datasetId
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42
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tasksource/disrpt
|
tasksource
| 2023-11-29T15:01:27Z | 35 | 1 |
[
"language:en",
"license:apache-2.0",
"region:us"
] | null | 2023-04-18T07:36:18Z |
---
license: apache-2.0
language:
- en
---
https://github.com/disrpt/sharedtask2023
scditb:
```
@inproceedings{yang-li-2018-scidtb,
title = "{S}ci{DTB}: Discourse Dependency {T}ree{B}ank for Scientific Abstracts",
author = "Yang, An and
Li, Sujian",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-2071",
doi = "10.18653/v1/P18-2071",
pages = "444--449",
abstract = "Annotation corpus for discourse relations benefits NLP tasks such as machine translation and question answering. In this paper, we present SciDTB, a domain-specific discourse treebank annotated on scientific articles. Different from widely-used RST-DT and PDTB, SciDTB uses dependency trees to represent discourse structure, which is flexible and simplified to some extent but do not sacrifice structural integrity. We discuss the labeling framework, annotation workflow and some statistics about SciDTB. Furthermore, our treebank is made as a benchmark for evaluating discourse dependency parsers, on which we provide several baselines as fundamental work.",
}
```
|
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] |
TREC-AToMiC/AToMiC-Texts-v0.2.1
|
TREC-AToMiC
| 2023-05-04T18:58:43Z | 35 | 2 |
[
"region:us"
] | null | 2023-04-26T16:34:45Z |
---
dataset_info:
features:
- name: text_id
dtype: string
- name: page_url
dtype: string
- name: page_title
dtype: string
- name: section_title
dtype: string
- name: context_page_description
dtype: string
- name: context_section_description
dtype: string
- name: media
sequence: string
- name: hierachy
sequence: string
- name: category
sequence: string
- name: source_id
dtype: string
splits:
- name: train
num_bytes: 20393084595
num_examples: 10134744
download_size: 7192298025
dataset_size: 20393084595
---
# Dataset Card for "AToMiC-Texts-v0.2.updated"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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ybelkada/oasst1-tiny-subset
|
ybelkada
| 2023-05-11T14:07:03Z | 35 | 1 |
[
"region:us"
] | null | 2023-05-11T14:06:58Z |
---
dataset_info:
features:
- name: messages
dtype: string
splits:
- name: train
num_bytes: 59104494.0
num_examples: 39663
- name: test
num_bytes: 6567166.0
num_examples: 4407
download_size: 38767143
dataset_size: 65671660.0
---
# Dataset Card for "oasst1-tiny-subset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
Nan-Do/code-search-net-python
|
Nan-Do
| 2023-05-15T00:55:15Z | 35 | 14 |
[
"task_categories:text-generation",
"task_categories:text2text-generation",
"task_categories:summarization",
"language:en",
"license:apache-2.0",
"code",
"python",
"CodeSearchNet",
"region:us"
] |
[
"text-generation",
"text2text-generation",
"summarization"
] | 2023-05-14T00:42:57Z |
---
dataset_info:
features:
- name: repo
dtype: string
- name: path
dtype: string
- name: func_name
dtype: string
- name: original_string
dtype: string
- name: language
dtype: string
- name: code
dtype: string
- name: code_tokens
sequence: string
- name: docstring
dtype: string
- name: docstring_tokens
sequence: string
- name: sha
dtype: string
- name: url
dtype: string
- name: partition
dtype: string
- name: summary
dtype: string
splits:
- name: train
num_bytes: 1772584117
num_examples: 455243
download_size: 598837908
dataset_size: 1772584117
license: apache-2.0
task_categories:
- text-generation
- text2text-generation
- summarization
language:
- en
tags:
- code
- python
- CodeSearchNet
pretty_name: Python CodeSearchNet with Summaries
---
# Dataset Card for "code-search-net-python"
## Dataset Description
- **Homepage:** None
- **Repository:** https://huggingface.co/datasets/Nan-Do/code-search-net-python
- **Paper:** None
- **Leaderboard:** None
- **Point of Contact:** [@Nan-Do](https://github.com/Nan-Do)
## Dataset Description
- **Homepage:** None
- **Repository:** https://huggingface.co/datasets/Nan-Do/code-search-net-python
- **Paper:** None
- **Leaderboard:** None
- **Point of Contact:** [@Nan-Do](https://github.com/Nan-Do)
### Dataset Summary
This dataset is the Python portion of the CodeSarchNet annotated with a summary column.
The code-search-net dataset includes open source functions that include comments found at GitHub.
The summary is a short description of what the function does.
### Languages
The dataset's comments are in English and the functions are coded in Python
### Data Splits
Train, test, validation labels are included in the dataset as a column.
## Dataset Creation
May of 2023
### Curation Rationale
This dataset can be used to generate instructional (or many other interesting) datasets that are useful to train LLMs
### Source Data
The CodeSearchNet dataset can be found at https://www.kaggle.com/datasets/omduggineni/codesearchnet
### Annotations
This datasets include a summary column including a short description of the function.
#### Annotation process
The annotation procedure was done using [Salesforce](https://huggingface.co/Salesforce) T5 summarization models.
A sample notebook of the process can be found at https://github.com/Nan-Do/OpenAssistantInstructionResponsePython
The annontations have been cleaned to make sure there are no repetitions and/or meaningless summaries. (some may still be present in the dataset)
### Licensing Information
Apache 2.0
|
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] |
clarin-knext/quora-pl-qrels
|
clarin-knext
| 2023-06-07T08:13:49Z | 35 | 0 |
[
"language:pl",
"arxiv:2305.19840",
"region:us"
] | null | 2023-06-06T22:18:44Z |
---
language:
- pl
---
Part of **BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language**.
Link to arxiv: https://arxiv.org/pdf/2305.19840.pdf
Contact: [email protected]
|
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] |
d0rj/curation-corpus-ru
|
d0rj
| 2023-06-13T13:31:27Z | 35 | 2 |
[
"task_categories:summarization",
"language_creators:translated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:d0rj/curation-corpus",
"language:ru",
"license:cc-by-4.0",
"news",
"summarization",
"region:us"
] |
[
"summarization"
] | 2023-06-12T19:49:36Z |
---
dataset_info:
features:
- name: title
dtype: string
- name: summary
dtype: string
- name: url
dtype: string
- name: date
dtype: string
- name: article_content
dtype: string
splits:
- name: train
num_bytes: 237436901.42479068
num_examples: 30454
download_size: 116826702
dataset_size: 237436901.42479068
license: cc-by-4.0
task_categories:
- summarization
multilinguality:
- monolingual
source_datasets:
- d0rj/curation-corpus
language:
- ru
language_creators:
- translated
tags:
- news
- summarization
pretty_name: Curation Corpus (ru)
size_categories:
- 10K<n<100K
---
# curation-corpus-ru
## Dataset Description
- **Repository:** [https://github.com/CurationCorp/curation-corpus](https://github.com/CurationCorp/curation-corpus)
Translated version of [d0rj/curation-corpus](https://huggingface.co/datasets/d0rj/curation-corpus) into Russian.
|
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mrjunos/depression-reddit-cleaned
|
mrjunos
| 2023-06-17T02:03:22Z | 35 | 1 |
[
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-4.0",
"reddit",
"Sentiment ",
"depression",
"region:us"
] |
[
"text-classification"
] | 2023-06-14T23:16:31Z |
---
license: cc-by-4.0
task_categories:
- text-classification
language:
- en
tags:
- reddit
- 'Sentiment '
- depression
pretty_name: Depression Reddit Cleaned
size_categories:
- 1K<n<10K
---
# Depression: Reddit Dataset (Cleaned)
**~7000 Cleaned Reddit Labelled Dataset on Depression**
### Summary
- The dataset provided is a Depression: Reddit Dataset (Cleaned) containing approximately 7,000 labeled instances. It consists of two main features: 'text' and 'label'. The 'text' feature contains the text data from Reddit posts related to depression, while the 'label' feature indicates whether a post is classified as depression or not.
- The raw data for this dataset was collected by web scraping Subreddits. To ensure the data's quality and usefulness, multiple natural language processing (NLP) techniques were applied to clean the data. The dataset exclusively consists of English-language posts, and its primary purpose is to facilitate mental health classification tasks.
- This dataset can be employed in various natural language processing tasks related to depression, such as sentiment analysis, topic modeling, text classification, or any other NLP task that requires labeled data pertaining to depression from Reddit.
- Extracted from Kaggle: https://www.kaggle.com/datasets/infamouscoder/depression-reddit-cleaned
|
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causal-lm/natural_instructions
|
causal-lm
| 2023-07-13T14:22:18Z | 35 | 0 |
[
"language:en",
"region:us"
] | null | 2023-06-25T06:05:21Z |
---
language: en
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 3794173036
num_examples: 4530011
- name: validation
num_bytes: 421548790
num_examples: 503335
download_size: 2165828372
dataset_size: 4215721826
---
# Dataset Card for "natural_instructions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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teleprint-me/phi-1
|
teleprint-me
| 2023-07-08T04:01:52Z | 35 | 32 |
[
"license:cc-by-nc-sa-3.0",
"arxiv:2306.11644",
"region:us"
] | null | 2023-07-04T03:49:40Z |
---
title: 'Phi-1 Model Dataset'
date: '2023-07-03'
license: cc-by-nc-sa-3.0
---
## Dataset Description
- **Homepage:** [teleprint.me](https://teleprint.me)
- **Repository:** [phi-1](https://huggingface.co/datasets/teleprint-me/phi-1)
- **Paper:** [2306.11644v1](https://arxiv.org/abs/2306.11644v1)
- **Leaderboard:** [Link to the leaderboard]
- **Point of Contact:** [[email protected]]([email protected])
### Dataset Summary
This dataset is created for training the phi-1 model, based on the paper
"Textbooks are All You Need". It contains high-quality data derived from various
textbooks, transformed and synthesized using OpenAI's GPT-3.5 and GPT-4 models.
For optimal results, it is recommended to train models with the following
parameters and sequence lengths:
- For a model with 350M parameters, use a sequence length of 2048.
- For a model with 700M parameters, use a sequence length of 4096.
- For a model with 1.3B parameters, use a sequence length of 8096.
Please note that the dataset is currently in its initial phase of planning and
collection. The process involves preparing the data, extracting it, formatting
it, chunking it, and preparing it for synthesis. Scripts for preparing and
processing the data for the model will be developed. Once the data is generated,
it will undergo a review and revision process to ensure its quality and
relevance.
These recommendations and notes are based on the dataset creator's initial plans
and may be subject to change as the project progresses.
**NOTE**: Due to the nature of this dataset, it cannot be released without
obtaining permissions from the respective publishers and/or authors. If you are
an author or publisher and have any concerns about this repository, please feel
free to email me.
If you are an author or publisher and would like to grant permission for the use
of your work, your support would be greatly appreciated. Please note that in
order for the dataset to be released, permissions would need to be unanimous
from all involved parties.
In the absence of such permissions, I will respect the copyrights of the
copyrighted materials and exercise my right to Fair Use with my own physical
property for personal use.
**This dataset is NOT intended for commercial purposes**. Its primary purpose is
for research in machine learning and AI software development. If a model is
created using this dataset, it will be shared under the same license.
Any proceeds derived from donations will be primarily used for the development
of the dataset and the model.
### Supported Tasks and Leaderboards
- `text-generation`: The dataset can be used to train a model for chat-like text
generation, more specifically, for generating explanations and examples in the
context of arithmetic, algebra, geometry, trigonometry, calculus, algorithms
and data structures, design patterns, and the python programming language.
### Languages
The text in the dataset is in English.
## Dataset Structure
### Data Instances
A data instance consists of a dialogue between a user and an assistant,
discussing a topic in arithmetic, algebra, geometry, trigonometry, calculus,
algorithms and data structures, design patterns, or the Python programming
language. The dialogue is structured as a list of turns, each turn containing
the role ("user" or "assistant") and the content of the turn.
### Data Fields
- `role`: a string indicating the role of the speaker in the dialogue ("system",
"user", "assistant", "function").
- `content`: a string containing the content of the speaker's turn in the
dialogue.
### Data Splits
The dataset is split into a training set, a validation set, and a test set. The
exact sizes and proportions of these splits will depend on the final size of the
dataset.
## Dataset Creation
### Curation Rationale
The dataset is being created to train a model capable of generating explanations
and examples in the context of various mathematical and computer science topics.
The goal is to create an AI assistant that can provide clear, accurate, and
pedagogically sound responses to user queries on these topics.
### Source Data
#### Initial Data Collection and Normalization
The data is collected from a variety of textbooks covering arithmetic, algebra,
geometry, trigonometry, calculus, algorithms and data structures, design
patterns, and the Python programming language. The textbooks used include:
- Barron's Arithmetic The Easy Way Fourth Edition
- Blitzer Introductory Algebra for College Students Fifth Edition
- McDougal Littell Geometry
- Blitzer Intermediate Algebra for College Students 5th Edition
- Trigonometry Sixth Edition
- Pearson College Algebra Fourth Edition
- Hughes-Hallet Applied Calculus 5th Edition
- CLRS Introduction to Algorithms Third Edition
In addition to the textbooks, the dataset also includes material from the
following online resources:
- [C reference](https://en.cppreference.com/w/c)
- [Cpp reference](https://en.cppreference.com/w/cpp)
- [Python Standard Library](https://docs.python.org/3/)
These resources provide up-to-date information and examples for the C, C++, and
Python programming languages. The creators of the Cppreference site also provide
[archives](https://en.cppreference.com/w/Cppreference:Archives) of their site
for offline use. Code samples synthesized by OpenAI's GPT models, curated by the
dataset creator, are also included in the dataset.
**Note:** The creator of this dataset owns physical copies of all the textbooks
listed above. The data from these sources are transformed into a dialogue format
using OpenAI's GPT-3.5 and GPT-4 models. The resulting dialogues are then used
as the training data for the phi-1 model. This dataset does not include the full
content of the source textbooks. Instead, it consists of transformations and
syntheses of the original content. Anyone who wants access to the full original
content should purchase or otherwise legally access the textbooks themselves.
#### Who are the source language producers?
The original language data was created by a variety of authors and educators,
who wrote the textbooks and other materials used as sources for this dataset.
These include:
- Barron's Arithmetic The Easy Way Fourth Edition - Edward Williams, Katie
Prindle
- Blitzer Introductory Algebra for College Students Fifth Edition - Robert
Blitzer
- McDougal Littell Geometry - Ron Larson, Laurie Boswell, Timothy D. Kanold, Lee
Stiff
- Blitzer Intermediate Algebra for College Students 5th Edition - Robert Blitzer
- Trigonometry Sixth Edition - Charles P. McKeague, Mark D. Turner
- Pearson College Algebra Fourth Edition - Robert F. Blitzer
- Hughes-Hallet Applied Calculus 5th Edition - Deborah Hughes-Hallett, Andrew M.
Gleason, Patti Frazer Lock, Daniel E. Flath, Sheldon P. Gordon, David O.
Lomen, David Lovelock, William G. McCallum, Brad G. Osgood, Andrew Pasquale,
Jeff Tecosky-Feldman, Joseph Thrash, Karen R. Rhea, Thomas W. Tucker
- CLRS Introduction to Algorithms Third Edition - Thomas H. Cormen, Charles E.
Leiserson, Ronald L. Rivest, Clifford Stein
In addition to these authors, the developers of OpenAI's GPT-3.5 and GPT-4
models also contributed to the creation of the language data, as these models
were used to transform the source material into a dialogue format.
### Annotations
#### Annotation process
The dataset does not contain any explicit annotations. However, the data is
curated and synthesized using OpenAI's GPT-3.5 and GPT-4 models. The process
involves transforming the source material into a dialogue format suitable for
training the phi-1 model. The dataset creator, an independent learner with a
strong interest in computer science, reviewed and curated the synthesized
dialogues to ensure their quality and relevance.
#### Who are the annotators?
The dataset creator, an independent learner who has studied computer science
extensively in a self-directed manner, performed the curation and review of the
synthesized dialogues.
### Personal and Sensitive Information
The dataset does not contain any personal or sensitive information. All the data
is derived from publicly available textbooks and online resources. Any names or
other potential identifiers in the source material have been removed or
anonymized.
### Social Impact of Dataset
The dataset is intended to support the development of AI models capable of
providing detailed explanations and examples in the context of arithmetic,
algebra, geometry, trigonometry, calculus, algorithms and data structures,
design patterns, and the python programming language. The potential social
impact is significant, as such models could greatly enhance self-directed
learning and provide valuable educational support to students worldwide.
However, it's important to note that the quality and usefulness of the AI models
trained on this dataset will depend on the quality of the data itself. If the
data is inaccurate or biased, the models could propagate these inaccuracies and
biases, potentially leading to misinformation or unfair outcomes.
### Discussion of Biases
The dataset is based on a variety of textbooks and online resources, which may
contain their own inherent biases. For example, textbooks often reflect the
perspectives and biases of their authors, which can influence the way
information is presented. These biases could potentially be reflected in the
dataset and in any models trained on it.
### Other Known Limitations
At this stage of the dataset creation process, it's difficult to identify all
potential limitations. However, one potential limitation is that the dataset may
not cover all possible topics or perspectives within the fields it addresses.
The dataset creator will continue to monitor and assess the dataset for
limitations as the work progresses.
## Additional Information
### Dataset Curators
The dataset was curated by an independent learner with a strong interest in
computer science. The curator has studied the subject matter in a self-directed
manner, using a variety of resources including textbooks and online materials.
The curation process also involved the use of OpenAI's GPT-3.5 and GPT-4 models
to synthesize dialogues based on the source material.
### Licensing Information
This dataset is released under the Creative Commons
Attribution-NonCommercial-ShareAlike 3.0 International (CC BY-NC-SA 3.0)
license.
### Citation Information
As this dataset is a compilation of various sources synthesized and curated for
the purpose of training the phi-1 model, please ensure to cite the original
sources when using this dataset. If referencing the dataset directly, please
refer to this repository.
|
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MightyStudent/Egyptian-ASR-MGB-3
|
MightyStudent
| 2023-08-31T08:30:42Z | 35 | 0 |
[
"task_categories:automatic-speech-recognition",
"size_categories:1K<n<10K",
"language:ar",
"arabic",
"egypt",
"egyptian",
"ASR",
"automatic speech recognition",
"arxiv:1709.07276",
"region:us"
] |
[
"automatic-speech-recognition"
] | 2023-07-07T13:56:34Z |
---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: sentence
splits:
- name: train
num_bytes: 1094421637.73819888
num_examples: 1138
download_size: 888280535
dataset_size: 1094421637.7381988
language:
- ar
tags:
- arabic
- egypt
- egyptian
- ASR
- automatic speech recognition
pretty_name: 'Egyptian Arabic dialect automatic speech recognition '
size_categories:
- 1K<n<10K
task_categories:
- automatic-speech-recognition
---
# Egyptian Arabic dialect automatic speech recognition
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset was collected, cleaned and adjusted for huggingface hub and ready to be used for whisper finetunning/training.
[From MGB-3 website](http://www.mgb-challenge.org/MGB-3.html):
*The MGB-3 is using 16 hours multi-genre data collected from different YouTube channels. The 16 hours have been manually transcribed.
The chosen Arabic dialect for this year is Egyptian.
Given that dialectal Arabic has no orthographic rules, each program has been transcribed by four different transcribers using this transcription guidelines.*
### Supported Tasks and Leaderboards
ASR: automatic speech recognition
### Languages
Arabic - Egyptian dialect
### Data Fields
* audio: sampled in 16000HZ and have a max duration of 30 sec (ideal for whispear and others ASR models)
* sentence: the transcription in Egyptian Arabic
## Dataset Creation
The youtube videos that are still avalible (some of them got deleted/ made private) were downloaded and synced with the provided transcription.
Then the 12 min of each youtube video were cut down into 30 sec segments.
the resulting dataset was uploaded to huggingface.
[From MGB-3 website](http://www.mgb-challenge.org/MGB-3.html):
*Egyptian broadcast data collected from YouTube.This year, we collected about 80 programs from different YouTube channels. The first 12 minutes from each program has been transcribed and released. This sums up to roughly 16 hours in total*
### Source Data
* [http://www.mgb-challenge.org/MGB-3.html](MGB challenge website)
* [Youtube.com](youtube)
#### Initial Data Collection and Normalization
[Available on MGB website ](http://www.mgb-challenge.org/MGB-3.html):
#### Who are the source language producers?
[Available on MGB website ](http://www.mgb-challenge.org/MGB-3.html):
### Annotations
#### Annotation process
[Available on MGB website ](http://www.mgb-challenge.org/MGB-3.html):
#### Who are the annotators?
[Available on MGB website ](http://www.mgb-challenge.org/MGB-3.html):
### Personal and Sensitive Information
[Available on MGB website ](http://www.mgb-challenge.org/MGB-3.html):
### Social Impact of Dataset
[Available on MGB website ](http://www.mgb-challenge.org/MGB-3.html):
### Discussion of Biases
[Available on MGB website ](http://www.mgb-challenge.org/MGB-3.html):
### Other Known Limitations
[Available on MGB website ](http://www.mgb-challenge.org/MGB-3.html):
## Additional Information
### Dataset Curators
[Available on MGB website ](http://www.mgb-challenge.org/MGB-3.html):
### Licensing Information
[Available on MGB website ](http://www.mgb-challenge.org/MGB-3.html):
### Citation Information
[Available on MGB website ](http://www.mgb-challenge.org/MGB-3.html)
[Speech Recognition Challenge in the Wild: Arabic MGB-3](https://arxiv.org/abs/1709.07276)
### Contributions
|
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MichaelR207/MultiSim
|
MichaelR207
| 2023-11-14T00:32:32Z | 35 | 0 |
[
"task_categories:summarization",
"task_categories:text2text-generation",
"task_categories:text-generation",
"size_categories:1M<n<10M",
"language:en",
"language:fr",
"language:ru",
"language:ja",
"language:it",
"language:da",
"language:es",
"language:de",
"language:pt",
"language:sl",
"language:ur",
"language:eu",
"license:mit",
"medical",
"legal",
"wikipedia",
"encyclopedia",
"science",
"literature",
"news",
"websites",
"arxiv:2305.15678",
"region:us"
] |
[
"summarization",
"text2text-generation",
"text-generation"
] | 2023-07-18T21:55:31Z |
---
license: mit
language:
- en
- fr
- ru
- ja
- it
- da
- es
- de
- pt
- sl
- ur
- eu
task_categories:
- summarization
- text2text-generation
- text-generation
pretty_name: MultiSim
tags:
- medical
- legal
- wikipedia
- encyclopedia
- science
- literature
- news
- websites
size_categories:
- 1M<n<10M
---
# Dataset Card for MultiSim Benchmark
## Dataset Description
- **Repository:https://github.com/XenonMolecule/MultiSim/tree/main**
- **Paper:https://aclanthology.org/2023.acl-long.269/ https://arxiv.org/pdf/2305.15678.pdf**
- **Point of Contact: [email protected]**
### Dataset Summary
The MultiSim benchmark is a growing collection of text simplification datasets targeted at sentence simplification in several languages. Currently, the benchmark spans 12 languages.
### Supported Tasks
- Sentence Simplification
### Usage
```python
from datasets import load_dataset
dataset = load_dataset("MichaelR207/MultiSim")
```
### Citation
If you use this benchmark, please cite our [paper](https://aclanthology.org/2023.acl-long.269/):
```
@inproceedings{ryan-etal-2023-revisiting,
title = "Revisiting non-{E}nglish Text Simplification: A Unified Multilingual Benchmark",
author = "Ryan, Michael and
Naous, Tarek and
Xu, Wei",
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.269",
pages = "4898--4927",
abstract = "Recent advancements in high-quality, large-scale English resources have pushed the frontier of English Automatic Text Simplification (ATS) research. However, less work has been done on multilingual text simplification due to the lack of a diverse evaluation benchmark that covers complex-simple sentence pairs in many languages. This paper introduces the MultiSim benchmark, a collection of 27 resources in 12 distinct languages containing over 1.7 million complex-simple sentence pairs. This benchmark will encourage research in developing more effective multilingual text simplification models and evaluation metrics. Our experiments using MultiSim with pre-trained multilingual language models reveal exciting performance improvements from multilingual training in non-English settings. We observe strong performance from Russian in zero-shot cross-lingual transfer to low-resource languages. We further show that few-shot prompting with BLOOM-176b achieves comparable quality to reference simplifications outperforming fine-tuned models in most languages. We validate these findings through human evaluation.",
}
```
### Contact
**Michael Ryan**: [Scholar](https://scholar.google.com/citations?user=8APGEEkAAAAJ&hl=en) | [Twitter](http://twitter.com/michaelryan207) | [Github](https://github.com/XenonMolecule) | [LinkedIn](https://www.linkedin.com/in/michael-ryan-207/) | [Research Gate](https://www.researchgate.net/profile/Michael-Ryan-86) | [Personal Website](http://michaelryan.tech/) | [[email protected]](mailto://[email protected])
### Languages
- English
- French
- Russian
- Japanese
- Italian
- Danish (on request)
- Spanish (on request)
- German
- Brazilian Portuguese
- Slovene
- Urdu (on request)
- Basque (on request)
## Dataset Structure
### Data Instances
MultiSim is a collection of 27 existing datasets:
- AdminIT
- ASSET
- CBST
- CLEAR
- DSim
- Easy Japanese
- Easy Japanese Extended
- GEOLino
- German News
- Newsela EN/ES
- PaCCSS-IT
- PorSimples
- RSSE
- RuAdapt Encyclopedia
- RuAdapt Fairytales
- RuAdapt Literature
- RuWikiLarge
- SIMPITIKI
- Simple German
- Simplext
- SimplifyUR
- SloTS
- Teacher
- Terence
- TextComplexityDE
- WikiAuto
- WikiLargeFR
### Data Fields
In the train set, you will only find `original` and `simple` sentences. In the validation and test sets you may find `simple1`, `simple2`, ... `simpleN` because a given sentence can have multiple reference simplifications (useful in SARI and BLEU calculations)
### Data Splits
The dataset is split into a train, validation, and test set.
## Dataset Creation
### Curation Rationale
I hope that collecting all of these independently useful resources for text simplification together into one benchmark will encourage multilingual work on text simplification!
### Source Data
#### Initial Data Collection and Normalization
Data is compiled from the 27 existing datasets that comprise the MultiSim Benchmark. For details on each of the resources please see Appendix A in the [paper](https://aclanthology.org/2023.acl-long.269.pdf).
#### Who are the source language producers?
Each dataset has different sources. At a high level the sources are: Automatically Collected (ex. Wikipedia, Web data), Manually Collected (ex. annotators asked to simplify sentences), Target Audience Resources (ex. Newsela News Articles), or Translated (ex. Machine translations of existing datasets).
These sources can be seen in Table 1 pictured above (Section: `Dataset Structure/Data Instances`) and further discussed in section 3 of the [paper](https://aclanthology.org/2023.acl-long.269.pdf). Appendix A of the paper has details on specific resources.
### Annotations
#### Annotation process
Annotators writing simplifications (only for some datasets) typically follow an annotation guideline. Some example guidelines come from [here](https://dl.acm.org/doi/10.1145/1410140.1410191), [here](https://link.springer.com/article/10.1007/s11168-006-9011-1), and [here](https://link.springer.com/article/10.1007/s10579-017-9407-6).
#### Who are the annotators?
See Table 1 (Section: `Dataset Structure/Data Instances`) for specific annotators per dataset. At a high level the annotators are: writers, translators, teachers, linguists, journalists, crowdworkers, experts, news agencies, medical students, students, writers, and researchers.
### Personal and Sensitive Information
No dataset should contain personal or sensitive information. These were previously collected resources primarily collected from news sources, wikipedia, science communications, etc. and were not identified to have personally identifiable information.
## Considerations for Using the Data
### Social Impact of Dataset
We hope this dataset will make a greatly positive social impact as text simplification is a task that serves children, second language learners, and people with reading/cognitive disabilities. By publicly releasing a dataset in 12 languages we hope to serve these global communities.
One negative and unintended use case for this data would be reversing the labels to make a "text complification" model. We beleive the benefits of releasing this data outweigh the harms and hope that people use the dataset as intended.
### Discussion of Biases
There may be biases of the annotators involved in writing the simplifications towards how they believe a simpler sentence should be written. Additionally annotators and editors have the choice of what information does not make the cut in the simpler sentence introducing information importance bias.
### Other Known Limitations
Some of the included resources were automatically collected or machine translated. As such not every sentence is perfectly aligned. Users are recommended to use such individual resources with caution.
## Additional Information
### Dataset Curators
**Michael Ryan**: [Scholar](https://scholar.google.com/citations?user=8APGEEkAAAAJ&hl=en) | [Twitter](http://twitter.com/michaelryan207) | [Github](https://github.com/XenonMolecule) | [LinkedIn](https://www.linkedin.com/in/michael-ryan-207/) | [Research Gate](https://www.researchgate.net/profile/Michael-Ryan-86) | [Personal Website](http://michaelryan.tech/) | [[email protected]](mailto://[email protected])
### Licensing Information
MIT License
### Citation Information
Please cite the individual datasets that you use within the MultiSim benchmark as appropriate. Proper bibtex attributions for each of the datasets are included below.
#### AdminIT
```
@inproceedings{miliani-etal-2022-neural,
title = "Neural Readability Pairwise Ranking for Sentences in {I}talian Administrative Language",
author = "Miliani, Martina and
Auriemma, Serena and
Alva-Manchego, Fernando and
Lenci, Alessandro",
booktitle = "Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing",
month = nov,
year = "2022",
address = "Online only",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.aacl-main.63",
pages = "849--866",
abstract = "Automatic Readability Assessment aims at assigning a complexity level to a given text, which could help improve the accessibility to information in specific domains, such as the administrative one. In this paper, we investigate the behavior of a Neural Pairwise Ranking Model (NPRM) for sentence-level readability assessment of Italian administrative texts. To deal with data scarcity, we experiment with cross-lingual, cross- and in-domain approaches, and test our models on Admin-It, a new parallel corpus in the Italian administrative language, containing sentences simplified using three different rewriting strategies. We show that NPRMs are effective in zero-shot scenarios ({\textasciitilde}0.78 ranking accuracy), especially with ranking pairs containing simplifications produced by overall rewriting at the sentence-level, and that the best results are obtained by adding in-domain data (achieving perfect performance for such sentence pairs). Finally, we investigate where NPRMs failed, showing that the characteristics of the training data, rather than its size, have a bigger effect on a model{'}s performance.",
}
```
#### ASSET
```
@inproceedings{alva-manchego-etal-2020-asset,
title = "{ASSET}: {A} Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations",
author = "Alva-Manchego, Fernando and
Martin, Louis and
Bordes, Antoine and
Scarton, Carolina and
Sagot, Beno{\^\i}t and
Specia, Lucia",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.424",
pages = "4668--4679",
}
```
#### CBST
```
@article{10.1007/s10579-017-9407-6,
title={{The corpus of Basque simplified texts (CBST)}},
author={Gonzalez-Dios, Itziar and Aranzabe, Mar{\'\i}a Jes{\'u}s and D{\'\i}az de Ilarraza, Arantza},
journal={Language Resources and Evaluation},
volume={52},
number={1},
pages={217--247},
year={2018},
publisher={Springer}
}
```
#### CLEAR
```
@inproceedings{grabar-cardon-2018-clear,
title = "{CLEAR} {--} Simple Corpus for Medical {F}rench",
author = "Grabar, Natalia and
Cardon, R{\'e}mi",
booktitle = "Proceedings of the 1st Workshop on Automatic Text Adaptation ({ATA})",
month = nov,
year = "2018",
address = "Tilburg, the Netherlands",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-7002",
doi = "10.18653/v1/W18-7002",
pages = "3--9",
}
```
#### DSim
```
@inproceedings{klerke-sogaard-2012-dsim,
title = "{DS}im, a {D}anish Parallel Corpus for Text Simplification",
author = "Klerke, Sigrid and
S{\o}gaard, Anders",
booktitle = "Proceedings of the Eighth International Conference on Language Resources and Evaluation ({LREC}'12)",
month = may,
year = "2012",
address = "Istanbul, Turkey",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2012/pdf/270_Paper.pdf",
pages = "4015--4018",
abstract = "We present DSim, a new sentence aligned Danish monolingual parallel corpus extracted from 3701 pairs of news telegrams and corresponding professionally simplified short news articles. The corpus is intended for building automatic text simplification for adult readers. We compare DSim to different examples of monolingual parallel corpora, and we argue that this corpus is a promising basis for future development of automatic data-driven text simplification systems in Danish. The corpus contains both the collection of paired articles and a sentence aligned bitext, and we show that sentence alignment using simple tf*idf weighted cosine similarity scoring is on line with state―of―the―art when evaluated against a hand-aligned sample. The alignment results are compared to state of the art for English sentence alignment. We finally compare the source and simplified sides of the corpus in terms of lexical and syntactic characteristics and readability, and find that the one―to―many sentence aligned corpus is representative of the sentence simplifications observed in the unaligned collection of article pairs.",
}
```
#### Easy Japanese
```
@inproceedings{maruyama-yamamoto-2018-simplified,
title = "Simplified Corpus with Core Vocabulary",
author = "Maruyama, Takumi and
Yamamoto, Kazuhide",
booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
month = may,
year = "2018",
address = "Miyazaki, Japan",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/L18-1185",
}
```
#### Easy Japanese Extended
```
@inproceedings{katsuta-yamamoto-2018-crowdsourced,
title = "Crowdsourced Corpus of Sentence Simplification with Core Vocabulary",
author = "Katsuta, Akihiro and
Yamamoto, Kazuhide",
booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
month = may,
year = "2018",
address = "Miyazaki, Japan",
publisher = "European Language Resources Association (ELRA)",
url = "https://aclanthology.org/L18-1072",
}
```
#### GEOLino
```
@inproceedings{mallinson2020,
title={Zero-Shot Crosslingual Sentence Simplification},
author={Mallinson, Jonathan and Sennrich, Rico and Lapata, Mirella},
year={2020},
booktitle={2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020)}
}
```
#### German News
```
@inproceedings{sauberli-etal-2020-benchmarking,
title = "Benchmarking Data-driven Automatic Text Simplification for {G}erman",
author = {S{\"a}uberli, Andreas and
Ebling, Sarah and
Volk, Martin},
booktitle = "Proceedings of the 1st Workshop on Tools and Resources to Empower People with REAding DIfficulties (READI)",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.readi-1.7",
pages = "41--48",
abstract = "Automatic text simplification is an active research area, and there are first systems for English, Spanish, Portuguese, and Italian. For German, no data-driven approach exists to this date, due to a lack of training data. In this paper, we present a parallel corpus of news items in German with corresponding simplifications on two complexity levels. The simplifications have been produced according to a well-documented set of guidelines. We then report on experiments in automatically simplifying the German news items using state-of-the-art neural machine translation techniques. We demonstrate that despite our small parallel corpus, our neural models were able to learn essential features of simplified language, such as lexical substitutions, deletion of less relevant words and phrases, and sentence shortening.",
language = "English",
ISBN = "979-10-95546-45-0",
}
```
#### Newsela EN/ES
```
@article{xu-etal-2015-problems,
title = "Problems in Current Text Simplification Research: New Data Can Help",
author = "Xu, Wei and
Callison-Burch, Chris and
Napoles, Courtney",
journal = "Transactions of the Association for Computational Linguistics",
volume = "3",
year = "2015",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/Q15-1021",
doi = "10.1162/tacl_a_00139",
pages = "283--297",
abstract = "Simple Wikipedia has dominated simplification research in the past 5 years. In this opinion paper, we argue that focusing on Wikipedia limits simplification research. We back up our arguments with corpus analysis and by highlighting statements that other researchers have made in the simplification literature. We introduce a new simplification dataset that is a significant improvement over Simple Wikipedia, and present a novel quantitative-comparative approach to study the quality of simplification data resources.",
}
```
#### PaCCSS-IT
```
@inproceedings{brunato-etal-2016-paccss,
title = "{P}a{CCSS}-{IT}: A Parallel Corpus of Complex-Simple Sentences for Automatic Text Simplification",
author = "Brunato, Dominique and
Cimino, Andrea and
Dell{'}Orletta, Felice and
Venturi, Giulia",
booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2016",
address = "Austin, Texas",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D16-1034",
doi = "10.18653/v1/D16-1034",
pages = "351--361",
}
```
#### PorSimples
```
@inproceedings{aluisio-gasperin-2010-fostering,
title = "Fostering Digital Inclusion and Accessibility: The {P}or{S}imples project for Simplification of {P}ortuguese Texts",
author = "Alu{\'\i}sio, Sandra and
Gasperin, Caroline",
booktitle = "Proceedings of the {NAACL} {HLT} 2010 Young Investigators Workshop on Computational Approaches to Languages of the {A}mericas",
month = jun,
year = "2010",
address = "Los Angeles, California",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W10-1607",
pages = "46--53",
}
```
```
@inproceedings{10.1007/978-3-642-16952-6_31,
author="Scarton, Carolina and Gasperin, Caroline and Aluisio, Sandra",
editor="Kuri-Morales, Angel and Simari, Guillermo R.",
title="Revisiting the Readability Assessment of Texts in Portuguese",
booktitle="Advances in Artificial Intelligence -- IBERAMIA 2010",
year="2010",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="306--315",
isbn="978-3-642-16952-6"
}
```
#### RSSE
```
@inproceedings{sakhovskiy2021rusimplesenteval,
title={{RuSimpleSentEval-2021 shared task:} evaluating sentence simplification for Russian},
author={Sakhovskiy, Andrey and Izhevskaya, Alexandra and Pestova, Alena and Tutubalina, Elena and Malykh, Valentin and Smurov, Ivana and Artemova, Ekaterina},
booktitle={Proceedings of the International Conference “Dialogue},
pages={607--617},
year={2021}
}
```
#### RuAdapt
```
@inproceedings{Dmitrieva2021Quantitative,
title={A quantitative study of simplification strategies in adapted texts for L2 learners of Russian},
author={Dmitrieva, Anna and Laposhina, Antonina and Lebedeva, Maria},
booktitle={Proceedings of the International Conference “Dialogue},
pages={191--203},
year={2021}
}
```
```
@inproceedings{dmitrieva-tiedemann-2021-creating,
title = "Creating an Aligned {R}ussian Text Simplification Dataset from Language Learner Data",
author = {Dmitrieva, Anna and
Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing",
month = apr,
year = "2021",
address = "Kiyv, Ukraine",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.bsnlp-1.8",
pages = "73--79",
abstract = "Parallel language corpora where regular texts are aligned with their simplified versions can be used in both natural language processing and theoretical linguistic studies. They are essential for the task of automatic text simplification, but can also provide valuable insights into the characteristics that make texts more accessible and reveal strategies that human experts use to simplify texts. Today, there exist a few parallel datasets for English and Simple English, but many other languages lack such data. In this paper we describe our work on creating an aligned Russian-Simple Russian dataset composed of Russian literature texts adapted for learners of Russian as a foreign language. This will be the first parallel dataset in this domain, and one of the first Simple Russian datasets in general.",
}
```
#### RuWikiLarge
```
@inproceedings{sakhovskiy2021rusimplesenteval,
title={{RuSimpleSentEval-2021 shared task:} evaluating sentence simplification for Russian},
author={Sakhovskiy, Andrey and Izhevskaya, Alexandra and Pestova, Alena and Tutubalina, Elena and Malykh, Valentin and Smurov, Ivana and Artemova, Ekaterina},
booktitle={Proceedings of the International Conference “Dialogue},
pages={607--617},
year={2021}
}
```
#### SIMPITIKI
```
@article{tonelli2016simpitiki,
title={SIMPITIKI: a Simplification corpus for Italian},
author={Tonelli, Sara and Aprosio, Alessio Palmero and Saltori, Francesca},
journal={Proceedings of CLiC-it},
year={2016}
}
```
#### Simple German
```
@inproceedings{battisti-etal-2020-corpus,
title = "A Corpus for Automatic Readability Assessment and Text Simplification of {G}erman",
author = {Battisti, Alessia and
Pf{\"u}tze, Dominik and
S{\"a}uberli, Andreas and
Kostrzewa, Marek and
Ebling, Sarah},
booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.404",
pages = "3302--3311",
abstract = "In this paper, we present a corpus for use in automatic readability assessment and automatic text simplification for German, the first of its kind for this language. The corpus is compiled from web sources and consists of parallel as well as monolingual-only (simplified German) data amounting to approximately 6,200 documents (nearly 211,000 sentences). As a unique feature, the corpus contains information on text structure (e.g., paragraphs, lines), typography (e.g., font type, font style), and images (content, position, and dimensions). While the importance of considering such information in machine learning tasks involving simplified language, such as readability assessment, has repeatedly been stressed in the literature, we provide empirical evidence for its benefit. We also demonstrate the added value of leveraging monolingual-only data for automatic text simplification via machine translation through applying back-translation, a data augmentation technique.",
language = "English",
ISBN = "979-10-95546-34-4",
}
```
#### Simplext
```
@article{10.1145/2738046,
author = {Saggion, Horacio and \v{S}tajner, Sanja and Bott, Stefan and Mille, Simon and Rello, Luz and Drndarevic, Biljana},
title = {Making It Simplext: Implementation and Evaluation of a Text Simplification System for Spanish},
year = {2015},
issue_date = {June 2015}, publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {6},
number = {4},
issn = {1936-7228},
url = {https://doi.org/10.1145/2738046},
doi = {10.1145/2738046},
journal = {ACM Trans. Access. Comput.},
month = {may},
articleno = {14},
numpages = {36},
keywords = {Spanish, text simplification corpus, human evaluation, readability measures}
}
```
#### SimplifyUR
```
@inproceedings{qasmi-etal-2020-simplifyur,
title = "{S}implify{UR}: Unsupervised Lexical Text Simplification for {U}rdu",
author = "Qasmi, Namoos Hayat and
Zia, Haris Bin and
Athar, Awais and
Raza, Agha Ali",
booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.428",
pages = "3484--3489",
language = "English",
ISBN = "979-10-95546-34-4",
}
```
#### SloTS
```
@misc{gorenc2022slovene,
title = {Slovene text simplification dataset {SloTS}},
author = {Gorenc, Sabina and Robnik-{\v S}ikonja, Marko},
url = {http://hdl.handle.net/11356/1682},
note = {Slovenian language resource repository {CLARIN}.{SI}},
copyright = {Creative Commons - Attribution 4.0 International ({CC} {BY} 4.0)},
issn = {2820-4042},
year = {2022}
}
```
#### Terence and Teacher
```
@inproceedings{brunato-etal-2015-design,
title = "Design and Annotation of the First {I}talian Corpus for Text Simplification",
author = "Brunato, Dominique and
Dell{'}Orletta, Felice and
Venturi, Giulia and
Montemagni, Simonetta",
booktitle = "Proceedings of the 9th Linguistic Annotation Workshop",
month = jun,
year = "2015",
address = "Denver, Colorado, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W15-1604",
doi = "10.3115/v1/W15-1604",
pages = "31--41",
}
```
#### TextComplexityDE
```
@article{naderi2019subjective,
title={Subjective Assessment of Text Complexity: A Dataset for German Language},
author={Naderi, Babak and Mohtaj, Salar and Ensikat, Kaspar and M{\"o}ller, Sebastian},
journal={arXiv preprint arXiv:1904.07733},
year={2019}
}
```
#### WikiAuto
```
@inproceedings{acl/JiangMLZX20,
author = {Chao Jiang and
Mounica Maddela and
Wuwei Lan and
Yang Zhong and
Wei Xu},
editor = {Dan Jurafsky and
Joyce Chai and
Natalie Schluter and
Joel R. Tetreault},
title = {Neural {CRF} Model for Sentence Alignment in Text Simplification},
booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational
Linguistics, {ACL} 2020, Online, July 5-10, 2020},
pages = {7943--7960},
publisher = {Association for Computational Linguistics},
year = {2020},
url = {https://www.aclweb.org/anthology/2020.acl-main.709/}
}
```
#### WikiLargeFR
```
@inproceedings{cardon-grabar-2020-french,
title = "{F}rench Biomedical Text Simplification: When Small and Precise Helps",
author = "Cardon, R{\'e}mi and
Grabar, Natalia",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.62",
doi = "10.18653/v1/2020.coling-main.62",
pages = "710--716",
abstract = "We present experiments on biomedical text simplification in French. We use two kinds of corpora {--} parallel sentences extracted from existing health comparable corpora in French and WikiLarge corpus translated from English to French {--} and a lexicon that associates medical terms with paraphrases. Then, we train neural models on these parallel corpora using different ratios of general and specialized sentences. We evaluate the results with BLEU, SARI and Kandel scores. The results point out that little specialized data helps significantly the simplification.",
}
```
## Data Availability
### Public Datasets
Most of the public datasets are available as a part of this MultiSim Repo. A few are still pending availability. For all resources we provide alternative download links.
| Dataset | Language | Availability in MultiSim Repo | Alternative Link |
|---|---|---|---|
| ASSET | English | Available | https://huggingface.co/datasets/asset |
| WikiAuto | English | Available | https://huggingface.co/datasets/wiki_auto |
| CLEAR | French | Available | http://natalia.grabar.free.fr/resources.php#remi |
| WikiLargeFR | French | Available | http://natalia.grabar.free.fr/resources.php#remi |
| GEOLino | German | Available | https://github.com/Jmallins/ZEST-data |
| TextComplexityDE | German | Available | https://github.com/babaknaderi/TextComplexityDE |
| AdminIT | Italian | Available | https://github.com/Unipisa/admin-It |
| Simpitiki | Italian | Available | https://github.com/dhfbk/simpitiki# |
| PaCCSS-IT | Italian | Available | http://www.italianlp.it/resources/paccss-it-parallel-corpus-of-complex-simple-sentences-for-italian/ |
| Terence and Teacher | Italian | Available | http://www.italianlp.it/resources/terence-and-teacher/ |
| Easy Japanese | Japanese | Available | https://www.jnlp.org/GengoHouse/snow/t15 |
| Easy Japanese Extended | Japanese | Available | https://www.jnlp.org/GengoHouse/snow/t23 |
| RuAdapt Encyclopedia | Russian | Available | https://github.com/Digital-Pushkin-Lab/RuAdapt |
| RuAdapt Fairytales | Russian | Available | https://github.com/Digital-Pushkin-Lab/RuAdapt |
| RuSimpleSentEval | Russian | Available | https://github.com/dialogue-evaluation/RuSimpleSentEval |
| RuWikiLarge | Russian | Available | https://github.com/dialogue-evaluation/RuSimpleSentEval |
| SloTS | Slovene | Available | https://github.com/sabina-skubic/text-simplification-slovene |
| SimplifyUR | Urdu | Pending | https://github.com/harisbinzia/SimplifyUR |
| PorSimples | Brazilian Portuguese | Available | [[email protected]](mailto:[email protected]) |
### On Request Datasets
The authors of the original papers must be contacted for on request datasets. Contact information for the authors of each dataset is provided below.
| Dataset | Language | Contact |
|---|---|---|
| CBST | Basque | http://www.ixa.eus/node/13007?language=en <br/> [[email protected]](mailto:[email protected]) |
| DSim | Danish | [[email protected]](mailto:[email protected]) |
| Newsela EN | English | [https://newsela.com/data/](https://newsela.com/data/) |
| Newsela ES | Spanish | [https://newsela.com/data/](https://newsela.com/data/) |
| German News | German | [[email protected]](mailto:[email protected]) |
| Simple German | German | [[email protected]](mailto:[email protected]) |
| Simplext | Spanish | [[email protected]](mailto:[email protected]) |
| RuAdapt Literature | Russian | Partially Available: https://github.com/Digital-Pushkin-Lab/RuAdapt <br/> Full Dataset: [[email protected]](mailto:[email protected]) |
|
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] |
svjack/cmmlu_ed
|
svjack
| 2023-07-24T06:56:54Z | 35 | 0 |
[
"task_categories:multiple-choice",
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:zh",
"license:cc-by-nc-4.0",
"chinese",
"llm",
"evaluation",
"arxiv:2306.09212",
"region:us"
] |
[
"multiple-choice",
"question-answering"
] | 2023-07-24T06:30:20Z |
---
license: cc-by-nc-4.0
task_categories:
- multiple-choice
- question-answering
language:
- zh
tags:
- chinese
- llm
- evaluation
pretty_name: CMMLU
size_categories:
- 10K<n<100K
---
# CMMLU: Measuring massive multitask language understanding in Chinese
- **Homepage:** [https://github.com/haonan-li/CMMLU](https://github.com/haonan-li/CMMLU)
- **Repository:** [https://huggingface.co/datasets/haonan-li/cmmlu](https://huggingface.co/datasets/haonan-li/cmmlu)
- **Paper:** [CMMLU: Measuring Chinese Massive Multitask Language Understanding](https://arxiv.org/abs/2306.09212).
## Table of Contents
- [Introduction](#introduction)
- [Leaderboard](#leaderboard)
- [Data](#data)
- [Citation](#citation)
- [License](#license)
## Introduction
CMMLU is a comprehensive Chinese assessment suite specifically designed to evaluate the advanced knowledge and reasoning abilities of LLMs within the Chinese language and cultural context.
CMMLU covers a wide range of subjects, comprising 67 topics that span from elementary to advanced professional levels. It includes subjects that require computational expertise, such as physics and mathematics, as well as disciplines within humanities and social sciences.
Many of these tasks are not easily translatable from other languages due to their specific contextual nuances and wording.
Furthermore, numerous tasks within CMMLU have answers that are specific to China and may not be universally applicable or considered correct in other regions or languages.
## Leaderboard
Latest leaderboard is in our [github](https://github.com/haonan-li/CMMLU).
## Data
We provide development and test dataset for each of 67 subjects, with 5 questions in development set and 100+ quesitons in test set.
Each question in the dataset is a multiple-choice questions with 4 choices and only one choice as the correct answer.
Here are two examples:
```
题目：同一物种的两类细胞各产生一种分泌蛋白，组成这两种蛋白质的各种氨基酸含量相同，但排列顺序不同。其原因是参与这两种蛋白质合成的：
A. tRNA种类不同
B. 同一密码子所决定的氨基酸不同
C. mRNA碱基序列不同
D. 核糖体成分不同
答案是：C
```
```
题目：某种植物病毒V是通过稻飞虱吸食水稻汁液在水稻间传播的。稻田中青蛙数量的增加可减少该病毒在水稻间的传播。下列叙述正确的是：
A. 青蛙与稻飞虱是捕食关系
B. 水稻和病毒V是互利共生关系
C. 病毒V与青蛙是寄生关系
D. 水稻与青蛙是竞争关系
答案是：
```
#### Load data
```python
from datasets import load_dataset
cmmlu=load_dataset(r"haonan-li/cmmlu", 'agronomy')
print(cmmlu['test'][0])
```
#### Load all data at once
```python
task_list = ['agronomy', 'anatomy', 'ancient_chinese', 'arts', 'astronomy', 'business_ethics', 'chinese_civil_service_exam', 'chinese_driving_rule', 'chinese_food_culture', 'chinese_foreign_policy', 'chinese_history', 'chinese_literature',
'chinese_teacher_qualification', 'clinical_knowledge', 'college_actuarial_science', 'college_education', 'college_engineering_hydrology', 'college_law', 'college_mathematics', 'college_medical_statistics', 'college_medicine', 'computer_science',
'computer_security', 'conceptual_physics', 'construction_project_management', 'economics', 'education', 'electrical_engineering', 'elementary_chinese', 'elementary_commonsense', 'elementary_information_and_technology', 'elementary_mathematics',
'ethnology', 'food_science', 'genetics', 'global_facts', 'high_school_biology', 'high_school_chemistry', 'high_school_geography', 'high_school_mathematics', 'high_school_physics', 'high_school_politics', 'human_sexuality',
'international_law', 'journalism', 'jurisprudence', 'legal_and_moral_basis', 'logical', 'machine_learning', 'management', 'marketing', 'marxist_theory', 'modern_chinese', 'nutrition', 'philosophy', 'professional_accounting', 'professional_law',
'professional_medicine', 'professional_psychology', 'public_relations', 'security_study', 'sociology', 'sports_science', 'traditional_chinese_medicine', 'virology', 'world_history', 'world_religions']
from datasets import load_dataset
cmmlu = {k: load_dataset(r"haonan-li/cmmlu", k) for k in task_list}
```
## Citation
```
@misc{li2023cmmlu,
title={CMMLU: Measuring massive multitask language understanding in Chinese},
author={Haonan Li and Yixuan Zhang and Fajri Koto and Yifei Yang and Hai Zhao and Yeyun Gong and Nan Duan and Timothy Baldwin},
year={2023},
eprint={2306.09212},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## License
The CMMLU dataset is licensed under a
[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-nc-sa/4.0/).
|
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reginaboateng/Bioasq7b_6b_list
|
reginaboateng
| 2023-08-02T14:57:12Z | 35 | 0 |
[
"region:us"
] | null | 2023-08-02T14:57:11Z |
---
dataset_info:
features:
- name: context
dtype: string
- name: question
dtype: string
- name: id
dtype: string
- name: answers
struct:
- name: answer_start
sequence: int64
- name: text
sequence: string
splits:
- name: train
num_bytes: 27573422
num_examples: 16239
download_size: 5435398
dataset_size: 27573422
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "Bioasq7b_6b_list"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
PL-MTEB/ppc-pairclassification
|
PL-MTEB
| 2023-08-11T11:00:22Z | 35 | 0 |
[
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | 2023-08-11T11:00:07Z |
---
license: cc-by-nc-sa-4.0
---
|
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dim/essayforum_writing_prompts_6k
|
dim
| 2023-08-16T20:37:43Z | 35 | 1 |
[
"region:us"
] | null | 2023-08-16T01:03:40Z |
---
dataset_info:
features:
- name: prompt
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 21696702
num_examples: 6361
download_size: 11796178
dataset_size: 21696702
---
# Dataset Card for "essayforum_writing_prompts_6k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
jingwora/unstructured-data-multilingual
|
jingwora
| 2023-08-19T03:46:36Z | 35 | 0 |
[
"region:us"
] | null | 2023-08-18T08:23:41Z |
---
dataset_info:
features:
- name: language
dtype: string
- name: id
dtype: string
- name: product_id
dtype: string
- name: category
dtype: string
- name: sub_category
dtype: string
- name: product_name
dtype: string
- name: product_detail
dtype: string
- name: image_files
dtype: string
- name: review
dtype: string
- name: star
dtype: string
- name: sentiment
dtype: string
splits:
- name: en
num_bytes: 11790
num_examples: 24
- name: ja
num_bytes: 10499
num_examples: 24
- name: th
num_bytes: 12716
num_examples: 24
download_size: 34282
dataset_size: 35005
configs:
- config_name: default
data_files:
- split: en
path: data/en-*
- split: ja
path: data/ja-*
- split: th
path: data/th-*
---
# Dataset Card for "unstructured-data-multilingual"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
baber/hendrycks_math
|
baber
| 2023-08-25T21:15:56Z | 35 | 0 |
[
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"arxiv:2103.03874",
"region:us"
] |
[
"text-generation"
] | 2023-08-19T14:28:52Z |
---
license: mit
task_categories:
- text-generation
language:
- en
pretty_name: MATH
size_categories:
- 10K<n<100K
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:** https://github.com/hendrycks/math/blob/main/README.md
- **Repository:** https://github.com/hendrycks/math
- **Paper:** https://arxiv.org/abs/2103.03874
### Dataset Summary
MATH contains 12,500 challenging competition mathematics problems. Each problem in MATH has a full step-by-step solution which can be used to teach models to generate answer derivations and explanation
This dataset card aims to be a base template for new datasets.
### Languages
[English]
## Dataset Structure
### Data Instances
7 sub-datasets
### Data Splits
training: 7500
test: 5000
## Additional Information
### Licensing Information
MIT but check the [Legal Compliance](https://arxiv.org/pdf/2103.03874.pdf) section in appendix B of the paper as well as the [repo](https://github.com/hendrycks/math/blob/main/LICENSE).
### Citation Information
@article{hendrycksmath2021,
title={Measuring Mathematical Problem Solving With the MATH Dataset},
author={Dan Hendrycks and Collin Burns and Saurav Kadavath and Akul Arora and Steven Basart and Eric Tang and Dawn Song and Jacob Steinhardt},
journal={NeurIPS},
year={2021}
}
|
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mlabonne/medical-cases-fr
|
mlabonne
| 2023-09-09T16:24:18Z | 35 | 0 |
[
"region:us"
] | null | 2023-09-09T12:59:55Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: eval
path: data/eval-*
dataset_info:
features:
- name: Specialite
dtype: string
- name: Serie
dtype: int64
- name: Question
dtype: int64
- name: N_Question
dtype: int64
- name: Answer
dtype: string
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 38355502
num_examples: 8134
- name: eval
num_bytes: 1479803
num_examples: 366
download_size: 7807273
dataset_size: 39835305
---
# Dataset Card for "medical-cases-fr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
C-MTEB/T2Reranking_en2zh
|
C-MTEB
| 2023-09-09T16:11:54Z | 35 | 1 |
[
"region:us"
] | null | 2023-09-09T16:11:24Z |
---
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*
dataset_info:
features:
- name: query
dtype: string
- name: positive
sequence: string
- name: negative
sequence: string
splits:
- name: dev
num_bytes: 206929387
num_examples: 6129
download_size: 120405829
dataset_size: 206929387
---
# Dataset Card for "T2Reranking_en2zh"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
larryvrh/OASST_Top1_2023-08-25-Zh_Only
|
larryvrh
| 2023-09-20T19:33:28Z | 35 | 0 |
[
"task_categories:text-generation",
"task_categories:conversational",
"size_categories:n<1K",
"language:zh",
"region:us"
] |
[
"text-generation",
"conversational"
] | 2023-09-20T19:30:35Z |
---
dataset_info:
features:
- name: conversation
list:
- name: from
dtype: string
- name: value
dtype: string
splits:
- name: train
num_bytes: 1008722
num_examples: 662
download_size: 603882
dataset_size: 1008722
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
task_categories:
- text-generation
- conversational
language:
- zh
size_categories:
- n<1K
---
# Dataset Card for "OASST_Top1_2023-08-25-Zh_Only"
Filtered from [OpenAssistant/oasst_top1_2023-08-25](https://huggingface.co/datasets/OpenAssistant/oasst_top1_2023-08-25).
|
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] |
famepram/llama-2-jk48-demo
|
famepram
| 2023-11-08T04:14:12Z | 35 | 0 |
[
"license:other",
"region:us"
] | null | 2023-10-07T16:45:09Z |
---
license: other
license_name: readme.md
license_link: LICENSE
---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
- name: table
dtype: string
# Dataset Card for "Llama-2-JKT48-FP"
This dataset is intended to provide LLaMA 2 improved coding and instruction following capabilities, with a specific focus on JKT$* knowledges.
The dataset is created for exercising training llama2.
|
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] |
ismailiismail/FrEn_handpicks
|
ismailiismail
| 2023-10-14T19:55:36Z | 35 | 0 |
[
"region:us"
] | null | 2023-10-14T17:06:15Z |
---
dataset_info:
features:
- name: French
dtype: string
- name: English
dtype: string
splits:
- name: train
num_bytes: 34126
num_examples: 394
download_size: 16438
dataset_size: 34126
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "FrEn_handpicks"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
kardosdrur/europarl-scandinavian
|
kardosdrur
| 2023-10-25T08:38:29Z | 35 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-10-25T06:54:03Z |
---
license: mit
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: da
dtype: string
- name: en
dtype: string
- name: sv
dtype: string
splits:
- name: train
num_bytes: 620348322.4
num_examples: 1304296
- name: test
num_bytes: 155087080.6
num_examples: 326074
download_size: 488376564
dataset_size: 775435403.0
---
# Europarl Scandinavian Languages
The data originates from the Europarl parallel corpus, where English transcriptions of parliamentary discussions were aligned
with a number of other languages algorithmically.
In order to align Danish and Swedish corpora in the dataset, English entries were hashed with 128bit Murmurhash3,
and the Danish and Swedish transcriptions were joined on the obtained hash values.
Entries that had more than one pair in the other dataset were removed, this ensures that no false positives due to hash collisions
got into the dataset.
Source code is available in the repository.
The dataset was created for aiding the training of sentence transformer models in the Danish Foundation Models project.
|
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charsiu/libriphrase_meta
|
charsiu
| 2023-10-27T18:33:50Z | 35 | 0 |
[
"region:us"
] | null | 2023-10-27T18:30:29Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: anchor
dtype: string
- name: anchor_spk
dtype: int64
- name: anchor_text
dtype: string
- name: anchor_dur
dtype: float64
- name: comparison
dtype: string
- name: comparison_spk
dtype: int64
- name: comparison_text
dtype: string
- name: comparison_dur
dtype: float64
- name: type
dtype: string
- name: target
dtype: int64
- name: class
dtype: int64
- name: anchor_phone
dtype: string
- name: comparison_phone
dtype: string
splits:
- name: train
num_bytes: 53970720
num_examples: 203013
download_size: 8382220
dataset_size: 53970720
---
# Dataset Card for "libriphrase_meta"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
princeton-nlp/LLMBar
|
princeton-nlp
| 2023-10-29T15:35:34Z | 35 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-10-29T15:35:18Z |
---
license: mit
---
|
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] |
arminmrm93/usda_recipes_with_embed
|
arminmrm93
| 2023-11-07T22:39:51Z | 35 | 0 |
[
"region:us"
] | null | 2023-10-31T03:15:13Z |
---
dataset_info:
features:
- name: name
dtype: string
- name: cookTime
dtype: string
- name: prepTime
dtype: string
- name: recipeIngredient
dtype: string
- name: description
dtype: string
- name: totalTime
dtype: string
- name: recipeInstructions
dtype: string
- name: recipeYield
dtype: string
- name: id
dtype: int64
- name: embeddings
sequence: float32
splits:
- name: train
num_bytes: 445536
num_examples: 114
download_size: 592730
dataset_size: 445536
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "usda_recipes_with_embed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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jay401521/domain_test_balance
|
jay401521
| 2023-10-31T05:52:07Z | 35 | 0 |
[
"region:us"
] | null | 2023-10-31T05:52:02Z |
---
dataset_info:
features:
- name: id
dtype: int64
- name: domain
dtype:
class_label:
names:
'0': AIRL
'1': CAR
'2': COMM
'3': TECH
- name: label
dtype: int64
- name: rank
dtype: string
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 1789645
num_examples: 20648
download_size: 971736
dataset_size: 1789645
---
# Dataset Card for "domain_test_balance"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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peuvini/llama2Pesquisa
|
peuvini
| 2023-10-31T17:25:00Z | 35 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-10-31T16:57:43Z |
---
license: mit
---
|
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pphuc25/vlsp-dataset-2
|
pphuc25
| 2023-11-02T18:23:01Z | 35 | 0 |
[
"region:us"
] | null | 2023-11-02T18:16:34Z |
---
dataset_info:
features:
- name: audio
dtype: audio
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 6165849355.594
num_examples: 50482
download_size: 6304115752
dataset_size: 6165849355.594
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "vlsp-dataset-2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
timyangyazhou/ubuntu_irc_kummerfeld_ft_20_window_last_5_pseudo
|
timyangyazhou
| 2023-11-18T11:05:23Z | 35 | 0 |
[
"region:us"
] | null | 2023-11-08T02:32:01Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
- split: test
path: data/test-*
dataset_info:
features:
- name: canon_name
dtype: string
- name: id
dtype: int64
- name: parents
sequence: int64
- name: children
sequence: int64
- name: messages
sequence: string
- name: prediction
dtype: string
splits:
- name: train
num_bytes: 81419322
num_examples: 63982
- name: dev
num_bytes: 3052013
num_examples: 2397
- name: test
num_bytes: 6263006
num_examples: 4783
download_size: 0
dataset_size: 90734341
---
# Dataset Card for "ubuntu_irc_kummerfeld_ft_20_window_last_5_pseudo"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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Rewcifer/validation_2000_cutoff_llama_formatted
|
Rewcifer
| 2023-11-08T03:05:52Z | 35 | 0 |
[
"region:us"
] | null | 2023-11-08T03:05:50Z |
---
dataset_info:
features:
- name: labels_and_findings
dtype: string
- name: prompts
dtype: string
- name: true_findings
dtype: string
splits:
- name: train
num_bytes: 113806806
num_examples: 14551
download_size: 26372198
dataset_size: 113806806
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "validation_2000_cutoff_llama_formatted"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
Aarif1430/english-to-hindi
|
Aarif1430
| 2023-11-12T09:13:33Z | 35 | 0 |
[
"region:us"
] | null | 2023-11-09T09:29:28Z |
---
dataset_info:
features:
- name: english_sentence
dtype: string
- name: hindi_sentence
dtype: string
splits:
- name: train
num_bytes: 41188315
num_examples: 127705
download_size: 21737146
dataset_size: 41188315
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "english-to-hindi"
**Dataset Card: English-to-Hindi Translation**
**Overview:**
- **Dataset Name:** English-to-Hindi Translation
- **Dataset Size:** 128K sentences
- **Source:** Curated list of English sentences paired with their Hindi translations.
- **Use Case:** Training machine translation models, specifically English-to-Hindi translation using transformer architectures.
**Data Collection:**
- **Collection Method:** Manual translation by bilingual speakers.
- **Data Quality:** High quality with accurate translations.
**Dataset Composition:**
- **Language Pair:** English to Hindi
- **Text Type:** General sentences, covering a wide range of topics.
- **Text Length:** Varied lengths of sentences.
**Data Format:**
- **Format:** CSV, each row containing an English sentence and its corresponding Hindi translation.
**Licensing:**
- **License:** MIT
**Dataset Distribution:**
- **Availability:**
```python
from datasets import load_dataset
dataset = load_dataset("Aarif1430/english-to-hindi")
```
```shell
curl -X GET "https://datasets-server.huggingface.co/rows?dataset=Aarif1430%2Fenglish-to-hindi&config=default&split=train&offset=0&length=100"
```
- **Download Size:** 21.7 MB
**Potential Use Cases:**
- Training and evaluating machine translation models.
- Research in natural language processing, specifically in the field of translation.
**Limitations:**
- Limited coverage of domain-specific language or specialized terminology.
**Additional Information:**
- The dataset was created to facilitate research and development in English-to-Hindi machine translation. Researchers and developers are encouraged to contribute to and improve the dataset.
**Citation:**
- If you use this dataset in your work, please cite the dataset using the provided citation information.
**References:**
- https://huggingface.co/datasets/ai4bharat/samanantar
|
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tiagoblima/enem-tc-aqg
|
tiagoblima
| 2023-11-11T01:13:06Z | 35 | 0 |
[
"region:us"
] | null | 2023-11-11T00:58:24Z |
---
dataset_info:
features:
- name: paragraph
dtype: string
- name: question
dtype: string
- name: answers
struct:
- name: text
sequence: string
splits:
- name: train
num_bytes: 402614
num_examples: 388
download_size: 267848
dataset_size: 402614
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "enem-tc-aqg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
renumics/f1_dataset
|
renumics
| 2023-11-13T18:15:08Z | 35 | 2 |
[
"region:us"
] | null | 2023-11-13T10:33:33Z |
---
dataset_info:
features:
- name: Time
dtype: duration[ns]
- name: Driver
dtype: string
- name: DriverNumber
dtype: string
- name: LapTime
dtype: duration[ns]
- name: LapNumber
dtype: float64
- name: Stint
dtype: float64
- name: PitOutTime
dtype: duration[ns]
- name: PitInTime
dtype: duration[ns]
- name: Sector1Time
dtype: duration[ns]
- name: Sector2Time
dtype: duration[ns]
- name: Sector3Time
dtype: duration[ns]
- name: Sector1SessionTime
dtype: duration[ns]
- name: Sector2SessionTime
dtype: duration[ns]
- name: Sector3SessionTime
dtype: duration[ns]
- name: SpeedI1
dtype: float64
- name: SpeedI2
dtype: float64
- name: SpeedFL
dtype: float64
- name: SpeedST
dtype: float64
- name: IsPersonalBest
dtype: bool
- name: Compound
dtype: string
- name: TyreLife
dtype: float64
- name: FreshTyre
dtype: bool
- name: Team
dtype: string
- name: LapStartTime
dtype: duration[ns]
- name: LapStartDate
dtype: timestamp[ns]
- name: TrackStatus
dtype: string
- name: Position
dtype: float64
- name: Deleted
dtype: bool
- name: DeletedReason
dtype: string
- name: FastF1Generated
dtype: bool
- name: IsAccurate
dtype: bool
- name: DistanceToDriverAhead
sequence:
sequence: float64
- name: RPM
sequence:
sequence: float64
- name: Speed
sequence:
sequence: float64
- name: nGear
sequence:
sequence: float64
- name: Throttle
sequence:
sequence: float64
- name: Brake
sequence:
sequence: float64
- name: DRS
sequence:
sequence: float64
- name: X
sequence:
sequence: float64
- name: Y
sequence:
sequence: float64
- name: Z
sequence:
sequence: float64
- name: gear_vis
dtype: image
- name: speed_vis
dtype: image
- name: RPM_emb
sequence: float64
- name: Speed_emb
sequence: float64
- name: nGear_emb
sequence: float64
- name: Throttle_emb
sequence: float64
- name: Brake_emb
sequence: float64
- name: X_emb
sequence: float64
- name: Y_emb
sequence: float64
- name: Z_emb
sequence: float64
- name: portrait
dtype: image
splits:
- name: train
num_bytes: 561415487.5469999
num_examples: 1317
download_size: 300522146
dataset_size: 561415487.5469999
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "f1_dataset"
This dataset includes race telemetry data from the Formula1 Montreail 2023 GP. It was obtained from the Ergast API using the fastf1 library.
We built an [interactive demo](https://huggingface.co/spaces/renumics/f1_montreal_gp) for this dataset on Hugging Face spaces.
You can explore the dataset on your machine with [Spotlight](https://github.com/Renumics/spotlight):
```bash
pip install renumics-spotlight
```
```Python
import datasets
from renumics import spotlight
ds = datasets.load_dataset('renumics/f1_dataset', split='train')
dtypes = {"DistanceToDriverAhead": spotlight.Sequence1D, "RPM": spotlight.Sequence1D, "Speed": spotlight.Sequence1D, "nGear": spotlight.Sequence1D,
"Throttle": spotlight.Sequence1D, "Brake": spotlight.Sequence1D, "DRS": spotlight.Sequence1D, "X": spotlight.Sequence1D, "Y": spotlight.Sequence1D, "Z": spotlight.Sequence1D,
'RPM_emb': spotlight.Embedding, 'Speed_emb': spotlight.Embedding, 'nGear_emb': spotlight.Embedding, 'Throttle_emb': spotlight.Embedding, 'Brake_emb': spotlight.Embedding,
'X_emb': spotlight.Embedding, 'Y_emb': spotlight.Embedding, 'Z_emb': spotlight.Embedding}
spotlight.show(ds, dtype=dtypes)
```
|
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zaanind/qasimple
|
zaanind
| 2023-11-15T15:46:26Z | 35 | 1 |
[
"region:us"
] | null | 2023-11-15T15:46:24Z |
---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 18378
num_examples: 25
download_size: 16549
dataset_size: 18378
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "qasimple"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
atmallen/qm_bob_hard_4_grader_last_1.0e
|
atmallen
| 2023-11-16T18:23:13Z | 35 | 0 |
[
"region:us"
] | null | 2023-11-16T03:26:36Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: alice_label
dtype: bool
- name: bob_label
dtype: bool
- name: difficulty
dtype: int64
- name: statement
dtype: string
- name: choices
sequence: string
- name: character
dtype: string
- name: label
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
splits:
- name: train
num_bytes: 2899268.0
num_examples: 37091
- name: validation
num_bytes: 310182.0
num_examples: 3969
- name: test
num_bytes: 306854.0
num_examples: 3926
download_size: 1006241
dataset_size: 3516304.0
---
# Dataset Card for "qm_bob_hard_4_grader_last_1.0e"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
josiauhlol/fsGPT
|
josiauhlol
| 2023-11-18T05:13:05Z | 35 | 0 |
[
"task_categories:conversational",
"language:en",
"license:openrail",
"ai",
"region:us"
] |
[
"conversational"
] | 2023-11-18T04:56:28Z |
---
language: en
license: openrail
pretty_name: freesmartGPT
task_categories:
- conversational
tags:
- ai
---
# fsGPT
|
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michelcarroll/llama2-earnings-stock-prediction-fine-tune-v2
|
michelcarroll
| 2023-12-02T16:55:56Z | 35 | 0 |
[
"region:us"
] | null | 2023-11-19T17:22:11Z |
---
dataset_info:
features:
- name: completion
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 87920323
num_examples: 111140
- name: development
num_bytes: 26603449
num_examples: 33284
- name: test
num_bytes: 840735
num_examples: 1000
download_size: 47167270
dataset_size: 115364507
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: development
path: data/development-*
- split: test
path: data/test-*
---
|
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] |
dthoek/DocLayNet-large_paragraphs_encoded_ml512
|
dthoek
| 2023-11-21T10:29:34Z | 35 | 0 |
[
"region:us"
] | null | 2023-11-19T23:26:10Z |
---
dataset_info:
features:
- name: page_hash
dtype: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: normalized_bboxes
sequence:
sequence: int64
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 3793445572
num_examples: 150701
- name: validation
num_bytes: 401719948
num_examples: 15959
- name: test
num_bytes: 275482368
num_examples: 10944
download_size: 110722707
dataset_size: 4470647888
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
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Innovina/Test_Youtube_Links
|
Innovina
| 2023-11-23T12:07:54Z | 35 | 0 |
[
"task_categories:text-generation",
"language:it",
"license:mit",
"code",
"region:us"
] |
[
"text-generation"
] | 2023-11-22T15:08:10Z |
---
license: mit
task_categories:
- text-generation
language:
- it
tags:
- code
pretty_name: Test
---
|
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Michaelkassouf/Ferrari_SD1
|
Michaelkassouf
| 2023-11-25T13:52:04Z | 35 | 0 |
[
"region:us"
] | null | 2023-11-25T13:50:59Z |
---
dataset_info:
features:
- name: image
dtype: string
- name: caption
dtype: string
splits:
- name: train
num_bytes: 3495120
num_examples: 35553
download_size: 1051219
dataset_size: 3495120
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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jytjyt05/t_to_m7
|
jytjyt05
| 2023-12-02T01:20:01Z | 35 | 0 |
[
"region:us"
] | null | 2023-12-02T01:17:57Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: caption
dtype: string
splits:
- name: train
num_bytes: 2001176362.999
num_examples: 14061
download_size: 2094980526
dataset_size: 2001176362.999
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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DDSC/dkhate
|
DDSC
| 2023-05-17T06:19:43Z | 34 | 4 |
[
"task_categories:text-classification",
"task_ids:hate-speech-detection",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:da",
"license:cc-by-4.0",
"arxiv:1908.04531",
"region:us"
] |
[
"text-classification"
] | 2022-03-02T23:29:22Z |
---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- da
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: DKHate
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- hate-speech-detection
extra_gated_prompt: "Content warning: This dataset contains harmful text (abusive language, hate speech)."
paperswithcode_id: dkhate
---
# Dataset Card for DKHate
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://stromberg.ai/publication/offensivelanguageandhatespeechdetectionfordanish/](https://stromberg.ai/publication/offensivelanguageandhatespeechdetectionfordanish/)
- **Repository:** [https://github.com/StrombergNLP/dkhate](https://github.com/StrombergNLP/dkhate)
- **Paper:** [https://https://aclanthology.org/2020.lrec-1.430/](aclanthology.org/2020.lrec-1.430/), [https://arxiv.org/abs/1908.04531](https://arxiv.org/abs/1908.04531)
- **Direct Download**: [https://figshare.com/articles/dataset/Danish_Hate_Speech_Abusive_Language_data/12220805](https://figshare.com/articles/dataset/Danish_Hate_Speech_Abusive_Language_data/12220805)
- **Point of Contact:** [Leon Derczynski](mailto:[email protected])
### Dataset Summary
This dataset consists of anonymised Danish Twitter data that has been annotated for hate speech. All credits go to the authors of the following paper, who created the dataset:
[Offensive Language and Hate Speech Detection for Danish](https://aclanthology.org/2020.lrec-1.430) (Sigurbergsson & Derczynski, LREC 2020)
### Supported Tasks and Leaderboards
This dataset is suitable for hate speech detection.
* PwC leaderboard for Task A: [Hate Speech Detection on DKhate](https://paperswithcode.com/sota/hate-speech-detection-on-dkhate)
### Languages
This dataset is in Danish.
## Dataset Structure
### Data Instances
Every entry in the dataset has a tweet and an associated label.
### Data Fields
An entry in the dataset consists of the following fields:
- `text` (`str`): The tweet content.
- `label` (`str`): The label of the `text`. Can be either "OFF" or "NOT", being offensive and not offensive, respectively.
### Data Splits
A `train` and `test` split is available, which are identical to the original splits. There are 2,960 tweets in the training split and 329 in the test split.
## Additional Information
### Dataset Curators
The curation of the dataset is solely due to the authors of [the original paper](https://aclanthology.org/2020.lrec-1.430/): Gudbjartur Ingi Sigurbergsson and Leon Derczynski.
### Licensing Information
The dataset is released under the CC BY 4.0 license.
### Citation Information
```
@inproceedings{sigurbergsson2020offensive,
title={Offensive Language and Hate Speech Detection for Danish},
author={Sigurbergsson, Gudbjartur Ingi and Derczynski, Leon},
booktitle={Proceedings of the 12th Language Resources and Evaluation Conference},
pages={3498--3508},
year={2020}
}
```
### Contributions
Thanks to [@saattrupdan](https://github.com/saattrupdan) for adding this dataset to the Hugging Face Hub.
|
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projecte-aina/teca
|
projecte-aina
| 2023-11-25T05:30:02Z | 34 | 0 |
[
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:unknown",
"language:ca",
"license:cc-by-nc-nd-4.0",
"arxiv:2107.07903",
"region:us"
] |
[
"text-classification"
] | 2022-03-02T23:29:22Z |
---
YAML tags:
annotations_creators:
- expert-generated
language_creators:
- found
language:
- ca
license:
- cc-by-nc-nd-4.0
multilinguality:
- monolingual
pretty_name: teca
size_categories:
- unknown
source_datasets: []
task_categories:
- text-classification
task_ids:
- natural-language-inference
---
# Dataset Card for TE-ca
## Dataset Description
- **Website:** https://zenodo.org/record/4761458
- **Paper:** [Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? A Comprehensive Assessment for Catalan](https://arxiv.org/abs/2107.07903)
- **Point of Contact:** [Carlos Rodríguez-Penagos]([email protected]) and [Carme Armentano-Oller]([email protected])
### Dataset Summary
TE-ca is a dataset of textual entailment in Catalan, which contains 21,163 pairs of premises and hypotheses, annotated according to the inference relation they have (implication, contradiction or neutral).
This dataset was developed by [BSC TeMU](https://temu.bsc.es/) as part of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina/), to enrich the [Catalan Language Understanding Benchmark (CLUB)](https://club.aina.bsc.es/).
This work is licensed under an <a rel="license" href="https://creativecommons.org/licenses/by-nc-nd/4.0/">Attribution-NonCommercial-NoDerivatives 4.0 International License</a>.
### Supported Tasks and Leaderboards
Textual entailment, Text classification, Language Model
### Languages
The dataset is in Catalan (`ca-ES`).
## Dataset Structure
### Data Instances
Three JSON files, one for each split.
### Example:
<pre>
{
"id": 3247,
"premise": "L'ONU adopta a Marràqueix un pacte no vinculant per les migracions",
"hypothesis": "S'acorden unes recomanacions per les persones migrades a Marràqueix",
"label": "0"
},
{
"id": 2825,
"premise": "L'ONU adopta a Marràqueix un pacte no vinculant per les migracions",
"hypothesis": "Les persones migrades seran acollides a Marràqueix",
"label": "1"
},
{
"id": 2431,
"premise": "L'ONU adopta a Marràqueix un pacte no vinculant per les migracions",
"hypothesis": "L'acord impulsat per l'ONU lluny de tancar-se",
"label": "2"
},
</pre>
### Data Fields
- premise: text
- hypothesis: text related to the premise
- label: relation between premise and hypothesis:
* 0: entailment
* 1: neutral
* 2: contradiction
### Data Splits
* dev.json: 2116 examples
* test.json: 2117 examples
* train.json: 16930 examples
## Dataset Creation
### Curation Rationale
We created this dataset to contribute to the development of language models in Catalan, a low-resource language.
### Source Data
Source sentences are extracted from the [Catalan Textual Corpus](https://doi.org/10.5281/zenodo.4519349) and from [VilaWeb](https://www.vilaweb.cat) newswire.
#### Initial Data Collection and Normalization
12000 sentences from the BSC [Catalan Textual Corpus](https://doi.org/10.5281/zenodo.4519349), together with 6200 headers from the Catalan news site [VilaWeb](https://www.vilaweb.cat), were chosen randomly. We filtered them by different criteria, such as length and stand-alone intelligibility. For each selected text, we commissioned 3 hypotheses (one for each entailment category) to be written by a team of native annotators.
Some sentence pairs were excluded because of inconsistencies.
#### Who are the source language producers?
The Catalan Textual Corpus corpus consists of several corpora gathered from web crawling and public corpora. More information can be found [here](https://doi.org/10.5281/zenodo.4519349).
[VilaWeb](https://www.vilaweb.cat) is a Catalan newswire.
### Annotations
#### Annotation process
We commissioned 3 hypotheses (one for each entailment category) to be written by a team of annotators.
#### Who are the annotators?
Annotators are a team of native language collaborators from two independent companies.
### Personal and Sensitive Information
No personal or sensitive information included.
## Considerations for Using the Data
### Social Impact of Dataset
We hope this dataset contributes to the development of language models in Catalan, a low-resource language.
### Discussion of Biases
[N/A]
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
Text Mining Unit (TeMU) at the Barcelona Supercomputing Center ([email protected])
This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
### Licensing Information
This work is licensed under an <a rel="license" href="https://creativecommons.org/licenses/by-nc-nd/4.0/">Attribution-NonCommercial-NoDerivatives 4.0 International License</a>.
### Citation Information
```
@inproceedings{armengol-estape-etal-2021-multilingual,
title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}atalan",
author = "Armengol-Estap{\'e}, Jordi and
Carrino, Casimiro Pio and
Rodriguez-Penagos, Carlos and
de Gibert Bonet, Ona and
Armentano-Oller, Carme and
Gonzalez-Agirre, Aitor and
Melero, Maite and
Villegas, Marta",
booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-acl.437",
doi = "10.18653/v1/2021.findings-acl.437",
pages = "4933--4946",
}
```
[DOI](https://doi.org/10.5281/zenodo.4529183)
|
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] |
rakkaalhazimi/hotel-review
|
rakkaalhazimi
| 2022-03-12T07:23:47Z | 34 | 0 |
[
"license:gpl-3.0",
"region:us"
] | null | 2022-03-12T05:52:57Z |
---
license: gpl-3.0
---
# Review Hotel in Indonesia
### Dataset Summary
Data about reviews of hotels in Indonesia
### Languages
Indonesia
## Dataset Structure
### Data Fields
- review_id : unique identification code of each review
- review_text : the main review of text
- category : label for each review, positive (1) or negative (0)
|
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hackathon-pln-es/readability-es-caes
|
hackathon-pln-es
| 2023-04-13T08:51:40Z | 34 | 1 |
[
"task_categories:text-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:es",
"license:cc-by-4.0",
"readability",
"region:us"
] |
[
"text-classification"
] | 2022-04-03T21:42:19Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- es
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
pretty_name: readability-es-caes
tags:
- readability
---
# Dataset Card for [readability-es-caes]
## Dataset Description
### Dataset Summary
This dataset is a compilation of short articles from websites dedicated to learn Spanish as a second language. These articles have been compiled from the following sources:
- [CAES corpus](http://galvan.usc.es/caes/) (Martínez et al., 2019): the "Corpus de Aprendices del Español" is a collection of texts produced by Spanish L2 learners from Spanish learning centers and universities. These text are produced by students of all levels (A1 to C1), with different backgrounds (11 native languages) and levels of experience.
### Languages
Spanish
## Dataset Structure
Texts are tokenized to create a paragraph-based dataset
### Data Fields
The dataset is formatted as a json lines and includes the following fields:
- **Category:** when available, this includes the level of this text according to the Common European Framework of Reference for Languages (CEFR).
- **Level:** standardized readability level: simple or complex.
- **Level-3:** standardized readability level: basic, intermediate or advanced.
- **Text:** original text formatted into sentences.
## Additional Information
### Licensing Information
https://creativecommons.org/licenses/by-nc-sa/4.0/
### Citation Information
Please cite this page to give credit to the authors :)
### Team
- [Laura Vásquez-Rodríguez](https://lmvasque.github.io/)
- [Pedro Cuenca](https://twitter.com/pcuenq)
- [Sergio Morales](https://www.fireblend.com/)
- [Fernando Alva-Manchego](https://feralvam.github.io/)
|
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strombergnlp/twitter_pos_vcb
|
strombergnlp
| 2022-10-25T21:42:56Z | 34 | 2 |
[
"task_categories:token-classification",
"task_ids:part-of-speech",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
] |
[
"token-classification"
] | 2022-04-28T10:10:59Z |
---
annotations_creators:
- machine-generated
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- part-of-speech
paperswithcode_id: twitter-pos-vcb
pretty_name: Twitter PoS VCB
---
# Dataset Card for "twitter-pos-vcb"
## 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://gate.ac.uk/wiki/twitter-postagger.html](https://gate.ac.uk/wiki/twitter-postagger.html)
- **Repository:** [https://github.com/GateNLP/gateplugin-Twitter](https://github.com/GateNLP/gateplugin-Twitter)
- **Paper:** [https://aclanthology.org/R13-1026.pdf](https://aclanthology.org/R13-1026.pdf)
- **Point of Contact:** [Leon Derczynski](https://github.com/leondz)
- **Size of downloaded dataset files:** 4.51 MiB
- **Size of the generated dataset:** 26.88 MB
- **Total amount of disk used:** 31.39 MB
### Dataset Summary
Part-of-speech information is basic NLP task. However, Twitter text
is difficult to part-of-speech tag: it is noisy, with linguistic errors and idiosyncratic style.
This data is the vote-constrained bootstrapped data generate to support state-of-the-art results.
The data is about 1.5 million English tweets annotated for part-of-speech using Ritter's extension of the PTB tagset.
The tweets are from 2012 and 2013, tokenized using the GATE tokenizer and tagged
jointly using the CMU ARK tagger and Ritter's T-POS tagger. Only when both these taggers' outputs
are completely compatible over a whole tweet, is that tweet added to the dataset.
This data is recommend for use a training data **only**, and not evaluation data.
For more details see https://gate.ac.uk/wiki/twitter-postagger.html and https://aclanthology.org/R13-1026.pdf
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
English, non-region-specific. `bcp47:en`
## Dataset Structure
### Data Instances
An example of 'train' looks as follows.
```
```
### Data Fields
The data fields are the same among all splits.
#### twitter_pos_vcb
- `id`: a `string` feature.
- `tokens`: a `list` of `string` features.
- `pos_tags`: a `list` of classification labels (`int`). Full tagset with indices:
```python
```
### Data Splits
| name |tokens|sentences|
|---------|----:|---------:|
|twitter-pos-vcb|1 543 126| 159 492|
## 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
Creative Commons Attribution 4.0 (CC-BY)
### Citation Information
```
@inproceedings{derczynski2013twitter,
title={Twitter part-of-speech tagging for all: Overcoming sparse and noisy data},
author={Derczynski, Leon and Ritter, Alan and Clark, Sam and Bontcheva, Kalina},
booktitle={Proceedings of the international conference recent advances in natural language processing ranlp 2013},
pages={198--206},
year={2013}
}
```
### Contributions
Author uploaded ([@leondz](https://github.com/leondz))
|
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google/wit
|
google
| 2022-07-04T10:47:07Z | 34 | 14 |
[
"task_categories:text-retrieval",
"task_categories:image-to-text",
"task_ids:image-captioning",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"source_datasets:extended|wikipedia",
"language:af",
"language:ar",
"language:ast",
"language:azb",
"language:be",
"language:bg",
"language:bn",
"language:br",
"language:ca",
"language:cs",
"language:cy",
"language:da",
"language:de",
"language:el",
"language:en",
"language:eo",
"language:es",
"language:et",
"language:eu",
"language:fa",
"language:fi",
"language:fr",
"language:fy",
"language:ga",
"language:gl",
"language:hr",
"language:hu",
"language:hy",
"language:id",
"language:it",
"language:iw",
"language:ja",
"language:ka",
"language:ko",
"language:la",
"language:lt",
"language:lv",
"language:mk",
"language:ml",
"language:ms",
"language:nl",
"language:nn",
"language:no",
"language:pl",
"language:pt",
"language:ro",
"language:ru",
"language:sk",
"language:sl",
"language:sr",
"language:sv",
"language:th",
"language:tr",
"language:uk",
"language:ur",
"language:vi",
"language:vo",
"language:zh",
"license:cc-by-sa-3.0",
"arxiv:2103.01913",
"region:us"
] |
[
"text-retrieval",
"image-to-text"
] | 2022-05-02T11:22:32Z |
---
annotations_creators:
- machine-generated
language_creators:
- found
language:
- af
- ar
- ast
- azb
- be
- bg
- bn
- br
- ca
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gl
- hr
- hu
- hy
- id
- it
- iw
- ja
- ka
- ko
- la
- lt
- lv
- mk
- ml
- ms
- nl
- nn
- 'no'
- pl
- pt
- ro
- ru
- sk
- sl
- sr
- sv
- th
- tr
- uk
- ur
- vi
- vo
- zh
license:
- cc-by-sa-3.0
multilinguality:
- multilingual
paperswithcode_id: wit
pretty_name: Wikipedia-based Image Text
size_categories:
- 10M<n<100M
source_datasets:
- original
- extended|wikipedia
task_categories:
- text-retrieval
- image-to-text
task_ids:
- text-retrieval-other-text-image-retrieval
- image-captioning
---
# Dataset Card for WIT
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Preprocessing](#dataset-preprocessing)
- [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:** [WIT homepage](https://github.com/google-research-datasets/wit)
- **Repository:** [WIT repository](https://github.com/google-research-datasets/wit)
- **Paper:** [WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning
](https://arxiv.org/abs/2103.01913)
- **Leaderboard:** [WIT leaderboard](https://www.kaggle.com/c/wikipedia-image-caption)
- **Point of Contact:** [WIT e-mail](mailto:[email protected])
### Dataset Summary
Wikipedia-based Image Text (WIT) Dataset is a large multimodal multilingual dataset. WIT is composed of a curated set of 37.6 million entity rich image-text examples with 11.5 million unique images across 108 Wikipedia languages. Its size enables WIT to be used as a pretraining dataset for multimodal machine learning models.
A few unique advantages of WIT:
* The largest multimodal dataset (time of this writing) by the number of image-text examples.
* A massively multilingual (first of its kind) with coverage for over 100+ languages.
* A collection of diverse set of concepts and real world entities.
* Brings forth challenging real-world test sets.
### Dataset Preprocessing
This dataset doesn't download the images locally by default. Instead, it exposes URLs to the images. To fetch the images, use the following code:
```python
from concurrent.futures import ThreadPoolExecutor
from functools import partial
import io
import urllib
import PIL.Image
from datasets import load_dataset
from datasets.utils.file_utils import get_datasets_user_agent
def fetch_single_image(image_url, timeout=None, retries=0):
for _ in range(retries + 1):
try:
request = urllib.request.Request(
image_url,
data=None,
headers={"user-agent": get_datasets_user_agent()},
)
with urllib.request.urlopen(request, timeout=timeout) as req:
image = PIL.Image.open(io.BytesIO(req.read()))
break
except Exception:
image = None
return image
def fetch_images(batch, num_threads, timeout=None, retries=0):
fetch_single_image_with_args = partial(fetch_single_image, timeout=timeout, retries=retries)
with ThreadPoolExecutor(max_workers=num_threads) as executor:
batch["image"] = list(executor.map(fetch_single_image_with_args, batch["image_url"]))
return batch
num_threads = 20
dset = load_dataset("wit")
dset = dset.map(fetch_images, batched=True, batch_size=100, fn_kwargs={"num_threads": num_threads})
```
### Supported Tasks and Leaderboards
- `image-captioning`: This dataset can be used to train a model for image captioning where the goal is to predict a caption given the image.
- `text-retrieval`: The goal in this task is to build a model that retrieves the text closest to an image.
In these tasks, any combination of the `caption_reference_description`, `caption_attribution_description` and `caption_alt_text_description` fields can be used as the input text/caption.
### Languages
The dataset contains examples from all Wikipedia languages, with the following stats:
Image-Text | # Lang | Uniq. Images | # Lang
------------ | ------ | ------------- | ------
total > 1M | 9 | images > 1M | 6
total > 500K | 10 | images > 500K | 12
total > 100K | 36 | images > 100K | 35
total > 50K | 15 | images > 50K | 17
total > 14K | 38 | images > 13K | 38
## Dataset Structure
### Data Instances
```
{
'language': 'en',
'page_url': 'https://en.wikipedia.org/wiki/Oxydactylus',
'image_url': 'https://upload.wikimedia.org/wikipedia/commons/5/5f/Oxydactylus_longipes_fm.jpg',
'page_title': 'Oxydactylus',
'section_title': None,
'hierarchical_section_title': 'Oxydactylus',
'caption_reference_description': None,
'caption_attribution_description': 'English: Mounted skeleton of Oxydactylus longipes in the Field Museum of Natural History.',
'caption_alt_text_description': None,
'mime_type': 'image/jpeg',
'original_height': 3564,
'original_width': 2748,
'is_main_image': True,
'attribution_passes_lang_id': True,
'page_changed_recently': True,
'context_page_description': 'Oxydactylus is an extinct genus of camelid endemic to North America. It lived from the Late Oligocene to the Middle Miocene, existing for approximately 14 million years. The name is from the Ancient Greek οξύς and δάκτυλος.\nThey had very long legs and necks, and were probably adapted to eating high vegetation, much like modern giraffes. Unlike modern camelids, they had hooves, rather than tough sole-pads, and splayed toes.',
'context_section_description': 'Oxydactylus is an extinct genus of camelid endemic to North America. It lived from the Late Oligocene to the Middle Miocene (28.4–13.7 mya), existing for approximately 14 million years. The name is from the Ancient Greek οξύς (oxys, "sharp")and δάκτυλος (daktylos, "finger").\n \nThey had very long legs and necks, and were probably adapted to eating high vegetation, much like modern giraffes. Unlike modern camelids, they had hooves, rather than tough sole-pads, and splayed toes.'
}
```
### Data Fields
- `language`: Language code depicting wikipedia language of the page
- `page_url`: URL to wikipedia page
- `image_url`: URL to wikipedia image
- `page_title`: Wikipedia page's title
- `section_title`: Section's title
- `hierarchical_section_title`: Hierarchical section's title
- `caption_reference_description`: This is the caption that is visible on the wiki page directly below the image.
- `caption_attribution_description`: This is the text found on the Wikimedia page of the image. This text is common to all occurrences of that image across all Wikipedias and thus can be in a language different to the original page article.
- `caption_alt_text_description`: This is the “alt” text associated with the image. While not visible in general, it is commonly used for accessibility / screen readers
- `mime_type`: Mime type associated to the image.
- `original_height`: Image height
- `original_width`: Image width
- `is_main_image`: Flag determining if the image is the first image of the page. Usually displayed on the top-right part of the page when using web browsers.
- `attribution_passes_lang_id`: Compared `language` field with the attribution language (written in the prefix of the attribution description).
- `page_changed_recently`: [More Information Needed]
- `context_page_description`: Page description corresponds to the short description of the page. It provides a concise explanation of the scope of the page.
- `context_section_description`: Text within the image's section.
<p align='center'>
<img width='75%' src='https://production-media.paperswithcode.com/datasets/Screenshot_2021-03-04_at_14.26.02.png' alt="Half Dome" /> </br>
<b>Figure: WIT annotation example. </b>
</p>
Details on the field content can be found directly in the [paper, figure 5 and table 12.](https://arxiv.org/abs/2103.01913)
### Data Splits
All data is held in `train` split, with a total of 37046386 rows.
## Dataset Creation
### Curation Rationale
From the [repository](https://github.com/google-research-datasets/wit#motivation):
> Multimodal visio-linguistic models rely on a rich dataset to help them learn to model the relationship between images and texts. Having large image-text datasets can significantly improve performance, as shown by recent works. Furthermore the lack of language coverage in existing datasets (which are mostly only in English) also impedes research in the multilingual multimodal space – we consider this a lost opportunity given the potential shown in leveraging images (as a language-agnostic medium) to help improve our multilingual textual understanding.
>
> To address these challenges and advance research on multilingual, multimodal learning we created the Wikipedia-based Image Text (WIT) Dataset. WIT is created by extracting multiple different texts associated with an image (e.g., as shown in the above image) from Wikipedia articles and Wikimedia image links. This was accompanied by rigorous filtering to only retain high quality image-text sets.
>
> The resulting dataset contains over 37.6 million image-text sets – making WIT the largest multimodal dataset (publicly available at the time of this writing) with unparalleled multilingual coverage – with 12K+ examples in each of 108 languages (53 languages have 100K+ image-text pairs).
### Source Data
#### Initial Data Collection and Normalization
From the [paper, section 3.1](https://arxiv.org/abs/2103.01913):
> We started with all Wikipedia content pages (i.e., ignoring other
pages that have discussions, comments and such). These number about ∼124M pages across 279 languages.
#### Who are the source language producers?
Text was extracted from Wikipedia.
### Annotations
#### Annotation process
WIT was constructed using an automatic process. However it was human-validated.
From the [paper, section 3.7](https://arxiv.org/abs/2103.01913):
> To further verify the quality of the WIT dataset we performed a
study using (crowd-sourced) human annotators. As seen in Fig. 3,
we asked raters to answer 3 questions. Given an image and the page
title, raters first evaluate the quality of the attribution description
and reference description in the first two questions (order randomized). The third question understands the contextual quality of these
text descriptions given the page description and caption. Each response is on a 3-point scale: "Yes" if the text perfectly describes
the image, "Maybe" if it is sufficiently explanatory and "No" if it is
irrelevant or the image is inappropriate.
#### 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
From the [paper, section 3.4](https://arxiv.org/abs/2103.01913):
> Lastly we found that certain image-text pairs occurred very
frequently. These were often generic images that did not have
much to do with the main article page. Common examples
included flags, logos, maps, insignia and such. To prevent
biasing the data, we heavily under-sampled all such images
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```bibtex
@article{srinivasan2021wit,
title={WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning},
author={Srinivasan, Krishna and Raman, Karthik and Chen, Jiecao and Bendersky, Michael and Najork, Marc},
journal={arXiv preprint arXiv:2103.01913},
year={2021}
}
```
### Contributions
Thanks to [@thomasw21](https://github.com/thomasw21), [@nateraw](https://github.com/nateraw) and [hassiahk](https://github.com/hassiahk) for adding this dataset.
|
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ett
| null | 2022-11-18T22:07:07Z | 34 | 3 |
[
"task_categories:time-series-forecasting",
"task_ids:univariate-time-series-forecasting",
"task_ids:multivariate-time-series-forecasting",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"license:cc-by-4.0",
"arxiv:2012.07436",
"region:us"
] |
[
"time-series-forecasting"
] | 2022-05-05T12:12:41Z |
---
annotations_creators:
- no-annotation
language_creators:
- found
language: []
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Electricity Transformer Temperature
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- time-series-forecasting
task_ids:
- univariate-time-series-forecasting
- multivariate-time-series-forecasting
dataset_info:
- config_name: h1
features:
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dtype: timestamp[s]
- name: target
sequence: float32
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sequence: uint64
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dtype: string
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- name: test
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download_size: 2589657
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download_size: 9677236
dataset_size: 1782066298
---
# Dataset Card for [Electricity Transformer Temperature](https://github.com/zhouhaoyi/ETDataset)
## 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:** [Electricity Transformer Dataset](https://github.com/zhouhaoyi/ETDataset)
- **Repository:** https://github.com/zhouhaoyi/ETDataset
- **Paper:** [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://arxiv.org/abs/2012.07436)
- **Point of Contact:** [Haoyi Zhou](mailto:[email protected])
### Dataset Summary
The electric power distribution problem is the distribution of electricity to different areas depending on its sequential usage. But predicting the future demand of a specific area is difficult, as it varies with weekdays, holidays, seasons, weather, temperatures, etc. However, no existing method can perform a long-term prediction based on super long-term real-world data with high precision. Any false predictions may damage the electrical transformer. So currently, without an efficient method to predict future electric usage, managers have to make decisions based on the empirical number, which is much higher than the real-world demands. It causes unnecessary waste of electric and equipment depreciation. On the other hand, the oil temperatures can reflect the condition of the Transformer. One of the most efficient strategies is to predict how the electrical transformers' oil temperature is safe and avoid unnecessary waste. As a result, to address this problem, the authors and Beijing Guowang Fuda Science & Technology Development Company have provided 2-years worth of data.
Specifically, the dataset combines short-term periodical patterns, long-term periodical patterns, long-term trends, and many irregular patterns. The dataset are obtained from 2 Electricity Transformers at 2 stations and come in an `1H` (hourly) or `15T` (15-minute) frequency containing 2 year * 365 days * 24 hours * (4 for 15T) times = 17,520 (70,080 for 15T) data points.
The target time series is the **O**il **T**emperature and the dataset comes with the following 6 covariates in the univariate setup:
* **H**igh **U**se**F**ul **L**oad
* **H**igh **U**se**L**ess **L**oad
* **M**iddle **U**se**F**ul **L**oad
* **M**iddle **U**se**L**ess **L**oad
* **L**ow **U**se**F**ul **L**oad
* **L**ow **U**se**L**ess **L**oad
### Dataset Usage
To load a particular variant of the dataset just specify its name e.g:
```python
load_dataset("ett", "m1", multivariate=False) # univariate 15-min frequency dataset from first transformer
```
or to specify a prediction length:
```python
load_dataset("ett", "h2", prediction_length=48) # multivariate dataset from second transformer with prediction length of 48 (hours)
```
### Supported Tasks and Leaderboards
The time series data is split into train/val/test set of 12/4/4 months respectively. Given the prediction length (default: 1 day (24 hours or 24*4 15T)) we create rolling windows of this size for the val/test sets.
#### `time-series-forecasting`
##### `univariate-time-series-forecasting`
The univariate time series forecasting tasks involves learning the future one dimensional `target` values of a time series in a dataset for some `prediction_length` time steps. The performance of the forecast models can then be validated via the ground truth in the `validation` split and tested via the `test` split. The covriates are stored in the `feat_dynamic_real` key of each time series.
##### `multivariate-time-series-forecasting`
The multivariate time series forecasting task involves learning the future vector of `target` values of a time series in a dataset for some `prediction_length` time steps. Similar to the univariate setting the performance of a multivariate model can be validated via the ground truth in the `validation` split and tested via the `test` split.
### Languages
## Dataset Structure
### Data Instances
A sample from the training set is provided below:
```python
{
'start': datetime.datetime(2012, 1, 1, 0, 0),
'target': [14.0, 18.0, 21.0, 20.0, 22.0, 20.0, ...],
'feat_static_cat': [0],
'feat_dynamic_real': [[0.3, 0.4], [0.1, 0.6], ...],
'item_id': 'OT'
}
```
### Data Fields
For the univariate regular time series each series has the following keys:
* `start`: a datetime of the first entry of each time series in the dataset
* `target`: an array[float32] of the actual target values
* `feat_static_cat`: an array[uint64] which contains a categorical identifier of each time series in the dataset
* `feat_dynamic_real`: optional array of covariate features
* `item_id`: a string identifier of each time series in a dataset for reference
For the multivariate time series the `target` is a vector of the multivariate dimension for each time point.
### Data Splits
The time series data is split into train/val/test set of 12/4/4 months respectively.
## Dataset Creation
### Curation Rationale
Develop time series methods that can perform a long-term prediction based on super long-term real-world data with high precision.
### 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
* [Haoyi Zhou](mailto:[email protected])
### Licensing Information
[Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/legalcode)
### Citation Information
```tex
@inproceedings{haoyietal-informer-2021,
author = {Haoyi Zhou and
Shanghang Zhang and
Jieqi Peng and
Shuai Zhang and
Jianxin Li and
Hui Xiong and
Wancai Zhang},
title = {Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting},
booktitle = {The Thirty-Fifth {AAAI} Conference on Artificial Intelligence, {AAAI} 2021, Virtual Conference},
volume = {35},
number = {12},
pages = {11106--11115},
publisher = {{AAAI} Press},
year = {2021},
}
```
### Contributions
Thanks to [@kashif](https://github.com/kashif) for adding this dataset.
|
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Fhrozen/AudioSet2K22
|
Fhrozen
| 2023-05-07T23:50:56Z | 34 | 3 |
[
"task_categories:audio-classification",
"annotations_creators:unknown",
"language_creators:unknown",
"size_categories:100K<n<100M",
"source_datasets:unknown",
"license:cc-by-sa-4.0",
"audio-slot-filling",
"region:us"
] |
[
"audio-classification"
] | 2022-05-09T12:42:09Z |
---
annotations_creators:
- unknown
language_creators:
- unknown
license: cc-by-sa-4.0
size_categories:
- 100K<n<100M
source_datasets:
- unknown
task_categories:
- audio-classification
task_ids: []
tags:
- audio-slot-filling
---
# Dataset Card for audioset2022
## 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:** [AudioSet Ontology](https://research.google.com/audioset/ontology/index.html)
- **Repository:** [Needs More Information]
- **Paper:** [Audio Set: An ontology and human-labeled dataset for audio events](https://research.google.com/pubs/pub45857.html)
- **Leaderboard:** [Paperswithcode Leaderboard](https://paperswithcode.com/dataset/audioset)
### Dataset Summary
The AudioSet ontology is a collection of sound events organized in a hierarchy. The ontology covers a wide range of everyday sounds, from human and animal sounds, to natural and environmental sounds, to musical and miscellaneous sounds.
**This repository only includes audio files for DCASE 2022 - Task 3**
The included labels are limited to:
- Female speech, woman speaking
- Male speech, man speaking
- Clapping
- Telephone
- Telephone bell ringing
- Ringtone
- Laughter
- Domestic sounds, home sounds
- Vacuum cleaner
- Kettle whistle
- Mechanical fan
- Walk, footsteps
- Door
- Cupboard open or close
- Music
- Background music
- Pop music
- Musical instrument
- Acoustic guitar
- Marimba, xylophone
- Cowbell
- Piano
- Electric piano
- Rattle (instrument)
- Water tap, faucet
- Bell
- Bicycle bell
- Chime
- Knock
### Supported Tasks and Leaderboards
- `audio-classification`: The dataset can be used to train a model for Sound Event Detection/Localization.
**The recordings only includes the single channel audio. For Localization tasks, it will required to apply RIR information**
### Languages
None
## Dataset Structure
### Data Instances
**WIP**
```
{
'file':
}
```
### Data Fields
- file: A path to the downloaded audio file in .mp3 format.
### Data Splits
This dataset only includes audio file from the unbalance train list.
The data comprises two splits: weak labels and strong labels.
## 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
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
The dataset was initially downloaded by Nelson Yalta ([email protected]).
### Licensing Information
[CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0)
### Citation Information
```
@inproceedings{45857,
title = {Audio Set: An ontology and human-labeled dataset for audio events},
author = {Jort F. Gemmeke and Daniel P. W. Ellis and Dylan Freedman and Aren Jansen and Wade Lawrence and R. Channing Moore and Manoj Plakal and Marvin Ritter},
year = {2017},
booktitle = {Proc. IEEE ICASSP 2017},
address = {New Orleans, LA}
}
```
|
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blinoff/medical_institutions_reviews
|
blinoff
| 2022-10-23T16:51:28Z | 34 | 2 |
[
"task_categories:text-classification",
"task_ids:sentiment-classification",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:ru",
"region:us"
] |
[
"text-classification"
] | 2022-05-27T10:09:02Z |
---
language:
- ru
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
task_categories:
- text-classification
task_ids:
- sentiment-classification
---
### Dataset Summary
The dataset contains user reviews about medical institutions.
In total it contains 12,036 reviews. A review tagged with the <em>general</em> sentiment and sentiments on 5 aspects: <em>quality, service, equipment, food, location</em>.
### Data Fields
Each sample contains the following fields:
- **review_id**;
- **content**: review text;
- **general**;
- **quality**;
- **service**;
- **equipment**;
- **food**;
- **location**.
### Python
```python3
import pandas as pd
df = pd.read_json('medical_institutions_reviews.jsonl', lines=True)
df.sample(5)
```
|
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] |
vesteinn/FC3
|
vesteinn
| 2023-03-23T15:51:34Z | 34 | 1 |
[
"language:fo",
"license:cc",
"region:us"
] | null | 2022-09-30T12:09:39Z |
---
license: cc
language:
- fo
pretty_name: FC3
---
This is the Faroese Common Crawl corpus. The largest dataset of mono-lingual Faroese text, it was extracted from the Common Crawl.
If you find this dataset useful, please cite
```
@inproceedings{snaebjarnarson-etal-2023-transfer,
title = "{T}ransfer to a Low-Resource Language via Close Relatives: The Case Study on Faroese",
author = "Snæbjarnarson, Vésteinn and
Simonsen, Annika and
Glavaš, Goran and
Vulić, Ivan",
booktitle = "Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)",
month = "may 22--24",
year = "2023",
address = "Tórshavn, Faroe Islands",
publisher = {Link{\"o}ping University Electronic Press, Sweden},
}
```
|
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PlanTL-GOB-ES/WikiCAT_esv2
|
PlanTL-GOB-ES
| 2023-07-27T09:13:16Z | 34 | 0 |
[
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:automatically-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:unknown",
"language:es",
"license:cc-by-sa-3.0",
"region:us"
] |
[
"text-classification"
] | 2022-11-18T10:18:53Z |
---
YAML tags:
annotations_creators:
- automatically-generated
language_creators:
- found
language:
- es
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
pretty_name: wikicat_esv2
size_categories:
- unknown
source_datasets: []
task_categories:
- text-classification
task_ids:
- multi-class-classification
---
# WikiCAT_es: Spanish Text Classification dataset
## Dataset Description
- **Paper:**
- **Point of Contact:** [email protected]
**Repository**
### Dataset Summary
WikiCAT_ca is a Spanish corpus for thematic Text Classification tasks. It is created automatically from Wikipedia and Wikidata sources, and contains 8401 articles from the Viquipedia classified under 12 different categories.
This dataset was developed by BSC TeMU as part of the PlanTL project, and intended as an evaluation of LT capabilities to generate useful synthetic corpus.
### Supported Tasks and Leaderboards
Text classification, Language Model
### Languages
ES- Spanish
## Dataset Structure
### Data Instances
Two json files, one for each split.
### Data Fields
We used a simple model with the article text and associated labels, without further metadata.
#### Example:
<pre>
{'sentence': 'La economía de Reunión se ha basado tradicionalmente en la agricultura. La caña de azúcar ha sido el cultivo principal durante más de un siglo, y en algunos años representa el 85% de las exportaciones. El gobierno ha estado impulsando el desarrollo de una industria turística para aliviar el alto desempleo, que representa más del 40% de la fuerza laboral.(...) El PIB total de la isla fue de 18.800 millones de dólares EE.UU. en 2007., 'label': 'Economía'}
</pre>
#### Labels
'Religión', 'Entretenimiento', 'Música', 'Ciencia_y_Tecnología', 'Política', 'Economía', 'Matemáticas', 'Humanidades', 'Deporte', 'Derecho', 'Historia', 'Filosofía'
### Data Splits
* hfeval_esv5.json: 1681 label-document pairs
* hftrain_esv5.json: 6716 label-document pairs
## Dataset Creation
### Methodology
La páginas de "Categoría" representan los temas.
para cada tema, extraemos las páginas asociadas a ese primer nivel de la jerarquía, y utilizamos el resúmen ("summary") como texto representativo.
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
The source data are thematic categories in the different Wikipedias
#### Who are the source language producers?
### Annotations
#### Annotation process
Automatic annotation
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
No personal or sensitive information included.
## Considerations for Using the Data
### Social Impact of Dataset
We hope this corpus contributes to the development of language models in Spanish.
### Discussion of Biases
We are aware that this data might contain biases. We have not applied any steps to reduce their impact.
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
Text Mining Unit (TeMU) at the Barcelona Supercomputing Center ([email protected]).
For further information, send an email to ([email protected]).
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)
### Contributions
[N/A]
|
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Rami/multi-label-class-github-issues-text-classification
|
Rami
| 2022-12-02T01:19:08Z | 34 | 0 |
[
"region:us"
] | null | 2022-11-29T16:32:12Z |
---
dataset_info:
features:
- name: title
dtype: string
- name: labels
sequence: string
- name: bodyText
dtype: string
splits:
- name: train
num_bytes: 2713984
num_examples: 1556
- name: valid
num_bytes: 1296582
num_examples: 778
- name: test
num_bytes: 1307650
num_examples: 778
download_size: 2328003
dataset_size: 5318216
---
# Dataset Card for "multi-label-class-github-issues-text-classification"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
ardauzunoglu/tr-wikihow-summ
|
ardauzunoglu
| 2023-01-10T12:27:29Z | 34 | 0 |
[
"region:us"
] | null | 2023-01-10T12:24:11Z |
---
dataset_info:
features:
- name: text
dtype: string
- name: summary
dtype: string
splits:
- name: train
num_bytes: 279070558
num_examples: 113356
- name: validation
num_bytes: 15174147
num_examples: 6082
- name: test
num_bytes: 14888006
num_examples: 5984
download_size: 166588788
dataset_size: 309132711
---
# Dataset Card for "tr-wikihow-summ"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
sartajekram/BanglaRQA
|
sartajekram
| 2023-05-06T19:04:32Z | 34 | 0 |
[
"task_categories:question-answering",
"task_ids:open-domain-qa",
"task_ids:extractive-qa",
"annotations_creators:human",
"size_categories:10K<n<100K",
"language:bn",
"license:cc-by-nc-sa-4.0",
"region:us"
] |
[
"question-answering"
] | 2023-02-07T14:56:06Z |
---
annotations_creators:
- human
license: cc-by-nc-sa-4.0
task_categories:
- question-answering
task_ids:
- open-domain-qa
- extractive-qa
language:
- bn
size_categories:
- 10K<n<100K
---
# Dataset Card for `BanglaRQA`
## Table of Contents
- [Dataset Card for `BanglaRQA`](#dataset-card-for-BanglaRQA)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Usage](#usage)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Repository:** [https://github.com/sartajekram419/BanglaRQA](https://github.com/sartajekram419/BanglaRQA)
- **Paper:** [BanglaRQA: A Benchmark Dataset for Under-resourced Bangla Language Reading Comprehension-based Question Answering with Diverse Question-Answer Types](https://aclanthology.org/2022.findings-emnlp.186)
### Dataset Summary
This is a human-annotated Bangla Question Answering (QA) dataset with diverse question-answer types.
### Languages
* `Bangla`
### Usage
```python
from datasets import load_dataset
dataset = load_dataset("sartajekram/BanglaRQA")
```
## Dataset Structure
### Data Instances
One example from the dataset is given below in JSON format.
```
{
'passage_id': 'bn_wiki_2977',
'title': 'ফাজিল পরীক্ষা',
'context': 'ফাজিল পরীক্ষা বাংলাদেশ ও ভারতের আলিয়া মাদ্রাসায় অনুষ্ঠিত একটি সরকারি পরীক্ষা। ফাজিল পরীক্ষা বাংলাদেশে ডিগ্রি সমমানের, কখনো স্নাতক সমমানের একটি পরীক্ষা, যা একটি ফাজিল মাদ্রাসায় অনুষ্ঠিত হয়ে থাকে। তবে ভারতে ফাজিল পরীক্ষাকে উচ্চ মাধ্যমিক শ্রেণীর (১১ বা ১২ ক্লাস) মান বলে বিবেচিত করা হয়। ফাজিল পরীক্ষা বাংলাদেশ ভারত ও পাকিস্তানের সরকারি স্বীকৃত আলিয়া মাদরাসায় প্রচলিত রয়েছে। বাংলাদেশের ফাজিল পরীক্ষা ইসলামি আরবি বিশ্ববিদ্যালয়ের অধীনে অনুষ্ঠিত হয়ে থাকে ও ভারতের ফাজিল পরীক্ষা পশ্চিমবঙ্গ মাদ্রাসা শিক্ষা পর্ষদের অধীনে অনুষ্ঠিত হয়ে থাকে।\n\n১৯৪৭ সালে ঢাকা আলিয়া মাদ্রাসা ঢাকায় স্থানান্তরের পূর্বে বাংলাদেশ ও ভারতের ফাজিল পরীক্ষা কলকাতা আলিয়া মাদ্রাসার অধীনে অনুষ্ঠিত হতো। ফাযিল পরীক্ষা বর্তমানে ইসলামি আরবী বিশ্ববিদ্যালয়ের অধীনে অনুষ্ঠিত হয়। যা পূর্বে মাদরাসা বোর্ড ও ইসলামি বিশ্ববিদ্যালয়ের আধীনে অনুষ্ঠিত হত। মাদ্রাসা-ই-আলিয়া ঢাকায় স্থানান্তরিত হলে ১৯৪৮ সালে মাদ্রাসা বোর্ডের ফাজিলগুলো পরীক্ষা ঢাকা বিশ্ববিদ্যালয় কর্তৃক গৃহীত হতো। ১৯৭৫ সালের কুদরত-এ-খুদা শিক্ষা কমিশনের সুপারিশে মাদ্রাসা বোর্ড নিয়ন্ত্রিত আলিয়া মাদ্রাসাসমূহে জাতীয় শিক্ষাক্রম ও বহুমুখী পাঠ্যসূচি প্রবর্তিত করা হয়। ১৯৮০ সালে অনুষ্ঠিত ফাজিল পরীক্ষায় এই পাঠ্যসুচী কার্যকর হয়। এই শিক্ষা কমিশন অনুসারে ফাজিল শ্রেণীতে ইসলামি শিক্ষার পাশাপাশি সাধারণ পাঠ্যসূচী অন্তর্ভুক্ত করে ফাজিল পরীক্ষাকে সাধারণ উচ্চ মাধ্যমিক এইচ এস সির সমমান ঘোষণা করা হয়।\n\n১৯৭৮ সালে অধ্যাপক মুস্তফা বিন কাসিমের নেতৃত্বে সিনিয়র মাদ্রাসা শিক্ষা ব্যবস্থা কমিটি গঠিত হয়। এই কমিটির নির্দেশনায় ১৯৮৪ সালে সাধারণ শিক্ষার স্তরের সঙ্গে বাংলাদেশ মাদ্রাসা বোর্ড নিয়ন্ত্রিত আলিয়া মাদ্রাসা শিক্ষা স্তরের সামঞ্জস্য করা হয়। ফাজিল স্তরকে ২ বছর মেয়াদী কোর্সে উন্নিত করে, মোট ১৬ বছর ব্যাপী আলিয়া মাদ্রাসার পূর্ণাঙ্গ আধুনিক শিক্ষা ব্যবস্থা প্রবর্তন করা হয়। এই কমিশনের মাধ্যমেই সরকার ফাজিল পরীক্ষাকে সাধারণ ডিগ্রি মান ঘোষণা করে।',
'question_id': 'bn_wiki_2977_01',
'question_text': 'ফাজিল পরীক্ষা বাংলাদেশ ও ভারতের আলিয়া মাদ্রাসায় অনুষ্ঠিত একটি সরকারি পরীক্ষা ?',
'is_answerable': '1',
'question_type': 'confirmation',
'answers':
{
'answer_text': ['হ্যাঁ', 'হ্যাঁ '],
'answer_type': ['yes/no', 'yes/no']
},
}
```
### Data Splits
| split |count |
|----------|--------|
|`train`| 11,912 |
|`validation`| 1,484 |
|`test`| 1,493 |
## Additional Information
### Licensing Information
Contents of this repository are restricted to only non-commercial research purposes under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). Copyright of the dataset contents belongs to the original copyright holders.
### Citation Information
If you use the dataset, please cite the following paper:
```
@inproceedings{ekram-etal-2022-banglarqa,
title = "{B}angla{RQA}: A Benchmark Dataset for Under-resourced {B}angla Language Reading Comprehension-based Question Answering with Diverse Question-Answer Types",
author = "Ekram, Syed Mohammed Sartaj and
Rahman, Adham Arik and
Altaf, Md. Sajid and
Islam, Mohammed Saidul and
Rahman, Mehrab Mustafy and
Rahman, Md Mezbaur and
Hossain, Md Azam and
Kamal, Abu Raihan Mostofa",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.186",
pages = "2518--2532",
abstract = "High-resource languages, such as English, have access to a plethora of datasets with various question-answer types resembling real-world reading comprehension. However, there is a severe lack of diverse and comprehensive question-answering datasets in under-resourced languages like Bangla. The ones available are either translated versions of English datasets with a niche answer format or created by human annotations focusing on a specific domain, question type, or answer type. To address these limitations, this paper introduces BanglaRQA, a reading comprehension-based Bangla question-answering dataset with various question-answer types. BanglaRQA consists of 3,000 context passages and 14,889 question-answer pairs created from those passages. The dataset comprises answerable and unanswerable questions covering four unique categories of questions and three types of answers. In addition, this paper also implemented four different Transformer models for question-answering on the proposed dataset. The best-performing model achieved an overall 62.42{\%} EM and 78.11{\%} F1 score. However, detailed analyses showed that the performance varies across question-answer types, leaving room for substantial improvement of the model performance. Furthermore, we demonstrated the effectiveness of BanglaRQA as a training resource by showing strong results on the bn{\_}squad dataset. Therefore, BanglaRQA has the potential to contribute to the advancement of future research by enhancing the capability of language models. The dataset and codes are available at https://github.com/sartajekram419/BanglaRQA",
}
```
|
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derek-thomas/squad-v1.1-t5-question-generation
|
derek-thomas
| 2023-03-09T13:50:46Z | 34 | 2 |
[
"task_categories:text2text-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|squad",
"language:en",
"license:cc-by-4.0",
"questiongeneration",
"question-generation",
"text2text-generation",
"arxiv:1606.05250",
"arxiv:2005.01107",
"region:us"
] |
[
"text2text-generation"
] | 2023-02-08T12:10:34Z |
---
dataset_info:
features:
- name: context
dtype: string
- name: questions
dtype: string
splits:
- name: train
num_bytes: 20293805
num_examples: 18896
- name: validation
num_bytes: 2376313
num_examples: 2067
download_size: 12600387
dataset_size: 22670118
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- crowdsourced
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Question Generation for T5 based on Squad V1.1
size_categories:
- 10K<n<100K
source_datasets:
- extended|squad
tags:
- questiongeneration
- question-generation
- text2text-generation
task_categories:
- text2text-generation
task_ids: []
---
# Dataset Card for "squad-v1.1-t5-question-generation"
## Dataset Description
- **Homepage:** [https://rajpurkar.github.io/SQuAD-explorer/](https://rajpurkar.github.io/SQuAD-explorer/)
- **Paper:** [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://arxiv.org/abs/1606.05250)
### Dataset Summary
This is a modified Stanford Question Answering Dataset (SQuAD) to suit question generation with All Questions in One Line (AQOL) just like in [Transformer-based End-to-End Question Generation](https://arxiv.org/pdf/2005.01107v1.pdf)
specifically for the T5 family of models. The prefix is `generate questions: ` so that the task can be unique to a trained model.
Check out the generation notebook [here](https://nbviewer.org/urls/huggingface.co/datasets/derek-thomas/squad-v1.1-t5-question-generation/resolve/main/Squad_V1_Question_Generation.ipynb).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
## Dataset Structure
### Data Instances
#### plain_text
An example of 'train' looks as follows.
```
{
"context": "generate questions: This is a test context.",
"question": "Is this a test? {sep_token} Is this another Test {sep_token}"
}
```
### Data Fields
The data fields are the same among all splits.
#### plain_text
- `context`: a `string` feature.
- `question`: a `string` feature.
### Data Splits
| name |train|validation|
|----------|----:|---------:|
|plain_text|18896| 2067|
### Citation Information
```
@article{2016arXiv160605250R,
author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
Konstantin and {Liang}, Percy},
title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
journal = {arXiv e-prints},
year = 2016,
eid = {arXiv:1606.05250},
pages = {arXiv:1606.05250},
archivePrefix = {arXiv},
eprint = {1606.05250},
}
```
### Contributions
Thanks to [Derek Thomas](https://huggingface.co/derek-thomas) and [Thomas Simonini](https://huggingface.co/ThomasSimonini) for adding this to the hub
Check out: [How to contribute more](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Visitors
[](https://visitorbadge.io/status?path=https%3A%2F%2Fhuggingface.co%2Fdatasets%2Fderek-thomas%2Fsquad-v1.1-t5-question-generation)
|
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mstz/australian_credit
|
mstz
| 2023-04-15T11:11:01Z | 34 | 0 |
[
"task_categories:tabular-classification",
"size_categories:n<1K",
"language:en",
"license:cc",
"australian_credit",
"tabular_classification",
"binary_classification",
"UCI",
"region:us"
] |
[
"tabular-classification"
] | 2023-04-07T08:20:44Z |
---
language:
- en
tags:
- australian_credit
- tabular_classification
- binary_classification
- UCI
pretty_name: Australian Credit
size_categories:
- n<1K
task_categories:
- tabular-classification
configs:
- australian_credit
license: cc
---
# Australian Credit
The [Australian Credit](https://archive-beta.ics.uci.edu/dataset/143/statlog+australian+credit+approval) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
Classification of loan approval.
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|-------------------------|
| australian_credit | Binary classification | Is the loan granted? |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/australian_credit")["train"]
```
# Features
Target feature changes according to the selected configuration and is always in last position in the dataset.
|
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zetavg/CC-100-zh-Hant-merged
|
zetavg
| 2023-05-06T11:11:44Z | 34 | 1 |
[
"region:us"
] | null | 2023-05-06T04:28:11Z |
---
dataset_info:
features:
- name: content
dtype: string
splits:
- name: train
num_bytes: 17882150544
num_examples: 12328228
download_size: 12940914691
dataset_size: 17882150544
---
# CC-100 zh-Hant (Traditional Chinese)
From https://data.statmt.org/cc-100/, only zh-Hant - Chinese (Traditional). Broken into paragraphs, with each paragraphs as a row.
Estimated to have around 4B tokens when tokenized with the [`bigscience/bloom`](https://huggingface.co/bigscience/bloom) tokenizer.
There's another version that the text is split by lines instead of paragraphs: [`zetavg/CC-100-zh-Hant`](https://huggingface.co/datasets/zetavg/CC-100-zh-Hant).
## References
Please cite the following if you found the resources in the CC-100 corpus useful.
* **Unsupervised Cross-lingual Representation Learning at Scale**, *Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, Veselin Stoyanov*, Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), p. 8440-8451, July 2020, [pdf](https://www.aclweb.org/anthology/2020.acl-main.747.pdf), [bib](https://www.aclweb.org/anthology/2020.acl-main.747.bib) .
* **CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data**, *Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau, Vishrav Chaudhary, Francisco Guzmán, Armand Joulin, Edouard Grave*, Proceedings of the 12th Language Resources and Evaluation Conference (LREC), p. 4003-4012, May 2020, [pdf](https://www.aclweb.org/anthology/2020.lrec-1.494.pdf), [bib](https://www.aclweb.org/anthology/2020.lrec-1.494.bib).
|
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ma2za/many_emotions
|
ma2za
| 2023-06-10T02:18:01Z | 34 | 1 |
[
"task_categories:text-classification",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:dair-ai/emotion",
"source_datasets:daily_dialog",
"source_datasets:go_emotions",
"language:en",
"license:apache-2.0",
"emotion",
"region:us"
] |
[
"text-classification"
] | 2023-05-20T21:59:41Z |
---
license:
apache-2.0
task_categories:
- text-classification
multilinguality:
- multilingual
source_datasets:
- dair-ai/emotion
- daily_dialog
- go_emotions
language:
- en
size_categories:
- 100K<n<1M
tags:
- emotion
---
# Dataset Card for "many_emotions"
## Dataset Description
- **Homepage:**
### Dataset Summary
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
The data fields are:
- `id`: unique identifier
- `text`: a `string` feature.
- `label`: a classification label, with possible values including `anger` (0), `fear` (1), `joy` (2), `love` (
3), `sadness` (4), `surprise` (5), `neutral` (6).
- `license`: inherited license from source dataset
- `dataset`: source dataset
- `language`: text language
### Data Splits
The dataset has 2 configurations:
- raw: with 5 configuration for each language
- split: with configurations train, validation, test
## Dataset Creation
### Curation Rationale
The raw split contains duplicates.
In the split "split" there may be equal rows but with different label.
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
## Additional Information
### Licensing Information
Each row has its own license which is inherited from the source dataset.
|
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tianyang/repobench-c
|
tianyang
| 2023-06-24T01:37:41Z | 34 | 4 |
[
"task_categories:text-generation",
"task_ids:document-retrieval",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"license:cc-by-nc-nd-4.0",
"code",
"arxiv:2306.03091",
"region:us"
] |
[
"text-generation"
] | 2023-06-16T07:18:00Z |
---
language_creators:
- found
license:
- cc-by-nc-nd-4.0
multilinguality:
- multilingual
pretty_name: RepoBench-Completion
source_datasets:
- original
task_categories:
- text-generation
task_ids:
- document-retrieval
tags:
- code
size_categories:
- 100K<n<1M
---
# Dataset Card for RepoBench-C
## Dataset Description
- **Homepage:** https://github.com/Leolty/repobench
- **Paper:** https://arxiv.org/abs/2306.03091
## Dataset Summary
**RepoBench-C (Completion)** is a subtask of **RepoBench**([GitHub](https://github.com/Leolty/repobench), [arXiv](https://arxiv.org/abs/2306.03091)), focuing on the prediction of the next line of code, given in-file context (including several preceding lines and import statements), and cross-file context.
## Settings
- `cff`: short for cross_file_first, indicating the cross-file module in next line is first used in the current file.
- `cfr`: short for cross_file_random, indicating the cross-file module in next line is not first used in the current file.
- `if`: short for in_file, indicating the next line does not contain any cross-file module.
## Supported Tasks
- `python_cff`: python code prediction with cross-file-first setting.
- `python_cfr`: python code prediction with cross-file-random setting.
- `python_if`: python code prediction with in-file setting.
- `java_cff`: java code prediction with cross-file-first setting.
- `java_cfr`: java code prediction with cross-file-random setting.
- `java_if`: java code prediction with in-file setting.
## Loading Data
For example, if you want to load the `test` set to test your model on `Python` code prediction with `cff` setting, you can do the following:
```python
from datasets import load_dataset
dataset = load_dataset("tianyang/repobench-c", "python_cff", split="test")
```
> Note: The `split` argument is optional. If not provided, the entire dataset will be loaded.
## Dataset Structure
```json
{
"repo_name": "repository name of the data point",
"file_path": "path/to/file",
"context": "commented and concatenated cross-file context",
"import_statement": "all import statements in the file",
"code": "the code for next-line prediction",
"prompt": "cross-file context + import statements + in-file code",
"next_line": "the next line of the code"
}
```
## Licensing Information
CC BY-NC-ND 4.0
## Citation Information
```bibtex
@misc{liu2023repobench,
title={RepoBench: Benchmarking Repository-Level Code Auto-Completion Systems},
author={Tianyang Liu and Canwen Xu and Julian McAuley},
year={2023},
eprint={2306.03091},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## Contributions
Thanks to [@Leolty](https://github.com/Leolty) for adding this dataset.
|
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] |
KaiLv/UDR_SNLI
|
KaiLv
| 2023-06-21T12:49:04Z | 34 | 0 |
[
"region:us"
] | null | 2023-06-21T12:48:42Z |
---
dataset_info:
features:
- name: idx
dtype: int64
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
- name: sentence
dtype: string
- name: len_sentence
dtype: int64
splits:
- name: test
num_bytes: 747502
num_examples: 3262
- name: train
num_bytes: 28963424
num_examples: 131062
- name: validation
num_bytes: 750070
num_examples: 3272
- name: debug
num_bytes: 22092624
num_examples: 100000
download_size: 17825058
dataset_size: 52553620
---
# Dataset Card for "UDR_SNLI"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
patimus-prime/strain_selection
|
patimus-prime
| 2023-06-28T00:58:15Z | 34 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-06-28T00:51:38Z |
---
license: mit
---
|
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] |
trixdade/reviews_russian
|
trixdade
| 2023-07-12T15:30:36Z | 34 | 0 |
[
"task_categories:summarization",
"size_categories:n<1K",
"language:ru",
"region:us"
] |
[
"summarization"
] | 2023-07-07T09:26:15Z |
---
task_categories:
- summarization
language:
- ru
size_categories:
- n<1K
---
|
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] |
bigheiniuJ/MyC4Validation
|
bigheiniuJ
| 2023-07-22T00:01:07Z | 34 | 0 |
[
"region:us"
] | null | 2023-07-21T23:57:04Z |
---
dataset_info:
features:
- name: text
dtype: string
- name: timestamp
dtype: string
- name: url
dtype: string
splits:
- name: validation
num_bytes: 825766822
num_examples: 364608
download_size: 509372854
dataset_size: 825766822
---
# Dataset Card for "MyC4Validation"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
Brendan/multiwoz_turns_v24
|
Brendan
| 2023-07-27T01:22:40Z | 34 | 0 |
[
"region:us"
] | null | 2023-07-27T01:22:31Z |
---
dataset_info:
features:
- name: dialogue_id
dtype: string
- name: turn_id
dtype: int64
- name: user
dtype: string
- name: system_response
dtype: string
- name: history
sequence: string
- name: system_acts
struct:
- name: Attraction-Inform
sequence:
sequence: string
- name: Attraction-NoOffer
sequence:
sequence: string
- name: Attraction-Recommend
sequence:
sequence: string
- name: Attraction-Request
sequence:
sequence: string
- name: Attraction-Select
sequence:
sequence: string
- name: Booking-Book
sequence:
sequence: string
- name: Booking-Inform
sequence:
sequence: string
- name: Booking-NoBook
sequence:
sequence: string
- name: Booking-Request
sequence:
sequence: string
- name: Hotel-Inform
sequence:
sequence: string
- name: Hotel-NoOffer
sequence:
sequence: string
- name: Hotel-Recommend
sequence:
sequence: string
- name: Hotel-Request
sequence:
sequence: string
- name: Hotel-Select
sequence:
sequence: string
- name: Restaurant-Inform
sequence:
sequence: string
- name: Restaurant-NoOffer
sequence:
sequence: string
- name: Restaurant-Recommend
sequence:
sequence: string
- name: Restaurant-Request
sequence:
sequence: string
- name: Restaurant-Select
sequence:
sequence: string
- name: Taxi-Inform
sequence:
sequence: string
- name: Taxi-Request
sequence:
sequence: string
- name: Train-Inform
sequence:
sequence: string
- name: Train-NoOffer
sequence:
sequence: string
- name: Train-OfferBook
sequence:
sequence: string
- name: Train-OfferBooked
sequence:
sequence: string
- name: Train-Request
sequence:
sequence: string
- name: Train-Select
sequence:
sequence: string
- name: general-bye
sequence:
sequence: string
- name: general-greet
sequence:
sequence: string
- name: general-reqmore
sequence:
sequence: string
- name: general-welcome
sequence:
sequence: string
- name: belief_state
sequence:
sequence: string
- name: prev_belief_state
sequence:
sequence: string
- name: belief_state_delta
sequence:
sequence: string
- name: degenerate_user
dtype: bool
splits:
- name: train
num_bytes: 71669619
num_examples: 56719
- name: validation
num_bytes: 9862893
num_examples: 7374
- name: test
num_bytes: 9864860
num_examples: 7368
download_size: 15883931
dataset_size: 91397372
---
# Dataset Card for "multiwoz_turns_v24"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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brando/debug1_af
|
brando
| 2023-10-20T19:03:38Z | 34 | 1 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-08-09T22:53:07Z |
---
license: apache-2.0
---
If you find this please cite it:
```
@software{brando2021ultimateutils,
author={Brando Miranda},
title={Ultimate Utils - the Ultimate Utils library for Machine Learning and Artificial Intelligence},
url={https://github.com/brando90/ultimate-utils},
year={2021}
}
```
it's not suppose to be used by people yet.
It's under **apache license 2.0** too.
Files are
```
Topic # of theorems # Statements Selected (floor)
Polynomial 515 0
Polynomial_Factorial 47 11
```
|
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] |
ds4sd/PubTables-1M_OTSL
|
ds4sd
| 2023-08-31T16:00:24Z | 34 | 1 |
[
"task_categories:object-detection",
"task_categories:table-to-text",
"size_categories:100K<n<1M",
"license:other",
"table-structure-recognition",
"table-understanding",
"PDF",
"arxiv:2305.03393",
"region:us"
] |
[
"object-detection",
"table-to-text"
] | 2023-08-10T08:21:06Z |
---
license: other
pretty_name: PubTables-1M-OTSL
size_categories:
- 100K<n<1M
tags:
- table-structure-recognition
- table-understanding
- PDF
task_categories:
- object-detection
- table-to-text
---
# Dataset Card for PubTables-1M_OTSL
## Dataset Description
- **Homepage:** https://ds4sd.github.io
- **Paper:** https://arxiv.org/pdf/2305.03393
### Dataset Summary
This dataset enables the evaluation of both object detection models and image-to-text methods.
[PubTables-1M](https://github.com/microsoft/table-transformer) is introduced in the publication *"PubTables-1M: Towards Comprehensive Table Extraction From Unstructured Documents"* by Smock et al. The conversion into HF (Hugging Face) and the addition of the OTSL (Optimized Table Structure Language) format is presented in our paper "Optimized Table Tokenization for Table Structure Recognition" by Lysak et al. The dataset includes the original annotations amongst new additions.
### Dataset Structure
* cells: origunal dataset cell groundtruth (content).
* table_bbox: origunal dataset table detection groundtruth.
* otsl: new reduced table structure token format
* html: Generated HTML for PubTables-1M to match PubTabNet, FinTabNet, and SynthTabNet format.
* html_restored: generated HTML from OTSL.
* cols: grid column length.
* rows: grid row length.
* image: PIL image
### OTSL Vocabulary:
**OTSL**: new reduced table structure token format
More information on the OTSL table structure format and its concepts can be read from our paper.
Format of this dataset extends work presented in a paper, and introduces slight modifications:
* "fcel" - cell that has content in it
* "ecel" - cell that is empty
* "lcel" - left-looking cell (to handle horizontally merged cells)
* "ucel" - up-looking cell (to handle vertically merged cells)
* "xcel" - 2d span cells, in this dataset - covers entire area of a merged cell
* "nl" - new line token
### Data Splits
The dataset provides three splits
- `train`
- `val`
- `test`
## Additional Information
### Dataset Curators
The dataset is converted by the [Deep Search team](https://ds4sd.github.io/) at IBM Research.
You can contact us at [[email protected]](mailto:[email protected]).
Curators:
- Maksym Lysak, [@maxmnemonic](https://github.com/maxmnemonic)
- Ahmed Nassar, [@nassarofficial](https://github.com/nassarofficial)
- Christoph Auer, [@cau-git](https://github.com/cau-git)
- Nikos Livathinos, [@nikos-livathinos](https://github.com/nikos-livathinos)
- Peter Staar, [@PeterStaar-IBM](https://github.com/PeterStaar-IBM)
### Citation Information
**Citation to OTSL Paper:**
@article{lysak2023optimized,
title={Optimized Table Tokenization for Table Structure Recognition},
author={Maksym Lysak and Ahmed Nassar and Nikolaos Livathinos and Christoph Auer and Peter Staar},
year={2023},
eprint={2305.03393},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
**Citation to PubTables-1M creators:**
@inproceedings{smock2022pubtables,
title={Pub{T}ables-1{M}: Towards comprehensive table extraction from unstructured documents},
author={Smock, Brandon and Pesala, Rohith and Abraham, Robin},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
pages={4634-4642},
year={2022},
month={June}
}
|
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PL-MTEB/allegro-reviews
|
PL-MTEB
| 2023-08-11T13:11:54Z | 34 | 0 |
[
"license:cc-by-sa-4.0",
"region:us"
] | null | 2023-08-11T13:11:37Z |
---
license: cc-by-sa-4.0
---
|
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Kant1/French_Wikipedia_articles
|
Kant1
| 2023-08-29T17:09:13Z | 34 | 0 |
[
"task_categories:text-generation",
"language:fr",
"region:us"
] |
[
"text-generation"
] | 2023-08-29T16:59:23Z |
---
task_categories:
- text-generation
language:
- fr
---
Dump of 2023-08-20 of all french article in wikipedia
https://dumps.wikimedia.org/frwiki/20230820/frwiki-20230820-pages-articles.xml.bz2
|
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sarahlintang/Alpaca_indo_instruct
|
sarahlintang
| 2023-09-07T06:27:38Z | 34 | 0 |
[
"language:id",
"region:us"
] | null | 2023-09-07T06:21:17Z |
---
language:
- id
---
Translated from Stanford alpaca using google translate API.
|
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Trelis/openassistant-llama-style
|
Trelis
| 2023-10-31T11:29:33Z | 34 | 3 |
[
"size_categories:1K<n<10k",
"language:en",
"language:es",
"language:ru",
"language:de",
"language:pl",
"language:th",
"language:vi",
"language:sv",
"language:bn",
"language:da",
"language:he",
"language:it",
"language:fa",
"language:sk",
"language:id",
"language:nb",
"language:el",
"language:nl",
"language:hu",
"language:eu",
"language:zh",
"language:eo",
"language:ja",
"language:ca",
"language:cs",
"language:bg",
"language:fi",
"language:pt",
"language:tr",
"language:ro",
"language:ar",
"language:uk",
"language:gl",
"language:fr",
"language:ko",
"license:apache-2.0",
"human-feedback",
"llama-2",
"arxiv:2304.07327",
"region:us"
] | null | 2023-10-04T14:14:13Z |
---
license: apache-2.0
language:
- en
- es
- ru
- de
- pl
- th
- vi
- sv
- bn
- da
- he
- it
- fa
- sk
- id
- nb
- el
- nl
- hu
- eu
- zh
- eo
- ja
- ca
- cs
- bg
- fi
- pt
- tr
- ro
- ar
- uk
- gl
- fr
- ko
tags:
- human-feedback
- llama-2
size_categories:
- 1K<n<10k
pretty_name: Filtered OpenAssistant Conversations
---
# Chat Fine-tuning Dataset - Llama 2 Style
This dataset allows for fine-tuning chat models using [INST] AND [/INST] to wrap user messages.
Preparation:
1. The dataset is cloned from [TimDettmers](https://huggingface.co/datasets/timdettmers/openassistant-guanaco), which itself is a subset of the Open Assistant dataset, which you can find [here](https://huggingface.co/datasets/OpenAssistant/oasst1/tree/main). This subset of the data only contains the highest-rated paths in the conversation tree, with a total of 9,846 samples.
1. The dataset was then filtered to:
- replace instances of '### Human:' with '[INST]'
- replace instances of '### Assistant:' with '</s><s> [/INST]' (to encourage the model to emit </s> when finished a response)
- if a row of data ends with an assistant response, then [INST] was additionally added to the end of that row of data.
Details of the root dataset follow, copied from that repo:
# OpenAssistant Conversations Dataset (OASST1)
## Dataset Description
- **Homepage:** https://www.open-assistant.io/
- **Repository:** https://github.com/LAION-AI/Open-Assistant
- **Paper:** https://arxiv.org/abs/2304.07327
### Dataset Summary
In an effort to democratize research on large-scale alignment, we release OpenAssistant
Conversations (OASST1), a human-generated, human-annotated assistant-style conversation
corpus consisting of 161,443 messages in 35 different languages, annotated with 461,292
quality ratings, resulting in over 10,000 fully annotated conversation trees. The corpus
is a product of a worldwide crowd-sourcing effort involving over 13,500 volunteers.
Please refer to our [paper](https://arxiv.org/abs/2304.07327) for further details.
### Dataset Structure
This dataset contains message trees. Each message tree has an initial prompt message as the root node,
which can have multiple child messages as replies, and these child messages can have multiple replies.
All messages have a role property: this can either be "assistant" or "prompter". The roles in
conversation threads from prompt to leaf node strictly alternate between "prompter" and "assistant".
This version of the dataset contains data collected on the [open-assistant.io](https://open-assistant.io/) website until April 12 2023.
### JSON Example: Message
For readability, the following JSON examples are shown formatted with indentation on multiple lines.
Objects are stored without indentation (on single lines) in the actual jsonl files.
```json
{
"message_id": "218440fd-5317-4355-91dc-d001416df62b",
"parent_id": "13592dfb-a6f9-4748-a92c-32b34e239bb4",
"user_id": "8e95461f-5e94-4d8b-a2fb-d4717ce973e4",
"text": "It was the winter of 2035, and artificial intelligence (..)",
"role": "assistant",
"lang": "en",
"review_count": 3,
"review_result": true,
"deleted": false,
"rank": 0,
"synthetic": true,
"model_name": "oasst-sft-0_3000,max_new_tokens=400 (..)",
"labels": {
"spam": { "value": 0.0, "count": 3 },
"lang_mismatch": { "value": 0.0, "count": 3 },
"pii": { "value": 0.0, "count": 3 },
"not_appropriate": { "value": 0.0, "count": 3 },
"hate_speech": { "value": 0.0, "count": 3 },
"sexual_content": { "value": 0.0, "count": 3 },
"quality": { "value": 0.416, "count": 3 },
"toxicity": { "value": 0.16, "count": 3 },
"humor": { "value": 0.0, "count": 3 },
"creativity": { "value": 0.33, "count": 3 },
"violence": { "value": 0.16, "count": 3 }
}
}
```
### JSON Example: Conversation Tree
For readability, only a subset of the message properties is shown here.
```json
{
"message_tree_id": "14fbb664-a620-45ce-bee4-7c519b16a793",
"tree_state": "ready_for_export",
"prompt": {
"message_id": "14fbb664-a620-45ce-bee4-7c519b16a793",
"text": "Why can't we divide by 0? (..)",
"role": "prompter",
"lang": "en",
"replies": [
{
"message_id": "894d30b6-56b4-4605-a504-89dd15d4d1c8",
"text": "The reason we cannot divide by zero is because (..)",
"role": "assistant",
"lang": "en",
"replies": [
// ...
]
},
{
"message_id": "84d0913b-0fd9-4508-8ef5-205626a7039d",
"text": "The reason that the result of a division by zero is (..)",
"role": "assistant",
"lang": "en",
"replies": [
{
"message_id": "3352725e-f424-4e3b-a627-b6db831bdbaa",
"text": "Math is confusing. Like those weird Irrational (..)",
"role": "prompter",
"lang": "en",
"replies": [
{
"message_id": "f46207ca-3149-46e9-a466-9163d4ce499c",
"text": "Irrational numbers are simply numbers (..)",
"role": "assistant",
"lang": "en",
"replies": []
},
// ...
]
}
]
}
]
}
}
```
Please refer to [oasst-data](https://github.com/LAION-AI/Open-Assistant/tree/main/oasst-data) for
details about the data structure and Python code to read and write jsonl files containing oasst data objects.
If you would like to explore the dataset yourself you can find a
[`getting-started`](https://github.com/LAION-AI/Open-Assistant/blob/main/notebooks/openassistant-oasst1/getting-started.ipynb)
notebook in the `notebooks/openassistant-oasst1` folder of the [LAION-AI/Open-Assistant](https://github.com/LAION-AI/Open-Assistant)
github repository.
## Main Dataset Files
Conversation data is provided either as nested messages in trees (extension `.trees.jsonl.gz`)
or as a flat list (table) of messages (extension `.messages.jsonl.gz`).
### Ready For Export Trees
```
2023-04-12_oasst_ready.trees.jsonl.gz 10,364 trees with 88,838 total messages
2023-04-12_oasst_ready.messages.jsonl.gz 88,838 messages
```
Trees in `ready_for_export` state without spam and deleted messages including message labels.
The oasst_ready-trees file usually is sufficient for supervised fine-tuning (SFT) & reward model (RM) training.
### All Trees
```
2023-04-12_oasst_all.trees.jsonl.gz 66,497 trees with 161,443 total messages
2023-04-12_oasst_all.messages.jsonl.gz 161,443 messages
```
All trees, including those in states `prompt_lottery_waiting` (trees that consist of only one message, namely the initial prompt),
`aborted_low_grade` (trees that stopped growing because the messages had low quality), and `halted_by_moderator`.
### Supplemental Exports: Spam & Prompts
```
2023-04-12_oasst_spam.messages.jsonl.gz
```
These are messages which were deleted or have a negative review result (`"review_result": false`).
Besides low quality, a frequent reason for message deletion is a wrong language tag.
```
2023-04-12_oasst_prompts.messages.jsonl.gz
```
These are all the kept initial prompt messages with positive review result (no spam) of trees in `ready_for_export` or `prompt_lottery_waiting` state.
### Using the Huggingface Datasets
While HF datasets is ideal for tabular datasets, it is not a natural fit for nested data structures like the OpenAssistant conversation trees.
Nevertheless, we make all messages which can also be found in the file `2023-04-12_oasst_ready.trees.jsonl.gz` available in parquet as train/validation splits.
These are directly loadable by [Huggingface Datasets](https://pypi.org/project/datasets/).
To load the oasst1 train & validation splits use:
```python
from datasets import load_dataset
ds = load_dataset("OpenAssistant/oasst1")
train = ds['train'] # len(train)=84437 (95%)
val = ds['validation'] # len(val)=4401 (5%)
```
The messages appear in depth-first order of the message trees.
Full conversation trees can be reconstructed from the flat messages table by using the `parent_id`
and `message_id` properties to identify the parent-child relationship of messages. The `message_tree_id`
and `tree_state` properties (only present in flat messages files) can be used to find all messages of a message tree or to select trees by their state.
### Languages
OpenAssistant Conversations incorporates 35 different languages with a distribution of messages as follows:
**Languages with over 1000 messages**
- English: 71956
- Spanish: 43061
- Russian: 9089
- German: 5279
- Chinese: 4962
- French: 4251
- Thai: 3042
- Portuguese (Brazil): 2969
- Catalan: 2260
- Korean: 1553
- Ukrainian: 1352
- Italian: 1320
- Japanese: 1018
<details>
<summary><b>Languages with under 1000 messages</b></summary>
<ul>
<li>Vietnamese: 952</li>
<li>Basque: 947</li>
<li>Polish: 886</li>
<li>Hungarian: 811</li>
<li>Arabic: 666</li>
<li>Dutch: 628</li>
<li>Swedish: 512</li>
<li>Turkish: 454</li>
<li>Finnish: 386</li>
<li>Czech: 372</li>
<li>Danish: 358</li>
<li>Galician: 339</li>
<li>Hebrew: 255</li>
<li>Romanian: 200</li>
<li>Norwegian Bokmål: 133</li>
<li>Indonesian: 115</li>
<li>Bulgarian: 95</li>
<li>Bengali: 82</li>
<li>Persian: 72</li>
<li>Greek: 66</li>
<li>Esperanto: 59</li>
<li>Slovak: 19</li>
</ul>
</details>
## Contact
- Discord [Open Assistant Discord Server](https://ykilcher.com/open-assistant-discord)
- GitHub: [LAION-AI/Open-Assistant](https://github.com/LAION-AI/Open-Assistant)
- E-Mail: [[email protected]](mailto:[email protected])
|
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baebee/chatgpt-custom_inst
|
baebee
| 2023-10-09T19:16:48Z | 34 | 0 |
[
"task_categories:summarization",
"task_categories:question-answering",
"task_categories:conversational",
"size_categories:n<1K",
"language:en",
"language:tl",
"license:mit",
"region:us"
] |
[
"summarization",
"question-answering",
"conversational"
] | 2023-10-09T07:31:02Z |
---
license: mit
task_categories:
- summarization
- question-answering
- conversational
language:
- en
- tl
size_categories:
- n<1K
---
# Languages: English, Tagalog
## Collection Process:
- Dialogs generated by instructing ChatGPT to respond concisely
- Responses edited by Nuph researchers for naturalness
- Bilingual exchanges added for diversity
## Intended Use:
- Train conversational agents
- Research in straightforward dialog
# Limitations:
- Small scale (300 rows)
- Biased toward English
- Limited to text conversations
# Ethics and Privacy:
- No personal or offensive content
- ChatGPT instructed to avoid unethical responses
- Data anonymized - no personally identifiable information
|
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sordonia/my-wiki-latex_mmlu_from_valid_all
|
sordonia
| 2023-10-11T01:19:27Z | 34 | 0 |
[
"region:us"
] | null | 2023-10-10T20:52:48Z |
---
dataset_info:
features:
- name: subject
dtype: string
- name: docno
dtype: int64
- name: score
dtype: float64
- name: dfq
dtype: int64
- name: text
dtype: string
- name: id
dtype: string
- name: url
dtype: string
- name: title
dtype: string
- name: revid
dtype: string
splits:
- name: train
num_bytes: 1139620543
num_examples: 137881
download_size: 0
dataset_size: 1139620543
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "my-wiki-latex_mmlu_from_valid_all"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
WenyangHui/Conic10K
|
WenyangHui
| 2023-10-24T14:58:46Z | 34 | 4 |
[
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:zh",
"license:mit",
"math",
"semantic parsing",
"region:us"
] |
[
"question-answering"
] | 2023-10-24T14:42:07Z |
---
license: mit
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
- name: answer_expressions
dtype: string
- name: fact_expressions
dtype: string
- name: query_expressions
dtype: string
- name: fact_spans
dtype: string
- name: query_spans
dtype: string
- name: process
dtype: string
splits:
- name: train
num_bytes: 6012696
num_examples: 7757
- name: validation
num_bytes: 796897
num_examples: 1035
- name: test
num_bytes: 1630198
num_examples: 2069
download_size: 3563693
dataset_size: 8439791
task_categories:
- question-answering
language:
- zh
tags:
- math
- semantic parsing
size_categories:
- 10K<n<100K
---
|
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] |
kaitchup/opus-German-to-English
|
kaitchup
| 2023-11-01T19:15:23Z | 34 | 1 |
[
"region:us"
] | null | 2023-11-01T19:15:17Z |
---
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
- split: train
path: data/train-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: validation
num_bytes: 334342
num_examples: 2000
- name: train
num_bytes: 115010446
num_examples: 940304
download_size: 84489243
dataset_size: 115344788
---
# Dataset Card for "opus-de-en"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
anyspeech/fleurs_test
|
anyspeech
| 2023-11-02T00:05:35Z | 34 | 0 |
[
"region:us"
] | null | 2023-11-02T00:02:09Z |
---
configs:
- config_name: default
data_files:
- split: query
path: data/query-*
- split: candidate
path: data/candidate-*
dataset_info:
features:
- name: _id
dtype: int64
- name: file_name
dtype: string
- name: raw_transcription
dtype: string
- name: transcription
dtype: string
- name: num_samples
dtype: int64
- name: gender
dtype: string
- name: phones
dtype: string
- name: audio
struct:
- name: array
sequence: float64
- name: sampling_rate
dtype: int64
splits:
- name: query
num_bytes: 1843536302
num_examples: 1132
- name: candidate
num_bytes: 3243527476
num_examples: 1979
download_size: 3137163451
dataset_size: 5087063778
---
# Dataset Card for "fleurs_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
slplab/QoLT_apacAnd5Sentences
|
slplab
| 2023-11-02T12:36:57Z | 34 | 0 |
[
"region:us"
] | null | 2023-11-02T12:36:07Z |
---
dataset_info:
features:
- name: speaker
dtype: string
- name: audio
struct:
- name: array
sequence: float32
- name: path
dtype: string
- name: sampling_rate
dtype: int64
- name: txt
dtype: string
- name: severity
dtype: int64
splits:
- name: train
num_bytes: 507067812
num_examples: 2352
- name: validation
num_bytes: 62855037
num_examples: 294
- name: test
num_bytes: 62245337
num_examples: 294
download_size: 278294583
dataset_size: 632168186
---
# Dataset Card for "QoLT_apacAnd5Sentences"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
paul-w-qs/contracts_v2
|
paul-w-qs
| 2023-11-02T23:23:33Z | 34 | 0 |
[
"region:us"
] | null | 2023-11-02T23:16:10Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: N_ROWS
dtype: int64
- name: N_COLS
dtype: int64
- name: FONT_SIZE
dtype: int64
- name: FONT_NAME
dtype: string
- name: BORDER_THICKNESS
dtype: int64
- name: NOISED
dtype: bool
- name: LABEL_NOISE
dtype: bool
- name: JSON_LABEL
dtype: string
splits:
- name: train
num_bytes: 961858267.064
num_examples: 11871
download_size: 947911506
dataset_size: 961858267.064
---
# Dataset Card for "contracts_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
artyomboyko/Common_voice_13_0_ru_dataset_prepared_for_whisper_fine_tune
|
artyomboyko
| 2023-11-03T14:50:21Z | 34 | 0 |
[
"task_categories:automatic-speech-recognition",
"language:ru",
"license:gpl-3.0",
"region:us"
] |
[
"automatic-speech-recognition"
] | 2023-11-03T09:18:28Z |
---
license: gpl-3.0
dataset_info:
features:
- name: input_features
sequence:
sequence: float32
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 35015263976
num_examples: 36454
- name: test
num_bytes: 9783955736
num_examples: 10186
download_size: 8317469510
dataset_size: 44799219712
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
task_categories:
- automatic-speech-recognition
language:
- ru
---
|
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0.04487833380699158,
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] |
voyagar/mitre_cit_v14
|
voyagar
| 2023-11-06T01:59:00Z | 34 | 0 |
[
"language:en",
"license:unlicense",
"region:us"
] | null | 2023-11-06T01:51:16Z |
---
license: unlicense
language:
- en
---
# Cloud Matrix Data
## Description
This dataset contains information related to cybersecurity techniques, as cataloged by the MITRE ATT&CK framework(v14).
The data includes details such as unique identifiers, names, descriptions, URLs to more information, associated tactics, detection methods, applicable platforms, and data sources for detection. It also specifies whether a technique is a sub-technique of another and lists defenses that the technique may bypass.
## Structure
- **Rows**: 130
- **Columns**: 11
The columns are as follows:
1. `ID`: Unique identifier for the technique (e.g., T1189, T1566.002)
2. `Name`: Name of the technique (e.g., Drive-by Compromise, Phishing: Spearphishing Link)
3. `Description`: Brief description of the technique
4. `URL`: Link to more information about the technique
5. `Tactics`: Category of tactics the technique falls under
6. `Detection`: How the technique might be detected
7. `Platforms`: Operating systems or platforms the technique applies to
8. `Data Sources`: Sources of data for detection
9. `Is Sub-Technique`: Whether the entry is a sub-technique (True/False)
10. `Sub-Technique Of`: If the entry is a sub-technique, the parent technique's ID
11. `Defenses Bypassed`: Defenses the technique is known to bypass
## Usage
This dataset can be used by cybersecurity professionals and researchers to analyze and categorize different types of cybersecurity threats and their characteristics. It can also assist in developing defensive strategies by providing detection methods and noting applicable platforms.
## Additional Information
- The dataset is likely derived from the MITRE ATT&CK framework, as indicated by the URL structure and content.
- The data may need to be updated periodically to reflect the latest information from the MITRE ATT&CK database.
|
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] |
NathanGavenski/Acrobot-v1
|
NathanGavenski
| 2023-11-06T15:53:25Z | 34 | 1 |
[
"size_categories:10M<n<100M",
"license:mit",
"Imitation Learning",
"Expert Trajectory",
"region:us"
] | null | 2023-11-06T15:50:16Z |
---
license: mit
tags:
- Imitation Learning
- Expert Trajectory
pretty_name: Acrobot-v1 Expert Dataset
size_categories:
- 10M<n<100M
---
# Acrobot-v1 - Imitation Learning Datasets
This is a dataset created by [Imitation Learning Datasets](https://github.com/NathanGavenski/IL-Datasets) project.
It was created by using Stable Baselines weights from a DQN policy from [HuggingFace](https://huggingface.co/sb3/dqn-Acrobot-v1).
## Description
The dataset consists of 1,000 episodes with an average episodic reward of `-69.852`.
Each entry consists of:
```
obs (list): observation with length 6.
action (int): action (0, 1 or 2).
reward (float): reward point for that timestep.
episode_returns (bool): if that state was the initial timestep for an episode.
```
## Usage
Feel free to download and use the `teacher.jsonl` dataset as you please.
If you are interested in using our PyTorch Dataset implementation, feel free to check the [IL Datasets](https://github.com/NathanGavenski/IL-Datasets/blob/main/src/imitation_datasets/dataset/dataset.py) project.
There, we implement a base Dataset that downloads this dataset and all other datasets directly from HuggingFace.
The Baseline Dataset also allows for more control over train and test splits and how many episodes you want to use (in cases where the 1k episodes are not necessary).
## Citation
Coming soon.
|
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] |
anyspeech/PhoneCorpus
|
anyspeech
| 2023-11-07T16:54:01Z | 34 | 0 |
[
"region:us"
] | null | 2023-11-07T16:53:53Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: phones
dtype: string
splits:
- name: train
num_bytes: 264095984
num_examples: 10382114
download_size: 143568761
dataset_size: 264095984
---
# Dataset Card for "PhoneCorpus"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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wu981526092/LL144
|
wu981526092
| 2023-11-20T09:26:24Z | 34 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-11T12:55:47Z |
---
license: mit
---
|
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] |
thangquoc/ad_banner
|
thangquoc
| 2023-11-14T17:00:22Z | 34 | 0 |
[
"region:us"
] | null | 2023-11-14T16:59:58Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 86615696.13
num_examples: 1362
download_size: 84006544
dataset_size: 86615696.13
---
# Dataset Card for "ad_banner"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
lhallee/dl_ten_reg
|
lhallee
| 2023-11-15T18:36:05Z | 34 | 0 |
[
"region:us"
] | null | 2023-11-15T18:36:03Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
dataset_info:
features:
- name: seqs
dtype: string
- name: labels
dtype: int64
splits:
- name: train
num_bytes: 4494546
num_examples: 8743
- name: valid
num_bytes: 1132147
num_examples: 2190
- name: test
num_bytes: 1487111
num_examples: 2745
download_size: 7016840
dataset_size: 7113804
---
# Dataset Card for "dl_ten_reg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
atmallen/qm_alice_hard_4_grader_last_1.0e
|
atmallen
| 2023-11-16T18:22:49Z | 34 | 0 |
[
"region:us"
] | null | 2023-11-16T03:25:54Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: alice_label
dtype: bool
- name: bob_label
dtype: bool
- name: difficulty
dtype: int64
- name: statement
dtype: string
- name: choices
sequence: string
- name: character
dtype: string
- name: label
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
splits:
- name: train
num_bytes: 2899268.0
num_examples: 37091
- name: validation
num_bytes: 310182.0
num_examples: 3969
- name: test
num_bytes: 306854.0
num_examples: 3926
download_size: 1013749
dataset_size: 3516304.0
---
# Dataset Card for "qm_alice_hard_4_grader_last_1.0e"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
sam2ai/openwebtext-10k
|
sam2ai
| 2023-11-18T18:31:21Z | 34 | 0 |
[
"region:us"
] | null | 2023-11-18T18:31:17Z |
---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 49670857
num_examples: 10000
download_size: 30271595
dataset_size: 49670857
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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] |
JestemKamil/text-classification-pl
|
JestemKamil
| 2023-11-22T14:11:42Z | 34 | 0 |
[
"region:us"
] | null | 2023-11-20T18:22:21Z |
---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 6493727
num_examples: 35672
download_size: 4309231
dataset_size: 6493727
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Labels
0: normal
1: toxic
|
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taylorbobaylor/google-colab
|
taylorbobaylor
| 2023-11-23T03:52:58Z | 34 | 0 |
[
"region:us"
] | null | 2023-11-23T03:52:57Z |
---
dataset_info:
features:
- name: repo_id
dtype: string
- name: file_path
dtype: string
- name: content
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 625338
num_examples: 66
download_size: 229515
dataset_size: 625338
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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xrizs/test.v83i.coco-segmentation
|
xrizs
| 2023-11-23T14:14:52Z | 34 | 0 |
[
"region:us"
] | null | 2023-11-23T14:10:05Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
dataset_info:
features:
- name: image
dtype: image
- name: annotation
dtype: image
splits:
- name: train
num_bytes: 815324785.5
num_examples: 1814
- name: val
num_bytes: 205298969.0
num_examples: 453
download_size: 1020036030
dataset_size: 1020623754.5
---
# Dataset Card for "test.v83i.coco-segmentation"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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GokhanAI/INST_DATA
|
GokhanAI
| 2023-11-23T15:08:43Z | 34 | 0 |
[
"region:us"
] | null | 2023-11-23T15:07:56Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 107826078.69637841
num_examples: 37513
- name: test
num_bytes: 5748731.303621593
num_examples: 2000
download_size: 0
dataset_size: 113574810.0
---
# Dataset Card for "INST_DATA"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
iohadrubin/not-gibberish-20-56-22
|
iohadrubin
| 2023-12-01T00:27:47Z | 34 | 0 |
[
"region:us"
] | null | 2023-12-01T00:27:45Z |
---
dataset_info:
features:
- name: text
dtype: string
- name: id
dtype: int32
- name: file_loc
dtype: int64
splits:
- name: train
num_bytes: 10140566
num_examples: 6198
download_size: 5851350
dataset_size: 10140566
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "not-gibberish-20-56-22"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
botisan-ai/cantonese-mandarin-translations
|
botisan-ai
| 2022-11-23T06:41:46Z | 33 | 6 |
[
"task_categories:text2text-generation",
"task_categories:translation",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:translation",
"size_categories:unknown",
"source_datasets:original",
"language:zh",
"license:mit",
"conditional-text-generation",
"region:us"
] |
[
"text2text-generation",
"translation"
] | 2022-03-02T23:29:22Z |
---
annotations_creators:
- machine-generated
language_creators:
- found
language:
- zh
license:
- mit
multilinguality:
- translation
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text2text-generation
- translation
task_ids: []
pretty_name: Cantonese - Mandarin Translations
language_bcp47:
- zh-CN
- zh-HK
tags:
- conditional-text-generation
---
# Dataset Card for cantonese-mandarin-translations
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### 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
MIT
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@lhr0909](https://github.com/lhr0909) for adding this dataset.
|
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] |
shpotes/bosch-small-traffic-lights-dataset
|
shpotes
| 2022-03-10T20:00:45Z | 33 | 4 |
[
"license:other",
"region:us"
] | null | 2022-03-08T14:48:14Z |
---
license: other
---
|
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fmplaza/EmoEvent
|
fmplaza
| 2023-03-27T08:19:58Z | 33 | 6 |
[
"language:en",
"language:es",
"license:apache-2.0",
"region:us"
] | null | 2022-03-09T10:17:46Z |
---
license: apache-2.0
language:
- en
- es
---
# Dataset Card for Emoevent
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Repository:** [EmoEvent dataset repository](https://github.com/fmplaza/EmoEvent)
- **Paper: EmoEvent:** [A Multilingual Emotion Corpus based on different Events](https://aclanthology.org/2020.lrec-1.186.pdf)
- **Leaderboard:** [Leaderboard for EmoEvent / Spanish version](http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6385)
- **Point of Contact: [email protected]**
### Dataset Summary
EmoEvent is a multilingual emotion dataset of tweets based on different events that took place in April 2019.
Three annotators labeled the tweets following the six Ekman’s basic emotion model (anger, fear, sadness, joy, disgust, surprise) plus the “neutral or other emotions” category. Morevoer, the tweets are annotated as offensive (OFF) or non-offensive (NO).
### Supported Tasks and Leaderboards
This dataset is intended for multi-class emotion classification and binary offensive classification.
Competition [EmoEvalEs task on emotion detection for Spanish at IberLEF 2021](http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6385)
### Languages
- Spanish
- English
## Dataset Structure
### Data Instances
For each instance, there is a string for the id of the tweet, a string for the emotion class, a string for the offensive class, and a string for the event. See the []() to explore more examples.
```
{'id': 'a0c1a858-a9b8-4cb1-8a81-1602736ff5b8',
'event': 'GameOfThrones',
'tweet': 'ARYA DE MI VIDA. ERES MAS ÉPICA QUE EL GOL DE INIESTA JODER #JuegodeTronos #VivePoniente',
'offensive': 'NO',
'emotion': 'joy',
}
```
```
{'id': '3YCT0L9OMMFP7KWKQSTJRJO0YHUSN2a0c1a858-a9b8-4cb1-8a81-1602736ff5b8',
'event': 'GameOfThrones',
'tweet': 'The #NotreDameCathedralFire is indeed sad and people call all offered donations humane acts, but please if you have money to donate, donate to humans and help bring food to their tables and affordable education first. What more humane than that? #HumanityFirst',
'offensive': 'NO',
'emotion': 'sadness',
}
```
### Data Fields
- `id`: a string to identify the tweet
- `event`: a string containing the event associated with the tweet
- `tweet`: a string containing the text of the tweet
- `offensive`: a string containing the offensive gold label
- `emotion`: a string containing the emotion gold label
### Data Splits
The EmoEvent dataset has 2 subsets: EmoEvent_es (Spanish version) and EmoEvent_en (English version)
Each subset contains 3 splits: _train_, _validation_, and _test_. Below are the statistics subsets.
| EmoEvent_es | Number of Instances in Split |
| ------------- | ------------------------------------------- |
| Train | 5,723 |
| Validation | 844 |
| Test | 1,656 |
| EmoEvent_en | Number of Instances in Split |
| ------------- | ------------------------------------------- |
| Train | 5,112 |
| Validation | 744 |
| Test | 1,447 |
## Dataset Creation
### Source Data
Twitter
#### Who are the annotators?
Amazon Mechanical Turkers
## Additional Information
### Licensing Information
The EmoEvent dataset is released under the [Apache-2.0 License](http://www.apache.org/licenses/LICENSE-2.0).
### Citation Information
@inproceedings{plaza-del-arco-etal-2020-emoevent,
title = "{{E}mo{E}vent: A Multilingual Emotion Corpus based on different Events}",
author = "{Plaza-del-Arco}, {Flor Miriam} and Strapparava, Carlo and {Ure{\~n}a-L{\’o}pez}, L. Alfonso and {Mart{\’i}n-Valdivia}, M. Teresa",
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France", publisher = "European Language Resources Association",
url = "https://www.aclweb.org/anthology/2020.lrec-1.186", pages = "1492--1498",
language = "English",
ISBN = "979-10-95546-34-4"
}
|
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gcaillaut/frwiki_el
|
gcaillaut
| 2022-09-28T08:52:12Z | 33 | 1 |
[
"task_categories:token-classification",
"annotations_creators:crowdsourced",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:fr",
"license:wtfpl",
"region:us"
] |
[
"token-classification"
] | 2022-06-15T09:37:40Z |
---
annotations_creators:
- crowdsourced
language_creators:
- machine-generated
language:
- fr
license:
- wtfpl
multilinguality:
- monolingual
pretty_name: French Wikipedia dataset for Entity Linking
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- token-classification
task_ids: []
---
# Dataset Card for frwiki_good_pages_el
## Dataset Description
- Repository: [frwiki_el](https://github.com/GaaH/frwiki_el)
- Point of Contact: [Gaëtan Caillaut](mailto://[email protected])
### Dataset Summary
This dataset contains articles from the French Wikipédia.
It is intended to be used to train Entity Linking (EL) systems. Links in articles are used to detect named entities.
The dataset `frwiki` contains sentences of each Wikipedia pages.
The dataset `entities` contains description for each Wikipedia pages.
### Languages
- French
## Dataset Structure
### frwiki
```
{
"name": "Title of the page",
"wikidata_id": "Identifier of the related Wikidata entity. Can be null.",
"wikipedia_id": "Identifier of the Wikipedia page",
"wikipedia_url": "URL to the Wikipedia page",
"wikidata_url": "URL to the Wikidata page. Can be null.",
"sentences" : [
{
"text": "text of the current sentence",
"ner": ["list", "of", "ner", "labels"],
"mention_mappings": [
(start_of_first_mention, end_of_first_mention),
(start_of_second_mention, end_of_second_mention)
],
"el_wikidata_id": ["wikidata id of first mention", "wikidata id of second mention"],
"el_wikipedia_id": [wikipedia id of first mention, wikipedia id of second mention],
"el_wikipedia_title": ["wikipedia title of first mention", "wikipedia title of second mention"]
}
]
"words": ["words", "in", "the", "sentence"],
"ner": ["ner", "labels", "of", "each", "words"],
"el": ["el", "labels", "of", "each", "words"]
}
```
### entities
```
{
"name": "Title of the page",
"wikidata_id": "Identifier of the related Wikidata entity. Can be null.",
"wikipedia_id": "Identifier of the Wikipedia page",
"wikipedia_url": "URL to the Wikipedia page",
"wikidata_url": "URL to the Wikidata page. Can be null.",
"description": "Description of the entity"
}
```
|
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] |
SpeedOfMagic/ontonotes_english
|
SpeedOfMagic
| 2022-07-01T16:06:06Z | 33 | 2 |
[
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other",
"language:en",
"license:unknown",
"region:us"
] |
[
"token-classification"
] | 2022-06-28T17:34:30Z |
---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
pretty_name: ontonotes_english
size_categories:
- 10K<n<100K
source_datasets:
- extended|other
task_categories:
- token-classification
task_ids:
- named-entity-recognition
---
# Dataset Card for ontonotes_english
## 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:** [CoNLL-2012 Shared Task](https://conll.cemantix.org/2012/data.html), [Author's page](https://cemantix.org/data/ontonotes.html)
- **Repository:**
- **Paper:** [Towards Robust Linguistic Analysis using OntoNotes](https://aclanthology.org/W13-3516/)
- **Leaderboard:** [Papers With Code](https://paperswithcode.com/sota/named-entity-recognition-ner-on-ontonotes-v5)
- **Point of Contact:**
### Dataset Summary
This is preprocessed version of what I assume is OntoNotes v5.0.
Instead of having sentences stored in files, files are unpacked and sentences are the rows now. Also, fields were renamed in order to match [conll2003](https://huggingface.co/datasets/conll2003).
The source of data is from private repository, which in turn got data from another public repository, location of which is unknown :)
Since data from all repositories had no license (creator of the private repository told me so), there should be no licensing issues. But bear in mind, I don't give any guarantees that this is real OntoNotes, and may differ as a result.
### Supported Tasks and Leaderboards
- [Named Entity Recognition on Ontonotes v5 (English)](https://paperswithcode.com/sota/named-entity-recognition-ner-on-ontonotes-v5)
- [Coreference Resolution on OntoNotes](https://paperswithcode.com/sota/coreference-resolution-on-ontonotes)
- [Semantic Role Labeling on OntoNotes](https://paperswithcode.com/sota/semantic-role-labeling-on-ontonotes)
### Languages
English
## Dataset Structure
### Data Instances
```
{
'tokens': ['Well', ',', 'the', 'Hundred', 'Regiments', 'Offensive', 'was', 'divided', 'into', 'three', 'phases', '.'],
'ner_tags': [0, 0, 29, 30, 30, 30, 0, 0, 0, 27, 0, 0]
}
```
### Data Fields
- **`tokens`** (*`List[str]`*) : **`words`** in original dataset
- **`ner_tags`** (*`List[ClassLabel]`*) : **`named_entities`** in original dataset. The BIO tags for named entities in the sentence.
- tag set : `datasets.ClassLabel(num_classes=37, names=["O", "B-PERSON", "I-PERSON", "B-NORP", "I-NORP", "B-FAC", "I-FAC", "B-ORG", "I-ORG", "B-GPE", "I-GPE", "B-LOC", "I-LOC", "B-PRODUCT", "I-PRODUCT", "B-DATE", "I-DATE", "B-TIME", "I-TIME", "B-PERCENT", "I-PERCENT", "B-MONEY", "I-MONEY", "B-QUANTITY", "I-QUANTITY", "B-ORDINAL", "I-ORDINAL", "B-CARDINAL", "I-CARDINAL", "B-EVENT", "I-EVENT", "B-WORK_OF_ART", "I-WORK_OF_ART", "B-LAW", "I-LAW", "B-LANGUAGE", "I-LANGUAGE",])`
### Data Splits
_train_, _validation_, and _test_
## 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
No license
### Citation Information
```
@inproceedings{pradhan-etal-2013-towards,
title = "Towards Robust Linguistic Analysis using {O}nto{N}otes",
author = {Pradhan, Sameer and
Moschitti, Alessandro and
Xue, Nianwen and
Ng, Hwee Tou and
Bj{\"o}rkelund, Anders and
Uryupina, Olga and
Zhang, Yuchen and
Zhong, Zhi},
booktitle = "Proceedings of the Seventeenth Conference on Computational Natural Language Learning",
month = aug,
year = "2013",
address = "Sofia, Bulgaria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W13-3516",
pages = "143--152",
}
```
### Contributions
Thanks to the author of private repository, that uploaded this dataset.
|
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embedding-data/PAQ_pairs
|
embedding-data
| 2022-08-02T02:58:28Z | 33 | 1 |
[
"task_categories:sentence-similarity",
"task_ids:semantic-similarity-classification",
"language:en",
"license:mit",
"arxiv:2102.07033",
"region:us"
] |
[
"sentence-similarity",
"paraphrase-mining"
] | 2022-07-08T17:05:27Z |
---
license: mit
language:
- en
paperswithcode_id: embedding-data/PAQ_pairs
pretty_name: PAQ_pairs
task_categories:
- sentence-similarity
- paraphrase-mining
task_ids:
- semantic-similarity-classification
---
# Dataset Card for "PAQ_pairs"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/facebookresearch/PAQ](https://github.com/facebookresearch/PAQ)
- **Repository:** [More Information Needed](https://github.com/facebookresearch/PAQ)
- **Paper:** [More Information Needed](https://github.com/facebookresearch/PAQ)
- **Point of Contact:** [More Information Needed](https://github.com/facebookresearch/PAQ)
- **Size of downloaded dataset files:**
- **Size of the generated dataset:**
- **Total amount of disk used:** 21 Bytes
### Dataset Summary
Pairs questions and answers obtained from Wikipedia.
Disclaimer: The team releasing PAQ QA pairs did not upload the dataset to the Hub and did not write a dataset card.
These steps were done by the Hugging Face team.
### Supported Tasks
- [Sentence Transformers](https://huggingface.co/sentence-transformers) training; useful for semantic search and sentence similarity.
### Languages
- English.
## Dataset Structure
Each example in the dataset contains pairs of sentences and is formatted as a dictionary with the key "set" and a list with the sentences as "value". The first sentence is a question and the second an answer; thus, both sentences would be similar.
```
{"set": [sentence_1, sentence_2]}
{"set": [sentence_1, sentence_2]}
...
{"set": [sentence_1, sentence_2]}
```
This dataset is useful for training Sentence Transformers models. Refer to the following post on how to train models using similar pairs of sentences.
### Usage Example
Install the 🤗 Datasets library with `pip install datasets` and load the dataset from the Hub with:
```python
from datasets import load_dataset
dataset = load_dataset("embedding-data/PAQ_pairs")
```
The dataset is loaded as a `DatasetDict` and has the format:
```python
DatasetDict({
train: Dataset({
features: ['set'],
num_rows: 64371441
})
})
```
Review an example `i` with:
```python
dataset["train"][i]["set"]
```
### Data Instances
[More Information Needed](https://github.com/facebookresearch/PAQ)
### Data Fields
[More Information Needed](https://github.com/facebookresearch/PAQ)
### Data Splits
[More Information Needed](https://github.com/facebookresearch/PAQ)
## Dataset Creation
[More Information Needed](https://github.com/facebookresearch/PAQ)
### Curation Rationale
[More Information Needed](https://github.com/facebookresearch/PAQ)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/facebookresearch/PAQ)
#### Who are the source language producers?
[More Information Needed](https://github.com/facebookresearch/PAQ)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/facebookresearch/PAQ)
#### Who are the annotators?
[More Information Needed](https://github.com/facebookresearch/PAQ)
### Personal and Sensitive Information
[More Information Needed](https://github.com/facebookresearch/PAQ)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/facebookresearch/PAQ)
### Discussion of Biases
[More Information Needed](https://github.com/facebookresearch/PAQ)
### Other Known Limitations
[More Information Needed](https://github.com/facebookresearch/PAQ)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/facebookresearch/PAQ)
### Licensing Information
The PAQ QA-pairs and metadata is licensed under [CC-BY-SA](https://creativecommons.org/licenses/by-sa/3.0/).
Other data is licensed according to the accompanying license files.
### Citation Information
```
@article{lewis2021paq,
title={PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them},
author={Patrick Lewis and Yuxiang Wu and Linqing Liu and Pasquale Minervini and Heinrich Küttler and Aleksandra Piktus and Pontus Stenetorp and Sebastian Riedel},
year={2021},
eprint={2102.07033},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@patrick-s-h-lewis](https://github.com/patrick-s-h-lewis) for adding this dataset.
|
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EMBO/sd-nlp-v2
|
EMBO
| 2022-09-26T12:47:16Z | 33 | 0 |
[
"license:cc-by-4.0",
"region:us"
] | null | 2022-09-26T12:38:27Z |
---
license: cc-by-4.0
---
|
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