTC timestamp for the data point.
- 'permalink': a reference link to the data point on Reddit.
- 'score': score of the data point on Reddit.
- 'sentiment': the evaluated sentiment of the data point, if any.
- 'body': the text of the data point.
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
CC-BY v4.0
### Contributions
[Needs More Information] | 3,408 | [
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Tevatron/wikipedia-squad-corpus | 2021-09-23T02:17:59.000Z | [
"region:us"
] | Tevatron | null | @inproceedings{karpukhin-etal-2020-dense,
title = "Dense Passage Retrieval for Open-Domain Question Answering",
author = "Karpukhin, Vladimir and Oguz, Barlas and Min, Sewon and Lewis, Patrick and Wu, Ledell and Edunov, Sergey and Chen, Danqi and Yih, Wen-tau",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.550",
doi = "10.18653/v1/2020.emnlp-main.550",
pages = "6769--6781",
} | 1 | 83 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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TurkuNLP/register_mc4 | 2021-12-27T11:35:12.000Z | [
"region:us"
] | TurkuNLP | null | null | 0 | 83 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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VadorMazer/skyrimdialogstest | 2021-11-19T00:32:06.000Z | [
"region:us"
] | VadorMazer | null | null | 0 | 83 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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Wikidepia/IndoParaCrawl | 2021-04-13T10:22:22.000Z | [
"region:us"
] | Wikidepia | null | null | 2 | 83 | 2022-03-02T23:29:22 | # IndoParaCrawl
IndoParaCrawl is ParaCrawl v7.1 dataset bulk-translated to Indonesian using Google Translate.
Thanks HuggingFace for providing free storage for datasets <3.
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Yeva/arm-summary | 2023-02-09T08:03:13.000Z | [
"language:hy",
"region:us"
] | Yeva | null | null | 0 | 83 | 2022-03-02T23:29:22 | ---
language:
- hy
---
annotations_creators:
- other
language_creators:
- other
languages:
- hy-AM
licenses:
- unknown
multilinguality:
- monolingual
pretty_name: arm-sum
size_categories:
- unknown
source_datasets:
- original
task_categories:
- conditional-text-generation
task_ids:
- summarization | 298 | [
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abdusah/masc_dev | 2022-07-01T15:28:05.000Z | [
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language:ar",
"license:cc-by-nc-4.0",
"region:us"
] | abdusah | null | null | 0 | 83 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- ar
license:
- cc-by-nc-4.0
multilinguality: []
paperswithcode_id: []
pretty_name: 'MASC'
size_categories:
source_datasets: []
task_categories: []
task_ids: []
---
# Dataset Card for MASC: MASSIVE ARABIC SPEECH CORPUS
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://ieee-dataport.org/open-access/masc-massive-arabic-speech-corpus
- **Repository:**
- **Paper:** https://dx.doi.org/10.21227/e1qb-jv46
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This corpus is a dataset that contains 1,000 hours of speech sampled at 16~kHz and crawled from over 700 YouTube channels. MASC is multi-regional, multi-genre, and multi-dialect dataset that is intended to advance the research and development of Arabic speech technology with the special emphasis on Arabic speech recognition
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Multi-dialect Arabic
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
#### masc_dev
- speech
- sampling_rate
- target_text (label)
### Data Splits
#### masc_dev
- train: 100
- test: 40
## 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
Note: this is a small development set for testing.
### Dataset Curators
[More Information Needed]
### Licensing Information
CC 4.0
### Citation Information
[More Information Needed]
### Contributions
Mohammad Al-Fetyani, Muhammad Al-Barham, Gheith Abandah, Adham Alsharkawi, Maha Dawas, August 18, 2021, "MASC: Massive Arabic Speech Corpus", IEEE Dataport, doi: https://dx.doi.org/10.21227/e1qb-jv46.
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adalbertojunior/MININER | 2022-02-19T01:25:43.000Z | [
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albertvillanova/lm_en_dummy0 | 2022-02-02T09:09:43.000Z | [
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albertvillanova/lm_en_dummy1 | 2022-02-02T09:10:11.000Z | [
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albertvillanova/lm_en_dummy2 | 2022-02-02T08:51:44.000Z | [
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albertvillanova/lm_en_dummy3 | 2022-02-02T08:58:10.000Z | [
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albertvillanova/lm_en_dummy4 | 2022-02-03T06:58:10.000Z | [
"region:us"
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albertvillanova/tests-public-raw-jsonl | 2021-07-28T15:32:34.000Z | [
"region:us"
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albertvillanova/tmp-tests-zip | 2021-12-08T15:46:56.000Z | [
"region:us"
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aliabd/crowdsourced-calculator-demo | 2023-04-30T17:10:14.000Z | [
"region:us"
] | aliabd | null | null | 0 | 83 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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aliabd/hello-world | 2022-01-21T18:44:39.000Z | [
"region:us"
] | aliabd | null | null | 0 | 83 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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alperiox/autonlp-data-user-review-classification | 2022-10-25T09:07:13.000Z | [
"task_categories:text-classification",
"language:en",
"region:us"
] | alperiox | null | null | 0 | 83 | 2022-03-02T23:29:22 | ---
language:
- en
task_categories:
- text-classification
---
# AutoNLP Dataset for project: user-review-classification
## Table of content
- [Dataset Description](#dataset-description)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
## Dataset Descritpion
This dataset has been automatically processed by AutoNLP for project user-review-classification.
### Languages
The BCP-47 code for the dataset's language is en.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "awful",
"target": 3
},
{
"text": "it says you can only read three stories a month and yet everything i clicked on was blank and now it[...]",
"target": 2
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"target": "ClassLabel(num_classes=4, names=['CONTENT', 'INTERFACE', 'SUBSCRIPTION', 'USER_EXPERIENCE'], names_file=None, id=None)",
"text": "Value(dtype='string', id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 275 |
| valid | 71 |
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anuragshas/ha_cc100_processed | 2022-02-03T22:51:56.000Z | [
"region:us"
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corypaik/coda | 2022-10-20T16:57:23.000Z | [
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"arxiv:2110.08182",
"region:us"
] | corypaik | *The Color Dataset* (CoDa) is a probing dataset to evaluate the representation of visual properties in language models. CoDa consists of color distributions for 521 common objects, which are split into 3 groups: Single, Multi, and Any. | @misc{paik2021world,
title={The World of an Octopus: How Reporting Bias Influences a Language Model's Perception of Color},
author={Cory Paik and Stéphane Aroca-Ouellette and Alessandro Roncone and Katharina Kann},
year={2021},
eprint={2110.08182},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | 2 | 83 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- expert-generated
language:
- en
language_bcp47:
- en-US
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: CoDa
paperswithcode_id: coda
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-scoring
task_ids:
- text-scoring-other-distribution-prediction
---
# Dataset Card for CoDa
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [nala-cub/coda](https://github.com/nala-cub/coda)
- **Paper:** [The World of an Octopus: How Reporting Bias Influences a Language Model's Perception of Color](https://arxiv.org/abs/2110.08182)
- **Point of Contact:** [Cory Paik](cory.paik@colorado.edu)
### Dataset Summary
*The Color Dataset* (CoDa) is a probing dataset to evaluate the representation of visual properties in language models. CoDa consists of color distributions for 521 common objects, which are split into 3 groups. We denote these groups as Single, Multi, and Any, which represents the typical object of each group.
The default configuration of CoDa uses 10 CLIP-style templates (e.g. "A photo of a [object]"), and 10 cloze-style templates (e.g. "Everyone knows most [object] are
[color]." )
### Supported Tasks and Leaderboards
This version of the dataset consists of the filtered and templated examples as cloze style questions. See the [GitHub](https://github.com/nala-cub/coda) repo for the raw data (e.g. unfiltered annotations) as well as example usage with GPT-2, RoBERTa, ALBERT, and CLIP.
### Languages
The text in the dataset is in English. The associated BCP-47 code is `en-US`.
## Dataset Structure
### Data Instances
An example looks like this:
```json
{
"text": "All rulers are [MASK].",
"label": [
0.0181818176, 0.0363636352, 0.3077272773, 0.0181818176, 0.0363636352,
0.086363636, 0.0363636352, 0.0363636352, 0.0363636352, 0.086363636,
0.301363647
],
"template_group": 1,
"template_idx": 0,
"class_id": "/m/0hdln",
"display_name": "Ruler",
"object_group": 2,
"ngram": "ruler"
}
```
### Data Fields
- `text`: The templated example. What this is depends on the value of `template_group`.
- `template_group=0`: A CLIP style example. There are no `[MASK]` tokens in these examples.
- `template_group=1`: A cloze style example. Note that all templates have `[MASK]` as the last word, but in most cases, the period should be included.
- `label`: A list of probability values for the 11 colors. Note that these are sorted by the alphabetic order of the 11 colors (black, blue, brown, gray, green, orange, pink, purple, red, white, yellow).
- `template_group`: Type of template, `0` corresponds to A CLIP style template (`clip-imagenet`), and `1` corresponds to A cloze style templates (`text-masked`).
- `template_idx`: The index of the template out of all templates
- `class_id`: The Corresponding [OpenImages v6](https://storage.googleapis.com/openimages/web/index.html) `ClassID`.
- `display_name`: The Corresponding [OpenImages v6](https://storage.googleapis.com/openimages/web/index.html) `DisplayName`.
- `object_group`: Object Group, values correspond to `Single`, `Multi`, and `Any`.
- `ngram`: Corresponding n-gram used for lookups.
### Data Splits
Object Splits:
| Group | All | Train | Valid | Test |
| ------ | --- | ----- | ----- | ---- |
| Single | 198 | 118 | 39 | 41 |
| Multi | 208 | 124 | 41 | 43 |
| Any | 115 | 69 | 23 | 23 |
| Total | 521 | 311 | 103 | 107 |
Example Splits:
| Group | All | Train | Valid | Test |
| ------ | ----- | ----- | ----- | ---- |
| Single | 3946 | 2346 | 780 | 820 |
| Multi | 4146 | 2466 | 820 | 860 |
| Any | 2265 | 1352 | 460 | 453 |
| Total | 10357 | 6164 | 2060 | 2133 |
## 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
CoDa is licensed under the Apache 2.0 license.
### Citation Information
```
@misc{paik2021world,
title={The World of an Octopus: How Reporting Bias Influences a Language Model's Perception of Color},
author={Cory Paik and Stéphane Aroca-Ouellette and Alessandro Roncone and Katharina Kann},
year={2021},
eprint={2110.08182},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.
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damlab/HIV_V3_coreceptor | 2022-02-08T21:09:21.000Z | [
"region:us"
] | damlab | null | null | 0 | 83 | 2022-03-02T23:29:22 | # Dataset Description
## Dataset Summary
This dataset was derived from the Los Alamos National Laboratory HIV sequence (LANL) database.
It contains 2,935 HIV V3 loop protein sequences, which can interact with either CCR5 receptors on T-Cells or CXCR4 receptors on macrophages.
Supported Tasks and Leaderboards: None
Languages: English
## Dataset Structure
### Data Instances
Data Instances: Each column represents the protein amino acid sequence of the HIV V3 loop.
The ID field indicates the Genbank reference ID for future cross-referencing.
There are 2,935 total V3 sequences, with 91% being CCR5 tropic and 23% CXCR4 tropic.
Data Fields: ID, sequence, fold, CCR5, CXCR4
Data Splits: None
## Dataset Creation
Curation Rationale: This dataset was curated to train a model (HIV-BERT-V3) designed to predict whether an HIV V3 loop would be CCR5 or CXCR4 tropic.
Initial Data Collection and Normalization: Dataset was downloaded and curated on 12/20/2021.
## Considerations for Using the Data
Social Impact of Dataset: This dataset can be used to study the mechanism by which HIV V3 loops allow for entry into T-cells and macrophages.
Discussion of Biases: Due to the sampling nature of this database, it is predominantly composed of subtype B sequences from North America and Europe with only minor contributions of Subtype C, A, and D.
Currently, there was no effort made to balance the performance across these classes.
As such, one should consider refinement with additional sequences to perform well on non-B sequences.
## Additional Information:
- Dataset Curators: Will Dampier
- Citation Information: TBA
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davanstrien/kitten | 2022-01-21T18:05:04.000Z | [
"region:us"
] | davanstrien | null | null | 0 | 83 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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dcfidalgo/test | 2022-02-17T16:42:36.000Z | [
"region:us"
] | dcfidalgo | null | null | 0 | 83 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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DFKI-SLT/mobie | 2022-10-24T06:32:09.000Z | [
"task_categories:other",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:de",
"license:cc-by-4.0",
"structure-prediction",
"region:us"
] | DFKI-SLT | MobIE is a German-language dataset which is human-annotated with 20 coarse- and fine-grained entity types and entity linking information for geographically linkable entities. The dataset consists of 3,232 social media texts and traffic reports with 91K tokens, and contains 20.5K annotated entities, 13.1K of which are linked to a knowledge base. A subset of the dataset is human-annotated with seven mobility-related, n-ary relation types, while the remaining documents are annotated using a weakly-supervised labeling approach implemented with the Snorkel framework. The dataset combines annotations for NER, EL and RE, and thus can be used for joint and multi-task learning of these fundamental information extraction tasks. | @inproceedings{hennig-etal-2021-mobie,
title = "{M}ob{IE}: A {G}erman Dataset for Named Entity Recognition, Entity Linking and Relation Extraction in the Mobility Domain",
author = "Hennig, Leonhard and
Truong, Phuc Tran and
Gabryszak, Aleksandra",
booktitle = "Proceedings of the 17th Conference on Natural Language Processing (KONVENS 2021)",
month = "6--9 " # sep,
year = "2021",
address = {D{\"u}sseldorf, Germany},
publisher = "KONVENS 2021 Organizers",
url = "https://aclanthology.org/2021.konvens-1.22",
pages = "223--227",
} | 0 | 83 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- de
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- other
task_ids:
- named-entity-recognition
paperswithcode_id: mobie
pretty_name: MobIE
tags:
- structure-prediction
---
# Dataset Card for "MobIE"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/dfki-nlp/mobie](https://github.com/dfki-nlp/mobie)
- **Repository:** [https://github.com/dfki-nlp/mobie](https://github.com/dfki-nlp/mobie)
- **Paper:** [https://aclanthology.org/2021.konvens-1.22/](https://aclanthology.org/2021.konvens-1.22/)
- **Point of Contact:** See [https://github.com/dfki-nlp/mobie](https://github.com/dfki-nlp/mobie)
- **Size of downloaded dataset files:** 7.8 MB
- **Size of the generated dataset:** 1.9 MB
- **Total amount of disk used:** 9.7 MB
### Dataset Summary
This script is for loading the MobIE dataset from https://github.com/dfki-nlp/mobie.
MobIE is a German-language dataset which is human-annotated with 20 coarse- and fine-grained entity types and entity linking information for geographically linkable entities. The dataset consists of 3,232 social media texts and traffic reports with 91K tokens, and contains 20.5K annotated entities, 13.1K of which are linked to a knowledge base. A subset of the dataset is human-annotated with seven mobility-related, n-ary relation types, while the remaining documents are annotated using a weakly-supervised labeling approach implemented with the Snorkel framework. The dataset combines annotations for NER, EL and RE, and thus can be used for joint and multi-task learning of these fundamental information extraction tasks.
This version of the dataset loader provides NER tags only. NER tags use the `BIO` tagging scheme.
For more details see https://github.com/dfki-nlp/mobie and https://aclanthology.org/2021.konvens-1.22/.
### Supported Tasks and Leaderboards
- **Tasks:** Named Entity Recognition
- **Leaderboards:**
### Languages
German
## Dataset Structure
### Data Instances
- **Size of downloaded dataset files:** 7.8 MB
- **Size of the generated dataset:** 1.9 MB
- **Total amount of disk used:** 9.7 MB
An example of 'train' looks as follows.
```json
{
'id': 'http://www.ndr.de/nachrichten/verkehr/index.html#2@2016-05-04T21:02:14.000+02:00',
'tokens': ['Vorsicht', 'bitte', 'auf', 'der', 'A28', 'Leer', 'Richtung', 'Oldenburg', 'zwischen', 'Zwischenahner', 'Meer', 'und', 'Neuenkruge', 'liegen', 'Gegenstände', '!'],
'ner_tags': [0, 0, 0, 0, 19, 13, 0, 13, 0, 11, 12, 0, 11, 0, 0, 0]
}
```
### Data Fields
The data fields are the same among all splits.
- `id`: a `string` feature.
- `tokens`: a `list` of `string` features.
- `ner_tags`: a `list` of classification labels, with possible values including `O` (0), `B-date` (1), `I-date` (2), `B-disaster-type` (3), `I-disaster-type` (4), ...
### Data Splits
| | Train | Dev | Test |
| ----- | ------ | ----- | ---- |
| MobIE | 4785 | 1082 | 1210 |
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[CC BY-SA 4.0 license](https://creativecommons.org/licenses/by-sa/4.0/)
### Citation Information
```
@inproceedings{hennig-etal-2021-mobie,
title = "{M}ob{IE}: A {G}erman Dataset for Named Entity Recognition, Entity Linking and Relation Extraction in the Mobility Domain",
author = "Hennig, Leonhard and
Truong, Phuc Tran and
Gabryszak, Aleksandra",
booktitle = "Proceedings of the 17th Conference on Natural Language Processing (KONVENS 2021)",
month = "6--9 " # sep,
year = "2021",
address = {D{\"u}sseldorf, Germany},
publisher = "KONVENS 2021 Organizers",
url = "https://aclanthology.org/2021.konvens-1.22",
pages = "223--227",
}
```
### Contributions
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ejjaffe/onion_headlines_2_sources | 2021-10-01T16:54:29.000Z | [
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elonmuskceo/wordle | 2022-01-14T12:06:47.000Z | [
"region:us"
] | elonmuskceo | null | null | 1 | 83 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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evageon/IADD | 2022-01-29T11:16:17.000Z | [
"license:cc-by-4.0",
"region:us"
] | evageon | null | null | 0 | 83 | 2022-03-02T23:29:22 | ---
license: cc-by-4.0
---
# IADD
IADD is an Integrated Dataset for Arabic Dialect iDentification Dataset. It contains 136,317 texts representing 5 regions (Maghrebi (MGH) , Levantine (LEV), Egypt (EGY) , Iraq (IRQ) and Gulf (GLF)) and 9 countries (Algeria, Morocco, Tunisia, Palestine, Jordan, Syria, Lebanon, Egypt and Iraq).
IADD is created from the combination of subsets of five corpora: DART, SHAMI, TSAC, PADIC and AOC. The Dialectal ARabic Tweets dataset (DART) [1] has about 25,000 tweets that are annotated via crowdsourcing while the SHAMI dataset [2] consists of 117,805 sentences and covers levantine dialects spoken in Palestine, Jordan, Lebanon and Syria. TSAC [3] is a Tunisian dialect corpus of 17,000 comments collected mainly from Tunisian Facebook pages. Parallel Arabic Dialect Corpus (PADIC) [4] is made of sentences transcribed from recordings or translated from MSA. Finally, the Arabic Online Commentary (AOC) dataset [5] is based on reader commentary from the online versions of three Arabic newspapers, and it consists of 1.4M comments.
IADD is stored in a JSON-like format with the following keys:
- Sentence: contains the sentence/ text;
- Region: stores the corresponding dialectal region (MGH, LEV, EGY, IRQ, GLF or general);
- Country: specifies the corresponding country, if available (MAR, TUN, DZ, EGY, IRQ, SYR, JOR, PSE, LBN);
- DataSource: indicates the source of the data (PADIC, DART, AOC, SHAMI or TSAC).
[1] Alsarsour, I., Mohamed, E., Suwaileh, R., & Elsayed, T. (2018, May). Dart: A large dataset of dialectal arabic tweets. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018).
[2] Abu Kwaik, K., Saad, M. K., Chatzikyriakidis, S., & Dobnik, S. (2018). Shami: A corpus of levantine arabic dialects. In Proceedings of the eleventh international conference on language resources and evaluation (LREC 2018).
[3] Mdhaffar, S., Bougares, F., Esteve, Y., & Hadrich-Belguith, L. (2017, April). Sentiment analysis of tunisian dialects: Linguistic ressources and experiments. In Third Arabic Natural Language Processing Workshop (WANLP) (pp. 55-61).
[4] Meftouh, K., Harrat, S., Jamoussi, S., Abbas, M., & Smaili, K. (2015, October). Machine translation experiments on PADIC: A parallel Arabic dialect corpus. In The 29th Pacific Asia conference on language, information and computation.
[5] Zaidan, O., & Callison-Burch, C. (2011, June). The arabic online commentary dataset: an annotated dataset of informal arabic with high dialectal content. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (pp. 37-41).
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flax-community/conceptual-12m-mbart-50-multilingual | 2021-07-21T05:25:46.000Z | [
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flax-community/conceptual-12m-multilingual-marian-128 | 2021-07-29T15:49:32.000Z | [
"region:us"
] | flax-community | null | null | 0 | 83 | 2022-03-02T23:29:22 | This dataset is created from subset of [Conceptual Captions](https://ai.google.com/research/ConceptualCaptions/). The original dataset has 12M captions but this dataset has around 10M image, caption pairs in different languages with 2.5M unique images. This dataset has captions translated from English to Spanish, German, French using language specific English to [Marian](https://huggingface.co/Helsinki-NLP) models (with sequence length 128). Data distribution is following:
`train_file_marian_final.tsv`: 10002432 captions (2500608 captions of English, German, Spanish, French each)
<br />
`val_file_marian_final.tsv`: 102400 captions (25600 captions of English, German, Spanish, French each) | 698 | [
[
-0.0226593017578125,
-0.0276947021484375,
0.0263824462890625,
0.040924072265625,
-0.046051025390625,
-0.0016031265258789062,
-0.01245880126953125,
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0.0296630859375,
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flax-sentence-embeddings/paws-jsonl | 2021-07-02T10:19:03.000Z | [
"region:us"
] | flax-sentence-embeddings | null | null | 0 | 83 | 2022-03-02T23:29:22 | # Introduction
This dataset is a jsonl format for PAWS dataset from: https://github.com/google-research-datasets/paws. It only contains the `PAWS-Wiki Labeled (Final)` and
`PAWS-Wiki Labeled (Swap-only)` training sections of the original PAWS dataset. Duplicates data are removed.
Each line contains a dict in the following format:
`{"guid": <id>, "texts": [anchor, positive]}` or
`{"guid": <id>, "texts": [anchor, positive, negative]}`
positives_negatives.jsonl.gz: 24,723
positives_only.jsonl.gz: 13,487
**Total**: 38,210
## Dataset summary
[**PAWS: Paraphrase Adversaries from Word Scrambling**](https://github.com/google-research-datasets/paws)
This dataset contains 108,463 human-labeled and 656k noisily labeled pairs that feature the importance of modeling structure, context, and word order information for the problem of paraphrase identification. The dataset has two subsets, one based on Wikipedia and the other one based on the Quora Question Pairs (QQP) dataset. | 985 | [
[
-0.0184173583984375,
-0.045806884765625,
0.0234527587890625,
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0.04437255859375,
0.047332763671875,
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-0.054412841796875,
-0.041229248046875,
... |
flexthink/ljspeech | 2022-02-06T00:09:16.000Z | [
"region:us"
] | flexthink | This is a public domain speech dataset consisting of 13,100 short audio
clips of a single speaker reading passages from 7 non-fiction books. A
transcription is provided for each clip. Clips vary in length from 1 to 10
seconds and have a total length of approximately 24 hours. | null | 1 | 83 | 2022-03-02T23:29:22 | # The LJ Speech Dataset
Version 1.0
July 5, 2017
https://keithito.com/LJ-Speech-Dataset
# Overview
This is a public domain speech dataset consisting of 13,100 short audio clips
of a single speaker reading passages from 7 non-fiction books. A transcription
is provided for each clip. Clips vary in length from 1 to 10 seconds and have
a total length of approximately 24 hours.
The texts were published between 1884 and 1964, and are in the public domain.
The audio was recorded in 2016-17 by the LibriVox project and is also in the
public domain.
The following files provide raw lavels for the train/validation/test split
* train.txt
* valid.txt
* test.txt
Friendly metadata with the split is provided in the following files:
* ljspeech_train.json
* ljspeech_test.json
* ljspeech_valid.json
The JSON files are formatted as follows:
```json
{
"<sample-id>": {
"char_raw": "<label text (raw)>",
"char": "<label text (preprocessed)",
"phn": "<experimental phoneme annotation obtained using a G2P model",
"wav": "<relative path to the file"
}
}
```
The dataset is also usable as a HuggingFace Arrow dataset:
https://huggingface.co/docs/datasets/
# FILE FORMAT
Original metadata is provided in metadata.csv. This file consists of one record per line, delimited by the pipe character (0x7c). The fields are:
1. ID: this is the name of the corresponding .wav file
2. Transcription: words spoken by the reader (UTF-8)
3. Normalized Transcription: transcription with numbers, ordinals, and
monetary units expanded into full words (UTF-8).
Each audio file is a single-channel 16-bit PCM WAV with a sample rate of
22050 Hz.
## Statistics
Total Clips 13,100
Total Words 225,715
Total Characters 1,308,674
Total Duration 23:55:17
Mean Clip Duration 6.57 sec
Min Clip Duration 1.11 sec
Max Clip Duration 10.10 sec
Mean Words per Clip 17.23
Distinct Words 13,821
## Miscellaneous
The audio clips range in length from approximately 1 second to 10 seconds.
They were segmented automatically based on silences in the recording. Clip
boundaries generally align with sentence or clause boundaries, but not always.
The text was matched to the audio manually, and a QA pass was done to ensure
that the text accurately matched the words spoken in the audio.
The original LibriVox recordings were distributed as 128 kbps MP3 files. As a
result, they may contain artifacts introduced by the MP3 encoding.
The following abbreviations appear in the text. They may be expanded as
follows:
Abbreviation Expansion
--------------------------
Mr. Mister
Mrs. Misess (*)
Dr. Doctor
No. Number
St. Saint
Co. Company
Jr. Junior
Maj. Major
Gen. General
Drs. Doctors
Rev. Reverend
Lt. Lieutenant
Hon. Honorable
Sgt. Sergeant
Capt. Captain
Esq. Esquire
Ltd. Limited
Col. Colonel
Ft. Fort
* there's no standard expansion of "Mrs."
19 of the transcriptions contain non-ASCII characters (for example, LJ016-0257
contains "raison d'être").
For more information or to report errors, please email kito@kito.us.
LICENSE
This dataset is in the public domain in the USA (and likely other countries as
well). There are no restrictions on its use. For more information, please see:
https://librivox.org/pages/public-domain.
CHANGELOG
* 1.0 (July 8, 2017):
Initial release
* 1.1 (Feb 19, 2018):
Version 1.0 included 30 .wav files with no corresponding annotations in
metadata.csv. These have been removed in version 1.1. Thanks to Rafael Valle
for spotting this.
CREDITS
This dataset consists of excerpts from the following works:
* Morris, William, et al. Arts and Crafts Essays. 1893.
* Griffiths, Arthur. The Chronicles of Newgate, Vol. 2. 1884.
* Roosevelt, Franklin D. The Fireside Chats of Franklin Delano Roosevelt.
1933-42.
* Harland, Marion. Marion Harland's Cookery for Beginners. 1893.
* Rolt-Wheeler, Francis. The Science - History of the Universe, Vol. 5:
Biology. 1910.
* Banks, Edgar J. The Seven Wonders of the Ancient World. 1916.
* President's Commission on the Assassination of President Kennedy. Report
of the President's Commission on the Assassination of President Kennedy.
1964.
Recordings by Linda Johnson. Alignment and annotation by Keith Ito. All text,
audio, and annotations are in the public domain.
There's no requirement to cite this work, but if you'd like to do so, you can
link to: https://keithito.com/LJ-Speech-Dataset
or use the following:
@misc{ljspeech17,
author = {Keith Ito},
title = {The LJ Speech Dataset},
howpublished = {\url{https://keithito.com/LJ-Speech-Dataset/}},
year = 2017
}
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-0.006702423095703125,
-0.016357421875,
-0.04010009765625,
0.03277587890625,
0.06805419921875,
-0.0384521484375,
-0.047882080078125,
-0.03411865234375,
0.0006... |
frtna/sabahaKKarsi | 2022-01-04T07:35:57.000Z | [
"region:us"
] | frtna | null | null | 0 | 83 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.0213775634765625,
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0.016998291015625,
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-0.0604248046875,
0.037... |
geninhu/vi_vivos-cv-tts-fpt_processed | 2022-01-29T03:30:35.000Z | [
"region:us"
] | geninhu | null | null | 0 | 83 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
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0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060394287109375,
0.0379... |
ghomasHudson/character_id | 2022-01-13T23:29:38.000Z | [
"region:us"
] | ghomasHudson | The character types identification dataset consists of movie
scripts annotated with character archetypes (Hero, Villain, Mentor, etc.). | \ | 0 | 83 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
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0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060455322265625,
0.03793334... |
gj1997/trial | 2022-02-23T05:29:09.000Z | [
"region:us"
] | gj1997 | null | null | 0 | 83 | 2022-03-02T23:29:22 | Entry not found | 15 | [
[
-0.02142333984375,
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0.057220458984375,
0.0288238525390625,
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0.0513916015625,
0.016998291015625,
-0.052093505859375,
-0.014984130859375,
-0.060455322265625,
0.03793334... |
huggingartists/aimer | 2022-10-25T09:22:51.000Z | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
] | huggingartists | This dataset is designed to generate lyrics with HuggingArtists. | @InProceedings{huggingartists:dataset,
title = {Lyrics dataset},
author={Aleksey Korshuk
},
year={2021}
} | 0 | 83 | 2022-03-02T23:29:22 | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/aimer"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.237926 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/123a0b2ef09a25207b610c5bd7b21d0f.1000x1000x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/aimer">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Aimer</div>
<a href="https://genius.com/artists/aimer">
<div style="text-align: center; font-size: 14px;">@aimer</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/aimer).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/aimer")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|171| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/aimer")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
| 7,140 | [
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huggingartists/ariana-grande | 2022-10-25T09:23:36.000Z | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
] | huggingartists | This dataset is designed to generate lyrics with HuggingArtists. | @InProceedings{huggingartists:dataset,
title = {Lyrics dataset},
author={Aleksey Korshuk
},
year={2021}
} | 0 | 83 | 2022-03-02T23:29:22 | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/ariana-grande"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.997954 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/d36a47955ac0ddb12748c5e7c2bd4b4b.640x640x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/ariana-grande">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Ariana Grande</div>
<a href="https://genius.com/artists/ariana-grande">
<div style="text-align: center; font-size: 14px;">@ariana-grande</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/ariana-grande).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/ariana-grande")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|596| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/ariana-grande")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
| 7,202 | [
[
-0.045074462890625,
-0.041351318359375,
0.0029048919677734375,
0.0238800048828125,
-0.0138397216796875,
-0.0032482147216796875,
-0.022796630859375,
-0.0338134765625,
0.0631103515625,
0.0280609130859375,
-0.06890869140625,
-0.060882568359375,
-0.04486083984375,
... |
huggingartists/ghostmane | 2022-10-25T09:30:32.000Z | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
] | huggingartists | This dataset is designed to generate lyrics with HuggingArtists. | @InProceedings{huggingartists:dataset,
title = {Lyrics dataset},
author={Aleksey Korshuk
},
year={2021}
} | 0 | 83 | 2022-03-02T23:29:22 | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/ghostmane"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.027776 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://assets.genius.com/images/default_avatar_300.png?1631290285')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/ghostmane">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Ghostmane</div>
<a href="https://genius.com/artists/ghostmane">
<div style="text-align: center; font-size: 14px;">@ghostmane</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/ghostmane).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/ghostmane")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|2| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/ghostmane")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
| 7,162 | [
[
-0.048095703125,
-0.037811279296875,
0.0111083984375,
0.0162200927734375,
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0.002532958984375,
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0.0684814453125,
0.0273284912109375,
-0.06695556640625,
-0.061553955078125,
-0.04205322265625,
0.00930786... |
huggingartists/kojey-radical | 2022-10-25T09:34:00.000Z | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
] | huggingartists | This dataset is designed to generate lyrics with HuggingArtists. | @InProceedings{huggingartists:dataset,
title = {Lyrics dataset},
author={Aleksey Korshuk
},
year={2021}
} | 1 | 83 | 2022-03-02T23:29:22 | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/kojey-radical"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.317423 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/69984b683bf9f7d43b1580896174bf9f.673x673x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/kojey-radical">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Kojey Radical</div>
<a href="https://genius.com/artists/kojey-radical">
<div style="text-align: center; font-size: 14px;">@kojey-radical</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/kojey-radical).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/kojey-radical")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|138| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/kojey-radical")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
| 7,202 | [
[
-0.04705810546875,
-0.0400390625,
0.0057373046875,
0.0197296142578125,
-0.0178680419921875,
0.003387451171875,
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0.06195068359375,
0.0271148681640625,
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-0.0399169921875,
0.00939178466796... |
huggingartists/miyagi | 2022-10-25T09:39:13.000Z | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
] | huggingartists | This dataset is designed to generate lyrics with HuggingArtists. | @InProceedings{huggingartists:dataset,
title = {Lyrics dataset},
author={Aleksey Korshuk
},
year={2021}
} | 0 | 83 | 2022-03-02T23:29:22 | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/miyagi"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.536065 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/10795217955d95e2543993f8e83fe5c8.960x960x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/miyagi">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">MiyaGi</div>
<a href="https://genius.com/artists/miyagi">
<div style="text-align: center; font-size: 14px;">@miyagi</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/miyagi).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/miyagi")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|147| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/miyagi")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
| 7,146 | [
[
-0.04833984375,
-0.03778076171875,
0.0025196075439453125,
0.0211334228515625,
-0.0196075439453125,
-0.0031414031982421875,
-0.023345947265625,
-0.034942626953125,
0.06488037109375,
0.02471923828125,
-0.07049560546875,
-0.060089111328125,
-0.043121337890625,
... |
huggingartists/slava-kpss | 2022-10-25T09:44:55.000Z | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
] | huggingartists | This dataset is designed to generate lyrics with HuggingArtists. | @InProceedings{huggingartists:dataset,
title = {Lyrics dataset},
author={Aleksey Korshuk
},
year={2021}
} | 1 | 83 | 2022-03-02T23:29:22 | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/slava-kpss"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 3.88329 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/e63e3a804916ed71bf2941ac4e190063.847x847x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/slava-kpss">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Слава КПСС (Slava KPSS)</div>
<a href="https://genius.com/artists/slava-kpss">
<div style="text-align: center; font-size: 14px;">@slava-kpss</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/slava-kpss).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/slava-kpss")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|897| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/slava-kpss")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
| 7,190 | [
[
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0.0670166015625,
0.0242462158203125,
-0.0645751953125,
-0.05780029296875,
-0.04443359375,
0.006... |
huggingartists/sugar-ray | 2022-10-25T09:45:22.000Z | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
] | huggingartists | This dataset is designed to generate lyrics with HuggingArtists. | @InProceedings{huggingartists:dataset,
title = {Lyrics dataset},
author={Aleksey Korshuk
},
year={2021}
} | 0 | 83 | 2022-03-02T23:29:22 | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/sugar-ray"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.164888 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/8b5c8fe74f6176047b2b5681e0e0e2d4.273x273x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/sugar-ray">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Sugar Ray</div>
<a href="https://genius.com/artists/sugar-ray">
<div style="text-align: center; font-size: 14px;">@sugar-ray</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/sugar-ray).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/sugar-ray")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|117| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/sugar-ray")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
| 6,713 | [
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0.02532958984375,
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0.... |
huggingartists/sum-41 | 2022-10-25T09:45:34.000Z | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
] | huggingartists | This dataset is designed to generate lyrics with HuggingArtists. | @InProceedings{huggingartists:dataset,
title = {Lyrics dataset},
author={Aleksey Korshuk
},
year={2021}
} | 0 | 83 | 2022-03-02T23:29:22 | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/sum-41"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.196472 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/7cf5f61ac4ffe9a0fd1f6a4b235b95eb.320x320x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/sum-41">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Sum 41</div>
<a href="https://genius.com/artists/sum-41">
<div style="text-align: center; font-size: 14px;">@sum-41</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/sum-41).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/sum-41")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|134| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/sum-41")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
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huggingartists/the-beatles | 2022-10-25T09:46:31.000Z | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
] | huggingartists | This dataset is designed to generate lyrics with HuggingArtists. | @InProceedings{huggingartists:dataset,
title = {Lyrics dataset},
author={Aleksey Korshuk
},
year={2021}
} | 0 | 83 | 2022-03-02T23:29:22 | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/the-beatles"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 1.07072 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/df75ede64ffcf049727bfbb01d323081.400x400x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/the-beatles">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">The Beatles</div>
<a href="https://genius.com/artists/the-beatles">
<div style="text-align: center; font-size: 14px;">@the-beatles</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/the-beatles).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/the-beatles")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|878| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/the-beatles")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
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Gxg/Math23K | 2022-10-06T05:21:22.000Z | [
"region:us"
] | Gxg | null | null | 14 | 83 | 2022-10-06T05:16:18 | Entry not found | 15 | [
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keremberke/smoke-object-detection | 2023-01-04T20:54:45.000Z | [
"task_categories:object-detection",
"roboflow",
"region:us"
] | keremberke | null | @misc{ smoke100-uwe4t_dataset,
title = { Smoke100 Dataset },
type = { Open Source Dataset },
author = { Smoke Detection },
howpublished = { \\url{ https://universe.roboflow.com/smoke-detection/smoke100-uwe4t } },
url = { https://universe.roboflow.com/smoke-detection/smoke100-uwe4t },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { dec },
note = { visited on 2023-01-02 },
} | 2 | 83 | 2023-01-04T20:41:37 | ---
task_categories:
- object-detection
tags:
- roboflow
---
### Roboflow Dataset Page
https://universe.roboflow.com/smoke-detection/smoke100-uwe4t/dataset/4
### Dataset Labels
```
['smoke']
```
### Citation
```
@misc{ smoke100-uwe4t_dataset,
title = { Smoke100 Dataset },
type = { Open Source Dataset },
author = { Smoke Detection },
howpublished = { \\url{ https://universe.roboflow.com/smoke-detection/smoke100-uwe4t } },
url = { https://universe.roboflow.com/smoke-detection/smoke100-uwe4t },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { dec },
note = { visited on 2023-01-02 },
}
```
### License
CC BY 4.0
### Dataset Summary
This dataset was exported via roboflow.ai on March 17, 2022 at 3:42 PM GMT
It includes 21578 images.
Smoke are annotated in COCO format.
The following pre-processing was applied to each image:
* Auto-orientation of pixel data (with EXIF-orientation stripping)
* Resize to 640x640 (Stretch)
No image augmentation techniques were applied.
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danielpark/MQuAD-v1 | 2023-04-07T12:21:48.000Z | [
"task_categories:question-answering",
"task_categories:text-generation",
"language:en",
"language:ko",
"license:apache-2.0",
"biology",
"region:us"
] | danielpark | null | null | 2 | 83 | 2023-04-06T06:31:15 | ---
license: apache-2.0
task_categories:
- question-answering
- text-generation
language:
- en
- ko
tags:
- biology
pretty_name: Medical domain QA dataset for training a medical chatbot.
---
# MQuAD
The Medical Question and Answering dataset(MQuAD) has been refined, including the following datasets. You can download it through the Hugging Face dataset. Use the DATASETS method as follows.
## Quick Guide
```python
from datasets import load_dataset
dataset = load_dataset("danielpark/MQuAD-v1")
```
Medical Q/A datasets gathered from the following websites.
- eHealth Forum
- iCliniq
- Question Doctors
- WebMD
Data was gathered at the 5th of May 2017.
The MQuAD provides embedded question and answer arrays in string format, so it is recommended to convert the string-formatted arrays into float format as follows. This measure has been applied to save resources and time used for embedding.
```python
from datasets import load_dataset
from utilfunction import col_convert
import pandas as pd
qa = load_dataset("danielpark/MQuAD-v1", "csv")
df_qa = pd.DataFrame(qa['train'])
df_qa = col_convert(df_qa, ['Q_FFNN_embeds', 'A_FFNN_embeds'])
```
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camel-ai/biology | 2023-05-23T21:11:56.000Z | [
"task_categories:text-generation",
"language:en",
"license:cc-by-nc-4.0",
"instruction-finetuning",
"arxiv:2303.17760",
"region:us"
] | camel-ai | null | null | 17 | 83 | 2023-04-16T01:30:03 | ---
license: cc-by-nc-4.0
language:
- en
tags:
- instruction-finetuning
pretty_name: CAMEL Biology
task_categories:
- text-generation
arxiv: 2303.17760
extra_gated_prompt: "By using this data, you acknowledge and agree to utilize it solely for research purposes, recognizing that the dataset may contain inaccuracies due to its artificial generation through ChatGPT."
extra_gated_fields:
Name: text
Email: text
I will adhere to the terms and conditions of this dataset: checkbox
---
# **CAMEL: Communicative Agents for “Mind” Exploration of Large Scale Language Model Society**
- **Github:** https://github.com/lightaime/camel
- **Website:** https://www.camel-ai.org/
- **Arxiv Paper:** https://arxiv.org/abs/2303.17760
## Dataset Summary
Biology dataset is composed of 20K problem-solution pairs obtained using gpt-4. The dataset problem-solutions pairs generating from 25 biology topics, 25 subtopics for each topic and 32 problems for each "topic,subtopic" pairs.
We provide the data in `biology.zip`.
## Data Fields
**The data fields for files in `biology.zip` are as follows:**
* `role_1`: assistant role
* `topic`: biology topic
* `sub_topic`: biology subtopic belonging to topic
* `message_1`: refers to the problem the assistant is asked to solve.
* `message_2`: refers to the solution provided by the assistant.
**Download in python**
```
from huggingface_hub import hf_hub_download
hf_hub_download(repo_id="camel-ai/biology", repo_type="dataset", filename="biology.zip",
local_dir="datasets/", local_dir_use_symlinks=False)
```
### Citation
```
@misc{li2023camel,
title={CAMEL: Communicative Agents for "Mind" Exploration of Large Scale Language Model Society},
author={Guohao Li and Hasan Abed Al Kader Hammoud and Hani Itani and Dmitrii Khizbullin and Bernard Ghanem},
year={2023},
eprint={2303.17760},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
```
## Disclaimer:
This data was synthetically generated by GPT4 and might contain incorrect information. The dataset is there only for research purposes.
---
license: cc-by-nc-4.0
---
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sam-mosaic/full-hh-rlhf-chatml | 2023-07-18T00:28:22.000Z | [
"language:en",
"region:us"
] | sam-mosaic | null | null | 2 | 83 | 2023-04-26T00:27:24 | ---
language: en
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 155301546
num_examples: 147351
- name: test
num_bytes: 16963667
num_examples: 16255
download_size: 68690705
dataset_size: 172265213
---
# Dataset Card for "full-hh-rlhf-chatml-chatml"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 490 | [
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opentensor/openvalidators | 2023-09-25T14:03:34.000Z | [
"size_categories:1M<n<10M",
"license:mit",
"region:us"
] | opentensor | null | null | 6 | 83 | 2023-06-15T15:29:34 | ---
license: mit
viewer: False
size_categories:
- 1M<n<10M
---
# Dataset Card for Openvalidators dataset
## Dataset Description
- **Repository:** https://github.com/opentensor/validators
- **Homepage:** https://bittensor.com/
### Dataset Summary
The OpenValidators dataset, created by the OpenTensor Foundation, is a continuously growing collection of data generated
by the [OpenValidators](https://github.com/opentensor/validators) project in [W&B](https://wandb.ai/opentensor-dev/openvalidators/table).
It contains millions of records and serves researchers, data scientists, and miners in the Bittensor network.
The dataset provides information on network performance, node behaviors, and wandb run details.
Researchers can gain insights and detect patterns, while data scientists can use it for training models and analysis.
Miners can use the generated data to fine-tune their models and enhance their incentives in the network.
The dataset's continuous updates support collaboration and innovation in decentralized computing.
### Version support and revisions
This dataset is in constant evolution, so in order to facilitate data management, each data schema is versioned in
a hugging face dataset branch, so legacy data can be easily retrieved.
The main branch (or default revision) will always be the latest version of the dataset, following the latest schema adopted
by the openvalidators.
The current state of data organization is as following:
- `v1.0`: All data collected from the first openvalidators schema, ranging from version `1.0.0` to `1.0.8`.
- `main`: Current state of the dataset, following the latest schema adopted by the openvalidators (>= `1.1.0`).
### How to use
The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale.
The OpenValidators dataset gives you the granularity of extracting data by **run_id**, by **OpenValidators version** and
by **multiple OpenValidators versions.**
The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function.
**Downloading by run id**
For example, to download the data for a specific run, simply specify the corresponding **OpenValidators version** and the **wandb run id** in the format `version/raw_data/run_id.parquet`:
```python
from datasets import load_dataset
version = '1.1.0' # OpenValidators version
run_id = '0drg98iy' # WandB run id
run_id_dataset = load_dataset('opentensor/openvalidators', data_files=f'{version}/raw_data/{run_id}.parquet')
```
_Please note that only completed run_ids are included in the dataset. Runs that are still in progress will be ingested shortly after they finish._
**Downloading by OpenValidators version**
One can also leverage the `datasets` library to download all the runs within a determined **OpenValidators** version. That can be useful for researchers and data enthusiasts that are looking to do analysis in a specific **OpenValidators** version state.
```python
from datasets import load_dataset
version = '1.1.0' # Openvalidators version
version_dataset = load_dataset('opentensor/openvalidators', data_files=f'{version}/raw_data/*')
```
**Downloading by multiple OpenValidators version**
Utilizing the `datasets` library, users can efficiently download runs from multiple **OpenValidators** versions. By accessing data from various OpenValidators versions, users can undertake downstream tasks such as data fine-tuning for mining or to perform big data analysis.
```python
from datasets import load_dataset
versions = ['1.1.0', '1.1.1', ...] # Desired versions for extraction
data_files = [f'{version}/raw_data/*' for version in versions] # Set data files directories
dataset = load_dataset('opentensor/openvalidators', data_files={ 'test': data_files })
```
**Downloading legacy data using revisions**
```python
from datasets import load_dataset
version = '1.0.4' # OpenValidators version
run_id = '0plco3n0' # WandB run id
revision = 'v1.0' # Dataset revision
run_id_dataset = load_dataset('opentensor/openvalidators', data_files=f'{version}/raw_data/{run_id}.parquet', revision=revision)
```
> Note: You can interact with legacy data in all the ways mentioned above, as long as your data scope is within the same revision.
**Analyzing metadata**
All the state related to the details of the wandb data ingestion can be accessed easily using pandas and hugging face datasets structure. This data contains relevant information regarding the metadata of the run, including user information, config information and ingestion state.
```python
import pandas as pd
version = '1.1.0' # OpenValidators version for metadata analysis
df = pd.read_csv(f'hf://datasets/opentensor/openvalidators/{version}/metadata.csv')
```
## Dataset Structure
### Data Instances
**versioned raw_data**
The data is provided as-in the wandb logs, without further preprocessing or tokenization. This data is located at `version/raw_data` where each file is a wandb run.
**metadata**
This dataset defines the current state of the wandb data ingestion by **run id**.
### Data Fields
**Raw data**
The versioned raw_data collected from W&B follows the following schema:
- `rewards`: (float64) Reward vector for given step
- `completion_times`: (float64) List of completion times for a given prompt
- `completions`: (string) List of completions received for a given prompt
- `_runtime`: (float64) Runtime of the event
- `_timestamp`: (float64) Timestamp of the event
- `name`: (string) Prompt type, e.g. 'followup', 'answer', 'augment'
- `block`: (float64) Current block at given step
- `gating_loss`: (float64) Gating model loss for given step
- `rlhf_reward_model`: (float64) Output vector of the rlhf reward model
- `relevance_filter`: (float64) Output vector of the relevance scoring reward model
- `dahoas_reward_model`: (float64) Output vector of the dahoas reward model
- `blacklist_filter`:(float64) Output vector of the blacklist filter
- `nsfw_filter`:(float64) Output vector of the nsfw filter
- `prompt_reward_model`:(float64) Output vector of the prompt reward model
- `reciprocate_reward_model`:(float64) Output vector of the reciprocate reward model
- `diversity_reward_model`:(float64) Output vector of the diversity reward model
- `set_weights`: (float64) Output vector of the set weights
- `uids`:(int64) Queried uids
- `_step`: (int64) Step of the event
- `prompt`: (string) Prompt text string
- `step_length`: (float64) Elapsed time between the beginning of a run step to the end of a run step
- `best`: (string) Best completion for given prompt
**Metadata**
- `run_id`: (string) Wandb Run Id
- `completed`: (boolean) Flag indicating if the run_id is completed (finished, crashed or killed)
- `downloaded`: (boolean) Flag indicating if the run_id data has been downloaded
- `last_checkpoint`: (string) Last checkpoint of the run_id
- `hotkey`: (string) Hotkey associated with the run_id
- `openvalidators_version`: (string) Version of OpenValidators associated with the run_id
- `problematic`: (boolean) Flag indicating if the run_id data had problems to be ingested
- `problematic_reason`: (string) Reason for the run_id being problematic (Exception message)
- `wandb_json_config`: (string) JSON configuration associated with the run_id in Wandb
- `wandb_run_name`: (string) Name of the Wandb run
- `wandb_user_info`: (string) Username information associated with the Wandb run
- `wandb_tags`: (list) List of tags associated with the Wandb run
- `wandb_createdAt`: (string) Timestamp of the run creation in Wandb
## Dataset Creation
### Curation Rationale
This dataset was curated to provide a comprehensive and reliable collection of historical data obtained by the execution of different OpenValidators in the bittensor network.
The goal is to support researchers, data scientists and developers with data generated in the network, facilitating the discovery of new insights, network analysis, troubleshooting, and data extraction for downstream tasks like mining.
### Source Data
#### Initial Data Collection and Normalization
The initial data collection process for this dataset involves recurrent collection by a specialized worker responsible for extracting data from wandb and ingesting it into the Hugging Face datasets structure. The collected data is organized based on the OpenValidators version and run ID to facilitate efficient data management and granular access. Each run is collected based on its corresponding OpenValidators version tag and grouped into version-specific folders. Within each version folder, a `metadata.csv` file is included to manage the collection state, while the raw data of each run is saved in the `.parquet` format with the file name corresponding to the run ID (e.g., `run_id.parquet`). Please note that the code for this data collection process will be released for transparency and reproducibility.
#### Who are the source language producers?
The language producers for this dataset are all the openvalidators that are logging their data into wandb in conjunction of other nodes of the bittensor network. The main wandb page where the data is sent can be accessed at https://wandb.ai/opentensor-dev/openvalidators/table.
### Licensing Information
The dataset is licensed under the [MIT License](https://github.com/opentensor/validators/blob/main/LICENSE)
### Supported Tasks and Leaderboards
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | 9,522 | [
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Jumtra/jglue_jsquads_with_input | 2023-06-21T00:25:40.000Z | [
"region:us"
] | Jumtra | null | null | 0 | 83 | 2023-06-21T00:25:38 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 44660349
num_examples: 67301
download_size: 8923113
dataset_size: 44660349
---
# Dataset Card for "jglue_jsquads_with_input"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 445 | [
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zzliang/GRIT | 2023-07-04T06:40:28.000Z | [
"task_categories:text-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:zero-shot-classification",
"task_ids:image-captioning",
"task_ids:visual-question-answering",
"multilinguality:monolingual",
"size_categories:100M<n<1B",
"source_datasets:COYO-700M"... | zzliang | null | null | 61 | 83 | 2023-07-04T03:33:28 | ---
license: ms-pl
language:
- en
multilinguality:
- monolingual
pretty_name: GRIT
size_categories:
- 100M<n<1B
source_datasets:
- COYO-700M
tags:
- image-text-bounding-box pairs
- image-text pairs
task_categories:
- text-to-image
- image-to-text
- object-detection
- zero-shot-classification
task_ids:
- image-captioning
- visual-question-answering
---
# GRIT: Large-Scale Training Corpus of Grounded Image-Text Pairs
### Dataset Description
- **Repository:** [Microsoft unilm](https://github.com/microsoft/unilm/tree/master/kosmos-2)
- **Paper:** [Kosmos-2](https://arxiv.org/abs/2306.14824)
### Dataset Summary
We introduce GRIT, a large-scale dataset of Grounded Image-Text pairs, which is created based on image-text pairs from [COYO-700M](https://github.com/kakaobrain/coyo-dataset) and LAION-2B. We construct a pipeline to extract and link text spans (i.e., noun phrases, and referring expressions) in the caption to their corresponding image regions. More details can be found in the [paper](https://arxiv.org/abs/2306.14824).
### Supported Tasks
During the construction, we excluded the image-caption pairs if no bounding boxes are retained. This procedure resulted in a high-quality image-caption subset of COYO-700M, which we will validate in the future.
Furthermore, this dataset contains text-span-bounding-box pairs. Thus, it can be used in many location-aware mono/multimodal tasks, such as phrase grounding, referring expression comprehension, referring expression generation, and open-world object detection.
### Data Instance
One instance is
```python
{
'key': '000373938',
'clip_similarity_vitb32': 0.353271484375,
'clip_similarity_vitl14': 0.2958984375,
'id': 1795296605919,
'url': "https://www.thestrapsaver.com/wp-content/uploads/customerservice-1.jpg",
'caption': 'a wire hanger with a paper cover that reads we heart our customers',
'width': 1024,
'height': 693,
'noun_chunks': [[19, 32, 0.019644069503434333, 0.31054004033406574, 0.9622142865754519, 0.9603442351023356, 0.79298526], [0, 13, 0.019422357885505368, 0.027634161214033764, 0.9593302408854166, 0.969467560450236, 0.67520964]],
'ref_exps': [[19, 66, 0.019644069503434333, 0.31054004033406574, 0.9622142865754519, 0.9603442351023356, 0.79298526], [0, 66, 0.019422357885505368, 0.027634161214033764, 0.9593302408854166, 0.969467560450236, 0.67520964]]
}
```
- `key`: The generated file name when using img2dataset to download COYO-700M (omit it).
- `clip_similarity_vitb32`: The cosine similarity between text and image(ViT-B/32) embeddings by [OpenAI CLIP](https://github.com/openai/CLIP), provided by COYO-700M.
- `clip_similarity_vitl14`: The cosine similarity between text and image(ViT-L/14) embeddings by [OpenAI CLIP](https://github.com/openai/CLIP), provided by COYO-700M.
- `id`: Unique 64-bit integer ID in COYO-700M.
- `url`: The image URL.
- `caption`: The corresponding caption.
- `width`: The width of the image.
- `height`: The height of the image.
- `noun_chunks`: The noun chunks (extracted by [spaCy](https://spacy.io/)) that have associated bounding boxes (predicted by [GLIP](https://github.com/microsoft/GLIP)). The items in the children list respectively represent 'Start of the noun chunk in caption', 'End of the noun chunk in caption', 'normalized x_min', 'normalized y_min', 'normalized x_max', 'normalized y_max', 'confidence score'.
- `ref_exps`: The corresponding referring expressions. If a noun chunk has no expansion, we just copy it.
### Download image
We recommend to use [img2dataset](https://github.com/rom1504/img2dataset) tool to download the images.
1. Download the metadata. You can download it by cloning current repository:
```bash
git lfs install
git clone https://huggingface.co/datasets/zzliang/GRIT
```
2. Install [img2dataset](https://github.com/rom1504/img2dataset).
```bash
pip install img2dataset
```
3. Download images
You need to replace `/path/to/GRIT_dataset/grit-20m` with the local path to this repository.
```bash
img2dataset --url_list /path/to/GRIT_dataset/grit-20m --input_format "parquet"\
--url_col "url" --caption_col "caption" --output_format webdataset \
--output_folder /tmp/grit --processes_count 4 --thread_count 64 --image_size 256 \
--resize_only_if_bigger=True --resize_mode="keep_ratio" --skip_reencode=True \
--save_additional_columns '["id","noun_chunks","ref_exps","clip_similarity_vitb32","clip_similarity_vitl14"]' \
--enable_wandb False
```
You can adjust some parameters according to your actual needs (e.g., `processes_count`, `thread_count`, `image_size`, `save_additional_columns`).
More img2dataset hyper-parameters can be found in [here](https://github.com/rom1504/img2dataset#api).
### Citation Information
If you apply this dataset to any project and research, please cite our paper and coyo-700m:
```
@article{Kosmos2,
title={Kosmos-2: Grounding Multimodal Large Language Models to the World},
author={Zhiliang Peng and Wenhui Wang and Li Dong and Yaru Hao and Shaohan Huang and Shuming Ma and Furu Wei},
journal={ArXiv},
year={2023},
volume={abs/2306.14824}
}
@misc{kakaobrain2022coyo-700m,
title = {COYO-700M: Image-Text Pair Dataset},
author = {Minwoo Byeon, Beomhee Park, Haecheon Kim, Sungjun Lee, Woonhyuk Baek, Saehoon Kim},
year = {2022},
howpublished = {\url{https://github.com/kakaobrain/coyo-dataset}},
}
``` | 5,394 | [
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yzhuang/autotree_automl_100000_electricity_sgosdt_l256_dim7_d3_sd0 | 2023-09-07T21:52:00.000Z | [
"region:us"
] | yzhuang | null | null | 0 | 83 | 2023-09-07T21:51:32 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_x
sequence:
sequence: float32
- name: input_y
sequence:
sequence: float32
- name: input_y_clean
sequence:
sequence: float32
- name: rtg
sequence: float64
- name: status
sequence:
sequence: float32
- name: split_threshold
sequence:
sequence: float32
- name: split_dimension
sequence: int64
splits:
- name: train
num_bytes: 2057200000
num_examples: 100000
- name: validation
num_bytes: 205720000
num_examples: 10000
download_size: 578994225
dataset_size: 2262920000
---
# Dataset Card for "autotree_automl_100000_electricity_sgosdt_l256_dim7_d3_sd0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 852 | [
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longhoang06/text-recognition | 2023-09-30T15:08:12.000Z | [
"region:us"
] | longhoang06 | null | null | 0 | 83 | 2023-09-30T15:03:06 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 6858787617.0
num_examples: 100000
download_size: 6858941356
dataset_size: 6858787617.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "text-recognition"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 492 | [
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kye/metamath-mistal-tokenized-16384 | 2023-10-05T18:28:20.000Z | [
"region:us"
] | kye | null | null | 1 | 83 | 2023-10-05T18:27:24 | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 485833040
num_examples: 5930
download_size: 131269443
dataset_size: 485833040
---
# Dataset Card for "metamath-mistal-tokenized-16384"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 430 | [
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Fraser/mnist-text-default | 2021-02-22T10:48:20.000Z | [
"region:us"
] | Fraser | MNIST dataset adapted to a text-based representation.
This allows testing interpolation quality for Transformer-VAEs.
System is heavily inspired by Matthew Rayfield's work https://youtu.be/Z9K3cwSL6uM
Works by quantising each MNIST pixel into one of 64 characters.
Every sample has an up & down version to encourage the model to learn rotation invarient features.
Use `.array_to_text(` and `.text_to_array(` methods to test your generated data.
Data format:
- text: (30 x 28 tokens, 840 tokens total): Textual representation of MNIST digit, for example:
```
00 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !
01 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !
02 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !
03 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !
04 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !
05 down ! ! ! ! ! ! ! ! ! ! ! ! ! % % % @ C L ' J a ^ @ ! ! ! !
06 down ! ! ! ! ! ! ! ! ( * 8 G K ` ` ` ` ` Y L ` ] Q 1 ! ! ! !
07 down ! ! ! ! ! ! ! - \ ` ` ` ` ` ` ` ` _ 8 5 5 / * ! ! ! ! !
08 down ! ! ! ! ! ! ! % W ` ` ` ` ` R N ^ ] ! ! ! ! ! ! ! ! ! !
09 down ! ! ! ! ! ! ! ! 5 H ; ` ` T # ! + G ! ! ! ! ! ! ! ! ! !
10 down ! ! ! ! ! ! ! ! ! $ ! G ` 7 ! ! ! ! ! ! ! ! ! ! ! ! ! !
11 down ! ! ! ! ! ! ! ! ! ! ! C ` P ! ! ! ! ! ! ! ! ! ! ! ! ! !
12 down ! ! ! ! ! ! ! ! ! ! ! # P ` 2 ! ! ! ! ! ! ! ! ! ! ! ! !
13 down ! ! ! ! ! ! ! ! ! ! ! ! ) ] Y I < ! ! ! ! ! ! ! ! ! ! !
14 down ! ! ! ! ! ! ! ! ! ! ! ! ! 5 ] ` ` > ' ! ! ! ! ! ! ! ! !
15 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! , O ` ` F ' ! ! ! ! ! ! ! !
16 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! % 8 ` ` O ! ! ! ! ! ! ! !
17 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! _ ` _ 1 ! ! ! ! ! ! !
18 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! , A N ` ` T ! ! ! ! ! ! ! !
19 down ! ! ! ! ! ! ! ! ! ! ! ! * F Z ` ` ` _ N ! ! ! ! ! ! ! !
20 down ! ! ! ! ! ! ! ! ! ! ' = X ` ` ` ` S 4 ! ! ! ! ! ! ! ! !
21 down ! ! ! ! ! ! ! ! & 1 V ` ` ` ` R 5 ! ! ! ! ! ! ! ! ! ! !
22 down ! ! ! ! ! ! % K W ` ` ` ` Q 5 # ! ! ! ! ! ! ! ! ! ! ! !
23 down ! ! ! ! . L Y ` ` ` ` ^ B # ! ! ! ! ! ! ! ! ! ! ! ! ! !
24 down ! ! ! ! C ` ` ` V B B % ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !
25 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !
26 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !
27 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !
```
- label: Just a number with the texts matching label. | @dataset{dataset,
author = {Fraser Greenlee},
year = {2021},
month = {1},
pages = {},
title = {MNIST text dataset.},
doi = {}
} | 0 | 82 | 2022-03-02T23:29:22 | MNIST dataset adapted to a text-based representation.
This allows testing interpolation quality for Transformer-VAEs.
System is heavily inspired by Matthew Rayfield's work https://youtu.be/Z9K3cwSL6uM
Works by quantising each MNIST pixel into one of 64 characters.
Every sample has an up & down version to encourage the model to learn rotation invarient features.
Use `.array_to_text(` and `.text_to_array(` methods to test your generated data.
Data format:
- text: (30 x 28 tokens, 840 tokens total): Textual representation of MNIST digit, for example:
```
00 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !
01 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !
02 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !
03 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !
04 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !
05 down ! ! ! ! ! ! ! ! ! ! ! ! ! % % % @ C L ' J a ^ @ ! ! ! !
06 down ! ! ! ! ! ! ! ! ( * 8 G K ` ` ` ` ` Y L ` ] Q 1 ! ! ! !
07 down ! ! ! ! ! ! ! - \ ` ` ` ` ` ` ` ` _ 8 5 5 / * ! ! ! ! !
08 down ! ! ! ! ! ! ! % W ` ` ` ` ` R N ^ ] ! ! ! ! ! ! ! ! ! !
09 down ! ! ! ! ! ! ! ! 5 H ; ` ` T # ! + G ! ! ! ! ! ! ! ! ! !
10 down ! ! ! ! ! ! ! ! ! $ ! G ` 7 ! ! ! ! ! ! ! ! ! ! ! ! ! !
11 down ! ! ! ! ! ! ! ! ! ! ! C ` P ! ! ! ! ! ! ! ! ! ! ! ! ! !
12 down ! ! ! ! ! ! ! ! ! ! ! # P ` 2 ! ! ! ! ! ! ! ! ! ! ! ! !
13 down ! ! ! ! ! ! ! ! ! ! ! ! ) ] Y I < ! ! ! ! ! ! ! ! ! ! !
14 down ! ! ! ! ! ! ! ! ! ! ! ! ! 5 ] ` ` > ' ! ! ! ! ! ! ! ! !
15 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! , O ` ` F ' ! ! ! ! ! ! ! !
16 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! % 8 ` ` O ! ! ! ! ! ! ! !
17 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! _ ` _ 1 ! ! ! ! ! ! !
18 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! , A N ` ` T ! ! ! ! ! ! ! !
19 down ! ! ! ! ! ! ! ! ! ! ! ! * F Z ` ` ` _ N ! ! ! ! ! ! ! !
20 down ! ! ! ! ! ! ! ! ! ! ' = X ` ` ` ` S 4 ! ! ! ! ! ! ! ! !
21 down ! ! ! ! ! ! ! ! & 1 V ` ` ` ` R 5 ! ! ! ! ! ! ! ! ! ! !
22 down ! ! ! ! ! ! % K W ` ` ` ` Q 5 # ! ! ! ! ! ! ! ! ! ! ! !
23 down ! ! ! ! . L Y ` ` ` ` ^ B # ! ! ! ! ! ! ! ! ! ! ! ! ! !
24 down ! ! ! ! C ` ` ` V B B % ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !
25 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !
26 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !
27 down ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !
```
- label: Just a number with the texts matching label. | 2,412 | [
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GEM/SciDuet | 2022-10-24T15:30:06.000Z | [
"task_categories:other",
"annotations_creators:none",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"text-to-slide",
"region:us"
] | GEM | SciDuet is the first publicaly available dataset for the challenging task of document2slides generation,
The dataset integrated into GEM is the ACL portion of the whole dataset described in "https://aclanthology.org/2021.naacl-main.111.pdf".
It contains the full Dev and Test sets, and a portion of the Train dataset.
We additionally create a challenge dataset in which the slide titles do not match with the
section headers of the corresponding paper.
Note that although we cannot release the whole training dataset due to copyright issues, researchers can still
use our released data procurement code from https://github.com/IBM/document2slides
to generate the training dataset from the online ICML/NeurIPS anthologies.
In the released dataset, the original papers and slides (both are in PDF format) are carefully processed by a combination of PDF/Image processing tookits.
The text contents from multiple slides that correspond to the same slide title are mreged. | @inproceedings{sun-etal-2021-d2s,
title = "{D}2{S}: Document-to-Slide Generation Via Query-Based Text Summarization",
author = "Sun, Edward and
Hou, Yufang and
Wang, Dakuo and
Zhang, Yunfeng and
Wang, Nancy X. R.",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = June,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.111",
doi = "10.18653/v1/2021.naacl-main.111",
pages = "1405--1418",
} | 1 | 82 | 2022-03-02T23:29:22 | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- en
license:
- apache-2.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- other
task_ids: []
pretty_name: SciDuet
tags:
- text-to-slide
---
# Dataset Card for GEM/SciDuet
## Dataset Description
- **Homepage:** https://huggingface.co/datasets/GEM/SciDuet
- **Repository:** https://github.com/IBM/document2slides/tree/main/SciDuet-ACL
- **Paper:** https://aclanthology.org/2021.naacl-main.111/
- **Leaderboard:** N/A
- **Point of Contact:** N/A
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/SciDuet).
### Dataset Summary
This dataset supports the document-to-slide generation task where a model has to generate presentation slide content from the text of a document.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/SciDuet')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/SciDuet).
#### website
[Huggingface](https://huggingface.co/datasets/GEM/SciDuet)
#### paper
[ACL Anthology](https://aclanthology.org/2021.naacl-main.111/)
#### authors
Edward Sun, Yufang Hou, Dakuo Wang, Yunfeng Zhang, Nancy Wang
## Dataset Overview
### Where to find the Data and its Documentation
#### Webpage
<!-- info: What is the webpage for the dataset (if it exists)? -->
<!-- scope: telescope -->
[Huggingface](https://huggingface.co/datasets/GEM/SciDuet)
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Github](https://github.com/IBM/document2slides/tree/main/SciDuet-ACL)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[ACL Anthology](https://aclanthology.org/2021.naacl-main.111/)
#### BibTex
<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
<!-- scope: microscope -->
```
@inproceedings{sun-etal-2021-d2s,
title = "{D}2{S}: Document-to-Slide Generation Via Query-Based Text Summarization",
author = "Sun, Edward and
Hou, Yufang and
Wang, Dakuo and
Zhang, Yunfeng and
Wang, Nancy X. R.",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.111",
doi = "10.18653/v1/2021.naacl-main.111",
pages = "1405--1418",
abstract = "Presentations are critical for communication in all areas of our lives, yet the creation of slide decks is often tedious and time-consuming. There has been limited research aiming to automate the document-to-slides generation process and all face a critical challenge: no publicly available dataset for training and benchmarking. In this work, we first contribute a new dataset, SciDuet, consisting of pairs of papers and their corresponding slides decks from recent years{'} NLP and ML conferences (e.g., ACL). Secondly, we present D2S, a novel system that tackles the document-to-slides task with a two-step approach: 1) Use slide titles to retrieve relevant and engaging text, figures, and tables; 2) Summarize the retrieved context into bullet points with long-form question answering. Our evaluation suggests that long-form QA outperforms state-of-the-art summarization baselines on both automated ROUGE metrics and qualitative human evaluation.",
}
```
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
no
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
no
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`English`
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
apache-2.0: Apache License 2.0
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
Promote research on the task of document-to-slides generation
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Text-to-Slide
### Credit
#### Curation Organization Type(s)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`industry`
#### Curation Organization(s)
<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
IBM Research
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Edward Sun, Yufang Hou, Dakuo Wang, Yunfeng Zhang, Nancy Wang
#### Funding
<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
IBM Research
#### Who added the Dataset to GEM?
<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
<!-- scope: microscope -->
Yufang Hou (IBM Research), Dakuo Wang (IBM Research)
### Dataset Structure
#### How were labels chosen?
<!-- info: How were the labels chosen? -->
<!-- scope: microscope -->
The original papers and slides (both are in PDF format) are carefully processed by a combination of PDF/Image processing tookits. The text contents from multiple slides that correspond to the same slide title are mreged.
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
Training, validation and testing data contain 136, 55, and 81 papers from ACL Anthology and their corresponding slides, respectively.
#### Splitting Criteria
<!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. -->
<!-- scope: microscope -->
The dataset integrated into GEM is the ACL portion of the whole dataset described in the [paper](https://aclanthology.org/2021.naacl-main.111), It contains the full Dev and Test sets, and a portion of the Train dataset.
Note that although we cannot release the whole training dataset due to copyright issues, researchers can still use our released data procurement code to generate the training dataset from the online ICML/NeurIPS anthologies.
## Dataset in GEM
### Rationale for Inclusion in GEM
#### Why is the Dataset in GEM?
<!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? -->
<!-- scope: microscope -->
SciDuet is the first publicaly available dataset for the challenging task of document2slides generation, which requires a model has a good ability to "understand" long-form text, choose appropriate content and generate key points.
#### Similar Datasets
<!-- info: Do other datasets for the high level task exist? -->
<!-- scope: telescope -->
no
#### Ability that the Dataset measures
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: periscope -->
content selection, long-form text undersanding and generation
### GEM-Specific Curation
#### Modificatied for GEM?
<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
<!-- scope: telescope -->
no
#### Additional Splits?
<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
no
### Getting Started with the Task
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
content selection, long-form text undersanding and key points generation
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`ROUGE`
#### Proposed Evaluation
<!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. -->
<!-- scope: microscope -->
Automatical Evaluation Metric: ROUGE
Human Evaluation: (Readability, Informativeness, Consistency)
1) Readability: The generated slide content is coherent, concise, and grammatically correct;
2) Informativeness: The generated slide provides sufficient and necessary information that corresponds to the given slide title, regardless of its similarity to the original slide;
3) Consistency: The generated slide content is similar to the original author’s reference slide.
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
yes
#### Other Evaluation Approaches
<!-- info: What evaluation approaches have others used? -->
<!-- scope: periscope -->
ROUGE + Human Evaluation
#### Relevant Previous Results
<!-- info: What are the most relevant previous results for this task/dataset? -->
<!-- scope: microscope -->
Paper "D2S: Document-to-Slide Generation Via Query-Based
Text Summarization" reports 20.47, 5.26 and 19.08 for ROUGE-1, ROUGE-2 and ROUGE-L (f-score).
## Dataset Curation
### Original Curation
#### Original Curation Rationale
<!-- info: Original curation rationale -->
<!-- scope: telescope -->
Provide a benchmark dataset for the document-to-slides task.
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
no
### Language Data
#### How was Language Data Obtained?
<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Other`
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
not validated
#### Data Preprocessing
<!-- info: How was the text data pre-processed? (Enter N/A if the text was not pre-processed) -->
<!-- scope: microscope -->
Text on papers was extracted through Grobid. Figures andcaptions were extracted through pdffigures. Text on slides was extracted through IBM Watson Discovery package and OCR by pytesseract. Figures and tables that appear on slides and papers were linked through multiscale template matching by OpenCV. Further dataset
cleaning was performed with standard string-based
heuristics on sentence building, equation and floating caption removal, and duplicate line deletion.
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
algorithmically
#### Filter Criteria
<!-- info: What were the selection criteria? -->
<!-- scope: microscope -->
the slide context text shouldn't contain additional format information such as "*** University"
### Structured Annotations
#### Additional Annotations?
<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
none
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
no
### Consent
#### Any Consent Policy?
<!-- info: Was there a consent policy involved when gathering the data? -->
<!-- scope: telescope -->
yes
#### Consent Policy Details
<!-- info: What was the consent policy? -->
<!-- scope: microscope -->
The original dataset was open-sourced under Apache-2.0.
Some of the original dataset creators are part of the GEM v2 dataset infrastructure team and take care of integrating this dataset into GEM.
### Private Identifying Information (PII)
#### Contains PII?
<!-- quick -->
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
<!-- scope: telescope -->
yes/very likely
#### Categories of PII
<!-- info: What categories of PII are present or suspected in the data? -->
<!-- scope: periscope -->
`generic PII`
#### Any PII Identification?
<!-- info: Did the curators use any automatic/manual method to identify PII in the dataset? -->
<!-- scope: periscope -->
no identification
### Maintenance
#### Any Maintenance Plan?
<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
no
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
no
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
no
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
unsure
## Considerations for Using the Data
### PII Risks and Liability
### Licenses
#### Copyright Restrictions on the Dataset
<!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? -->
<!-- scope: periscope -->
`non-commercial use only`
#### Copyright Restrictions on the Language Data
<!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
<!-- scope: periscope -->
`research use only`
### Known Technical Limitations
| 14,553 | [
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HenryAI/KerasBERTv1-Data | 2021-12-15T16:05:48.000Z | [
"region:us"
] | HenryAI | null | null | 0 | 82 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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Iskaj/dutch_corpora_parliament_processed | 2022-01-27T11:42:10.000Z | [
"region:us"
] | Iskaj | null | null | 0 | 82 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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KTH/nst | 2023-09-05T14:29:06.000Z | [
"task_categories:automatic-speech-recognition",
"language:sv",
"license:cc0-1.0",
"region:us"
] | KTH | This database was created by Nordic Language Technology for the development of automatic speech recognition and dictation in Swedish. In this updated version, the organization of the data have been altered to improve the usefulness of the database.
In the original version of the material, the files were organized in a specific folder structure where the folder names were meaningful. However, the file names were not meaningful, and there were also cases of files with identical names in different folders. This proved to be impractical, since users had to keep the original folder structure in order to use the data. The files have been renamed, such that the file names are unique and meaningful regardless of the folder structure. The original metadata files were in spl format. These have been converted to JSON format. The converted metadata files are also anonymized and the text encoding has been converted from ANSI to UTF-8. | null | 0 | 82 | 2022-03-02T23:29:22 | ---
license: cc0-1.0
task_categories:
- automatic-speech-recognition
language:
- sv
---
# NST Swedish ASR Database (16 kHz) – reorganized
This database was created by Nordic Language Technology for the development of automatic speech recognition and dictation in Swedish. In this updated version, the organization of the data have been altered to improve the usefulness of the database.
In the original version of the material, the files were organized in a specific folder structure where the folder names were meaningful. However, the file names were not meaningful, and there were also cases of files with identical names in different folders. This proved to be impractical, since users had to keep the original folder structure in order to use the data. The files have been renamed, such that the file names are unique and meaningful regardless of the folder structure. The original metadata files were in spl format. These have been converted to JSON format. The converted metadata files are also anonymized and the text encoding has been converted from ANSI to UTF-8.
See the documentation file for a full description of the data and the changes made to the database.
The data is originally hosted on the National Library of Norway website.
https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-56/
Hosting on Hugging Face datasets for convenience.
Licence
CC0 1.0 Universal (CC0 1.0)
Public Domain Dedication | 1,434 | [
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Karavet/ARPA-Armenian-Paraphrase-Corpus | 2022-10-21T16:04:07.000Z | [
"multilinguality:monolingual",
"language:hy",
"arxiv:2009.12615",
"region:us"
] | Karavet | null | null | 0 | 82 | 2022-03-02T23:29:22 | ---
language:
- hy
task_categories: [paraphrase, paraphrase detection]
multilinguality: [monolingual]
task_ids: [paraphrase, paraphrase detection]
---
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Structure](#dataset-structure)
- [Dataset Evaluation](#dataset-evaluation)
- [Additional Information](#additional-information)
## Dataset Description
We provide sentential paraphrase detection train, test datasets as well as BERT-based models for the Armenian language.
### Dataset Summary
The sentences in the dataset are taken from [Hetq](https://hetq.am/) and [Panarmenian](http://www.panarmenian.net/) news articles. To generate paraphrase for the sentences, we used back translation from Armenian to English. We repeated the step twice, after which the generated paraphrases were manually reviewed. Invalid sentences were filtered out, while the rest were labelled as either paraphrase, near paraphrase or non-paraphrase. Test examples were reviewed by 3 different annotators. In addition, to increase the number of non-paraphrase pairs, we padded the dataset with automatically generated negative examples, including pairs of consecutive sentences and random pairs.
## Dataset Structure
Each row consists of 2 sentences and their label. This sentences were labelled as either paraphrase, near paraphrase or non-paraphrase (with 1, 0, -1 labels respectively). The sentences are divided into train and test sets.
|Number of examples|Total|Paraphrase|Non-paraphrase (near paraphrase)|
|:-- | :---: | :---: | :---: |
|Train | 4233 |1339 |2683 (211) |
|Test | 1682 |1021 |448 (213) |
### Dataset Evaluation
We finetuned Multilingual BERT on several training sets, including the proposed ARPA dataset, and evaluated their performance on our test set. During training and
evaluation, near paraphrase and non-paraphrase pairs were combined into one class. The results are provided below:
|BERT Model | Train set | F1 | Acc. |
|:-- | :---: | :---: | :---: |
|Multilingual BERT | ARPA train set| 84.27| 78.06|
|Multilingual BERT | Paraphraser.ru train set machine-translated into Armenian | 83.81 | 77.09 |
|Multilingual BERT | MRPC train set machine-translated into Armenian | 80.07 | 69.87 |
|Multilingual BERT | All of the above combined | 84 |77.6 |
#### Additional Information
The model trained on ARPA is available for use, and can be downloaded using this [link](https://drive.google.com/uc?id=14owW5kkZ1JiNa6P-676e-QIj8m8i5e_8).
For more details about the models and dataset construction, refer to the [paper](https://arxiv.org/pdf/2009.12615).
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LeverageX/klue-mrc | 2022-01-12T05:01:00.000Z | [
"region:us"
] | LeverageX | Klue Machine Reading Comprehension Data | null | 0 | 82 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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