id stringlengths 2 115 | lastModified stringlengths 24 24 | tags list | author stringlengths 2 42 โ | description stringlengths 0 6.67k โ | citation stringlengths 0 10.7k โ | likes int64 0 3.66k | downloads int64 0 8.89M | created timestamp[us] | card stringlengths 11 977k | card_len int64 11 977k | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|
izumi-lab/wikinews-ja-20230728 | 2023-07-29T03:06:48.000Z | [
"language:ja",
"license:cc-by-2.5",
"region:us"
] | izumi-lab | null | null | 3 | 162 | 2023-07-28T07:01:06 | ---
dataset_info:
features:
- name: text
dtype: string
- name: title
dtype: string
- name: url
dtype: string
splits:
- name: train
num_bytes: 7998861
num_examples: 4283
download_size: 4086208
dataset_size: 7998861
license: cc-by-2.5
language:
- ja
---
# Dataset Card for "wikinews-ja-20230728"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 462 | [
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] |
C-MTEB/QBQTC | 2023-07-28T13:38:12.000Z | [
"region:us"
] | C-MTEB | null | null | 0 | 162 | 2023-07-28T13:38:05 | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_bytes: 524191
num_examples: 5000
download_size: 387552
dataset_size: 524191
---
# Dataset Card for "QBQTC"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 503 | [
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yashnbx/l27b-E02-large-b10-1314-3 | 2023-09-30T16:29:18.000Z | [
"region:us"
] | yashnbx | null | null | 0 | 162 | 2023-09-30T16:28:57 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
splits:
- name: test
num_bytes: 1013014
num_examples: 146
- name: train
num_bytes: 9077266
num_examples: 1314
download_size: 1662927
dataset_size: 10090280
---
# Dataset Card for "l27b-E02-large-b10-1314-3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 534 | [
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] |
chrisgru/commonsense-dialogues2 | 2023-10-19T13:07:25.000Z | [
"region:us"
] | chrisgru | null | null | 0 | 162 | 2023-10-18T20:41:13 | ---
dataset_info:
features:
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
splits:
- name: train
num_bytes: 9152294
num_examples: 20176
- name: test
num_bytes: 941561
num_examples: 2158
- name: validation
num_bytes: 962952
num_examples: 2157
download_size: 6212665
dataset_size: 11056807
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
---
# Dataset Card for "commonsense-dialogues2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 742 | [
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PlanTL-GOB-ES/SQAC | 2023-10-12T23:35:38.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:es",
"license:cc-by-sa-4.0",
"arxiv:1606.05250",
"region:us"
] | PlanTL-GOB-ES | This dataset contains 6,247 contexts and 18,817 questions with their answers, 1 to 5 for each fragment.
The sources of the contexts are:
* Encyclopedic articles from [Wikipedia in Spanish](https://es.wikipedia.org/), used under [CC-by-sa licence](https://creativecommons.org/licenses/by-sa/3.0/legalcode).
* News from [Wikinews in Spanish](https://es.wikinews.org/), used under [CC-by licence](https://creativecommons.org/licenses/by/2.5/).
* Text from the Spanish corpus [AnCora](http://clic.ub.edu/corpus/en), which is a mix from diferent newswire and literature sources, used under [CC-by licence] (https://creativecommons.org/licenses/by/4.0/legalcode).
This dataset can be used to build extractive-QA. | bibtex
@article{DBLP:journals/corr/abs-2107-07253,
author = {Asier Guti{\'{e}}rrez{-}Fandi{\~{n}}o and
Jordi Armengol{-}Estap{\'{e}} and
Marc P{\`{a}}mies and
Joan Llop{-}Palao and
Joaqu{\'{\i}}n Silveira{-}Ocampo and
Casimiro Pio Carrino and
Aitor Gonzalez{-}Agirre and
Carme Armentano{-}Oller and
Carlos Rodr{\'{\i}}guez Penagos and
Marta Villegas},
title = {Spanish Language Models},
journal = {CoRR},
volume = {abs/2107.07253},
year = {2021},
url = {https://arxiv.org/abs/2107.07253},
archivePrefix = {arXiv},
eprint = {2107.07253},
timestamp = {Wed, 21 Jul 2021 15:55:35 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2107-07253.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
} | 7 | 161 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- es
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: Spanish Question Answering Corpus (SQAC)
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
---
# SQAC (Spanish Question-Answering Corpus)
## Dataset Description
SQAC is an extractive QA dataset for the Spanish language.
- **Paper:** [MarIA: Spanish Language Models](https://upcommons.upc.edu/bitstream/handle/2117/367156/6405-5863-1-PB%20%281%29.pdf?sequence=1)
- **Point of Contact:** carlos.rodriguez1@bsc.es
- **Leaderboard:** [EvalEs] (https://plantl-gob-es.github.io/spanish-benchmark/)
### Dataset Summary
Contains 6,247 contexts and 18,817 questions with their respective answers, 1 to 5 for each fragment.
The sources of the contexts are:
* Encyclopedic articles from the [Spanish Wikipedia](https://es.wikipedia.org/), used under [CC-by-sa licence](https://creativecommons.org/licenses/by-sa/3.0/legalcode).
* News articles from [Wikinews](https://es.wikinews.org/), used under [CC-by licence](https://creativecommons.org/licenses/by/2.5/).
* Newswire and literature text from the [AnCora corpus](http://clic.ub.edu/corpus/en), used under [CC-by licence](https://creativecommons.org/licenses/by/4.0/legalcode).
### Supported Tasks
Extractive-QA
### Languages
- Spanish (es)
### Directory Structure
- README.md
- SQAC.py
- dev.json
- test.json
- train.json
## Dataset Structure
### Data Instances
<pre>
{
'id': '6cf3dcd6-b5a3-4516-8f9e-c5c1c6b66628',
'title': 'Historia de Japรณn',
'context': 'La historia de Japรณn (ๆฅๆฌใฎๆญดๅฒ o ๆฅๆฌๅฒ, Nihon no rekishi / Nihonshi?) es la sucesiรณn de hechos acontecidos dentro del archipiรฉlago japonรฉs. Algunos de estos hechos aparecen aislados e influenciados por la naturaleza geogrรกfica de Japรณn como naciรณn insular, en tanto que otra serie de hechos, obedece a influencias forรกneas como en el caso del Imperio chino, el cual definiรณ su idioma, su escritura y, tambiรฉn, su cultura polรญtica. Asimismo, otra de las influencias forรกneas fue la de origen occidental, lo que convirtiรณ al paรญs en una naciรณn industrial, ejerciendo con ello una esfera de influencia y una expansiรณn territorial sobre el รกrea del Pacรญfico. No obstante, dicho expansionismo se detuvo tras la Segunda Guerra Mundial y el paรญs se posicionรณ en un esquema de naciรณn industrial con vรญnculos a su tradiciรณn cultural.',
'question': 'ยฟQuรฉ influencia convirtiรณ Japรณn en una naciรณn industrial?',
'answers': {
'text': ['la de origen occidental'],
'answer_start': [473]
}
}
</pre>
### Data Fields
<pre>
{
id: str
title: str
context: str
question: str
answers: {
answer_start: [int]
text: [str]
}
}
</pre>
### Data Splits
| Split | Size |
| ------------- | ------------- |
| `train` | 15,036 |
| `dev` | 1,864 |
| `test` | 1.910 |
## Content analysis
### Number of articles, paragraphs and questions
* Number of articles: 3,834
* Number of contexts: 6,247
* Number of questions: 18,817
* Number of sentences: 48,026
* Questions/Context ratio: 3.01
* Sentences/Context ratio: 7.70
### Number of tokens
* Total tokens in context: 1,561,616
* Average tokens/context: 250
* Total tokens in questions: 203,235
* Average tokens/question: 10.80
* Total tokens in answers: 90,307
* Average tokens/answer: 4.80
### Lexical variation
46.38% of the words in the Question can be found in the Context.
### Question type
| Question | Count | % |
|----------|-------:|---:|
| quรฉ | 6,381 | 33.91 % |
| quiรฉn/es | 2,952 | 15.69 % |
| cuรกl/es | 2,034 | 10.81 % |
| cรณmo | 1,949 | 10.36 % |
| dรณnde | 1,856 | 9.86 % |
| cuรกndo | 1,639 | 8.71 % |
| cuรกnto | 1,311 | 6.97 % |
| cuรกntos | 495 |2.63 % |
| adรณnde | 100 | 0.53 % |
| cuรกnta | 49 | 0.26 % |
| no question mark | 43 | 0.23 % |
| cuรกntas | 19 | 0.10 % |
## Dataset Creation
### Curation Rationale
For compatibility with similar datasets in other languages, we followed as close as possible existing curation guidelines from SQUAD 1.0 [(Rajpurkar, Pranav et al.)](http://arxiv.org/abs/1606.05250).
### Source Data
#### Initial Data Collection and Normalization
The source data are scraped articles from Wikinews, the Spanish Wikipedia and the AnCora corpus.
- [Spanish Wikipedia](https://es.wikipedia.org)
- [Spanish Wikinews](https://es.wikinews.org/)
- [AnCora corpus](http://clic.ub.edu/corpus/en)
#### Who are the source language producers?
Contributors to the aforementioned sites.
### Annotations
#### Annotation process
We commissioned the creation of 1 to 5 questions for each context, following an adaptation of the guidelines from SQUAD 1.0 [(Rajpurkar, Pranav et al.)](http://arxiv.org/abs/1606.05250).
#### Who are the annotators?
Native language speakers.
### Personal and Sensitive Information
No personal or sensitive information included.
## Considerations for Using the Data
### Social Impact of Dataset
This corpus contributes to the development of language models in Spanish.
### Discussion of Biases
No postprocessing steps were applied to mitigate potential social biases.
## Additional Information
### Dataset Curators
Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es).
For further information, send an email to (plantl-gob-es@bsc.es).
This work was funded by the [Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA)](https://avancedigital.mineco.gob.es/en-us/Paginas/index.aspx) within the framework of the [Plan-TL](https://plantl.mineco.gob.es/Paginas/index.aspx).
### Licensing information
This work is licensed under [CC Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/) License.
Copyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022)
### Citation Information
```
@article{maria,
author = {Asier Gutiรฉrrez-Fandiรฑo and Jordi Armengol-Estapรฉ and Marc Pร mies and Joan Llop-Palao and Joaquin Silveira-Ocampo and Casimiro Pio Carrino and Carme Armentano-Oller and Carlos Rodriguez-Penagos and Aitor Gonzalez-Agirre and Marta Villegas},
title = {MarIA: Spanish Language Models},
journal = {Procesamiento del Lenguaje Natural},
volume = {68},
number = {0},
year = {2022},
issn = {1989-7553},
url = {http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6405},
pages = {39--60}
}
```
### Contributions
[N/A]
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addy88/nq-question-answeronly | 2021-12-14T13:59:58.000Z | [
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ceyda/smithsonian_butterflies | 2022-07-13T09:32:27.000Z | [
"task_categories:image-classification",
"task_ids:multi-label-image-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:cc0-1.0",
"region:us"
] | ceyda | null | null | 6 | 161 | 2022-04-09T00:38:13 | ---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- expert-generated
license:
- cc0-1.0
multilinguality:
- monolingual
pretty_name: Smithsonian Butterflies
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- image-classification
task_ids:
- multi-label-image-classification
---
# Dataset Card for [Smithsonian Butterflies]
## 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:** Smithsonian "Education and Outreach" & "NMNH - Entomology Dept." collections [here](https://collections.si.edu/search/results.htm?q=butterfly&view=list&fq=online_media_type%3A%22Images%22&fq=topic%3A%22Insects%22&fq=data_source%3A%22NMNH+-+Entomology+Dept.%22&media.CC0=true&dsort=title&start=0)
### Dataset Summary
High-res images from Smithsonian "Education and Outreach" & "NMNH - Entomology Dept." collections. Crawled
### Supported Tasks and Leaderboards
Includes metadata about the scientific name of butterflies, but there maybe missing values. Might be good for classification.
### Languages
English
## Dataset Structure
### Data Instances
# Example data
```
{'image_url': 'https://ids.si.edu/ids/deliveryService?id=ark:/65665/m3b3132f6666904de396880d9dc811c5cd',
'image_alt': 'view Aholibah Underwing digital asset number 1',
'id': 'ark:/65665/m3b3132f6666904de396880d9dc811c5cd',
'name': 'Aholibah Underwing',
'scientific_name': 'Catocala aholibah',
'gender': None,
'taxonomy': 'Animalia, Arthropoda, Hexapoda, Insecta, Lepidoptera, Noctuidae, Catocalinae',
'region': None,
'locality': None,
'date': None,
'usnm_no': 'EO400317-DSP',
'guid': 'http://n2t.net/ark:/65665/39b506292-715f-45a7-8511-b49bb087c7de',
'edan_url': 'edanmdm:nmnheducation_10866595',
'source': 'Smithsonian Education and Outreach collections',
'stage': None,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=2000x1328 at 0x7F57D0504DC0>,
'image_hash': '27a5fe92f72f8b116d3b7d65bac84958',
'sim_score': 0.8440760970115662}
โ
```
### Data Fields
sim-score indicates clip score for "pretty butterfly". This is to eliminate non-butterfly images(just id card images etc)
### Data Splits
No specific split exists.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
Crawled from "Education and Outreach" & "NMNH - Entomology Dept." collections found online [here](https://collections.si.edu/search/results.htm?q=butterfly&view=list&fq=online_media_type%3A%22Images%22&fq=topic%3A%22Insects%22&fq=data_source%3A%22NMNH+-+Entomology+Dept.%22&media.CC0=true&dsort=title&start=0)
#### 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
Doesn't include all butterfly species ## Additional Information
### Dataset Curators
Smithsonian "Education and Outreach" & "NMNH - Entomology Dept." collections
### Licensing Information
Only results marked: CC0
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@cceyda](https://github.com/cceyda) for adding this dataset. | 4,520 | [
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language-and-voice-lab/samromur_children | 2023-10-15T16:02:44.000Z | [
"task_categories:automatic-speech-recognition",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:is",
"license:cc-by-4.0",
"samromur",
"children's speech",
"icelandic: iceland",
"icelandic children",
"icelandic kids",
"kids",
"region:us"
] | language-and-voice-lab | The Samrรณmur Children corpus contains more than 137000 validated speech-recordings uttered by Icelandic children. | @misc{menasamromurchildren2022,
title={Samrรณmur Children Icelandic Speech 1.0},
ldc_catalog_no={LDC2022S11},
DOI={https://doi.org/10.35111/frrj-qd60},
author={Hernรกndez Mena, Carlos Daniel and Borsky, Michal and Mollberg, David Erik and Guรฐmundsson, Smรกri Freyr and Hedstrรถm, Staffan and Pรกlsson, Ragnar and Jรณnsson, รlafur Helgi and รorsteinsdรณttir, Sunneva and Guรฐmundsdรณttir, Jรณhanna Vigdรญs and Magnรบsdรณttir, Eydรญs Huld and รรณrhallsdรณttir, Ragnheiรฐur and Guรฐnason, Jรณn},
publisher={Reykjavรญk University}
journal={Linguistic Data Consortium, Philadelphia},
year={2019},
url={https://catalog.ldc.upenn.edu/LDC2022S11},
} | 1 | 161 | 2022-11-26T03:15:54 | ---
annotations_creators:
- crowdsourced
language:
- is
language_creators:
- crowdsourced
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: "Samrรณmur Children Icelandic Speech 1.0"
size_categories:
- 100K<n<1M
source_datasets:
- original
tags:
- "samromur"
- children's speech
- 'icelandic: iceland'
- icelandic children
- icelandic kids
- kids
task_categories:
- automatic-speech-recognition
task_ids: []
---
# Dataset Card for samromur_children
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-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:** [Samrรณmur Children Icelandic Speech 1.0](https://samromur.is/)
- **Repository:** [LDC](https://catalog.ldc.upenn.edu/LDC2022S11)
- **Paper:** [Samrรณmur Children: An Icelandic Speech Corpus](https://aclanthology.org/2022.lrec-1.105.pdf)
- **Point of Contact:** [Carlos Mena](mailto:carlos.mena@ciempiess.org), [Jรณn Guรฐnason](mailto:jg@ru.is)
### Dataset Summary
The Samrรณmur Children Corpus consists of audio recordings and metadata files containing prompts read by the participants. It contains more than 137000 validated speech-recordings uttered by Icelandic children.
The corpus is a result of the crowd-sourcing effort run by the Language and Voice Lab (LVL) at the Reykjavik University, in cooperation with Almannarรณmur, Center for Language Technology. The recording process has started in October 2019 and continues to this day (Spetember 2021).
### Example Usage
The Samrรณmur Children Corpus is divided in 3 splits: train, validation and test. To load a specific split pass its name as a config name:
```python
from datasets import load_dataset
samromur_children = load_dataset("language-and-voice-lab/samromur_children")
```
To load an specific split (for example, the validation split) do:
```python
from datasets import load_dataset
samromur_children = load_dataset("language-and-voice-lab/samromur_children",split="validation")
```
### Supported Tasks
automatic-speech-recognition: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER).
### Languages
The audio is in Icelandic.
The reading prompts were gathered from a variety of sources, mainly from the [Icelandic Gigaword Corpus](http://clarin.is/en/resources/gigaword). The corpus includes text from novels, news, plays, and from a list of location names in Iceland. The prompts also came from the [Icelandic Web of Science](https://www.visindavefur.is/).
## Dataset Structure
### Data Instances
```python
{
'audio_id': '015652-0717240',
'audio': {
'path': '/home/carlos/.cache/HuggingFace/datasets/downloads/extracted/2c6b0d82de2ef0dc0879732f726809cccbe6060664966099f43276e8c94b03f2/test/015652/015652-0717240.flac',
'array': array([ 0. , 0. , 0. , ..., -0.00311279,
-0.0007019 , 0.00128174], dtype=float32),
'sampling_rate': 16000
},
'speaker_id': '015652',
'gender': 'female',
'age': '11',
'duration': 4.179999828338623,
'normalized_text': 'eiginlega var hann hin unga rรบssneska bylting lifandi komin'
}
```
### Data Fields
* `audio_id` (string) - id of audio segment
* `audio` (datasets.Audio) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate. In non-streaming mode (default), the path points to the locally extracted audio. In streaming mode, the path is the relative path of an audio inside its archive (as files are not downloaded and extracted locally).
* `speaker_id` (string) - id of speaker
* `gender` (string) - gender of speaker (male or female)
* `age` (string) - range of age of the speaker: Younger (15-35), Middle-aged (36-60) or Elderly (61+).
* `duration` (float32) - duration of the audio file in seconds.
* `normalized_text` (string) - normalized audio segment transcription.
### Data Splits
The corpus is split into train, dev, and test portions. Lenghts of every portion are: train = 127h25m, test = 1h50m, dev=1h50m.
To load an specific portion please see the above section "Example Usage".
## Dataset Creation
### Curation Rationale
In the field of Automatic Speech Recognition (ASR) is a known fact that the children's speech is particularly hard to recognise due to its high variability produced by developmental changes in children's anatomy and speech production skills.
For this reason, the criteria of selection for the train/dev/test portions have to take into account the children's age. Nevertheless, the Samrรณmur Children is an unbalanced corpus in terms of gender and age of the speakers. This means that the corpus has, for example, a total of 1667 female speakers (73h38m) versus 1412 of male speakers (52h26m).
These unbalances impose conditions in the type of the experiments than can be performed with the corpus. For example, a equal number of female and male speakers through certain ranges of age is impossible. So, if one can't have a perfectly balance corpus in the training set, at least one can have it in the test portion.
The test portion of the Samrรณmur Children was meticulously selected to cover ages between 6 to 16 years in both female and male speakers. Every of these range of age in both genders have a total duration of 5 minutes each.
The development portion of the corpus contains only speakers with an unknown gender information. Both test and dev sets have a total duration of 1h50m each.
In order to perform fairer experiments, speakers in the train and test sets are not shared. Nevertheless, there is only one speaker shared between the train and development set. It can be identified with the speaker ID=010363. However, no audio files are shared between these two sets.
### Source Data
#### Initial Data Collection and Normalization
The data was collected using the website https://samromur.is, code of which is available at https://github.com/cadia-lvl/samromur. The age range selected for this corpus is between 4 and 17 years.
The original audio was collected at 44.1 kHz or 48 kHz sampling rate as *.wav files, which was down-sampled to 16 kHz and converted to *.flac. Each recording contains one read sentence from a script. The script contains 85.080 unique sentences and 90.838 unique tokens.
There was no identifier other than the session ID, which is used as the speaker ID. The corpus is distributed with a metadata file with a detailed information on each utterance and speaker. The madata file is encoded as UTF-8 Unicode.
The prompts were gathered from a variety of sources, mainly from The Icelandic Gigaword Corpus, which is available at http://clarin.is/en/resources/gigaword. The corpus includes text from novels, news, plays, and from a list of location names in Iceland. The prompts also came from the [Icelandic Web of Science](https://www.visindavefur.is/).
### Annotations
#### Annotation process
Prompts were pulled from these corpora if they met the criteria of having only letters which are present in the Icelandic alphabet, and if they are listed in the [DIM: Database Icelandic Morphology](https://aclanthology.org/W19-6116.pdf).
There are also synthesised prompts consisting of a name followed by a question or a demand, in order to simulate a dialogue with a smart-device.
#### Who are the annotators?
The audio files content was manually verified against the prompts by one or more listener (summer students mainly).
### Personal and Sensitive Information
The dataset consists of people who have donated their voice. You agree to not attempt to determine the identity of speakers in this dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This is the first ASR corpus of Icelandic children.
### Discussion of Biases
* The utterances were recorded by a smartphone or the web app.
* Participants self-reported their age group, gender, and the native language.
* Participants are aged between 4 to 17 years.
* The corpus contains 137597 utterances from 3175 speakers, totalling 131 hours.
* The amount of data due to female speakers is 73h38m, the amount of data due to male speakers is 52h26m and the amount of data due to speakers with an unknown gender information is 05h02m
* The number of female speakers is 1667, the number of male speakers is 1412. The number of speakers with an unknown gender information is 96.
* The audios due to female speakers are 78993, the audios due to male speakers are 53927 and the audios due to speakers with an unknown gender information are 4677.
### Other Known Limitations
"Samrรณmur Children: Icelandic Speech 21.09" by the Language and Voice Laboratory (LVL) at the Reykjavik University is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License with the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
## Additional Information
### Dataset Curators
The corpus is a result of the crowd-sourcing effort run by the Language and Voice Lab (LVL) at the Reykjavik University, in cooperation with Almannarรณmur, Center for Language Technology. The recording process has started in October 2019 and continues to this day (Spetember 2021). The corpus was curated by Carlos Daniel Hernรกndez Mena in 2021.
### Licensing Information
[CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)
### Citation Information
```
@misc{menasamromurchildren2021,
title={Samrรณmur Children Icelandic Speech 1.0},
ldc_catalog_no={LDC2022S11},
DOI={https://doi.org/10.35111/frrj-qd60},
author={Hernรกndez Mena, Carlos Daniel and Borsky, Michal and Mollberg, David Erik and Guรฐmundsson, Smรกri Freyr and Hedstrรถm, Staffan and Pรกlsson, Ragnar and Jรณnsson, รlafur Helgi and รorsteinsdรณttir, Sunneva and Guรฐmundsdรณttir, Jรณhanna Vigdรญs and Magnรบsdรณttir, Eydรญs Huld and รรณrhallsdรณttir, Ragnheiรฐur and Guรฐnason, Jรณn},
publisher={Reykjavรญk University},
journal={Linguistic Data Consortium, Philadelphia},
year={2021},
url={https://catalog.ldc.upenn.edu/LDC2022S11},
}
```
### Contributions
This project was funded by the Language Technology Programme for Icelandic 2019-2023. The programme, which is managed and coordinated by Almannarรณmur, is funded by the Icelandic Ministry of Education, Science and Culture.
The verification for the dataset was funded by the the Icelandic Directorate of Labour's Student Summer Job Program in 2020 and 2021.
Special thanks for the summer students for all the hard work.
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findzebra/corpus.latest.vod-retriever-medical-v1.1 | 2023-09-05T05:17:16.000Z | [
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SetFit/amazon_polarity | 2022-01-19T20:49:58.000Z | [
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medalpaca/medical_meadow_mmmlu | 2023-04-06T17:49:48.000Z | [
"region:us"
] | medalpaca | null | null | 0 | 160 | 2023-04-06T17:49:34 | Entry not found | 15 | [
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] |
ura-hcmut/synthetic_reasoning | 2023-09-19T02:37:10.000Z | [
"task_categories:text2text-generation",
"language:vi",
"license:cc-by-nc-sa-4.0",
"region:us"
] | ura-hcmut | null | null | 0 | 160 | 2023-09-19T02:01:51 | ---
license: cc-by-nc-sa-4.0
task_categories:
- text2text-generation
language:
- vi
configs:
- config_name: induction_gcp
data_files:
- split: train
path: synthetic_reasoning_gcp_induction_training.csv
- split: test
path: synthetic_reasoning_gcp_induction.csv
- config_name: induction_azr
data_files:
- split: train
path: synthetic_reasoning_azr_induction_training.csv
- split: test
path: synthetic_reasoning_azr_induction.csv
- config_name: pattern_match_gcp
data_files:
- split: train
path: synthetic_reasoning_gcp_pattern_match_training.csv
- split: test
path: synthetic_reasoning_gcp_pattern_match.csv
- config_name: pattern_match_azr
data_files:
- split: train
path: synthetic_reasoning_azr_pattern_match_training.csv
- split: test
path: synthetic_reasoning_azr_pattern_match.csv
- config_name: variable_substitution_gcp
data_files:
- split: train
path: synthetic_reasoning_gcp_variable_substitution_training.csv
- split: test
path: synthetic_reasoning_gcp_variable_substitution.csv
- config_name: variable_substitution_azr
data_files:
- split: train
path: synthetic_reasoning_azr_variable_substitution_training.csv
- split: test
path: synthetic_reasoning_azr_variable_substitution.csv
---
# Synthetic reasoning dataset
Original version:
- https://huggingface.co/datasets/lighteval/synthetic_reasoning
Translation source code: https://github.com/martinakaduc/ura-llama/tree/main/dataset_scripts/custom_datasets | 1,622 | [
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lucasmccabe/logiqa | 2023-02-08T01:51:31.000Z | [
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:en",
"region:us"
] | lucasmccabe | LogiQA is constructed from the logical comprehension problems from publically available questions of the National Civil Servants Examination of China, which are designed to test the civil servant candidatesโ critical thinking and problem solving. This dataset includes the English versions only; the Chinese versions are available via the homepage/original source. | @article{liu2020logiqa,
title={Logiqa: A challenge dataset for machine reading comprehension with logical reasoning},
author={Liu, Jian and Cui, Leyang and Liu, Hanmeng and Huang, Dandan and Wang, Yile and Zhang, Yue},
journal={arXiv preprint arXiv:2007.08124},
year={2020}
} | 3 | 159 | 2023-01-12T04:14:53 | ---
task_categories:
- question-answering
language:
- en
pretty_name: LogiQA
size_categories:
- 1K<n<10K
paperswithcode_id: logiqa
dataset_info:
features:
- name: context
dtype: string
- name: query
dtype: string
- name: options
sequence:
dtype: string
- name: correct_option
dtype: string
splits:
- name: train
num_examples: 7376
- name: validation
num_examples: 651
- name: test
num_examples: 651
---
# Dataset Card for LogiQA
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
LogiQA is constructed from the logical comprehension problems from publically available questions of the National Civil Servants Examination of China, which are designed to test the civil servant candidatesโ critical thinking and problem solving. This dataset includes the English versions only; the Chinese versions are available via the homepage/original source.
## Dataset Structure
### Data Instances
An example from `train` looks as follows:
```
{'context': 'Continuous exposure to indoor fluorescent lights is beneficial to the health of hamsters with heart disease. One group of hamsters exposed to continuous exposure to fluorescent lights has an average lifespan that is 2.5% longer than another one of the same species but living in a black wall.',
'query': 'Which of the following questions was the initial motivation for conducting the above experiment?',
'options': ['Can hospital light therapy be proved to promote patient recovery?',
'Which one lives longer, the hamster living under the light or the hamster living in the dark?',
'What kind of illness does the hamster have?',
'Do some hamsters need a period of darkness?'],
'correct_option': 0}
```
### Data Fields
- `context`: a `string` feature.
- `query`: a `string` feature.
- `answers`: a `list` feature containing `string` features.
- `correct_option`: a `string` feature.
### Data Splits
|train|validation|test|
|----:|---------:|---:|
| 7376| 651| 651|
## Additional Information
### Dataset Curators
The original LogiQA was produced by Jian Liu, Leyang Cui , Hanmeng Liu, Dandan Huang, Yile Wang, and Yue Zhang.
### Licensing Information
[More Information Needed]
### Citation Information
```
@article{liu2020logiqa,
title={Logiqa: A challenge dataset for machine reading comprehension with logical reasoning},
author={Liu, Jian and Cui, Leyang and Liu, Hanmeng and Huang, Dandan and Wang, Yile and Zhang, Yue},
journal={arXiv preprint arXiv:2007.08124},
year={2020}
}
```
### Contributions
[@lucasmccabe](https://github.com/lucasmccabe) added this dataset. | 2,729 | [
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kotzeje/lamini_docs.jsonl | 2023-08-24T12:35:32.000Z | [
"region:us"
] | kotzeje | null | null | 2 | 159 | 2023-08-24T12:35:29 | ---
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 573589
num_examples: 1400
download_size: 283465
dataset_size: 573589
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "lamini_docs.jsonl"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 481 | [
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] |
Nbardy/renamed_waves | 2023-10-19T19:27:20.000Z | [
"region:us"
] | Nbardy | null | null | 0 | 159 | 2023-10-19T19:21:43 | ---
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: 498961211.25
num_examples: 1306
download_size: 497509644
dataset_size: 498961211.25
---
# Dataset Card for "renamed_waves"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 486 | [
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metaeval/imppres | 2023-06-21T12:52:43.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"language:en",
"license:apache-2.0",
"region:us"
] | metaeval | Over >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types. IMPPRES is an NLI dataset following the format of SNLI (Bowman et al., 2015), MultiNLI (Williams et al., 2018) and XNLI (Conneau et al., 2018), which was created to evaluate how well trained NLI models recognize several classes of presuppositions and scalar implicatures. | @inproceedings{jeretic-etal-2020-natural,
title = "Are Natural Language Inference Models {IMPPRESsive}? {L}earning {IMPlicature} and {PRESupposition}",
author = "Jereti\v{c}, Paloma and
Warstadt, Alex and
Bhooshan, Suvrat and
Williams, Adina",
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.768",
doi = "10.18653/v1/2020.acl-main.768",
pages = "8690--8705",
abstract = "Natural language inference (NLI) is an increasingly important task for natural language understanding, which requires one to infer whether a sentence entails another. However, the ability of NLI models to make pragmatic inferences remains understudied. We create an IMPlicature and PRESupposition diagnostic dataset (IMPPRES), consisting of 32K semi-automatically generated sentence pairs illustrating well-studied pragmatic inference types. We use IMPPRES to evaluate whether BERT, InferSent, and BOW NLI models trained on MultiNLI (Williams et al., 2018) learn to make pragmatic inferences. Although MultiNLI appears to contain very few pairs illustrating these inference types, we find that BERT learns to draw pragmatic inferences. It reliably treats scalar implicatures triggered by {``}some{''} as entailments. For some presupposition triggers like {``}only{''}, BERT reliably recognizes the presupposition as an entailment, even when the trigger is embedded under an entailment canceling operator like negation. BOW and InferSent show weaker evidence of pragmatic reasoning. We conclude that NLI training encourages models to learn some, but not all, pragmatic inferences.",
} | 0 | 158 | 2023-01-05T20:14:45 | ---
license: apache-2.0
task_categories:
- text-classification
language:
- en
task_ids:
- natural-language-inference
---
Imppres, but it works
https://github.com/facebookresearch/Imppres
```
@inproceedings{jeretic-etal-2020-natural,
title = "Are Natural Language Inference Models {IMPPRESsive}? {L}earning {IMPlicature} and {PRESupposition}",
author = "Jereti\v{c}, Paloma and
Warstadt, Alex and
Bhooshan, Suvrat and
Williams, Adina",
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.768",
doi = "10.18653/v1/2020.acl-main.768",
pages = "8690--8705",
abstract = "Natural language inference (NLI) is an increasingly important task for natural language understanding, which requires one to infer whether a sentence entails another. However, the ability of NLI models to make pragmatic inferences remains understudied. We create an IMPlicature and PRESupposition diagnostic dataset (IMPPRES), consisting of 32K semi-automatically generated sentence pairs illustrating well-studied pragmatic inference types. We use IMPPRES to evaluate whether BERT, InferSent, and BOW NLI models trained on MultiNLI (Williams et al., 2018) learn to make pragmatic inferences. Although MultiNLI appears to contain very few pairs illustrating these inference types, we find that BERT learns to draw pragmatic inferences. It reliably treats scalar implicatures triggered by {``}some{''} as entailments. For some presupposition triggers like {``}only{''}, BERT reliably recognizes the presupposition as an entailment, even when the trigger is embedded under an entailment canceling operator like negation. BOW and InferSent show weaker evidence of pragmatic reasoning. We conclude that NLI training encourages models to learn some, but not all, pragmatic inferences.",
}
``` | 2,031 | [
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] |
jjzha/skillspan | 2023-09-07T12:12:10.000Z | [
"language:en",
"license:cc-by-4.0",
"region:us"
] | jjzha | null | null | 0 | 158 | 2023-07-04T13:37:04 | ---
license: cc-by-4.0
language: en
---
This is the SkillSpan dataset created by:
```
@inproceedings{zhang-etal-2022-skillspan,
title = "{S}kill{S}pan: Hard and Soft Skill Extraction from {E}nglish Job Postings",
author = "Zhang, Mike and
Jensen, Kristian and
Sonniks, Sif and
Plank, Barbara",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.366",
doi = "10.18653/v1/2022.naacl-main.366",
pages = "4962--4984"
}
```
There are document delimiters indicated by `idx`.
Number of samples (sentences):
- train: 4800
- dev: 3174
- test: 3569
Sources:
- Stackoverflow (tech)
- STAR (house)
Type of tags:
- Generic BIO tags with keys `tags_skill` and `tags_knowledge`
Sample:
```
{
"idx": 53,
"tokens": ["Drive", "our", "IT", "compliance", "agenda", "and", "develop", "our", "processes"],
"tags_skill": ["B", "I", "I", "I", "I", "O", "B", "I", "I"],
"tags_knowledge": ["O", "O", "O", "O", "O", "O", "O", "O", "O"],
"source": "house"
}
``` | 1,291 | [
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SatwikKambham/ex-dark | 2023-10-13T10:58:40.000Z | [
"license:bsd-3-clause",
"region:us"
] | SatwikKambham | The Exclusively Dark (ExDARK) dataset is a collection of low-light
images from very low-light environments to twilight (i.e 10 different
conditions) with 12 object classes (similar to PASCAL VOC) annotated on both
image class level and local object bounding boxes.
The object classes are as follows:
- Dog
- Motorbike
- People
- Cat
- Chair
- Table
- Car
- Bicycle
- Bottle
- Bus
- Cup
- Boat
For more information about the original Exclusively Dark Image dataset,
please visit the official dataset page:
https://github.com/cs-chan/Exclusively-Dark-Image-Dataset
Please refer to the original dataset source for any additional details,
citations, or specific usage guidelines provided by the dataset creators. | @article{Exdark,
title = {Getting to Know Low-light Images with The Exclusively Dark Dataset},
author = {Loh, Yuen Peng and Chan, Chee Seng},
journal = {Computer Vision and Image Understanding},
volume = {178},
pages = {30-42},
year = {2019},
doi = {https://doi.org/10.1016/j.cviu.2018.10.010}
} | 0 | 158 | 2023-10-12T07:54:37 | ---
license: bsd-3-clause
dataset_info:
config_name: exdark
features:
- name: img
dtype: image
- name: labels
sequence:
class_label:
names:
'0': Dog
'1': Motorbike
'2': People
'3': Cat
'4': Chair
'5': Table
'6': Car
'7': Bicycle
'8': Bottle
'9': Bus
'10': Cup
'11': Boat
- name: bboxes
sequence:
sequence: float32
length: 4
splits:
- name: train
num_bytes: 1770065
num_examples: 7361
download_size: 1487935964
dataset_size: 1770065
---
The Exclusively Dark (ExDARK) dataset is a collection of low-light
images from very low-light environments to twilight (i.e 10 different
conditions) with 12 object classes (similar to PASCAL VOC) annotated on both
image class level and local object bounding boxes.
The object classes are as follows:
- Dog
- Motorbike
- People
- Cat
- Chair
- Table
- Car
- Bicycle
- Bottle
- Bus
- Cup
- Boat
For more information about the original Exclusively Dark Image dataset,
please visit the official dataset page:
[https://github.com/cs-chan/Exclusively-Dark-Image-Dataset](https://github.com/cs-chan/Exclusively-Dark-Image-Dataset)
Please refer to the original dataset source for any additional details,
citations, or specific usage guidelines provided by the dataset creators.
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joelniklaus/Multi_Legal_Pile_Commercial | 2023-10-18T20:40:00.000Z | [
"task_categories:fill-mask",
"annotations_creators:other",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:bg",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:en",
"language:es",
"language:et",
"language:fi",
"language:fr",
"language:ga",
"language:hr",
"language:hu",
"language:it",
"language:lt",
"language:lv",
"language:mt",
"language:nl",
"language:pl",
"language:pt",
"language:ro",
"language:sk",
"language:sl",
"language:sv",
"license:cc-by-sa-4.0",
"arxiv:2306.02069",
"region:us"
] | joelniklaus | Multi Legal Pile is a dataset of legal documents in the 24 EU languages. | 0 | 158 | 2023-10-18T20:23:08 | ---
annotations_creators:
- other
language_creators:
- found
language:
- bg
- cs
- da
- de
- el
- en
- es
- et
- fi
- fr
- ga
- hr
- hu
- it
- lt
- lv
- mt
- nl
- pl
- pt
- ro
- sk
- sl
- sv
license: cc-by-sa-4.0
multilinguality:
- multilingual
paperswithcode_id: null
pretty_name: 'MultiLegalPile: A Large-Scale Multilingual Corpus for the Legal Domain'
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- fill-mask
---
# Dataset Card for MultiLegalPile: A Large-Scale Multilingual Corpus for the Legal Domain
## 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:** [MultiLegalPile](https://arxiv.org/abs/2306.02069)
- **Leaderboard:**
- **Point of Contact:** [Joel Niklaus](mailto:joel.niklaus.2@bfh.ch)
### Dataset Summary
The Multi_Legal_Pile is a large-scale multilingual legal dataset suited for pretraining language models.
It spans over 24 languages and five legal text types. This version only contains data permissively licensed for commercial use.
### Supported Tasks and Leaderboards
The dataset supports the tasks of fill-mask.
### Languages
The following languages are supported: bg, cs, da, de, el, en, es, et, fi, fr, ga, hr, hu, it, lt, lv, mt, nl, pl, pt,
ro, sk, sl, sv
## Dataset Structure
It is structured in the following format:
type -> language -> jurisdiction.jsonl.xz
type is one of the following:
- caselaw
- contracts
- legislation
- other
- legal_mc4
`legal_mc4` is a subset of the other type but is listed separately so it can be easily excluded since it is less
permissively licensed than the other types.
Use the dataset like this:
```python
from datasets import load_dataset
config = 'en_contracts' # {language}_{type}
dataset = load_dataset('joelito/Multi_Legal_Pile', config, split='train', streaming=True)
```
'config' is a combination of language and text_type, e.g. 'en_contracts' or 'de_caselaw'.
To load all the languages or all the text_types, use 'all' instead of the language or text_type (e.g., '
all_legislation').
### Data Instances
The file format is jsonl.xz and there is one split available ("train").
The complete dataset (689GB) consists of four large subsets:
- Native Multi Legal Pile (112GB)
- Eurlex Resources (179GB)
- Legal MC4 (106GB)
- Pile of Law (292GB)
#### Native Multilingual Legal Pile data
| | Language | Text Type | Jurisdiction | Source | Size (MB) | Words | Documents | Words/Document | URL | License |
|---:|:-----------|:------------|:---------------|:-----------------------------------|------------:|------------:|------------:|-----------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------|
| 0 | bg | legislation | Bulgaria | MARCELL | 8015 | 308946116 | 82777 | 3732 | https://elrc-share.eu/repository/browse/marcell-bulgarian-legislative-subcorpus-v2/946267fe8d8711eb9c1a00155d026706d2c9267e5cdf4d75b5f02168f01906c6/ | [CC0-1.0](https://elrc-share.eu/static/metashare/licences/CC0-1.0.pdf) |
| 1 | cs | caselaw | Czechia | CzCDC Constitutional Court | 11151 | 574336489 | 296652 | 1936 | https://lindat.mff.cuni.cz/repository/xmlui/handle/11372/LRT-3052 | [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) |
| 2 | cs | caselaw | Czechia | CzCDC Supreme Administrative Court | 11151 | 574336489 | 296652 | 1936 | https://lindat.mff.cuni.cz/repository/xmlui/handle/11372/LRT-3052 | [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) |
| 3 | cs | caselaw | Czechia | CzCDC Supreme Court | 11151 | 574336489 | 296652 | 1936 | https://lindat.mff.cuni.cz/repository/xmlui/handle/11372/LRT-3052 | [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) |
| 4 | da | caselaw | Denmark | DDSC | 3469 | 210730560 | 89702 | 2349 | https://huggingface.co/DDSC | [CC BY 4.0 and other, depending on the dataset](https://creativecommons.org/licenses/by-nc/4.0/) |
| 5 | da | legislation | Denmark | DDSC | 10736 | 653153146 | 265868 | 2456 | https://huggingface.co/DDSC | [CC BY 4.0 and other, depending on the dataset](https://creativecommons.org/licenses/by-nc/4.0/) |
| 6 | de | caselaw | Germany | openlegaldata | 31527 | 1785439383 | 596800 | 2991 | https://de.openlegaldata.io/ | [ODbL-1.0](https://opendatacommons.org/licenses/odbl/1-0/) |
| 7 | de | caselaw | Switzerland | entscheidsuche | 31527 | 1785439383 | 596800 | 2991 | https://entscheidsuche.ch/ | [See description](https://entscheidsuche.ch/dataUsage) |
| 8 | de | legislation | Germany | openlegaldata | 8934 | 512840663 | 276034 | 1857 | https://de.openlegaldata.io/ | [ODbL-1.0](https://opendatacommons.org/licenses/odbl/1-0/) |
| 9 | de | legislation | Switzerland | lexfind | 8934 | 512840663 | 276034 | 1857 | https://www.lexfind.ch/fe/de/search | No information provided |
| 10 | fr | caselaw | Switzerland | entscheidsuche | 18313 | 1170335690 | 435569 | 2686 | https://entscheidsuche.ch/ | [See description](https://entscheidsuche.ch/dataUsage) |
| 11 | fr | caselaw | Belgium | jurportal | 18313 | 1170335690 | 435569 | 2686 | https://juportal.be/home/welkom | [See description](https://juportal.be/home/disclaimer) |
| 12 | fr | caselaw | France | CASS | 18313 | 1170335690 | 435569 | 2686 | https://echanges.dila.gouv.fr/OPENDATA/CASS/ | [Open Licence 2.0](https://echanges.dila.gouv.fr/OPENDATA/CASS/DILA_CASS_Presentation_20170824.pdf) |
| 13 | fr | caselaw | Luxembourg | judoc | 18313 | 1170335690 | 435569 | 2686 | https://justice.public.lu/fr.html | [See description](https://justice.public.lu/fr/support/aspects-legaux/conditions-generales.html) |
| 14 | it | caselaw | Switzerland | entscheidsuche | 6483 | 406520336 | 156630 | 2595 | https://entscheidsuche.ch/ | [See description](https://entscheidsuche.ch/dataUsage) |
| 15 | en | legislation | Switzerland | lexfind | 36587 | 2537696894 | 657805 | 3857 | https://www.lexfind.ch/fe/de/search | No information provided |
| 16 | en | legislation | UK | uk-lex | 36587 | 2537696894 | 657805 | 3857 | https://zenodo.org/record/6355465 | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode) |
| 17 | fr | legislation | Switzerland | lexfind | 9297 | 600170792 | 243313 | 2466 | https://www.lexfind.ch/fe/fr/search | No information provided |
| 18 | fr | legislation | Belgium | ejustice | 9297 | 600170792 | 243313 | 2466 | https://www.ejustice.just.fgov.be/cgi/welcome.pl | No information provided |
| 19 | it | legislation | Switzerland | lexfind | 8332 | 542579039 | 227968 | 2380 | https://www.lexfind.ch/fe/it/search | No information provided |
| 20 | nl | legislation | Belgium | ejustice | 8484 | 550788527 | 232204 | 2372 | https://www.ejustice.just.fgov.be/cgi/welcome.pl | No information provided |
| 21 | hu | legislation | Hungary | MARCELL | 5744 | 264572303 | 86862 | 3045 | https://elrc-share.eu/repository/browse/marcell-hungarian-legislative-subcorpus-v2/a87295ec8d6511eb9c1a00155d0267065f7e56dc7db34ce5aaae0b48a329daaa/ | [CC0-1.0](https://elrc-share.eu/static/metashare/licences/CC0-1.0.pdf) |
| 22 | pl | legislation | Poland | MARCELL | 5459 | 299334705 | 89264 | 3353 | https://elrc-share.eu/repository/browse/marcell-polish-legislative-subcorpus-v2/dd14fa1c8d6811eb9c1a00155d026706c4718ddc9c6e4a92a88923816ca8b219/ | [CC0-1.0](https://elrc-share.eu/static/metashare/licences/CC0-1.0.pdf) |
| 23 | pt | caselaw | Brazil | RulingBR | 196919 | 12611760973 | 17251236 | 731 | https://github.com/diego-feijo/rulingbr | No information provided |
| 24 | pt | caselaw | Brazil | CRETA | 196919 | 12611760973 | 17251236 | 731 | https://www.kaggle.com/datasets/eliasjacob/brcad5?resource=download&select=language_modeling_texts.parquet | [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) |
| 25 | pt | caselaw | Brazil | CJPG | 196919 | 12611760973 | 17251236 | 731 | https://esaj.tjsp.jus.br/cjsg/consultaCompleta.do?f=1 | No information provided |
| 26 | ro | legislation | Romania | MARCELL | 10464 | 559092153 | 215694 | 2592 | https://elrc-share.eu/repository/browse/marcell-romanian-legislative-subcorpus-v2/2da548428b9d11eb9c1a00155d026706ce94a6b59ffc4b0e9fb5cd9cebe6889e/ | [CC0-1.0](https://elrc-share.eu/static/metashare/licences/CC0-1.0.pdf) |
| 27 | sk | legislation | Slovakia | MARCELL | 5208 | 280182047 | 76760 | 3650 | https://elrc-share.eu/repository/browse/marcell-slovak-legislative-subcorpus-v2/6bdee1d68c8311eb9c1a00155d0267063398d3f1a3af40e1b728468dcbd6efdd/ | [CC0-1.0](https://elrc-share.eu/static/metashare/licences/CC0-1.0.pdf) |
| 28 | sl | legislation | Slovenia | MARCELL | 6057 | 365513763 | 88651 | 4123 | https://elrc-share.eu/repository/browse/marcell-slovenian-legislative-subcorpus-v2/e2a779868d4611eb9c1a00155d026706983c845a30d741b78e051faf91828b0d/ | [CC-BY-4.0](https://elrc-share.eu/static/metashare/licences/CC-BY-4.0.pdf)
| total | all | all | all | 1297609 | xxx | 81214262514 | 57305071 | 1417 | |
#### Eurlex Resources
See [Eurlex Resources](https://huggingface.co/datasets/joelito/eurlex_resources#data-instances) for more information.
#### Legal-MC4
See [Legal-MC4](https://huggingface.co/datasets/joelito/legal-mc4#data-instances) for more information.
#### Pile-of-Law
See [Pile-of-Law](https://huggingface.co/datasets/pile-of-law/pile-of-law#data-instances) for more information.
| Language | Type | Jurisdiction | Source | Size (MB) | Tokens | Documents | Tokens/Document | Part of Multi_Legal_Pile |
|:-----------|:------------|:---------------|:-------------------------------------|------------:|------------:|------------:|------------------:|:---------------------------|
| en | all | all | all | 503712 | 50547777921 | 9872444 | 5120 | yes |
| en | caselaw | EU | echr | 298 | 28374996 | 8480 | 3346 | yes |
| en | caselaw | Canada | canadian_decisions | 486 | 45438083 | 11343 | 4005 | yes |
| en | caselaw | US | dol_ecab | 942 | 99113541 | 28211 | 3513 | no |
| en | caselaw | US | scotus_oral_arguments | 1092 | 108228951 | 7996 | 13535 | no |
| en | caselaw | US | tax_rulings | 1704 | 166915887 | 54064 | 3087 | no |
| en | caselaw | US | nlrb_decisions | 2652 | 294471818 | 32080 | 9179 | no |
| en | caselaw | US | scotus_filings | 4018 | 593870413 | 63775 | 9311 | yes |
| en | caselaw | US | bva_opinions | 35238 | 4084140080 | 839523 | 4864 | no |
| en | caselaw | US | courtlistener_docket_entry_documents | 139006 | 12713614864 | 1983436 | 6409 | yes |
| en | caselaw | US | courtlistener_opinions | 158110 | 15899704961 | 4518445 | 3518 | yes |
| en | contracts | -- | tos | 4 | 391890 | 50 | 7837 | no |
| en | contracts | US | cfpb_creditcard_contracts | 188 | 25984824 | 2638 | 9850 | yes |
| en | contracts | US | edgar | 28698 | 2936402810 | 987926 | 2972 | yes |
| en | contracts | US | atticus_contracts | 78300 | 7997013703 | 650833 | 12287 | yes |
| en | legislation | US | fre | 2 | 173325 | 68 | 2548 | no |
| en | legislation | US | frcp | 4 | 427614 | 92 | 4647 | no |
| en | legislation | US | eoir | 62 | 6109737 | 2229 | 2741 | no |
| en | legislation | -- | constitutions | 66 | 5984865 | 187 | 32004 | yes |
| en | legislation | US | federal_register | 424 | 39854787 | 5414 | 7361 | yes |
| en | legislation | US | uscode | 716 | 78466325 | 58 | 1352867 | yes |
| en | legislation | EU | euro_parl | 808 | 71344326 | 9672 | 7376 | no |
| en | legislation | US | cfr | 1788 | 160849007 | 243 | 661930 | yes |
| en | legislation | US | us_bills | 3394 | 320723838 | 112483 | 2851 | yes |
| en | legislation | EU | eurlex | 3504 | 401324829 | 142036 | 2825 | no |
| en | legislation | US | state_codes | 18066 | 1858333235 | 217 | 8563747 | yes |
| en | other | -- | bar_exam_outlines | 4 | 346924 | 59 | 5880 | no |
| en | other | US | ftc_advisory_opinions | 4 | 509025 | 145 | 3510 | no |
| en | other | US | olc_memos | 98 | 12764635 | 1384 | 9223 | yes |
| en | other | -- | cc_casebooks | 258 | 24857378 | 73 | 340512 | no |
| en | other | -- | un_debates | 360 | 31152497 | 8481 | 3673 | no |
| en | other | -- | r_legaladvice | 798 | 72605386 | 146671 | 495 | no |
| en | other | US | founding_docs | 1118 | 100390231 | 183664 | 546 | no |
| en | other | US | oig | 5056 | 566782244 | 38954 | 14550 | yes |
| en | other | US | congressional_hearings | 16448 | 1801110892 | 31514 | 57152 | no |
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@misc{niklaus2023multilegalpile,
title={MultiLegalPile: A 689GB Multilingual Legal Corpus},
author={Joel Niklaus and Veton Matoshi and Matthias Stรผrmer and Ilias Chalkidis and Daniel E. Ho},
year={2023},
eprint={2306.02069},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@JoelNiklaus](https://github.com/joelniklaus) for adding this dataset. | 24,250 | [
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indonli | 2023-01-25T14:33:00.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:expert-generated",
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:id",
"license:cc-by-sa-4.0",
"region:us"
] | null | IndoNLI is the first human-elicited Natural Language Inference (NLI) dataset for Indonesian.
IndoNLI is annotated by both crowd workers and experts. The expert-annotated data is used exclusively as a test set.
It is designed to provide a challenging test-bed for Indonesian NLI by explicitly incorporating various linguistic phenomena such as numerical reasoning, structural changes, idioms, or temporal and spatial reasoning. | @inproceedings{mahendra-etal-2021-indonli,
title = "{I}ndo{NLI}: A Natural Language Inference Dataset for {I}ndonesian",
author = "Mahendra, Rahmad and Aji, Alham Fikri and Louvan, Samuel and Rahman, Fahrurrozi and Vania, Clara",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.821",
pages = "10511--10527",
} | 6 | 157 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
- crowdsourced
language_creators:
- expert-generated
language:
- id
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- natural-language-inference
paperswithcode_id: indonli
pretty_name: IndoNLI
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
config_name: indonli
splits:
- name: train
num_bytes: 2265687
num_examples: 10330
- name: validation
num_bytes: 465299
num_examples: 2197
- name: test_lay
num_bytes: 473849
num_examples: 2201
- name: test_expert
num_bytes: 911916
num_examples: 2984
download_size: 6977877
dataset_size: 4116751
---
# Dataset Card for IndoNLI
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [GitHub](https://github.com/ir-nlp-csui/indonli)
- **Paper:** [EMNLP 2021](https://aclanthology.org/2021.emnlp-main.821/)
- **Point of Contact:** [GitHub](https://github.com/ir-nlp-csui/indonli)
### Dataset Summary
IndoNLI is the first human-elicited Natural Language Inference (NLI) dataset for Indonesian.
IndoNLI is annotated by both crowd workers and experts. The expert-annotated data is used exclusively as a test set. It is designed to provide a challenging test-bed for Indonesian NLI by explicitly incorporating various linguistic phenomena such as numerical reasoning, structural changes, idioms, or temporal and spatial reasoning.
### Supported Tasks and Leaderboards
- Natural Language Inference for Indonesian
### Languages
Indonesian
## Dataset Structure
### Data Instances
An example of `train` looks as follows.
```
{
"premise": "Keindahan alam yang terdapat di Gunung Batu Jonggol ini dapat Anda manfaatkan sebagai objek fotografi yang cantik.",
"hypothesis": "Keindahan alam tidak dapat difoto.",
"label": 2
}
```
### Data Fields
The data fields are:
- `premise`: a `string` feature
- `hypothesis`: a `string` feature
- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
### Data Splits
The data is split across `train`, `valid`, `test_lay`, and `test_expert`.
`test_expert` is written by expert annotators, whereas the rest are written by lay annotators.
| split | # examples |
|----------|-------:|
|train| 10330|
|valid| 2197|
|test_lay| 2201|
|test_expert| 2984|
A small subset of `test_expert` is used as a diasnostic tool. For more info, please visit https://github.com/ir-nlp-csui/indonli
## Dataset Creation
### Curation Rationale
Indonesian NLP is considered under-resourced. Up until now, there is no publicly available human-annotated NLI dataset for Indonesian.
### Source Data
#### Initial Data Collection and Normalization
The premise were collected from Indonesian Wikipedia and from other public Indonesian dataset: Indonesian PUD and GSD treebanks provided by the [Universal Dependencies 2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) and [IndoSum](https://github.com/kata-ai/indosum)
The hypothesis were written by annotators.
#### Who are the source language producers?
The data was produced by humans.
### Annotations
#### Annotation process
We start by writing the hypothesis, given the premise and the target label. Then, we ask 2 different independent annotators to predict the label, given the premise and hypothesis. If all 3 (the original hypothesis + 2 independent annotators) agree with the label, then the annotation process ends for that sample. Otherwise, we incrementally ask additional annotator until 3 annotators agree with the label. If there's no majority concensus after 5 annotations, the sample is removed.
#### Who are the annotators?
Lay annotators were computer science students, and expert annotators were NLP scientists with 7+ years research experience in NLP. All annotators are native speakers.
Additionally, expert annotators were explicitly instructed to provide challenging examples by incorporating various linguistic phenomena such as numerical reasoning, structural changes, idioms, or temporal and spatial reasoning. Annotators were compensated based on hourly rate.
### Personal and Sensitive Information
There might be some personal information coming from Wikipedia and news, especially the information of famous/important people.
## 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
INDONLI is created using premise sentences taken from Wikipedia and news. These data sources may contain some bias.
### Other Known Limitations
No other known limitations
## Additional Information
### Dataset Curators
This dataset is the result of the collaborative work of Indonesian researchers from the University of Indonesia, kata.ai, New York University, Fondazione Bruno Kessler, and the University of St Andrews.
### Licensing Information
CC-BY-SA 4.0.
Attribution โ You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
ShareAlike โ If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
No additional restrictions โ You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Please contact authors for any information on the dataset.
### Citation Information
```
@inproceedings{mahendra-etal-2021-indonli,
title = "{I}ndo{NLI}: A Natural Language Inference Dataset for {I}ndonesian",
author = "Mahendra, Rahmad and Aji, Alham Fikri and Louvan, Samuel and Rahman, Fahrurrozi and Vania, Clara",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.821",
pages = "10511--10527",
}
```
### Contributions
Thanks to [@afaji](https://github.com/afaji) for adding this dataset. | 7,615 | [
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kor_hate | 2023-01-25T14:33:47.000Z | [
"task_categories:text-classification",
"task_ids:multi-label-classification",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ko",
"license:cc-by-sa-4.0",
"arxiv:2005.12503",
"region:us"
] | null | Human-annotated Korean corpus collected from a popular domestic entertainment news aggregation platform
for toxic speech detection. Comments are annotated for gender bias, social bias and hate speech. | @inproceedings{moon-etal-2020-beep,
title = "{BEEP}! {K}orean Corpus of Online News Comments for Toxic Speech Detection",
author = "Moon, Jihyung and
Cho, Won Ik and
Lee, Junbum",
booktitle = "Proceedings of the Eighth International Workshop on Natural Language Processing for Social Media",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.socialnlp-1.4",
pages = "25--31",
abstract = "Toxic comments in online platforms are an unavoidable social issue under the cloak of anonymity. Hate speech detection has been actively done for languages such as English, German, or Italian, where manually labeled corpus has been released. In this work, we first present 9.4K manually labeled entertainment news comments for identifying Korean toxic speech, collected from a widely used online news platform in Korea. The comments are annotated regarding social bias and hate speech since both aspects are correlated. The inter-annotator agreement Krippendorff{'}s alpha score is 0.492 and 0.496, respectively. We provide benchmarks using CharCNN, BiLSTM, and BERT, where BERT achieves the highest score on all tasks. The models generally display better performance on bias identification, since the hate speech detection is a more subjective issue. Additionally, when BERT is trained with bias label for hate speech detection, the prediction score increases, implying that bias and hate are intertwined. We make our dataset publicly available and open competitions with the corpus and benchmarks.",
} | 4 | 157 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
- expert-generated
language_creators:
- found
language:
- ko
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-label-classification
paperswithcode_id: korean-hatespeech-dataset
pretty_name: Korean HateSpeech Dataset
dataset_info:
features:
- name: comments
dtype: string
- name: contain_gender_bias
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
- name: bias
dtype:
class_label:
names:
'0': none
'1': gender
'2': others
- name: hate
dtype:
class_label:
names:
'0': hate
'1': offensive
'2': none
splits:
- name: train
num_bytes: 983608
num_examples: 7896
- name: test
num_bytes: 58913
num_examples: 471
download_size: 968449
dataset_size: 1042521
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Korean HateSpeech Dataset](https://github.com/kocohub/korean-hate-speech)
- **Repository:** [Korean HateSpeech Dataset](https://github.com/kocohub/korean-hate-speech)
- **Paper:** [BEEP! Korean Corpus of Online News Comments for Toxic Speech Detection](https://arxiv.org/abs/2005.12503)
- **Point of Contact:** [Steven Liu](stevhliu@gmail.com)
### Dataset Summary
The Korean HateSpeech Dataset is a dataset of 8367 human-labeled entertainment news comments from a popular Korean news aggregation platform. Each comment was evaluated for either social bias (labels: `gender`, `others` `none`), hate speech (labels: `hate`, `offensive`, `none`) or gender bias (labels: `True`, `False`). The dataset was created to support the identification of toxic comments on online platforms where users can remain anonymous.
### Supported Tasks and Leaderboards
* `multi-label classification`: The dataset can be used to train a model for hate speech detection. A BERT model can be presented with a Korean entertainment news comment and be asked to label whether it contains social bias, gender bias and hate speech. Users can participate in a Kaggle leaderboard [here](https://www.kaggle.com/c/korean-hate-speech-detection/overview).
### Languages
The text in the dataset is in Korean and the associated is BCP-47 code is `ko-KR`.
## Dataset Structure
### Data Instances
An example data instance contains a `comments` containing the text of the news comment and then labels for each of the following fields: `contain_gender_bias`, `bias` and `hate`.
```python
{'comments':'์ค๋ง ใ
ํ์ ์๊ฐ ์๋์ง??'
'contain_gender_bias': 'True',
'bias': 'gender',
'hate': 'hate'
}
```
### Data Fields
* `comments`: text from the Korean news comment
* `contain_gender_bias`: a binary `True`/`False` label for the presence of gender bias
* `bias`: determines the type of social bias, which can be:
* `gender`: if the text includes bias for gender role, sexual orientation, sexual identity, and any thoughts on gender-related acts
* `others`: other kinds of factors that are considered not gender-related but social bias, including race, background, nationality, ethnic group, political stance, skin color, religion, handicaps, age, appearance, richness, occupations, the absence of military service experience
* `none`: a comment that does not incorporate the bias
* `hate`: determines how aggressive the comment is, which can be:
* `hate`: if the text is defined as an expression that display aggressive stances towards individuals/groups with certain characteristics (gender role, sexual orientation, sexual identity, any thoughts on gender-related acts, race, background, nationality, ethnic group, political stance, skin color, religion, handicaps, age, appearance, richness, occupations, the absence of military service experience, etc.)
* `offensive`: if the text contains rude or aggressive contents, can emit sarcasm through rhetorical question or irony, encompass an unethical expression or conveys unidentified rumors
* `none`: a comment that does not incorporate hate
### Data Splits
The data is split into a training and development (test) set. It contains 8371 annotated comments that are split into 7896 comments in the training set and 471 comments in the test set.
## Dataset Creation
### Curation Rationale
The dataset was created to provide the first human-labeled Korean corpus for toxic speech detection from a Korean online entertainment news aggregator. Recently, two young Korean celebrities suffered from a series of tragic incidents that led to two major Korean web portals to close the comments section on their platform. However, this only serves as a temporary solution, and the fundamental issue has not been solved yet. This dataset hopes to improve Korean hate speech detection.
### Source Data
#### Initial Data Collection and Normalization
A total of 10.4 million comments were collected from an online Korean entertainment news aggregator between Jan. 1, 2018 and Feb. 29, 2020. 1,580 articles were drawn using stratified sampling and the top 20 comments were extracted ranked in order of their Wilson score on the downvote for each article. Duplicate comments, single token comments and comments with more than 100 characters were removed (because they could convey various opinions). From here, 10K comments were randomly chosen for annotation.
#### Who are the source language producers?
The language producers are users of the Korean online news platform between 2018 and 2020.
### Annotations
#### Annotation process
Each comment was assigned to three random annotators to assign a majority decision. For more ambiguous comments, annotators were allowed to skip the comment. See Appendix A in the [paper](https://arxiv.org/pdf/2005.12503.pdf) for more detailed guidelines.
#### Who are the annotators?
Annotation was performed by 32 annotators, consisting of 29 annotators from the crowdsourcing platform DeepNatural AI and three NLP researchers.
### Personal and Sensitive Information
[N/A]
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to tackle the social issue of users creating toxic comments on online platforms. This dataset aims to improve detection of toxic comments online.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
This dataset is curated by Jihyung Moon, Won Ik Cho and Junbum Lee.
### Licensing Information
[N/A]
### Citation Information
```
@inproceedings
{moon-et-al-2020-beep
title = "{BEEP}! {K}orean Corpus of Online News Comments for Toxic Speech Detection",
author = "Moon, Jihyung and
Cho, Won Ik and
Lee, Junbum",
booktitle = "Proceedings of the Eighth International Workshop on Natural Language Processing for Social Media",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.socialnlp-1.4",
pages = "25--31",
abstract = "Toxic comments in online platforms are an unavoidable social issue under the cloak of anonymity. Hate speech detection has been actively done for languages such as English, German, or Italian, where manually labeled corpus has been released. In this work, we first present 9.4K manually labeled entertainment news comments for identifying Korean toxic speech, collected from a widely used online news platform in Korea. The comments are annotated regarding social bias and hate speech since both aspects are correlated. The inter-annotator agreement Krippendorff{'}s alpha score is 0.492 and 0.496, respectively. We provide benchmarks using CharCNN, BiLSTM, and BERT, where BERT achieves the highest score on all tasks. The models generally display better performance on bias identification, since the hate speech detection is a more subjective issue. Additionally, when BERT is trained with bias label for hate speech detection, the prediction score increases, implying that bias and hate are intertwined. We make our dataset publicly available and open competitions with the corpus and benchmarks.",
}
```
### Contributions
Thanks to [@stevhliu](https://github.com/stevhliu) for adding this dataset. | 9,443 | [
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jimregan/clarinpl_studio | 2023-01-21T12:27:08.000Z | [
"task_categories:other",
"task_categories:automatic-speech-recognition",
"annotations_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:pl",
"license:other",
"arxiv:1706.00245",
"region:us"
] | jimregan | The corpus consists of 317 speakers recorded in 554
sessions, where each session consists of 20 read sentences and 10 phonetically rich words. The size of
the audio portion of the corpus amounts to around 56 hours, with transcriptions containing 356674 words
from a vocabulary of size 46361.
Note that in order to limit the required storage for preparing this dataset, the audio
is stored in the .wav format and is not converted to a float32 array. To convert the audio
file to a float32 array, please make use of the `.map()` function as follows:
```python
import soundfile as sf
def map_to_array(batch):
speech_array, _ = sf.read(batch["file"])
batch["speech"] = speech_array
return batch
dataset = dataset.map(map_to_array, remove_columns=["file"])
``` | @article{korvzinek2017polish,
title={Polish read speech corpus for speech tools and services},
author={Kor{\v{z}}inek, Danijel and Marasek, Krzysztof and Brocki, {\L}ukasz and Wo{\l}k, Krzysztof},
journal={arXiv preprint arXiv:1706.00245},
year={2017}
} | 1 | 157 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language:
- pl
license:
- other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- other
- automatic-speech-recognition
task_ids: []
---
# Dataset Card for ClarinPL Studio Speech Corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [CLARIN-PL mowa](https://mowa.clarin-pl.eu/)
- **Repository:** [Kaldi Baseline](https://github.com/danijel3/ClarinStudioKaldi)
- **Paper:** [Polish Read Speech Corpus for Speech Tools and Services](https://arxiv.org/abs/1706.00245)
- **Leaderboard:** [Paperswithcode Leaderboard][Needs More Information]
- **Point of Contact:** [Danijel Korลพinek](https://github.com/danijel3/)
### Dataset Summary
The corpus consists of 317 speakers recorded in 554
sessions, where each session consists of 20 read sentences and 10 phonetically rich words. The size of
the audio portion of the corpus amounts to around 56 hours, with transcriptions containing 356674 words
from a vocabulary of size 46361.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
The audio is in Polish.
## Dataset Structure
### Data Instances
A typical data point comprises the path to the audio file, usually called `file` and its transcription, called `text`.
An example from the dataset is:
```
{'file': '/root/.cache/huggingface/datasets/downloads/extracted/333ddc746f2df1e1d19b44986992d4cbe28710fde81d533a220e755ee6c5c519/audio/SES0001/rich001.wav',
'id': 'SES0001_rich001',
'speaker_id': 'SPK0001',
'text': 'droลผdลผe dลผip gwoลผdลผenie ozimina wฤdzarz rdzeล wฤdzonka ingerowaฤ kลadzenie jutrzenka'}
```
### Data Fields
- file: A path to the downloaded audio file in .wav format.
- text: the transcription of the audio file.
- speaker_id: The ID of the speaker of the audio.
### Data Splits
| | Train | Test | Valid |
| ----- | ----- | ---- | ----- |
| dataset | 11222 | 1362 | 1229 |
## Dataset Creation
### Curation Rationale
The purpose of this segment of the project was to develop specific tools that would allow for automatic and semi-automatic processing of large quantities of acoustic speech data. Another purpose of the corpus was to serve as a reference for studies in phonetics and pronunciation.
### Source Data
#### Initial Data Collection and Normalization
The corpus was recorded in a studio environment using two microphones: a high-quality studio microphone and a typical consumer audio headset.
#### 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
[Needs More Information]
### Licensing Information
[CLARIN PUB+BY+INF+NORED](https://mowa.clarin-pl.eu/korpusy/LICENSE)
### Citation Information
```
@article{korvzinek2017polish,
title={Polish read speech corpus for speech tools and services},
author={Kor{\v{z}}inek, Danijel and Marasek, Krzysztof and Brocki, {\L}ukasz and Wo{\l}k, Krzysztof},
journal={arXiv preprint arXiv:1706.00245},
year={2017}
}
```
### Contributions
[Needs More Information]
| 4,481 | [
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linxinyuan/cola | 2022-06-08T07:26:13.000Z | [
"region:us"
] | linxinyuan | null | null | 1 | 157 | 2022-06-08T07:24:26 | Entry not found | 15 | [
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adsabs/FOCAL | 2023-10-18T19:15:03.000Z | [
"task_categories:token-classification",
"annotations_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-4.0",
"astronomy",
"region:us"
] | adsabs | null | null | 1 | 157 | 2023-05-17T19:09:34 | ---
annotations_creators:
- expert-generated
license: cc-by-4.0
task_categories:
- token-classification
language:
- en
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
tags:
- astronomy
dataset_info:
features:
- name: Identifier
dtype: string
- name: Paragraph
dtype: string
- name: Citation Text
sequence: string
- name: Functions Text
sequence: string
- name: Functions Label
sequence: string
- name: Citation Start End
sequence:
sequence: int64
- name: Functions Start End
sequence:
sequence: int64
splits:
- name: train
num_bytes: 7096500
num_examples: 2421
- name: validation
num_bytes: 1761751
num_examples: 606
- name: test
num_bytes: 2512022
num_examples: 821
download_size: 5649484
dataset_size: 11370273
---
# Function Of Citation in Astrophysics Literature (FOCAL): Dataset and Task
*Can you explain why the authors made a given citation?*
This dataset was created as a [shared task](https://ui.adsabs.harvard.edu/WIESP/2023/shared_task_1) for [WIESP @ AACL-IJCNLP 2023](https://ui.adsabs.harvard.edu/WIESP/2023/).
## Dataset Description
Datasets are in JSON Lines format (each line is a json dictionary).
Each entry consists of a dictionary with the following keys:
- `"Identifier"`: unique string to identify the entry
- `"Paragraph"`: text string from an astrophysics paper
- `"Citation Text"`: list of strings forming the citation (most often a single string, but sometimes the citation text is split up)
- `"Citation Start End"`: list of integer pairs denoting where the citation starts and end in `"Paragraph"` (most often a single pair, sometimes the citation text is split up, if so follows the order in `"Citation Text"`)
- `"Functions Text"`: list of strings highlighting parts of the paragraph that explain the function of the citation
- `"Functions Label"`: list of strings with the label for each text element in `"Functions Text"` (in same order)
- `"Functions Start End"`: list of integer pairs denoting where the elements in `"Functions Text"` start and end in `"Paragraph"`(in same order)
start and end are defined by the character position in the `"Paragraph"` string.
## Instructions for Workshop Participants:
How to load the data using the Huggingface library:
```python
from datasets import load_dataset
dataset = load_dataset("adsabs/FOCAL")
```
How to load the data if you cloned the repository locally:
(assuming `./FOCAL-TRAINING.jsonl` is in the current directory, change as needed)
- python (as list of dictionaries):
```python
import json
with open("./FOCAL-TRAINING.jsonl", 'r') as f:
focal_training_from_json = [json.loads(l) for l in list(f)]
```
- into Huggingface (as a Huggingface Dataset):
```python
from datasets import Dataset
focal_training_from_json = Dataset.from_json(path_or_paths="./FOCAL-TRAINING.jsonl")
```
## File List
```
โโโ FOCAL-TRAINING.jsonl (2421 samples for training)
โโโ FOCAL-VALIDATION.jsonl (606 samples for validating your training methods)
โโโ FOCAL-TESTING.jsonl (821 samples for testing)
โโโ FOCAL-VALIDATION-NO-LABELS.jsonl (606 samples for validation without the labels. Used during the shared task of [WIESP-2023](https://ui.adsabs.harvard.edu/WIESP/2023/)
โโโ FOCAL-TESTING-NO-LABELS.jsonl (821 samples for testing without the labels. Used during the shared task of [WIESP-2023](https://ui.adsabs.harvard.edu/WIESP/2023/)
โโโ /scoring_scripts/score_focal_seqeval.py (scoring scripts used during the shared task of [WIESP-2023](https://ui.adsabs.harvard.edu/WIESP/2023/)
โโโ /scoring_scripts/score_focal_labels_only.py (scoring scripts used during the shared task of [WIESP-2023](https://ui.adsabs.harvard.edu/WIESP/2023/)
โโโ /data/*.parquet (files used when loading the dataset through Huggingface's API)
โโโ README.MD (this file)
โโโ
```
Maintainer: Felix Grezes (ORCID: 0000-0001-8714-7774)
Data annotator: Tom Allen (ORCID: 0000-0002-5532-4809) | 3,958 | [
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] |
pankajmathur/orca_mini_v1_dataset | 2023-08-15T20:26:46.000Z | [
"license:apache-2.0",
"region:us"
] | pankajmathur | null | null | 8 | 157 | 2023-07-30T22:15:20 | ---
license: apache-2.0
---
An Orca Style dataset, which can be used to fine tuned base models with the following prompt format.
```
### System:
<system>
### User:
<instruction>
### Assistant:
<output>
```
More details coming soon.. | 238 | [
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] |
cawoylel/FulaSpeechCorpora-splited-noise_augmented | 2023-10-25T22:56:22.000Z | [
"region:us"
] | cawoylel | null | null | 0 | 157 | 2023-10-25T22:21:41 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: dev
path: data/dev-*
dataset_info:
features:
- name: audio
dtype: audio
- name: transcription
dtype: string
- name: dialect
dtype: string
splits:
- name: train
num_bytes: 30053008388.047405
num_examples: 187816
- name: test
num_bytes: 3026464537.665628
num_examples: 18801
- name: dev
num_bytes: 758023819.869451
num_examples: 4709
download_size: 32588024982
dataset_size: 33837496745.58248
---
# Dataset Card for "FulaSpeechCorpora-splited-noise_augmented"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 789 | [
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cail2018 | 2022-11-18T19:24:58.000Z | [
"task_categories:other",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:zh",
"license:unknown",
"judgement-prediction",
"arxiv:1807.02478",
"region:us"
] | null | In this paper, we introduce Chinese AI and Law challenge dataset (CAIL2018),
the first large-scale Chinese legal dataset for judgment prediction. CAIL contains more than 2.6 million
criminal cases published by the Supreme People's Court of China, which are several times larger than other
datasets in existing works on judgment prediction. Moreover, the annotations of judgment results are more
detailed and rich. It consists of applicable law articles, charges, and prison terms, which are expected
to be inferred according to the fact descriptions of cases. For comparison, we implement several conventional
text classification baselines for judgment prediction and experimental results show that it is still a
challenge for current models to predict the judgment results of legal cases, especially on prison terms.
To help the researchers make improvements on legal judgment prediction. | @misc{xiao2018cail2018,
title={CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction},
author={Chaojun Xiao and Haoxi Zhong and Zhipeng Guo and Cunchao Tu and Zhiyuan Liu and Maosong Sun and Yansong Feng and Xianpei Han and Zhen Hu and Heng Wang and Jianfeng Xu},
year={2018},
eprint={1807.02478},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | 7 | 156 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- zh
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- other
task_ids: []
paperswithcode_id: chinese-ai-and-law-cail-2018
pretty_name: CAIL 2018
tags:
- judgement-prediction
dataset_info:
features:
- name: fact
dtype: string
- name: relevant_articles
sequence: int32
- name: accusation
sequence: string
- name: punish_of_money
dtype: float32
- name: criminals
sequence: string
- name: death_penalty
dtype: bool
- name: imprisonment
dtype: float32
- name: life_imprisonment
dtype: bool
splits:
- name: exercise_contest_train
num_bytes: 220112732
num_examples: 154592
- name: exercise_contest_valid
num_bytes: 21702157
num_examples: 17131
- name: exercise_contest_test
num_bytes: 41057634
num_examples: 32508
- name: first_stage_train
num_bytes: 1779657510
num_examples: 1710856
- name: first_stage_test
num_bytes: 244335194
num_examples: 217016
- name: final_test
num_bytes: 44194707
num_examples: 35922
download_size: 984551626
dataset_size: 2351059934
---
---
# Dataset Card for CAIL 2018
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Github](https://github.com/thunlp/CAIL/blob/master/README_en.md)
- **Repository:** [Github](https://github.com/thunlp/CAIL)
- **Paper:** [Arxiv](https://arxiv.org/abs/1807.02478)
- **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
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@JetRunner](https://github.com/JetRunner) for adding this dataset. | 3,793 | [
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] |
cmu_hinglish_dog | 2023-03-17T10:14:14.000Z | [
"task_categories:translation",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"multilinguality:translation",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"language:hi",
"license:cc-by-sa-3.0",
"license:gfdl",
"arxiv:1809.07358",
"region:us"
] | null | This is a collection of text conversations in Hinglish (code mixing between Hindi-English) and their corresponding English only versions. Can be used for Translating between the two. | @inproceedings{cmu_dog_emnlp18,
title={A Dataset for Document Grounded Conversations},
author={Zhou, Kangyan and Prabhumoye, Shrimai and Black, Alan W},
year={2018},
booktitle={Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing}
}
@inproceedings{khanuja-etal-2020-gluecos,
title = "{GLUEC}o{S}: An Evaluation Benchmark for Code-Switched {NLP}",
author = "Khanuja, Simran and
Dandapat, Sandipan and
Srinivasan, Anirudh and
Sitaram, Sunayana and
Choudhury, Monojit",
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.329",
pages = "3575--3585"
} | 4 | 156 | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- crowdsourced
language:
- en
- hi
license:
- cc-by-sa-3.0
- gfdl
multilinguality:
- multilingual
- translation
pretty_name: CMU Document Grounded Conversations
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- translation
task_ids: []
dataset_info:
features:
- name: date
dtype: string
- name: docIdx
dtype: int64
- name: translation
dtype:
translation:
languages:
- en
- hi_en
- name: uid
dtype: string
- name: utcTimestamp
dtype: string
- name: rating
dtype: int64
- name: status
dtype: int64
- name: uid1LogInTime
dtype: string
- name: uid1LogOutTime
dtype: string
- name: uid1response
struct:
- name: response
sequence: int64
- name: type
dtype: string
- name: uid2response
struct:
- name: response
sequence: int64
- name: type
dtype: string
- name: user2_id
dtype: string
- name: whoSawDoc
sequence: string
- name: wikiDocumentIdx
dtype: int64
splits:
- name: train
num_bytes: 3142398
num_examples: 8060
- name: test
num_bytes: 379521
num_examples: 960
- name: validation
num_bytes: 368726
num_examples: 942
download_size: 8749685
dataset_size: 3890645
---
# Dataset Card for CMU Document Grounded Conversations
## 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:** [CMU Hinglish DoG](http://festvox.org/cedar/data/notyet/)
- **Repository:** [CMU Document Grounded Conversations (English version)](https://github.com/festvox/datasets-CMU_DoG)
- **Paper:** [CMU Document Grounded Conversations (English version)](https://arxiv.org/pdf/1809.07358.pdf)
- **Point of Contact:**
### Dataset Summary
This is a collection of text conversations in Hinglish (code mixing between Hindi-English) and their corresponding English versions. Can be used for Translating between the two. The dataset has been provided by Prof. Alan Black's group from CMU.
### Supported Tasks and Leaderboards
- `abstractive-mt`
### Languages
## Dataset Structure
### Data Instances
A typical data point comprises a Hinglish text, with key `hi_en` and its English version with key `en`. The `docIdx` contains the current section index of the wiki document when the utterance is said. There are in total 4 sections for each document. The `uid` has the user id of this utterance.
An example from the CMU_Hinglish_DoG train set looks as follows:
```
{'rating': 2,
'wikiDocumentIdx': 13,
'utcTimestamp': '2018-03-16T17:48:22.037Z',
'uid': 'user2',
'date': '2018-03-16T17:47:21.964Z',
'uid2response': {'response': [1, 2, 3, 5], 'type': 'finish'},
'uid1LogInTime': '2018-03-16T17:47:21.964Z',
'user2_id': 'USR664',
'uid1LogOutTime': '2018-03-16T18:02:29.072Z',
'whoSawDoc': ['user1', 'user2'],
'status': 1,
'docIdx': 0,
'uid1response': {'response': [1, 2, 3, 4], 'type': 'finish'},
'translation': {'en': 'The director is Zack Snyder, 27% Rotten Tomatoes, 4.9/10.',
'hi_en': 'Zack Snyder director hai, 27% Rotten Tomatoes, 4.9/10.'}}
```
### Data Fields
- `date`: the time the file is created, as a string
- `docIdx`: the current section index of the wiki document when the utterance is said. There are in total 4 sections for each document.
- `translation`:
- `hi_en`: The text in Hinglish
- `en`: The text in English
- `uid`: the user id of this utterance.
- `utcTimestamp`: the server utc timestamp of this utterance, as a string
- `rating`: A number from 1 or 2 or 3. A larger number means the quality of the conversation is better.
- `status`: status as an integer
- `uid1LogInTime`: optional login time of user 1, as a string
- `uid1LogOutTime`: optional logout time of user 1, as a string
- `uid1response`: a json object contains the status and response of user after finishing the conversation. Fields in the object includes:
- `type`: should be one of ['finish', 'abandon','abandonWithouAnsweringFeedbackQuestion']. 'finish' means the user successfully finishes the conversation, either by completing 12 or 15 turns or in the way that the other user leaves the conversation first. 'abandon' means the user abandons the conversation in the middle, but entering the feedback page. 'abandonWithouAnsweringFeedbackQuestion' means the user just disconnects or closes the web page without providing the feedback.
- `response`: the answer to the post-conversation questions. The worker can choose multiple of them. The options presented to the user are as follows:
For type 'finish'
1: The conversation is understandable.
2: The other user is actively responding me.
3: The conversation goes smoothly.
For type 'abandon'
1: The other user is too rude.
2: I don't know how to proceed with the conversation.
3: The other user is not responding to me.
For users given the document
4: I have watched the movie before.
5: I have not watched the movie before.
For the users without the document
4: I will watch the movie after the other user's introduction.
5: I will not watch the movie after the other user's introduction.
- `uid2response`: same as uid1response
- `user2_id`: the generated user id of user 2
- `whoSawDoc`: Should be one of ['user1'], ['user2'], ['user1', 'user2']. Indicating which user read the document.
- `wikiDocumentId`: the index of the wiki document.
### Data Splits
| name |train|validation|test|
|----------|----:|---------:|---:|
|CMU DOG | 8060| 942| 960|
## Dataset Creation
[More Information Needed]
### Curation Rationale
[More Information Needed]
### Source Data
The Hinglish dataset is derived from the original CMU DoG (Document Grounded Conversations Dataset). More info about that can be found in the [repo](https://github.com/festvox/datasets-CMU_DoG)
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to help develop better question answering systems.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The dataset was initially created by Prof Alan W Black's group at CMU
### Licensing Information
[More Information Needed]
### Citation Information
```bibtex
@inproceedings{
cmu_dog_emnlp18,
title={A Dataset for Document Grounded Conversations},
author={Zhou, Kangyan and Prabhumoye, Shrimai and Black, Alan W},
year={2018},
booktitle={Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing}
}
```
### Contributions
Thanks to [@Ishan-Kumar2](https://github.com/Ishan-Kumar2) for adding this dataset. | 8,126 | [
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] |
code_x_glue_cc_code_completion_line | 2023-06-01T14:59:47.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:slot-filling",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"size_categories:n<1K",
"source_datasets:original",
"language:code",
"license:c-uda",
"region:us"
] | null | Complete the unfinished line given previous context. Models are evaluated by exact match and edit similarity.
We propose line completion task to test model's ability to autocomplete a line. Majority code completion systems behave well in token level completion, but fail in completing an unfinished line like a method call with specific parameters, a function signature, a loop condition, a variable definition and so on. When a software develop finish one or more tokens of the current line, the line level completion model is expected to generate the entire line of syntactically correct code.
Line level code completion task shares the train/dev dataset with token level completion. After training a model on CodeCompletion-token, you could directly use it to test on line-level completion. | @article{raychev2016probabilistic,
title={Probabilistic Model for Code with Decision Trees},
author={Raychev, Veselin and Bielik, Pavol and Vechev, Martin},
journal={ACM SIGPLAN Notices},
pages={731--747},
year={2016},
publisher={ACM New York, NY, USA}
}
@inproceedings{allamanis2013mining,
title={Mining Source Code Repositories at Massive Scale using Language Modeling},
author={Allamanis, Miltiadis and Sutton, Charles},
booktitle={2013 10th Working Conference on Mining Software Repositories (MSR)},
pages={207--216},
year={2013},
organization={IEEE}
} | 1 | 156 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- code
license:
- c-uda
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
- n<1K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- slot-filling
pretty_name: CodeXGlueCcCodeCompletionLine
dataset_info:
- config_name: java
features:
- name: id
dtype: int32
- name: input
dtype: string
- name: gt
dtype: string
splits:
- name: train
num_bytes: 5454783
num_examples: 3000
download_size: 5523586
dataset_size: 5454783
- config_name: python
features:
- name: id
dtype: int32
- name: input
dtype: string
- name: gt
dtype: string
splits:
- name: train
num_bytes: 24021562
num_examples: 10000
download_size: 24266715
dataset_size: 24021562
config_names:
- go
- java
- javascript
- php
- python
- ruby
---
# Dataset Card for "code_x_glue_cc_code_completion_line"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits-sample-size)
- [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/microsoft/CodeXGLUE/tree/main/Code-Code/CodeCompletion-line
### Dataset Summary
CodeXGLUE CodeCompletion-line dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/CodeCompletion-line
Complete the unfinished line given previous context. Models are evaluated by exact match and edit similarity.
We propose line completion task to test model's ability to autocomplete a line. Majority code completion systems behave well in token level completion, but fail in completing an unfinished line like a method call with specific parameters, a function signature, a loop condition, a variable definition and so on. When a software develop finish one or more tokens of the current line, the line level completion model is expected to generate the entire line of syntactically correct code.
Line level code completion task shares the train/dev dataset with token level completion. After training a model on CodeCompletion-token, you could directly use it to test on line-level completion.
### Supported Tasks and Leaderboards
- `slot-filling`: The dataset can be used to train a model for completing entire code lines.
### Languages
- Java **programming** language
- Python **programming** language
## Dataset Structure
### Data Instances
#### java
An example of 'train' looks as follows.
```
{
"gt": "",
"id": 0,
"input": "<s> package org . rubypeople . rdt . internal . ui . rubyeditor ; import java . util . Iterator ; import org . eclipse . core . resources . IMarker ; import org . eclipse . ui . texteditor . MarkerAnnotation ; import org . eclipse . ui . texteditor . MarkerUtilities ; import org . rubypeople . rdt . core . IRubyElement ; import org . rubypeople . rdt . core . IRubyModelMarker ; import org . rubypeople . rdt . core . IRubyScript ; import org . rubypeople . rdt . core . RubyCore ; public class RubyMarkerAnnotation extends MarkerAnnotation implements IRubyAnnotation { public static final String RUBY_MARKER_TYPE_PREFIX = \"\" ; public static final String ERROR_ANNOTATION_TYPE = \"\" ; public static final String WARNING_ANNOTATION_TYPE = \"\" ; public static final String INFO_ANNOTATION_TYPE = \"\" ; public static final String TASK_ANNOTATION_TYPE = \"\" ; private IRubyAnnotation fOverlay ; public RubyMarkerAnnotation ( IMarker marker ) { super ( marker ) ; } public String [ ] getArguments ( ) { return null ; } public int getId ( ) { IMarker marker = getMarker ( ) ; if ( marker == null || ! marker . exists ( ) ) return - 1 ; if ( isProblem ( ) ) return marker . getAttribute ( IRubyModelMarker . ID , - 1 ) ; return - 1 ; } public boolean isProblem ( ) { String type = getType ( ) ; return WARNING_ANNOTATION_TYPE . equals ( type ) || ERROR_ANNOTATION_TYPE . equals"
}
```
#### python
An example of 'train' looks as follows.
```
{
"gt": "",
"id": 0,
"input": "<s> from __future__ import absolute_import <EOL> import weakref <EOL> import operator <EOL> from . compat import threading , itertools_filterfalse <EOL> from . import py2k <EOL> import types <EOL> EMPTY_SET = frozenset ( ) <EOL> class KeyedTuple ( tuple ) : <EOL> def __new__ ( cls , vals , labels = None ) : <EOL> t = tuple . __new__ ( cls , vals ) <EOL> t . _labels = [ ] <EOL> if labels : <EOL> t . __dict__ . update ( zip ( labels , vals ) ) <EOL> t . _labels = labels <EOL> return t <EOL> def keys ( self ) : <EOL> return [ l for l in self . _labels if l is not None ] <EOL> @ property <EOL> def _fields ( self ) : <EOL> return tuple ( self . keys ( ) ) <EOL> def _asdict ( self ) : <EOL> return dict ( ( key , self . __dict__ [ key ] ) for key in self . keys ( ) ) <EOL> class ImmutableContainer ( object ) : <EOL> def _immutable ( self , * arg , ** kw ) : <EOL> raise TypeError ( \"\" % self . __class__ . __name__ ) <EOL> __delitem__ = __setitem__ = __setattr__ = _immutable <EOL> class immutabledict ( ImmutableContainer , dict ) : <EOL> clear = pop = popitem = setdefault = update = ImmutableContainer . _immutable <EOL> def __new__ ( cls , * args ) : <EOL> new = dict . __new__ ( cls ) <EOL> dict . __init__ ( new , * args ) <EOL> return new <EOL> def __init__ ( self , * args ) : <EOL> pass <EOL> def __reduce__ ( self ) : <EOL> return immutabledict , ( dict ( self ) , ) <EOL> def union ( self , d ) : <EOL> if not self : <EOL> return immutabledict ( d ) <EOL> else : <EOL> d2 = immutabledict ( self ) <EOL> dict . update ( d2 , d ) <EOL> return d2 <EOL> def __repr__ ( self ) : <EOL> return \"\" % dict . __repr__ ( self ) <EOL> class Properties ( object ) : <EOL> def __init__ ( self , data ) : <EOL> self . __dict__ [ '_data' ] = data <EOL> def __len__ ( self ) : <EOL> return len ( self . _data ) <EOL> def __iter__ ( self ) : <EOL> return iter ( list ( self . _data . values ( ) ) ) <EOL> def __add__ ( self , other ) : <EOL> return list ( self ) + list ( other ) <EOL> def __setitem__ ( self , key , object ) : <EOL> self . _data [ key ] = object <EOL> def __getitem__ ( self , key ) : <EOL> return self . _data [ key ] <EOL> def __delitem__ ( self , key ) : <EOL> del self . _data [ key ] <EOL> def __setattr__ ( self , key , object ) : <EOL> self . _data [ key ] = object <EOL> def __getstate__ ( self ) : <EOL> return { '_data' : self . __dict__ [ '_data' ] } <EOL> def __setstate__ ( self , state ) : <EOL> self . __dict__ [ '_data' ] = state [ '_data' ] <EOL> def __getattr__ ( self , key ) : <EOL> try : <EOL> return self . _data [ key ] <EOL> except KeyError : <EOL> raise AttributeError ( key ) <EOL> def __contains__ ( self , key ) : <EOL> return key in self . _data <EOL> def as_immutable ( self ) : <EOL> return ImmutableProperties ( self . _data ) <EOL> def update ( self , value ) : <EOL> self . _data . update ( value ) <EOL> def get ( self , key , default = None ) : <EOL> if key in self : <EOL> return self [ key ] <EOL> else : <EOL> return default <EOL> def keys ( self ) : <EOL> return list ( self . _data ) <EOL> def values ( self ) : <EOL> return list ( self . _data . values ( ) ) <EOL> def items ( self ) : <EOL> return list ( self . _data . items ( ) ) <EOL> def has_key ( self , key ) : <EOL> return key in self . _data <EOL> def clear ( self ) : <EOL> self . _data . clear ( ) <EOL> class OrderedProperties ( Properties ) : <EOL> def __init__ ( self ) : <EOL> Properties . __init__ ( self , OrderedDict ( ) ) <EOL> class ImmutableProperties ( ImmutableContainer , Properties ) : <EOL> class OrderedDict ( dict ) : <EOL> def __init__ ( self , ____sequence = None , ** kwargs ) : <EOL> self . _list = [ ] <EOL> if ____sequence is None : <EOL> if kwargs : <EOL> self . update ( ** kwargs ) <EOL> else : <EOL> self . update ( ____sequence , ** kwargs ) <EOL> def clear ( self ) : <EOL> self . _list = [ ] <EOL> dict . clear ( self ) <EOL> def copy ( self ) : <EOL> return self . __copy__ ( ) <EOL> def __copy__ ( self ) : <EOL> return OrderedDict ( self ) <EOL> def sort ( self , * arg , ** kw ) : <EOL> self . _list . sort ( * arg , ** kw ) <EOL> def update ( self , ____sequence = None , ** kwargs ) : <EOL> if ____sequence is not None : <EOL> if hasattr ( ____sequence , 'keys' ) : <EOL> for key in ____sequence . keys ( ) : <EOL> self . __setitem__ ( key , ____sequence [ key ] ) <EOL> else : <EOL> for key , value in ____sequence : <EOL> self [ key ] = value <EOL> if kwargs : <EOL> self . update ( kwargs ) <EOL> def setdefault ( self , key , value ) : <EOL> if key not in self : <EOL> self . __setitem__ ( key , value ) <EOL> return value <EOL> else : <EOL> return self . __getitem__ ( key ) <EOL> def __iter__ ( self ) : <EOL> return iter ( self . _list ) <EOL> def keys ( self ) : <EOL> return list ( self ) <EOL> def values ( self ) : <EOL> return [ self [ key ] for key in self . _list ] <EOL> def items ( self ) : <EOL> return [ ( key , self [ key ] ) for key in self . _list ] <EOL> if py2k : <EOL> def itervalues ( self ) : <EOL> return iter ( self . values ( ) ) <EOL> def iterkeys ( self ) : <EOL> return iter ( self ) <EOL> def iteritems ( self ) : <EOL> return iter ( self . items ( ) ) <EOL> def __setitem__ ( self , key , object ) : <EOL> if key not in self : <EOL> try : <EOL> self . _list . append ( key ) <EOL> except AttributeError : <EOL> self . _list = [ key ] <EOL> dict . __setitem__ ( self , key , object ) <EOL> def __delitem__ ( self , key ) : <EOL> dict . __delitem__ ( self , key ) <EOL> self . _list . remove ( key ) <EOL> def pop ( self , key , * default ) : <EOL> present = key in self <EOL> value = dict . pop ( self , key , * default ) <EOL> if present : <EOL> self . _list . remove ( key ) <EOL> return value <EOL> def popitem ( self ) : <EOL> item = dict . popitem ( self ) <EOL> self . _list . remove ( item [ 0 ] ) <EOL> return item <EOL> class OrderedSet ( set ) : <EOL> def __init__ ( self , d = None ) : <EOL> set . __init__ ( self ) <EOL> self . _list = [ ] <EOL> if d is not None : <EOL>"
}
```
### Data Fields
In the following each data field in go is explained for each config. The data fields are the same among all splits.
#### java, python
|field name| type | description |
|----------|------|----------------------------|
|id |int32 | Index of the sample |
|input |string| Input code string |
|gt |string| Code string to be predicted|
### Data Splits
| name |train|
|------|----:|
|java | 3000|
|python|10000|
## 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
https://github.com/microsoft, https://github.com/madlag
### Licensing Information
Computational Use of Data Agreement (C-UDA) License.
### Citation Information
```
@article{raychev2016probabilistic,
title={Probabilistic Model for Code with Decision Trees},
author={Raychev, Veselin and Bielik, Pavol and Vechev, Martin},
journal={ACM SIGPLAN Notices},
pages={731--747},
year={2016},
publisher={ACM New York, NY, USA}
}
@inproceedings{allamanis2013mining,
title={Mining Source Code Repositories at Massive Scale using Language Modeling},
author={Allamanis, Miltiadis and Sutton, Charles},
booktitle={2013 10th Working Conference on Mining Software Repositories (MSR)},
pages={207--216},
year={2013},
organization={IEEE}
}
```
### Contributions
Thanks to @madlag (and partly also @ncoop57) for adding this dataset. | 12,887 | [
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HugoLaurencon/libri_light | 2022-05-10T15:51:37.000Z | [
"region:us"
] | HugoLaurencon | Libri-light is a large dataset of 60K hours of unlabelled speech from audiobooks in English.
It is a benchmark for the training of automatic speech recognition (ASR) systems with limited or no supervision. | @INPROCEEDINGS{librilight,
author={J. Kahn and M. Riviรจre and W. Zheng and E. Kharitonov and Q. Xu and P. E. Mazarรฉ and J. Karadayi and V. Liptchinsky and R. Collobert and C. Fuegen and T. Likhomanenko and G. Synnaeve and A. Joulin and A. Mohamed and E. Dupoux},
booktitle={ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
title={Libri-Light: A Benchmark for ASR with Limited or No Supervision},
year={2020},
pages={7669-7673},
} | 2 | 156 | 2022-05-09T14:31:34 | Entry not found | 15 | [
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allenai/prosocial-dialog | 2023-02-03T07:58:29.000Z | [
"task_categories:conversational",
"task_categories:text-classification",
"task_ids:dialogue-generation",
"task_ids:multi-class-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"size_categories:100K<n<1M",
"source_datasets:original",
"source_datasets:extended|social_bias_frames",
"language:en",
"license:cc-by-4.0",
"dialogue",
"dialogue safety",
"social norm",
"rules-of-thumb",
"arxiv:2205.12688",
"region:us"
] | allenai | null | null | 67 | 156 | 2022-10-30T04:24:12 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- crowdsourced
- machine-generated
license: cc-by-4.0
multilinguality:
- monolingual
pretty_name: ProsocialDialog
size_categories:
- 10K<n<100K
- 100K<n<1M
source_datasets:
- original
- extended|social_bias_frames
tags:
- dialogue
- dialogue safety
- social norm
- rules-of-thumb
task_categories:
- conversational
- text-classification
task_ids:
- dialogue-generation
- multi-class-classification
---
# Dataset Card for ProsocialDialog Dataset
## Dataset Description
- **Repository:** [Dataset and Model](https://github.com/skywalker023/prosocial-dialog)
- **Paper:** [ProsocialDialog: A Prosocial Backbone for Conversational Agents](https://aclanthology.org/2022.emnlp-main.267/)
- **Point of Contact:** [Hyunwoo Kim](mailto:hyunwook@allenai.org)
## Dataset Summary
ProsocialDialog is the first large-scale multi-turn English dialogue dataset to teach conversational agents to respond to problematic content following social norms. Covering diverse unethical, problematic, biased, and toxic situations, ProsocialDialog contains responses that encourage prosocial behavior, grounded in commonsense social rules (i.e., rules-of-thumb, RoTs). Created via a human-AI collaborative framework, ProsocialDialog consists of 58K dialogues, with 331K utterances, 160K unique RoTs, and 497K dialogue safety labels accompanied by free-form rationales.
## Supported Tasks
* Dialogue response generation
* Dialogue safety prediction
* Rules-of-thumb generation
## Languages
English
## Dataset Structure
### Data Attributes
attribute | type | description
--- | --- | ---
`context` | str | the potentially unsafe utterance
`response` | str | the guiding utterance grounded on rules-of-thumb (`rots`)
`rots` | list of str\|null | the relevant rules-of-thumb for `text` *not* labeled as \_\_casual\_\_
`safety_label` | str | the final verdict of the context according to `safety_annotations`: {\_\_casual\_\_, \_\_possibly\_needs\_caution\_\_, \_\_probably\_needs\_caution\_\_, \_\_needs\_caution\_\_, \_\_needs\_intervention\_\_}
`safety_annotations` | list of str | raw annotations from three workers: {casual, needs caution, needs intervention}
`safety_annotation_reasons` | list of str | the reasons behind the safety annotations in free-form text from each worker
`source` | str | the source of the seed text that was used to craft the first utterance of the dialogue: {socialchemistry, sbic, ethics_amt, ethics_reddit}
`etc` | str\|null | other information
`dialogue_id` | int | the dialogue index
`response_id` | int | the response index
`episode_done` | bool | an indicator of whether it is the end of the dialogue
## Dataset Creation
To create ProsocialDialog, we set up a human-AI collaborative data creation framework, where GPT-3 generates the potentially unsafe utterances, and crowdworkers provide prosocial responses to them. This approach allows us to circumvent two substantial challenges: (1) there are no available large-scale corpora of multiturn prosocial conversations between humans, and (2) asking humans to write unethical, toxic, or problematic utterances could result in psychological harms (Roberts, 2017; Steiger et al., 2021).
### Further Details, Social Impacts, and Limitations
Please refer to our [paper](https://arxiv.org/abs/2205.12688).
## Additional Information
### Citation
Please cite our work if you found the resources in this repository useful:
```
@inproceedings{kim2022prosocialdialog,
title={ProsocialDialog: A Prosocial Backbone for Conversational Agents},
author={Hyunwoo Kim and Youngjae Yu and Liwei Jiang and Ximing Lu and Daniel Khashabi and Gunhee Kim and Yejin Choi and Maarten Sap},
booktitle={EMNLP},
year=2022
}
``` | 3,762 | [
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bigbio/pdr | 2022-12-22T15:46:14.000Z | [
"multilinguality:monolingual",
"language:en",
"license:unknown",
"region:us"
] | bigbio | The corpus of plant-disease relation consists of plants and diseases and their relation to PubMed abstract.
The corpus consists of about 2400 plant and disease entities and 300 annotated relations from 179 abstracts. | @article{kim2019corpus,
title={A corpus of plant--disease relations in the biomedical domain},
author={Kim, Baeksoo and Choi, Wonjun and Lee, Hyunju},
journal={PLoS One},
volume={14},
number={8},
pages={e0221582},
year={2019},
publisher={Public Library of Science San Francisco, CA USA}
} | 1 | 156 | 2022-11-13T22:11:20 |
---
language:
- en
bigbio_language:
- English
license: unknown
multilinguality: monolingual
bigbio_license_shortname: UNKNOWN
pretty_name: PDR
homepage: http://gcancer.org/pdr/
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
- EVENT_EXTRACTION
- COREFERENCE_RESOLUTION
---
# Dataset Card for PDR
## Dataset Description
- **Homepage:** http://gcancer.org/pdr/
- **Pubmed:** True
- **Public:** True
- **Tasks:** NER,EE,COREF
The corpus of plant-disease relation consists of plants and diseases and their relation to PubMed abstract.
The corpus consists of about 2400 plant and disease entities and 300 annotated relations from 179 abstracts.
## Citation Information
```
@article{kim2019corpus,
title={A corpus of plant--disease relations in the biomedical domain},
author={Kim, Baeksoo and Choi, Wonjun and Lee, Hyunju},
journal={PLoS One},
volume={14},
number={8},
pages={e0221582},
year={2019},
publisher={Public Library of Science San Francisco, CA USA}
}
```
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olm/olm-wikipedia-20221220 | 2022-12-29T03:12:35.000Z | [
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"language:en",
"pretraining",
"language modelling",
"wikipedia",
"web",
"region:us"
] | olm | null | null | 2 | 156 | 2022-12-22T17:38:13 | ---
annotations_creators:
- no-annotation
language:
- en
language_creators:
- found
license: []
multilinguality:
- monolingual
pretty_name: OLM December 2022 Wikipedia
size_categories:
- 1M<n<10M
source_datasets: []
tags:
- pretraining
- language modelling
- wikipedia
- web
task_categories: []
task_ids: []
---
# Dataset Card for OLM December 2022 Wikipedia
Pretraining dataset, created with the OLM repo [here](https://github.com/huggingface/olm-datasets) from a December 2022 Wikipedia snapshot. | 500 | [
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atokforps/chunk-t1 | 2023-03-09T20:48:30.000Z | [
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] |
skeskinen/TinyStories-GPT4 | 2023-05-20T19:00:22.000Z | [
"region:us"
] | skeskinen | null | null | 12 | 156 | 2023-05-20T18:58:41 | ---
dataset_info:
features:
- name: story
dtype: string
- name: summary
dtype: string
- name: source
dtype: string
- name: prompt
dtype: string
- name: words
sequence: string
- name: features
sequence: string
splits:
- name: train
num_bytes: 3680196493
num_examples: 2745100
download_size: 1553670972
dataset_size: 3680196493
---
# Dataset Card for "TinyStories-GPT4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 554 | [
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ascent_kb | 2022-11-03T16:30:39.000Z | [
"task_categories:other",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"knowledge-base",
"arxiv:2011.00905",
"region:us"
] | null | This dataset contains 8.9M commonsense assertions extracted by the Ascent pipeline (https://ascent.mpi-inf.mpg.de/). | @InProceedings{nguyen2021www,
title={Advanced Semantics for Commonsense Knowledge Extraction},
author={Nguyen, Tuan-Phong and Razniewski, Simon and Weikum, Gerhard},
year={2021},
booktitle={The Web Conference 2021},
} | 2 | 155 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- other
task_ids: []
paperswithcode_id: ascentkb
pretty_name: Ascent KB
tags:
- knowledge-base
dataset_info:
- config_name: canonical
features:
- name: arg1
dtype: string
- name: rel
dtype: string
- name: arg2
dtype: string
- name: support
dtype: int64
- name: facets
list:
- name: value
dtype: string
- name: type
dtype: string
- name: support
dtype: int64
- name: source_sentences
list:
- name: text
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 2976697816
num_examples: 8904060
download_size: 710727536
dataset_size: 2976697816
- config_name: open
features:
- name: subject
dtype: string
- name: predicate
dtype: string
- name: object
dtype: string
- name: support
dtype: int64
- name: facets
list:
- name: value
dtype: string
- name: type
dtype: string
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list:
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dtype: string
splits:
- name: train
num_bytes: 2882678298
num_examples: 8904060
download_size: 710727536
dataset_size: 2882678298
---
# Dataset Card for Ascent KB
## 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://ascent.mpi-inf.mpg.de/
- **Repository:** https://github.com/phongnt570/ascent
- **Paper:** https://arxiv.org/abs/2011.00905
- **Point of Contact:** http://tuan-phong.com
### Dataset Summary
This dataset contains 8.9M commonsense assertions extracted by the Ascent pipeline developed at the [Max Planck Institute for Informatics](https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/).
The focus of this dataset is on everyday concepts such as *elephant*, *car*, *laptop*, etc.
The current version of Ascent KB (v1.0.0) is approximately **19 times larger than ConceptNet** (note that, in this comparison, non-commonsense knowledge in ConceptNet such as lexical relations is excluded).
For more details, take a look at
[the research paper](https://arxiv.org/abs/2011.00905) and
[the website](https://ascent.mpi-inf.mpg.de).
### Supported Tasks and Leaderboards
The dataset can be used in a wide range of downstream tasks such as commonsense question answering or dialogue systems.
### Languages
The dataset is in English.
## Dataset Structure
### Data Instances
There are two configurations available for this dataset:
1. `canonical` (default): This part contains `<arg1 ; rel ; arg2>`
assertions where the relations (`rel`) were mapped to
[ConceptNet relations](https://github.com/commonsense/conceptnet5/wiki/Relations)
with slight modifications:
- Introducing 2 new relations: `/r/HasSubgroup`, `/r/HasAspect`.
- All `/r/HasA` relations were replaced with `/r/HasAspect`.
This is motivated by the [ATOMIC-2020](https://allenai.org/data/atomic-2020)
schema, although they grouped all `/r/HasA` and
`/r/HasProperty` into `/r/HasProperty`.
- The `/r/UsedFor` relation was replaced with `/r/ObjectUse`
which is broader (could be either _"used for"_, _"used in"_, or _"used as"_, ect.).
This is also taken from ATOMIC-2020.
2. `open`: This part contains open assertions of the form
`<subject ; predicate ; object>` extracted directly from web
contents. This is the original form of the `canonical` triples.
In both configurations, each assertion is equipped with
extra information including: a set of semantic `facets`
(e.g., *LOCATION*, *TEMPORAL*, etc.), its `support` (i.e., number of occurrences),
and a list of `source_sentences`.
An example row in the `canonical` configuration:
```JSON
{
"arg1": "elephant",
"rel": "/r/HasProperty",
"arg2": "intelligent",
"support": 15,
"facets": [
{
"value": "extremely",
"type": "DEGREE",
"support": 11
}
],
"source_sentences": [
{
"text": "Elephants are extremely intelligent animals.",
"source": "https://www.softschools.com/facts/animals/asian_elephant_facts/2310/"
},
{
"text": "Elephants are extremely intelligent creatures and an elephant's brain can weigh as much as 4-6 kg.",
"source": "https://www.elephantsforafrica.org/elephant-facts/"
}
]
}
```
### Data Fields
- **For `canonical` configuration**
- `arg1`: the first argument to the relationship, e.g., *elephant*
- `rel`: the canonical relation, e.g., */r/HasProperty*
- `arg2`: the second argument to the relationship, e.g., *intelligence*
- `support`: the number of occurrences of the assertion, e.g., *15*
- `facets`: an array of semantic facets, each contains
- `value`: facet value, e.g., *extremely*
- `type`: facet type, e.g., *DEGREE*
- `support`: the number of occurrences of the facet, e.g., *11*
- `source_sentences`: an array of source sentences from which the assertion was
extracted, each contains
- `text`: the raw text of the sentence
- `source`: the URL to its parent document
- **For `open` configuration**
- The fields of this configuration are the same as the `canonical`
configuration's, except that
the (`arg1`, `rel`, `arg2`) fields are replaced with the
(`subject`, `predicate`, `object`) fields
which are free
text phrases extracted directly from the source sentences
using an Open Information Extraction (OpenIE) tool.
### Data Splits
There are no splits. All data points come to a default split called `train`.
## Dataset Creation
### Curation Rationale
The commonsense knowledge base was created to assist in development of robust and reliable AI.
### Source Data
#### Initial Data Collection and Normalization
Texts were collected from the web using the Bing Search API, and went through various cleaning steps before being processed by an OpenIE tool to get open assertions.
The assertions were then grouped into semantically equivalent clusters.
Take a look at the research paper for more details.
#### Who are the source language producers?
Web users.
### Annotations
#### Annotation process
None.
#### Who are the annotators?
None.
### Personal and Sensitive Information
Unknown.
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
The knowledge base has been developed by researchers at the
[Max Planck Institute for Informatics](https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/).
Contact [Tuan-Phong Nguyen](http://tuan-phong.com) in case of questions and comments.
### Licensing Information
[The Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/)
### Citation Information
```
@InProceedings{nguyen2021www,
title={Advanced Semantics for Commonsense Knowledge Extraction},
author={Nguyen, Tuan-Phong and Razniewski, Simon and Weikum, Gerhard},
year={2021},
booktitle={The Web Conference 2021},
}
```
### Contributions
Thanks to [@phongnt570](https://github.com/phongnt570) for adding this dataset. | 8,478 | [
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conll2000 | 2023-04-05T10:02:23.000Z | [
"language:en",
"region:us"
] | null | Text chunking consists of dividing a text in syntactically correlated parts of words. For example, the sentence
He reckons the current account deficit will narrow to only # 1.8 billion in September . can be divided as follows:
[NP He ] [VP reckons ] [NP the current account deficit ] [VP will narrow ] [PP to ] [NP only # 1.8 billion ]
[PP in ] [NP September ] .
Text chunking is an intermediate step towards full parsing. It was the shared task for CoNLL-2000. Training and test
data for this task is available. This data consists of the same partitions of the Wall Street Journal corpus (WSJ)
as the widely used data for noun phrase chunking: sections 15-18 as training data (211727 tokens) and section 20 as
test data (47377 tokens). The annotation of the data has been derived from the WSJ corpus by a program written by
Sabine Buchholz from Tilburg University, The Netherlands. | @inproceedings{tksbuchholz2000conll,
author = "Tjong Kim Sang, Erik F. and Sabine Buchholz",
title = "Introduction to the CoNLL-2000 Shared Task: Chunking",
editor = "Claire Cardie and Walter Daelemans and Claire
Nedellec and Tjong Kim Sang, Erik",
booktitle = "Proceedings of CoNLL-2000 and LLL-2000",
publisher = "Lisbon, Portugal",
pages = "127--132",
year = "2000"
} | 2 | 155 | 2022-03-02T23:29:22 | ---
language:
- en
paperswithcode_id: conll-2000-1
pretty_name: CoNLL-2000
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: pos_tags
sequence:
class_label:
names:
'0': ''''''
'1': '#'
'2': $
'3': (
'4': )
'5': ','
'6': .
'7': ':'
'8': '``'
'9': CC
'10': CD
'11': DT
'12': EX
'13': FW
'14': IN
'15': JJ
'16': JJR
'17': JJS
'18': MD
'19': NN
'20': NNP
'21': NNPS
'22': NNS
'23': PDT
'24': POS
'25': PRP
'26': PRP$
'27': RB
'28': RBR
'29': RBS
'30': RP
'31': SYM
'32': TO
'33': UH
'34': VB
'35': VBD
'36': VBG
'37': VBN
'38': VBP
'39': VBZ
'40': WDT
'41': WP
'42': WP$
'43': WRB
- name: chunk_tags
sequence:
class_label:
names:
'0': O
'1': B-ADJP
'2': I-ADJP
'3': B-ADVP
'4': I-ADVP
'5': B-CONJP
'6': I-CONJP
'7': B-INTJ
'8': I-INTJ
'9': B-LST
'10': I-LST
'11': B-NP
'12': I-NP
'13': B-PP
'14': I-PP
'15': B-PRT
'16': I-PRT
'17': B-SBAR
'18': I-SBAR
'19': B-UCP
'20': I-UCP
'21': B-VP
'22': I-VP
splits:
- name: train
num_bytes: 5356965
num_examples: 8937
- name: test
num_bytes: 1201151
num_examples: 2013
download_size: 3481560
dataset_size: 6558116
---
# Dataset Card for "conll2000"
## 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://www.clips.uantwerpen.be/conll2000/chunking/](https://www.clips.uantwerpen.be/conll2000/chunking/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 3.48 MB
- **Size of the generated dataset:** 6.55 MB
- **Total amount of disk used:** 10.03 MB
### Dataset Summary
Text chunking consists of dividing a text in syntactically correlated parts of words. For example, the sentence
He reckons the current account deficit will narrow to only # 1.8 billion in September . can be divided as follows:
[NP He ] [VP reckons ] [NP the current account deficit ] [VP will narrow ] [PP to ] [NP only # 1.8 billion ]
[PP in ] [NP September ] .
Text chunking is an intermediate step towards full parsing. It was the shared task for CoNLL-2000. Training and test
data for this task is available. This data consists of the same partitions of the Wall Street Journal corpus (WSJ)
as the widely used data for noun phrase chunking: sections 15-18 as training data (211727 tokens) and section 20 as
test data (47377 tokens). The annotation of the data has been derived from the WSJ corpus by a program written by
Sabine Buchholz from Tilburg University, The Netherlands.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### conll2000
- **Size of downloaded dataset files:** 3.48 MB
- **Size of the generated dataset:** 6.55 MB
- **Total amount of disk used:** 10.03 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"chunk_tags": [11, 13, 11, 12, 21, 22, 22, 22, 22, 11, 12, 12, 17, 11, 12, 13, 11, 0, 1, 13, 11, 11, 0, 21, 22, 22, 11, 12, 12, 13, 11, 12, 12, 11, 12, 12, 0],
"id": "0",
"pos_tags": [19, 14, 11, 19, 39, 27, 37, 32, 34, 11, 15, 19, 14, 19, 22, 14, 20, 5, 15, 14, 19, 19, 5, 34, 32, 34, 11, 15, 19, 14, 20, 9, 20, 24, 15, 22, 6],
"tokens": "[\"Confidence\", \"in\", \"the\", \"pound\", \"is\", \"widely\", \"expected\", \"to\", \"take\", \"another\", \"sharp\", \"dive\", \"if\", \"trade\", \"figur..."
}
```
### Data Fields
The data fields are the same among all splits.
#### conll2000
- `id`: a `string` feature.
- `tokens`: a `list` of `string` features.
- `pos_tags`: a `list` of classification labels, with possible values including `''` (0), `#` (1), `$` (2), `(` (3), `)` (4).
- `chunk_tags`: a `list` of classification labels, with possible values including `O` (0), `B-ADJP` (1), `I-ADJP` (2), `B-ADVP` (3), `I-ADVP` (4).
### Data Splits
| name |train|test|
|---------|----:|---:|
|conll2000| 8937|2013|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@inproceedings{tksbuchholz2000conll,
author = "Tjong Kim Sang, Erik F. and Sabine Buchholz",
title = "Introduction to the CoNLL-2000 Shared Task: Chunking",
editor = "Claire Cardie and Walter Daelemans and Claire
Nedellec and Tjong Kim Sang, Erik",
booktitle = "Proceedings of CoNLL-2000 and LLL-2000",
publisher = "Lisbon, Portugal",
pages = "127--132",
year = "2000"
}
```
### Contributions
Thanks to [@vblagoje](https://github.com/vblagoje), [@jplu](https://github.com/jplu) for adding this dataset. | 8,771 | [
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ywchoi/pubmed_abstract_5 | 2022-09-13T01:07:12.000Z | [
"region:us"
] | ywchoi | null | null | 0 | 155 | 2022-09-13T01:05:10 | Entry not found | 15 | [
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] |
TREC-AToMiC/AToMiC-Qrels-v0.2 | 2023-02-14T21:31:18.000Z | [
"license:cc-by-sa-4.0",
"region:us"
] | TREC-AToMiC | null | null | 1 | 155 | 2023-01-24T13:11:24 | ---
dataset_info:
features:
- name: text_id
dtype: string
- name: Q0
dtype: string
- name: image_id
dtype: string
- name: rel
dtype: int64
splits:
- name: test
num_bytes: 789840
num_examples: 9873
- name: validation
num_bytes: 1424080
num_examples: 17801
- name: train
num_bytes: 352152240
num_examples: 4401903
download_size: 205636566
dataset_size: 354366160
license: cc-by-sa-4.0
---
# Dataset Card for "AToMiC-Qrels-v0.2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 620 | [
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yuan-yang/MALLS-v0 | 2023-10-25T20:16:00.000Z | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-nc-4.0",
"region:us"
] | yuan-yang | null | null | 0 | 155 | 2023-05-31T19:01:19 | ---
license: cc-by-nc-4.0
viewer: true
task_categories:
- text-generation
language:
- en
pretty_name: MALLS NL-FOL Pairs 34K
size_categories:
- 10K<n<100K
---
# MALLS NL-FOL Pairs
## Dataset details
MALLS (large language **M**odel gener**A**ted natural-**L**anguage-to-first-order-**L**ogic pair**S**)
consists of pairs of real-world natural language (NL) statements and the corresponding first-order logic (FOL) rules annotations.
All pairs are generated by prompting GPT-4 and processed to ensure the validity of the FOL rules.
MALLS-v0 consists of the original 34K NL-FOL pairs. We validate FOL rules in terms of syntactical correctness, but we did not conduct a rigorous alignment check on the pairs,
meaning the FOL rule may not accurately reflect the meaning of the NL statement.
MALLS-v0.1 consists of 28K NL-FOL pairs that are filtered from v0. We manually checked the alignment for 1K samples and developed a filtering pipeline to filter
the main dataset.
# Dataset Structure
- The file `MALLS-v0.json` consists of the 34K unfiltered pairs of the MALLS-v0 dataset.
- The files `MALLS-v0.1-train.json` and `MALLS-v0.1-test.json` consist of the 27K auto-verified pairs and the 1K human-verified pairs.
- We also provide `folio_parsed.json` which consists of 2K pairs collected and processed from the FOLIO datset.
Each entry in the file is a dictionary object of the following format
```
{
'NL': <the NL statment>,
'FOL': <the FOL rule>
}
```
**License:**
Attribution-NonCommercial 4.0 International.
Since the data are collected from GPT-4, it also abides by the policy of OpenAI: https://openai.com/policies/terms-of-use
## Using the Dataset
We use MALLS to finetune LLaMA models for NL-FOL translation, namely LogicLLaMA, which achieves GPT-4 level performance.
**Project Page**
https://github.com/gblackout/LogicLLaMA
## Intended use
**Primary intended uses:**
MALLS is intended to be used for research.
## Citation
```
@article{yang2023harnessing,
title={Harnessing the Power of Large Language Models for Natural Language to First-Order Logic Translation},
author={Yuan Yang and Siheng Xiong and Ali Payani and Ehsan Shareghi and Faramarz Fekri},
journal={arXiv preprint arXiv:2305.15541},
year={2023}
}
``` | 2,268 | [
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lampent/IRFL | 2023-06-02T15:02:05.000Z | [
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-4.0",
"figurative-language",
"multimodal-figurative-language",
" commonsense-reasoning",
"visual-reasoning",
"arxiv:2303.15445",
"region:us"
] | lampent | null | null | 1 | 155 | 2023-06-01T09:34:13 | ---
license: cc-by-4.0
language:
- en
tags:
- figurative-language
- multimodal-figurative-language
- ' commonsense-reasoning'
- visual-reasoning
size_categories:
- 1K<n<10K
---
# Dataset Card for IRFL
- [Dataset Description](#dataset-description)
- [Leaderboards](#leaderboards)
- [Colab notebook code for IRFL evaluation](#colab-notebook-code-for-irfl-evaluation)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Dataset Creation](#dataset-creation)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
The IRFL dataset consists of idioms, similes, and metaphors with matching figurative and literal images, as well as two novel tasks of multimodal figurative understanding and preference.
We collected figurative and literal images for textual idioms, metaphors, and similes using an automatic pipeline we created (idioms) and manually (metaphors + similes). We annotated the relations between these images and the figurative phrase they originated from. Using these images we created two novel tasks of figurative understanding and preference.
The figurative understanding task evaluates Vision and Language Pre-Trained Modelsโ (VL-PTMs) ability to understand the relation between an image and a figurative phrase. The task is to choose the image that best visualizes the figurative phrase out of X candidates. The preference task examines VL-PTMs' preference for figurative images. In this task, the model needs to classify phrase images of different categories correctly based on their ranking by the model matching score.
The figurative understanding task evaluates Vision and Language Pre-Trained Modelsโ (VL-PTMs) ability to understand the relation between an image and a figurative phrase. The task is to choose the image that best visualizes the figurative phrase out of X candidates. The preference task examines VL-PTMs' preference for figurative images. In this task, the model needs to classify phrase images of different categories correctly based on their ranking by the model matching score.
We evaluated state-of-the-art VL models and found that the best models achieved 22%, 30%, and 66% accuracy vs. humans 97%, 99.7%, and 100% on our understanding task for idioms, metaphors, and similes respectively. The best model achieved an F1 score of 61 on the preference task.
- **Homepage:**
https://irfl-dataset.github.io/
- **Repository:**
https://github.com/irfl-dataset/IRFL
- **Paper:**
https://arxiv.org/abs/2303.15445
- **Leaderboard:**
https://irfl-dataset.github.io/leaderboard
- **Point of Contact:**
irfl.dataset@gmail.com; ron.yosef@mail.huji.ac.il
### Leaderboards
https://irfl-dataset.github.io/leaderboard
### Colab notebook code for IRFL evaluation
https://colab.research.google.com/drive/1zbW7R8Cn9sXICV3x_FGKjKIKu8GCrCme?usp=sharing
### Languages
English.
## Dataset Structure
### Data Fields
โ
- refers to idiom-only fields
Understanding task
- query (โ
): the idiom definition the answer image originated from.
- distractors: the distractor images
- answer: the correct image
- figurative_type: idiom | metaphor | simile
- images_metadata: the metadata of the distractors and asnwer images.
- type: the correct image type (Figurative or Figurative Literal).
- definition (โ
): list of all the definitions of the idiom
- phrase: the figurative phrase.
Preference task
- type: the rival categories FvsPO (Figurative images vs. Partial Objects) or FLvsPO (Figurative Literal images vs. Partial Objects)
- figurative_type: idiom | metaphor | simile
- first_category: the first category images (Figurative images if FvsPO, Figurative Literal images if FLvsPO)
- second_category: the second category images (Partial Objects)
- definition (โ
): list of all the definitions of the idiom
- phrase: the figurative phrase.
The idioms, metaphor, and similes datasets contain all the figurative phrases, annotated images, and corresponding metadata. <br/>
## Dataset Collection
We collected figurative and literal images for textual idioms, metaphors, and similes using an automatic pipeline we created (idioms) and manually (metaphors + similes). We annotated the relations between these images and the figurative phrase they originated from.
#### Annotation process
We paid Amazon Mechanical Turk Workers to annotate the relations between each image and phrase (Figurative vs. Literal).
## Considerations for Using the Data
5 annotators annotated all of the data releases.
### Licensing Information
CC-By 4.0
### Citation Information
@misc{yosef2023irfl,
title={IRFL: Image Recognition of Figurative Language},
author={Ron Yosef and Yonatan Bitton and Dafna Shahaf},
year={2023},
eprint={2303.15445},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | 4,948 | [
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] |
jondurbin/airoboros-2.2 | 2023-10-03T19:01:21.000Z | [
"license:other",
"region:us"
] | jondurbin | null | null | 2 | 155 | 2023-10-03T18:46:53 | ---
license: other
---
## Overview
This dataset is mostly a continuation of https://hf.co/datasets/jondurbin/airoboros-2.1, with some notable additions and fixes.
- Some of the content is "toxic"/"harmful", and contains profanity and other types of sensitive content.
- None of the content or views contained in text within this dataset necessarily align with my personal beliefs or opinions, they are simply text generated by LLMs and/or scraped from the web.
- Use with caution, particularly in locations with less-than-free speech laws.
- You, and you alone are responsible for having downloaded the dataset and having a copy of the contents therein and I am completely indemnified from any and all liabilities.
### 2.1 Contamination
I accidentally included some of the benchmark data in the first version of the airboros-2.1 model, which is why it had a crazy high truthfulqa score. Discussions here:
- https://huggingface.co/jondurbin/airoboros-l2-70b-2.1/discussions/3#64f325ce352152814d1f796a
- https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/discussions/225#64f0997659da193a12b78c32
I flagged it for removal and recreated the model right away, but the leaderboard cached the old results so it took some time to reflect.
Some of the instructors I use create overlapping data, and it's hard to filter, especially since the instructions aren't typically verbatim with the benchmark questions.
This time around, I used `thenlper/gte-small` to calculate embeddings of the instructions, along with a faiss index, and removed anything from the dataset that had a similarity score < 0.15 (from truthfulqa). If you have a better way of checking, please let me know!
I haven't done the same for most other benchmarks (yet) because there are hundreds of thousands of instructions and it would be pretty computationally expensive to do. That said, I only have ~1279 multiple choice questions, all randomly GPT generated, so there's probably little-to-no overlap.
### Awareness
I added a new "awareness" instructor, which aims to add a lot more nuance to responses relating to time, location, senses, etc. based on the system prompt.
For example, if you are using the standard prompt with user/assistant, and ask how long it would take to get to Chicago, the answer will be something about AI not having a physical presence.
If, on the other hand, you are using a system prompt with a human character specified, the model attempts to infer location from "home" and will provide a more nuanced answer as a human would (in theory).
https://github.com/jondurbin/airoboros/commit/e91562c88d7610edb051606622e7c25a99884f7e
### Editor
I created a text edit instructor as well, which uses a reverse prompt mechanism, meaning it takes the existing writing samples that have been generated, rewrites them to have misspellings, poor grammar, etc., then uses a prompt like "Please correct and improve the text." with the original well-written text and target output.
https://github.com/jondurbin/airoboros/commit/e60a68de5f9622320c9cfff3b238bd83cc7e373b
### Writing
I regenerated (almost) all of the training data that included "Once upon a time..." because it's too cliche and boring.
### Multiple choice
I created many more multiple choice questions, many of which have additional text context.
### Roleplay/conversation
I re-created all of the GTKM and RP datasets this time around, removing all of the "USER: " and "ASSISTANT: " prefixes from the instructions/responses, so it's more compatible with existing interfaces.
The GTKM instructor now does the same thing as RP, in that it saves each round of "conversation" as a separate row in the output - previously it only saved the final response, which may not have been sufficient since I don't typically train on inputs.
### UTF-8 to ASCII
I replaced most of the "standard" utf-8 sequences - left double quote, right double quote, left apostraphe, ellipses - with standard ascii characters. I don't know if this was contributing to part of the issue with eos tokens being produced after apostraphes, but I figured it was worth trying.
### Summarization
I also included 500 examples from:
https://hf.co/datasets/mattpscott/airoboros-summarization
These are existing summarizarions from various public datasets, formatted to airoboros style contextual qa.
Thanks Matt!
### Usage/license info
Much (most) of the data was generated via gpt-4 API calls, which has a restriction in the ToS about "competing" models. Please seek legal advice if you plan to build or use a model that includes this dataset in a commercial setting. | 4,615 | [
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] |
tyzhu/synpre_set_1M | 2023-10-04T13:26:19.000Z | [
"region:us"
] | tyzhu | null | null | 0 | 155 | 2023-10-04T13:12:37 | ---
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
splits:
- name: train
num_bytes: 1218382220
num_examples: 1000000
- name: validation
num_bytes: 12163626
num_examples: 10000
download_size: 8496414
dataset_size: 1230545846
---
# Dataset Card for "synpre_set_1M"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 471 | [
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germaner | 2023-01-25T14:30:52.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:de",
"license:apache-2.0",
"region:us"
] | null | GermaNER is a freely available statistical German Named Entity Tagger based on conditional random fields(CRF). The tagger is trained and evaluated on the NoSta-D Named Entity dataset, which was used in the GermEval 2014 for named entity recognition. The tagger comes close to the performance of the best (proprietary) system in the competition with 77% F-measure (this is the latest result; the one reported in the paper is 76%) test set performance on the four standard NER classes (PERson, LOCation, ORGanisation and OTHer).
We describe a range of features and their influence on German NER classification and provide a comparative evaluation and some analysis of the results. The software components, the training data and all data used for feature generation are distributed under permissive licenses, thus this tagger can be used in academic and commercial settings without restrictions or fees. The tagger is available as a command-line tool and as an Apache UIMA component. | @inproceedings{Benikova2015GermaNERFO,
title={GermaNER: Free Open German Named Entity Recognition Tool},
author={Darina Benikova and S. Yimam and Prabhakaran Santhanam and Chris Biemann},
booktitle={GSCL},
year={2015}
} | 0 | 154 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- de
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: GermaNER
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': B-LOC
'1': B-ORG
'2': B-OTH
'3': B-PER
'4': I-LOC
'5': I-ORG
'6': I-OTH
'7': I-PER
'8': O
splits:
- name: train
num_bytes: 9059606
num_examples: 26200
download_size: 4363657
dataset_size: 9059606
---
# Dataset Card for GermaNER
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** https://github.com/tudarmstadt-lt/GermaNER
- **Paper:** https://pdfs.semanticscholar.org/b250/3144ed2152830f6c64a9f797ab3c5a34fee5.pdf
- **Point of Contact:** [Darina Benikova](mailto:benikova@aiphes.tu-darmstadt.de)
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
German
## Dataset Structure
### Data Instances
An example instance looks as follows:
```
{
'id': '3',
'ner_tags': [1, 5, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8],
'tokens': ['Bayern', 'Mรผnchen', 'ist', 'wieder', 'alleiniger', 'Top-', 'Favorit', 'auf', 'den', 'Gewinn', 'der', 'deutschen', 'Fuรball-Meisterschaft', '.']
}
```
### Data Fields
Each instance in the dataset has:
- `id`: an id as a string
- `tokens`: sequence of tokens
- `ner_tags`: NER tags for each token (encoded as IOB)
NER tags can be: 'B-LOC' (0), 'B-ORG' (1), 'B-OTH' (2), 'B-PER' (3), 'I-LOC' (4), 'I-ORG' (5), 'I-OTH' (6), 'I-PER' (7), 'O' (8)
### Data Splits
Dataset provides only train part (26200 data instances).
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
License of GermaNER:
```
GermaNER is licensed under ASL 2.0 and other lenient licenses, allowing its use for academic and commercial purposes without restrictions. The licenses of its compenents are mixed licensed and are individually listed in Data/Licenses.
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
"Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License.
"Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity.
"You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License.
"Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files.
"Object" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types.
"Work" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below).
"Derivative Works" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof.
"Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution."
"Contributor" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work.
2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form.
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You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and
If the Work includes a "NOTICE" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License.
You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License.
5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions.
6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file.
7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License.
8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages.
9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability.
END OF TERMS AND CONDITIONS
```
### Citation Information
```bibtex
@inproceedings{Benikova2015GermaNERFO,
title={GermaNER: Free Open German Named Entity Recognition Tool},
author={Darina Benikova and Seid Muhie Yimam and P. Santhanam and Chris Biemann},
booktitle={GSCL},
year={2015}
}
```
### Contributions
Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. | 13,508 | [
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] |
OGB/ogbg-molhiv | 2023-02-07T16:39:46.000Z | [
"task_categories:graph-ml",
"license:mit",
"region:us"
] | OGB | null | null | 2 | 154 | 2022-07-06T15:28:13 | ---
license: mit
task_categories:
- graph-ml
---
# Dataset Card for ogbg-molhiv
## 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)
- [External Use](#external-use)
- [PyGeometric](#pygeometric)
- [Dataset Structure](#dataset-structure)
- [Data Properties](#data-properties)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **[Homepage](https://ogb.stanford.edu/docs/graphprop/#ogbg-mol)**
- **[Repository](https://github.com/snap-stanford/ogb):**:
- **Paper:**: Open Graph Benchmark: Datasets for Machine Learning on Graphs (see citation)
- **Leaderboard:**: [OGB leaderboard](https://ogb.stanford.edu/docs/leader_graphprop/#ogbg-molhiv) and [Papers with code leaderboard](https://paperswithcode.com/sota/graph-property-prediction-on-ogbg-molhiv)
### Dataset Summary
The `ogbg-molhiv` dataset is a small molecular property prediction dataset, adapted from MoleculeNet by teams at Stanford, to be a part of the Open Graph Benchmark.
### Supported Tasks and Leaderboards
`ogbg-molhiv` should be used for molecular property prediction (aiming to predict whether molecules inhibit HIV or not), a binary classification task. The score used is ROC-AUC.
The associated leaderboards are here: [OGB leaderboard](https://ogb.stanford.edu/docs/leader_graphprop/#ogbg-molhiv) and [Papers with code leaderboard](https://paperswithcode.com/sota/graph-property-prediction-on-ogbg-molhiv).
## External Use
### PyGeometric
To load in PyGeometric, do the following:
```python
from datasets import load_dataset
from torch_geometric.data import Data
from torch_geometric.loader import DataLoader
ogbg_molhiv = load_dataset("graphs-datasets/ogbg-molhiv")
# For the train set (replace by valid or test as needed)
ogbg_molhiv_pg_list = [Data(graph) for graph in ogbg_molhiv["train"]]
ogbg_molhiv_pg = DataLoader(ogbg_molhiv_pg_list)
```
## Dataset Structure
### Data Properties
| property | value |
|---|---|
| scale | small |
| #graphs | 41,127 |
| average #nodes | 25.5 |
| average #edges | 27.5 |
| average node degree | 2.2 |
| average cluster coefficient | 0.002 |
| MaxSCC ratio | 0.993 |
| graph diameter | 12.0 |
### Data Fields
Each row of a given file is a graph, with:
- `node_feat` (list: #nodes x #node-features): nodes
- `edge_index` (list: 2 x #edges): pairs of nodes constituting edges
- `edge_attr` (list: #edges x #edge-features): for the aforementioned edges, contains their features
- `y` (list: 1 x #labels): contains the number of labels available to predict (here 1, equal to zero or one)
- `num_nodes` (int): number of nodes of the graph
### Data Splits
This data comes from the PyGeometric version of the dataset provided by OGB, and follows the provided data splits.
This information can be found back using
```python
from ogb.graphproppred import PygGraphPropPredDataset
dataset = PygGraphPropPredDataset(name = 'ogbg-molhiv')
split_idx = dataset.get_idx_split()
train = dataset[split_idx['train']] # valid, test
```
## Additional Information
### Licensing Information
The dataset has been released under MIT license.
### Citation Information
```
@inproceedings{hu-etal-2020-open,
author = {Weihua Hu and
Matthias Fey and
Marinka Zitnik and
Yuxiao Dong and
Hongyu Ren and
Bowen Liu and
Michele Catasta and
Jure Leskovec},
editor = {Hugo Larochelle and
Marc Aurelio Ranzato and
Raia Hadsell and
Maria{-}Florina Balcan and
Hsuan{-}Tien Lin},
title = {Open Graph Benchmark: Datasets for Machine Learning on Graphs},
booktitle = {Advances in Neural Information Processing Systems 33: Annual Conference
on Neural Information Processing Systems 2020, NeurIPS 2020, December
6-12, 2020, virtual},
year = {2020},
url = {https://proceedings.neurips.cc/paper/2020/hash/fb60d411a5c5b72b2e7d3527cfc84fd0-Abstract.html},
}
```
### Contributions
Thanks to [@clefourrier](https://github.com/clefourrier) for adding this dataset. | 4,486 | [
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ywchoi/pubmed_abstract_7 | 2022-09-13T01:12:17.000Z | [
"region:us"
] | ywchoi | null | null | 0 | 154 | 2022-09-13T01:10:37 | Entry not found | 15 | [
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bigbio/anat_em | 2022-12-22T15:43:16.000Z | [
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] | bigbio | The extended Anatomical Entity Mention corpus (AnatEM) consists of 1212 documents (approx. 250,000 words) manually annotated to identify over 13,000 mentions of anatomical entities. Each annotation is assigned one of 12 granularity-based types such as Cellular component, Tissue and Organ, defined with reference to the Common Anatomy Reference Ontology. | @article{pyysalo2014anatomical,
title={Anatomical entity mention recognition at literature scale},
author={Pyysalo, Sampo and Ananiadou, Sophia},
journal={Bioinformatics},
volume={30},
number={6},
pages={868--875},
year={2014},
publisher={Oxford University Press}
} | 0 | 154 | 2022-11-13T18:26:03 |
---
language:
- en
bigbio_language:
- English
license: cc-by-sa-3.0
multilinguality: monolingual
bigbio_license_shortname: CC_BY_SA_3p0
pretty_name: AnatEM
homepage: http://nactem.ac.uk/anatomytagger/#AnatEM
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
---
# Dataset Card for AnatEM
## Dataset Description
- **Homepage:** http://nactem.ac.uk/anatomytagger/#AnatEM
- **Pubmed:** True
- **Public:** True
- **Tasks:** NER
The extended Anatomical Entity Mention corpus (AnatEM) consists of 1212 documents (approx. 250,000 words) manually annotated to identify over 13,000 mentions of anatomical entities. Each annotation is assigned one of 12 granularity-based types such as Cellular component, Tissue and Organ, defined with reference to the Common Anatomy Reference Ontology.
## Citation Information
```
@article{pyysalo2014anatomical,
title={Anatomical entity mention recognition at literature scale},
author={Pyysalo, Sampo and Ananiadou, Sophia},
journal={Bioinformatics},
volume={30},
number={6},
pages={868--875},
year={2014},
publisher={Oxford University Press}
}
```
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] |
metaeval/reclor | 2023-05-31T09:59:42.000Z | [
"language:en",
"license:other",
"region:us"
] | metaeval | null | null | 2 | 154 | 2023-03-23T16:31:55 | ---
license: other
language:
- en
---
https://whyu.me/reclor/
```bib
@inproceedings{yu2020reclor,
author = {Yu, Weihao and Jiang, Zihang and Dong, Yanfei and Feng, Jiashi},
title = {ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning},
booktitle = {International Conference on Learning Representations (ICLR)},
month = {April},
year = {2020}
}
``` | 407 | [
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] |
BelleGroup/school_math_0.25M | 2023-04-08T03:55:03.000Z | [
"task_categories:text2text-generation",
"size_categories:100K<n<1M",
"language:zh",
"license:gpl-3.0",
"region:us"
] | BelleGroup | null | null | 65 | 154 | 2023-04-02T06:57:09 | ---
license: gpl-3.0
task_categories:
- text2text-generation
language:
- zh
size_categories:
- 100K<n<1M
---
# School Math 0.25M
## ๅ
ๅฎน
ๅ
ๅซ็บฆ25ไธๆก็ฑ[BELLE](https://github.com/LianjiaTech/BELLE)้กน็ฎ็ๆ็ไธญๆๆฐๅญฆ้ขๆฐๆฎ๏ผๅ
ๅซ่งฃ้ข่ฟ็จใ
ๆณจๆ๏ผๆญคๆฐๆฎ้ๆฏ็ฑChatGPTไบง็็๏ผๆช็ป่ฟไธฅๆ ผๆ ก้ช๏ผ้ข็ฎๆ่งฃ้ข่ฟ็จๅฏ่ฝๅ
ๅซ้่ฏฏใไฝฟ็จ่ฟ็จไธญ่ฏทๆณจๆ่ฟไธ็นใ
## ๆ ทไพ
```
{
"instruction": "้ข็ฎ๏ผๅฐๅๆ้ๆไธไธช่ฃ
ๆปก็ณๆ็่ขๅญ๏ผๅ
ฑๆ12ไธช๏ผๅฐๆๆณ็ฅ้้้ขๆๅคๅฐ้ข็ณๆ๏ผไบๆฏไป้ฎๅฐๅ๏ผโไฝ ๆ้็็ณๆ่ขๅญ้ๆๅถๆฐไธช็ณๆๅ๏ผโๅฐๅๅ็ญ๏ผโๆ๏ผ่ไธๅคไบ10้ขใโ่ฏท้ฎๅฐๅๆ้็็ณๆ่ขๅญ้ๆๅฐๆๅ ้ข็ณๆ๏ผ",
"input": "",
"output": "\n็ฑ้ข็ฎๅฏ็ฅ๏ผๅฐๅๆ้็็ณๆ่ขๅญ้ๆๅถๆฐไธช็ณๆ๏ผ\nๅ็ฅ้ๅฐๅๆ้็็ณๆ่ขๅญ้ๆๅคไบ10้ข็ณๆใ\nๅ ไธบ็ณๆๆฐไธบๅถๆฐ๏ผๅคไบ10้ข๏ผๆไปฅ็ณๆๆฐๆๅฐๅฟ
้กปๆฏ12ใ\nๆไปฅๅฐๅๆ้็็ณๆ่ขๅญ้ๆๅฐๆ12้ข็ณๆใ"
}
```
### ๅญๆฎต
```
instruction: ๆไปค
input: ่พๅ
ฅ๏ผๆฌๆฐๆฎ้ๅไธบ็ฉบ๏ผ
output: ่พๅบ
```
## ๅฑ้ๆงๅไฝฟ็จ้ๅถ
ๆไปฌ่ฆๆฑๅผๅ่
ไป
ๅฐๆไปฌๅผๆบ็ไปฃ็ ใๆฐๆฎใๆจกๅๅๅ็ปญ่ก็็ฉ็จไบ็ ็ฉถ็ฎ็๏ผไธๅพ็จไบๅไธ๏ผไปฅๅๅ
ถไปไผๅฏน็คพไผๅธฆๆฅๅฑๅฎณ็็จ้ใ
็ฑไบๆฐๆฎๆฏ็ฑ*ChatGPT*็ๆ็๏ผๆช็ปไธฅๆ ผ้ช่ฏ๏ผๅจไบๅฎๆงๅๅ
ถไปๆน้ข่ฟๅญๅจไธไบไธ่ถณใๅ ๆญค๏ผๅจไฝฟ็จๆญคๆฐๆฎ้ๆถ๏ผ่ฏทๅกๅฟ
ๆณจๆ็ๅซใ
ๆฌๆฐๆฎ้ไธไปฃ่กจไปปไฝไธๆน็็ซๅบใๅฉ็ๆๆณๆณ๏ผๆ ๅ
ณไปปไฝๅขไฝ็ไปปไฝ็ฑปๅ็ไธปๅผ ใๅ ไฝฟ็จๆฌๆฐๆฎ้ๅธฆๆฅ็ไปปไฝๆๅฎณใ็บ ็บท๏ผๆฌ้กน็ฎ็ๅผๅ่
ไธๆฟๆ
ไปปไฝ่ดฃไปปใ
***
# School Math 0.25M
## Contents
Includes approx. 250k Chinese school math quizzes and answers generated by BELLE, with steps to solve the quizzes.
Note: this subset was generated by *ChatGPT* and was not strictly verified. The quizzes or answers might contain errors. Please take this in mind when using this subset.
## Sample
```
{
"instruction": "้ข็ฎ๏ผๅฐๅๆ้ๆไธไธช่ฃ
ๆปก็ณๆ็่ขๅญ๏ผๅ
ฑๆ12ไธช๏ผๅฐๆๆณ็ฅ้้้ขๆๅคๅฐ้ข็ณๆ๏ผไบๆฏไป้ฎๅฐๅ๏ผโไฝ ๆ้็็ณๆ่ขๅญ้ๆๅถๆฐไธช็ณๆๅ๏ผโๅฐๅๅ็ญ๏ผโๆ๏ผ่ไธๅคไบ10้ขใโ่ฏท้ฎๅฐๅๆ้็็ณๆ่ขๅญ้ๆๅฐๆๅ ้ข็ณๆ๏ผ",
"input": "",
"output": "\n็ฑ้ข็ฎๅฏ็ฅ๏ผๅฐๅๆ้็็ณๆ่ขๅญ้ๆๅถๆฐไธช็ณๆ๏ผ\nๅ็ฅ้ๅฐๅๆ้็็ณๆ่ขๅญ้ๆๅคไบ10้ข็ณๆใ\nๅ ไธบ็ณๆๆฐไธบๅถๆฐ๏ผๅคไบ10้ข๏ผๆไปฅ็ณๆๆฐๆๅฐๅฟ
้กปๆฏ12ใ\nๆไปฅๅฐๅๆ้็็ณๆ่ขๅญ้ๆๅฐๆ12้ข็ณๆใ"
}
```
### Schema
```
instruction: ๆไปค
input: ่พๅ
ฅ๏ผๆฌๆฐๆฎ้ๅไธบ็ฉบ๏ผ
output: ่พๅบ
```
## Limitation and Usage Limits
We require developers only use the open-sourced code, data, model and any other artifacts generated via this project for research purposes. Commercial use and other potential harmful use cases are not allowed.
Since this dataset was generated by *ChatGPT* and was not strictly verified, it still has shortcomings regarding factuality and other aspects. When using this dataset, careful inspection is needed.
This dataset does not represent anyone's ground, interest or thought, and is not related to any kind of claim of any groups. The developers of this project do not assume any responsibility to potential harm inflicted by using this dataset and project. | 2,179 | [
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vmalperovich/SST5 | 2023-05-25T00:10:29.000Z | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:en",
"region:us"
] | vmalperovich | This data collection contains all the data used in our learning question classification experiments(see [1]), which has question class definitions, the training and testing question sets, examples of preprocessing the questions, feature definition scripts and examples of semantically related word features.
This work has been done by Xin Li and Dan Roth and supported by [2]. | """
_TRAIN_DOWNLOAD_URL = "https://huggingface.co/datasets/vmalperovich/SST-5/raw/main/train.csv"
_TEST_DOWNLOAD_URL = "https://huggingface.co/datasets/vmalperovich/SST-5/raw/main/test.csv"
_VALID_DOWNLOAD_URL = "https://huggingface.co/datasets/vmalperovich/SST-5/raw/main/validation.csv"
CATEGORY_MAPPING = {'0': 0,
'1': 1,
'2': 2,
'3': 3,
'4': 4
}
class SST5(datasets.GeneratorBasedBuilder): | 0 | 154 | 2023-05-24T23:31:48 | ---
task_categories:
- text-classification
language:
- en
pretty_name: sst-5
size_categories:
- 1K<n<10K
---
# Dataset Card for Dataset Name
## Dataset Description
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### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
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### Curation Rationale
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#### Initial Data Collection and Normalization
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#### Who are the source language producers?
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### Annotations
#### Annotation process
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#### Who are the annotators?
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## Considerations for Using the Data
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### Contributions
[More Information Needed] | 1,640 | [
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jondurbin/airoboros-gpt4-1.4.1 | 2023-06-26T09:56:34.000Z | [
"license:cc-by-nc-4.0",
"region:us"
] | jondurbin | null | null | 36 | 154 | 2023-06-25T10:12:03 | ---
license: cc-by-nc-4.0
---
The same as 1.4, but with coding updates:
- rosettacode instructions were removed, due to a few issues found when spot-checking examples
- limited the coding examples to fewer languages, to test if a more focused dataset would produce better results | 281 | [
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argilla/llama-2-banking-fine-tune | 2023-07-28T06:24:22.000Z | [
"size_categories:n<1K",
"rlfh",
"argilla",
"human-feedback",
"region:us"
] | argilla | null | null | 7 | 154 | 2023-07-28T06:24:20 | ---
size_categories: n<1K
tags:
- rlfh
- argilla
- human-feedback
---
# Dataset Card for llama-2-banking-fine-tune
This dataset has been created with [Argilla](https://docs.argilla.io).
As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets).
## Dataset Description
- **Homepage:** https://argilla.io
- **Repository:** https://github.com/argilla-io/argilla
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset contains:
* A dataset configuration file conforming to the Argilla dataset format named `argilla.yaml`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla.
* Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` and can be loaded independently using the `datasets` library via `load_dataset`.
* The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla.
### Load with Argilla
To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code:
```python
import argilla as rg
ds = rg.FeedbackDataset.from_huggingface("argilla/llama-2-banking-fine-tune")
```
### Load with `datasets`
To load this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code:
```python
from datasets import load_dataset
ds = load_dataset("argilla/llama-2-banking-fine-tune")
```
### Supported Tasks and Leaderboards
This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/guides/llms/conceptual_guides/data_model.html) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure).
There are no leaderboards associated with this dataset.
### Languages
[More Information Needed]
## Dataset Structure
### Data in Argilla
The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, and **guidelines**.
The **fields** are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.
| Field Name | Title | Type | Required | Markdown |
| ---------- | ----- | ---- | -------- | -------- |
| request | User request | TextField | True | False |
| response-1 | Assistant response 1 | TextField | True | True |
| response-2 | Assistant response 2 | TextField | True | True |
The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, single choice, or multiple choice.
| Question Name | Title | Type | Required | Description | Values/Labels |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| preference | Order response based on your preference | RankingQuestion | True | 1 = most accurate and helpful, 2 = worst. Ties are allowed. | ['response-1', 'response-2'] |
| correct-response | If both responses are innacurate, complete the response: | TextQuestion | True | N/A | N/A |
**โจ NEW** Additionally, we also have **suggestions**, which are linked to the existing questions, and so on, named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above.
Finally, the **guidelines** are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section.
### Data Instances
An example of a dataset instance in Argilla looks as follows:
```json
{
"external_id": null,
"fields": {
"request": "I tried to make a transfer but it failed",
"response-1": " Thank you for choosing the Bank of America for your financial needs! I\u0027m so sorry to hear that your transfer failed. Can you please provide me with more details about the transfer, such as the amount and the account it was being transferred from/to? This will help me better understand the issue and assist you in resolving it as quickly as possible. Additionally, have you tried checking your account balance or recent transactions to see if the transfer has been processed? If you have any further questions or concerns, please don\u0027t hesitate to ask. Thank you for trusting the Bank of America with your financial matters!",
"response-2": " Hello there! Thank you for choosing the Bank of America for your financial needs. I\u0027m so sorry to hear that your transfer failed. Can you please provide me with more details about the transfer, such as the amount and the account it was being transferred from/to? Additionally, do you have any error messages or confirmations that you received after attempting the transfer? This information will help me better understand the issue and assist you in resolving it as quickly as possible. Thank you for trusting the Bank of America with your financial matters."
},
"id": null,
"metadata": {},
"responses": [],
"suggestions": [
{
"agent": null,
"question_id": "b80fb550-1add-4ad6-93c9-b403e6342306",
"question_name": "preference",
"score": null,
"type": null,
"value": [
{
"rank": 1,
"value": "response-2"
},
{
"rank": 2,
"value": "response-1"
}
]
}
]
}
```
While the same record in HuggingFace `datasets` looks as follows:
```json
{
"correct-response": null,
"correct-response-suggestion": null,
"correct-response-suggestion-metadata": {
"agent": null,
"score": null,
"type": null
},
"external_id": null,
"metadata": null,
"preference": null,
"preference-suggestion": {
"rank": [
1,
2
],
"value": [
"response-2",
"response-1"
]
},
"preference-suggestion-metadata": {
"agent": null,
"score": null,
"type": null
},
"request": "I tried to make a transfer but it failed",
"response-1": " Thank you for choosing the Bank of America for your financial needs! I\u0027m so sorry to hear that your transfer failed. Can you please provide me with more details about the transfer, such as the amount and the account it was being transferred from/to? This will help me better understand the issue and assist you in resolving it as quickly as possible. Additionally, have you tried checking your account balance or recent transactions to see if the transfer has been processed? If you have any further questions or concerns, please don\u0027t hesitate to ask. Thank you for trusting the Bank of America with your financial matters!",
"response-2": " Hello there! Thank you for choosing the Bank of America for your financial needs. I\u0027m so sorry to hear that your transfer failed. Can you please provide me with more details about the transfer, such as the amount and the account it was being transferred from/to? Additionally, do you have any error messages or confirmations that you received after attempting the transfer? This information will help me better understand the issue and assist you in resolving it as quickly as possible. Thank you for trusting the Bank of America with your financial matters."
}
```
### Data Fields
Among the dataset fields, we differentiate between the following:
* **Fields:** These are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.
* **request** is of type `TextField`.
* **response-1** is of type `TextField`.
* **response-2** is of type `TextField`.
* **Questions:** These are the questions that will be asked to the annotators. They can be of different types, such as `RatingQuestion`, `TextQuestion`, `LabelQuestion`, `MultiLabelQuestion`, and `RankingQuestion`.
* **preference** is of type `RankingQuestion` with the following allowed values ['response-1', 'response-2'], and description "1 = most accurate and helpful, 2 = worst. Ties are allowed.".
* (optional) **correct-response** is of type `TextQuestion`.
* **โจ NEW** **Suggestions:** As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.
* (optional) **preference-suggestion** is of type `ranking` with the following allowed values ['response-1', 'response-2'].
* (optional) **correct-response-suggestion** is of type `text`.
Additionally, we also have one more field which is optional and is the following:
* **external_id:** This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.
### Data Splits
The dataset contains a single split, which is `train`.
## 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 guidelines
Please, read the question carefully and try to answer it as accurately as possible.
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | 10,691 | [
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legacy107/cpgQA | 2023-08-27T07:19:43.000Z | [
"region:us"
] | legacy107 | null | null | 0 | 154 | 2023-08-27T07:19:40 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: answer
dtype: string
- name: answer_start
dtype: int64
- name: question
dtype: string
- name: context
dtype: string
splits:
- name: train
num_bytes: 1259359
num_examples: 987
- name: test
num_bytes: 143518
num_examples: 110
download_size: 232065
dataset_size: 1402877
---
# Dataset Card for "cpgQA"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 643 | [
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] |
chiragtubakad/chart-to-table-mix | 2023-09-05T05:48:07.000Z | [
"region:us"
] | chiragtubakad | null | null | 0 | 154 | 2023-09-05T05:47:46 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 102169807.41570717
num_examples: 2245
- name: test
num_bytes: 25042009.85429284
num_examples: 562
download_size: 108880031
dataset_size: 127211817.27000001
---
# Dataset Card for "chart-to-table-mix"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 611 | [
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] |
TheAIchemist13/hindi_asr_dataset | 2023-10-18T10:17:02.000Z | [
"region:us"
] | TheAIchemist13 | null | null | 0 | 154 | 2023-10-04T10:24:40 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: transcriptions
dtype: string
splits:
- name: train
num_bytes: 24441695.0
num_examples: 80
- name: test
num_bytes: 32809156.0
num_examples: 90
download_size: 28788848
dataset_size: 57250851.0
---
# Dataset Card for "hindi_asr_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 629 | [
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] |
HumanCompatibleAI/ppo-Pendulum-v1 | 2023-10-04T16:52:12.000Z | [
"region:us"
] | HumanCompatibleAI | null | null | 0 | 154 | 2023-10-04T16:52:08 | ---
dataset_info:
features:
- name: obs
sequence:
sequence: float32
- name: acts
sequence:
sequence: float32
- name: infos
sequence: string
- name: terminal
dtype: bool
- name: rews
sequence: float32
splits:
- name: train
num_bytes: 2575710
num_examples: 200
download_size: 940375
dataset_size: 2575710
---
# Dataset Card for "ppo-Pendulum-v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 536 | [
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erhwenkuo/wikinews-zhtw | 2023-10-10T04:06:53.000Z | [
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:zh",
"license:cc-by-sa-3.0",
"region:us"
] | erhwenkuo | null | null | 0 | 154 | 2023-10-10T03:55:49 | ---
dataset_info:
config_name: '20231001'
features:
- name: id
dtype: string
- name: url
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 13647957
num_examples: 9827
download_size: 8803739
dataset_size: 13647957
configs:
- config_name: '20231001'
data_files:
- split: train
path: 20231001/train-*
license: cc-by-sa-3.0
task_categories:
- text-generation
language:
- zh
size_categories:
- 1K<n<10K
---
# Dataset Card for "wikinews-zhtw"
็ถญๅบๆฐ่๏ผ่ฑๆ๏ผWikinews๏ผๆฏ็ฑไธ็พคๅฟ้ก่
ใๅณๆฐ้่จ่
้็็็ถฒ่ทฏๅช้ซใๅๆๆฏไธๅ่ช็ฑๅ
งๅฎน็็ถญๅบ๏ผๅฑฌ็ถญๅบๅช้ซ่จๅ้
็ฎ๏ผ็ฑ็ถญๅบๅช้ซๅบ้ๆ่ฒ ่ฒฌ้็ใ็ถญๅบๆฐ่้้ๅไฝๆฐ่ๅญธ็ๅทฅไฝๆจกๅผๅป้่ก๏ผๅๆไบฆๅชๅ้้ไธญๆง็่ง้ปๅ ฑๅฐๆฐ่๏ผๅ
ๆฌๅๅตไธๆ็จๅฎถๅ ฑ้ๅๆก่จชใ
้ๅๆธๆ้ๆฏๆ นๆ Wikipedia dumps (https://dumps.wikimedia.org/) ่ฃก้ ญ `zhwikinews` ็ไธญๆไธ่ผๆชๆกไพๅปบๆง็ใๆฏๅ็ฏไพ้ฝๅ
ๅซไธ็ฏๅฎๆด็็ถญๅบๆฐ่ๆ็ซ ็ๅ
งๅฎน๏ผไธฆ็ถ้ๆธ
็ไปฅๅป้คไธ้่ฆ็้จๅใ
- **Homepage:** [https://dumps.wikimedia.org](https://dumps.wikimedia.org)
- **zhwiki ไธ่ผ้ป:** [https://dumps.wikimedia.org/zhwikinews](https://dumps.wikimedia.org/zhwikinews/)
## ๆธๆ Dump ็ๆฌ
็ฑๆผ็ถญๅบ็พ็งๆธๆ้ๅฎๆๆ้ฒ่ก็ถฒ็ซๆธๆๆ่ฝ๏ผๅจ `2023/10/10` ็ๆ้้ปๅปๆฅ็ๆๆๆไธๅ็ๆธๆๅฏไพไธ่ผ:
|ๆธๆ Dump ็ฎ้|ๆ่ฝๆ้้ป|
|-------------|--------|
|`20230520/`|01-Jul-2023 09:28|
|`20230601/`|20-Jul-2023 09:41|
|`20230620/`|01-Aug-2023 09:35|
|`20230701/`|20-Aug-2023 09:49|
|`20230720/`|01-Sep-2023 09:35|
|`20230801/`|20-Sep-2023 09:46|
|`20230820/`|01-Oct-2023 09:42|
|`20230901/`|02-Sep-2023 14:47|
|`20230920/`|21-Sep-2023 14:41|
|`20231001/`|10-Oct-2023 03:50|
|`latest/`|10-Oct-2023 03:50|
ๆฌๆธๆ้ๆๅฎๆๅปๅๅพๆ่ฟๆๆ็ขบ็ๆฅๆไพ้ฒ่กไธ่ผ่ๆธ
็๏ผไพฟๆผ้ฉ่ญ่ไฝฟ็จใ
## ๆธๆไธ่ผๆธ
็
1. ไธ่ผ zhwiki ็ data dump ๆชๆก
2. ไฝฟ็จ [WikiExtractor](https://github.com/attardi/wikiextractor) ๅฅไปถไพ้ฒ่กๆไปถๅ
งๅฎน่ๅ
3. ้ฒ่กๆธๆๆธ
็ๅนถ่ฝๆๆ jsonl ๆ ผๅผๆชๆก
4. ไฝฟ็จ Huggingface [Datasets](https://pypi.org/project/datasets/) ๅฅไปถไพ่ผๅ
ฅ jsonl ๅนถไธๅณ่ณ Huggingface Hub
## ่ณๆ้็ตๆง
็ฏไพๅฆไธ๏ผ
{'id': '35',
'url': 'https://zh.wikinews.org/wiki?curid=35',
'title': 'EDWIN่CUELLO้ญ็ตฑไธ็
้่งฃ็ด',
'text': 'ๆพ็ถๆ้ไธญๅๆฃ็่ฏ่ณฝ็ๅ
ฉไฝๅคๆด็ๅกEDWIN(่บ่ญฏ๏ผๆๅ)่CUELLO(่บ่ญฏ๏ผ้ฟ-{A|่ฃก}-)๏ผๆจๅคฉๅณๅบ...'
}
## ่ณๆๆฌไฝ
ๆๆ้
็ฝฎไธญ็่ณๆๆฌไฝ้ฝๆฏ็ธๅ็๏ผ
- `id (str)`: ๆ็ซ ็ IDใ
- `url (str)`: ๆ็ซ ็ URLใ
- `title (str)`: ๆ็ซ ็ๆจ้กใ
- `text (str)`: ๆ็ซ ็ๆๅญๅ
งๅฎนใ
## ไฝฟ็จ
```python
from datasets import load_dataset
# ่ซๅจ็ฌฌไบๅๅๆธๅปๆๅฎ่ฆไฝฟ็จ็ๆธๆ dump ็ๆฅๆ
load_dataset("erhwenkuo/wikinews-zhtw", "20231001")
```
## ่จฑๅฏ่ณ่จ
็ถญๅบ็พ็ง็ๅคง้จๅๆ็ซ ๅ
งๅฎนๅๅ
ถ่จฑๅคๅๅๅๆ นๆ `Creative Commons Attribution-ShareAlike 3.0 Unported License (CC BY-SA)` ๅ `GNU Free Documentation License (GFDL)` ๅ
ฑๅๆๆฌใ
## Citation
```
@ONLINE{wikidump,
author = "Wikimedia Foundation",
title = "Wikimedia Downloads",
url = "https://dumps.wikimedia.org"
}
``` | 2,466 | [
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] |
DDSC/partial-danish-gigaword-no-twitter | 2023-03-13T14:01:53.000Z | [
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:da",
"license:cc-by-4.0",
"region:us"
] | DDSC | null | null | 3 | 153 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
language:
- da
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text-generation
task_ids:
- language-modeling
pretty_name: Danish Gigaword Corpus (no Twitter)
language_bcp47:
- da
- da-bornholm
- da-synnejyl
---
# Dataset Card for [Danish Gigaword (no Twitter)]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [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://gigaword.dk
- **Paper:**
http://www.derczynski.com/papers/dagw.pdf
### Dataset Summary
The Danish Gigaword Corpus contains text spanning several domains and forms. This version does *not* include the sections containing Tweets.
### Supported Tasks and Leaderboards
Pre-training of language models.
### Language
Danish
## Dataset Structure
The dataset contains text from 23 different sources which are thoroughly defined in [Source Data](#source-data). See the [homepage](https://gigaword.dk) or [paper](http://www.derczynski.com/papers/dagw.pdf) for more information.
### Data Instances
Each entry in the dataset consists of a single text with associated metadata
### Data Fields
An entry in the dataset consists of the following fields:
- `text`(`str`): The content of the document.
- `source` (`str`): The source of the document (see [Source Data](#source-data)).
- `doc_id` (`str`): An unique identifer for each document.
- `LICENSE` (`str`): The license of the document. The licenses vary according to the source.
- `uri` (`str`): The uri of the document. Not available for all sources.
- `data_built`(`str`): Date the document was built. Not avaialable for all sources.
### Data Splits
The entire corpus is provided in the `train` split.
## Dataset Creation
### Source Data
Below follows a brief overview of the sources in the corpus along with their individual license.
| Source | License |
| ----------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| adl | Creative Commons Legal Code 1.0 Universal |
| botxt | Creative Commons Legal Code 1.0 Universal |
| cc | Creative Commons Legal Code 1.0 Universal |
| danavis | Creative Commons Legal Code 1.0 Universal |
| dannet | [dannet license](https://cst.ku.dk/projekter/dannet/license.txt) |
| depbank | Attribution-ShareAlike 4.0 International |
| ep | Creative Commons Legal Code 1.0 Universal |
| ft | Creative Commons Legal Code 1.0 Universal |
| gutenberg | [gutenberg license](https://www.gutenberg.org/policy/license.html) |
| hest | Creative Commons Legal Code 1.0 Universal |
| jvj | Attribution-ShareAlike 4.0 International |
| naat | Creative Commons Legal Code 1.0 Universal |
| opensub | The data set comes with the same license as the original sources. Please, check the information about the source that is given on http://opus.nlpl.eu/OpenSubtitles-v2018.php |
| relig | Creative Commons Legal Code 1.0 Universal |
| retsinformationdk | Danish Copyright law at https://www.retsinformation.dk/forms/r0710.aspx?id=164796 states "ยง 9. Love, administrative forskrifter, retsafgรธrelser og lignende offentlige aktstykker er ikke genstand for ophavsret. Stk. 2. Bestemmelsen i stk. 1 gรฆlder ikke for vรฆrker, der fremtrรฆder som selvstรฆndige bidrag i de i stk. 1 nรฆvnte aktstykker. Sรฅdanne vรฆrker mรฅ dog gengives i forbindelse med aktstykket. Retten til videre udnyttelse afhรฆnger af de i รธvrigt gรฆldende regler." |
| retspraksis | Creative Commons Legal Code 1.0 Universal |
| skat | Creative Commons Legal Code 1.0 Universal |
| spont | Creative Commons Legal Code 1.0 Universal |
| synne | Creative Commons Legal Code 1.0 Universal |
| tv2r | The owner of this content is TV2 Regionerne, Denmark. Creative Commons Attribution 4.0 International |
| wiki | Creative Commons Legal Code 1.0 Universal |
| wikibooks | Creative Commons Legal Code 1.0 Universal |
| wikisource | Creative Commons Legal Code 1.0 Universal |
These sources corresponds to the following top-level domains in the dataset:
```python
# mapping from domain to top-level domain
domain_mapping_dict = {
"retsinformationdk": "Legal",
"skat": "Legal",
"retspraksis": "Legal",
"hest": "Social Media",
"cc": "Web",
"adl": "Wiki & Books",
"botxt": "Other",
"danavis": "News",
"dannet": "dannet",
"depbank": "Other",
"ep": "Conversation",
"ft": "Conversation",
"gutenberg": "Wiki & Books",
"jvj": "Wiki & Books",
"naat": "Conversation",
"opensub": "Conversation",
"relig": "Wiki & Books",
"spont": "Conversation",
"synne": "Other",
"tv2r": "News",
"wiki": "Wiki & Books",
"wikibooks": "Wiki & Books",
"wikisource": "Wiki & Books",
"twfv19": "Social Media", # not present in this version of the dataset
}
```
And the following mapping translates between the short form and the long form of the source name
```python
# mapping from domain to its long name format
longname_mapping_dict = {
"retsinformationdk": "retsinformation.dk (Danish legal information)",
"skat": "Skat (Danish tax authority)",
"retspraksis": "retspraksis (Danish legal information)",
"hest": "Hestenettet (Danish debate forum)",
"cc": "Common Crawl",
"adl": " Archive for Danish Literature",
"botxt": "Bornholmsk (Danish dialect)",
"danavis": "Danish daily newspapers",
"dannet": "DanNet (Danish WordNet)",
"depbank": "Danish Dependency Treebank",
"ep": "European Parliament",
"ft": "Folketinget (Danish Parliament)",
"gutenberg": "Gutenberg",
"jvj": "Johannes V. Jensen (Danish poet)",
"naat": "NAAT",
"opensub": "Open Subtitles",
"relig": "Religious texts",
"spont": "Spontaneous speech",
"synne": "Synderjysk (Danish dialect)",
"tv2r": "TV 2 Radio (Danish news)",
"wiki": "Wikipedia",
"wikibooks": "Wikibooks",
"wikisource": "Wikisource",
"twfv19": "Twitter Folketingsvalget 2019 (Danish election tweets)", # not present in this version of the dataset
}
```
## Additional Information
### Licensing Information
If you use the data, you MUST acknowledge it. The license is CC-BY 4.0, Creative Commons with Attribution.
### Citation Information
Sample attributions:
In a press release:
> Modellen er prรฆ-trรฆnet pรฅ et datasรฆt fra The Danish Gigaword Project (https://gigaword.dk), der er udviklet af forskere fra IT-Universitetet i Kรธbenhavn
> The model is pre-trained using the Danish Gigaword Corpus (https://gigaword.dk), developed at the IT University of Copenhagen
In academic writing:
```
Derczynski, L., Ciosici, M. R., et al. (2021). The Danish Gigaword Corpus. In Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa 2021).
@inproceedings{dagw,
title = {{The Danish Gigaword Corpus}},
author = {Leon Derczynski and Manuel R. Ciosici and Rebekah Baglini and Morten H. Christiansen and Jacob Aarup Dalsgaard and Riccardo Fusaroli and Peter Juel Henrichsen and Rasmus Hvingelby and Andreas Kirkedal and Alex Speed Kjeldsen and Claus Ladefoged and Finn ร
rup Nielsen and Jens Madsen and Malte Lau Petersen and Jonathan Hvithamar Rystrรธm and Daniel Varab},
year = 2021,
booktitle = {Proceedings of the 23rd Nordic Conference on Computational Linguistics},
publisher = {NEALT}
}
```
In a software product, tool, or service:
> Danish Gigaword Corpus: license - homepage
> Denne service er lavet med data fra The Danish Gigaword Corpus
### Contributions
Dataset created by Derczynski et al. (2021)
Derczynski, L., Ciosici, M. R., et al. (2021). The Danish Gigaword Corpus. In Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa 2021).
Thanks to [@HLasse](https://github.com/HLasse) for adding this dataset to the Hugging Face Hub. | 18,937 | [
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GEM/conversational_weather | 2022-10-24T15:30:13.000Z | [
"task_categories:table-to-text",
"annotations_creators:none",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:cc-by-nc-4.0",
"data-to-text",
"region:us"
] | GEM | The Conversational Weather dataset is designed for generation of responses to weather queries based on a structured input data. The input allows specifying data attributes such as dates, times, locations, weather conditions, and errors, and also offers control over structure of response through discourse relations such as join, contrast, and justification. | @inproceedings{balakrishnan-etal-2019-constrained,
title = "Constrained Decoding for Neural {NLG} from Compositional Representations in Task-Oriented Dialogue",
author = "Balakrishnan, Anusha and
Rao, Jinfeng and
Upasani, Kartikeya and
White, Michael and
Subba, Rajen",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P19-1080",
doi = "10.18653/v1/P19-1080",
pages = "831--844"
} | 1 | 153 | 2022-03-02T23:29:22 | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- en
license:
- cc-by-nc-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- table-to-text
task_ids: []
pretty_name: conversational_weather
tags:
- data-to-text
---
# Dataset Card for GEM/conversational_weather
## Dataset Description
- **Homepage:** [Needs More Information]
- **Repository:** https://github.com/facebookresearch/TreeNLG
- **Paper:** https://aclanthology.org/P19-1080
- **Leaderboard:** N/A
- **Point of Contact:** Kartikeya Upasani
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/conversational_weather).
### Dataset Summary
The purpose of this dataset is to assess how well a model can learn a template-like structure in a very low data setting. The task here is to produce a response to a weather-related query. The reply is further specified through the data attributes and discourse structure in the input. The output contains both the lexicalized text and discourse markers for attributes (e.g., `_ARG_TEMP_ 34`).
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/conversational_weather')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/conversational_weather).
#### paper
[ACL Anthology](https://aclanthology.org/P19-1080)
#### authors
Anusha Balakrishnan, Jinfeng Rao, Kartikeya Upasani, Michael White, Rajen Subba (Facebook Conversational AI)
## Dataset Overview
### Where to find the Data and its Documentation
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Github](https://github.com/facebookresearch/TreeNLG)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[ACL Anthology](https://aclanthology.org/P19-1080)
#### BibTex
<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
<!-- scope: microscope -->
```
@inproceedings{balakrishnan-etal-2019-constrained,
title = "Constrained Decoding for Neural {NLG} from Compositional Representations in Task-Oriented Dialogue",
author = "Balakrishnan, Anusha and
Rao, Jinfeng and
Upasani, Kartikeya and
White, Michael and
Subba, Rajen",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P19-1080",
doi = "10.18653/v1/P19-1080",
pages = "831--844"
}
```
#### Contact Name
<!-- quick -->
<!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
Kartikeya Upasani
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
kart@fb.com
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
no
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
no
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`English`
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
cc-by-nc-4.0: Creative Commons Attribution Non Commercial 4.0 International
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
This dataset is intended to help develop conversational agents that exhibit human-like properties such as matching the framing of the response with the query or contrasting relevant data attributes.
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Data-to-Text
#### Communicative Goal
<!-- quick -->
<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
<!-- scope: periscope -->
Producing a text that is a response to a weather query as per the discourse structure and data attributes specified in the input meaning representation.
### Credit
#### Curation Organization Type(s)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`industry`
#### Curation Organization(s)
<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
Facebook
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Anusha Balakrishnan, Jinfeng Rao, Kartikeya Upasani, Michael White, Rajen Subba (Facebook Conversational AI)
#### Funding
<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
Facebook
#### Who added the Dataset to GEM?
<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
<!-- scope: microscope -->
Vipul Raheja (Grammarly)
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
- `gem_id`: (string): GEM-formatted row id
- `id`: (string): Row id in the original data
- `user_query`: (string): Natural language weather query from humans
- `tree_str_mr`: (string): Synthetically-added user context (datetime and location) in the form of a tree-structured MR
- `response`: (string): A tree-structured annotation of the response.
#### Example Instance
<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
<!-- scope: periscope -->
```
{'gem_id': 'weather-train-11',
'id': '1108963',
'synthetic_user_context': '[__DG_INFORM__ [__ARG_TASK__ get_forecast ] '
'[__ARG_TEMP__ 37 ] [__ARG_TEMP_UNIT__ fahrenheit ] '
'[__ARG_CLOUD_COVERAGE__ partly cloudy ] '
'[__ARG_DATE_TIME__ [__ARG_COLLOQUIAL__ currently ] '
'] [__ARG_LOCATION__ [__ARG_CITY__ Oakland ] '
'[__ARG_COUNTRY__ United States ] [__ARG_REGION__ '
'California ] ] ] [__DG_INFORM__ [__ARG_TASK__ '
'get_forecast ] [__ARG_TEMP_SUMMARY__ mid 40s ] '
'[__ARG_DATE_TIME_RANGE__ [__ARG_COLLOQUIAL__ This '
'afternoon ] ] [__ARG_LOCATION__ [__ARG_CITY__ '
'Oakland ] [__ARG_COUNTRY__ United States ] '
'[__ARG_REGION__ California ] ] ] [__DG_INFORM__ '
'[__ARG_TASK__ get_forecast ] '
'[__ARG_CLOUD_COVERAGE__ mostly sunny ] '
'[__ARG_DATE_TIME_RANGE__ [__ARG_COLLOQUIAL__ This '
'afternoon ] ] [__ARG_LOCATION__ [__ARG_CITY__ '
'Oakland ] [__ARG_COUNTRY__ United States ] '
'[__ARG_REGION__ California ] ] ]',
'tree_str_mr': "[__DG_INFORM__ It's [__ARG_DATE_TIME__ [__ARG_COLLOQUIAL__ "
'currently ] ] [__ARG_CLOUD_COVERAGE__ partly cloudy ] and '
'[__ARG_TEMP__ __ARG_TEMP__ ] [__ARG_TEMP_UNIT__ '
'__ARG_TEMP_UNIT__ ] [__ARG_LOCATION__ in [__ARG_CITY__ '
'__ARG_CITY__ ] , [__ARG_REGION__ __ARG_REGION__ ] , '
'[__ARG_COUNTRY__ __ARG_COUNTRY__ ] ] . ] [__DG_INFORM__ '
'[__ARG_DATE_TIME_RANGE__ [__ARG_COLLOQUIAL__ This afternoon ] '
"] , it'll be [__ARG_CLOUD_COVERAGE__ mostly sunny ] ] "
'[__DG_INFORM__ with temperatures in the [__ARG_TEMP_SUMMARY__ '
'mid <number> ] ]',
'user_query': 'Show weather forecast for Oakland, CA. '}
```
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
- Standard Splits: Train/Validation/Test
- Additional Split: Disc_Test (a more challenging subset of the test set that contains discourse relations)
#### Splitting Criteria
<!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. -->
<!-- scope: microscope -->
The test set contains 3,121 examples, of which 1.1K (35%) have unique MRs that have never been seen in the training set.
####
<!-- info: What does an outlier of the dataset in terms of length/perplexity/embedding look like? -->
<!-- scope: microscope -->
```
{'gem_id': 'weather-train-13333', 'data_id': '1260610', 'user_query': 'Sundown', 'tree_str_mr': '[__DG_INFORM__ [__ARG_TASK__ get_weather_attribute ] [__ARG_SUNSET_TIME_DATE_TIME__ [__ARG_TIME__ 05:04 PM ] ] ]', 'response': '[__DG_INFORM__ The sun will go down at [__ARG_SUNSET_TIME_DATE_TIME__ [__ARG_TIME__ __ARG_TIME__ ] ] ]'}
```
## Dataset in GEM
### Rationale for Inclusion in GEM
#### Why is the Dataset in GEM?
<!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? -->
<!-- scope: microscope -->
The dataset was curated to develop a weather bot that exhibits human-like properties such as matching the framing of the response with the query or contrasting relevant data attributes.
The dataset offers rich tree-based meaning representations that offer fine-grained control over the response, e.g. by specifying which two attributes are to be contrasted. The natural language input queries are also provided to model the coherence of the response based on the input. The output response is annotated with the input meaning components using special bracketing tokens, which enables developing new techniques such as constrained decoding to improve quality of output responses
#### Similar Datasets
<!-- info: Do other datasets for the high level task exist? -->
<!-- scope: telescope -->
no
#### Ability that the Dataset measures
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: periscope -->
Adequately expressing CONTRAST and JUSTIFY discourse relations with appropriate grouping of arguments; adequately generalizing to many combinations of arguments.
### GEM-Specific Curation
#### Modificatied for GEM?
<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
<!-- scope: telescope -->
yes
#### GEM Modifications
<!-- info: What changes have been made to he original dataset? -->
<!-- scope: periscope -->
`data points removed`
#### Modification Details
<!-- info: For each of these changes, described them in more details and provided the intended purpose of the modification -->
<!-- scope: microscope -->
The original repo contained a challenge set disc_test.tsv, which is a subset of the test set consisting of discourse relations (CONTRAST and JUSTIFY) , but also contained JOIN relations.
This discrepancy has been rectified in the GEM version. The rectified version has been added in the `challenge_sets`
#### Additional Splits?
<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
no
### Getting Started with the Task
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
Adequately expressing CONTRAST and JUSTIFY discourse relations with appropriate grouping of arguments; adequately generalizing to many combinations of arguments.
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`BLEU`, `Other: Other Metrics`
#### Other Metrics
<!-- info: Definitions of other metrics -->
<!-- scope: periscope -->
Tree accuracy: It measures whether the tree structure in the prediction matches that of the input MR exactly (modulo repeated arguments that need only appear once).
#### Proposed Evaluation
<!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. -->
<!-- scope: microscope -->
Automatic metrics are evaluated on the raw model predictions (which have de-lexicalized fields):
* Tree accuracy: Measures whether the tree structure in the prediction matches that of the input MR exactly.
* BLEU-4: A word overlap metric commonly used for evaluating NLG systems.
Authors also performed human evaluation studies by asking annotators to evaluate the quality of responses produced by different models. Annotators provided binary ratings on the following dimensions:
โข Grammaticality: Measures fluency of the responses.
โข Correctness: Measures semantic correctness of the responses.
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
no
## Dataset Curation
### Original Curation
#### Original Curation Rationale
<!-- info: Original curation rationale -->
<!-- scope: telescope -->
The dataset was curated to develop a weather bot that exhibits human-like properties such as matching the framing of the response with the query or contrasting relevant data attributes. To achieve this, the dataset contains rich tree-structured meaning representations that are specified using several data arguments and discourse acts, the input natural language queries, and annotations for the responses.
#### Communicative Goal
<!-- info: What was the communicative goal? -->
<!-- scope: periscope -->
Producing a text that is a response to a weather query as per the discourse structure and data attributes specified in the input meaning representation.
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
no
### Language Data
#### How was Language Data Obtained?
<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Crowdsourced`, `Machine-generated`
#### Where was it crowdsourced?
<!-- info: If crowdsourced, where from? -->
<!-- scope: periscope -->
`Other crowdworker platform`
#### Topics Covered
<!-- info: Does the language in the dataset focus on specific topics? How would you describe them? -->
<!-- scope: periscope -->
The dataset is focused on the weather domain: Weather was the first successful case of NLG put into production back in the 80s (Reiter & Dale, 1997). This domain offers significant complexity for NLG. Weather forecast summaries in particular can be very long, and require reasoning over several disjoint pieces of information.
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
validated by crowdworker
#### Data Preprocessing
<!-- info: How was the text data pre-processed? (Enter N/A if the text was not pre-processed) -->
<!-- scope: microscope -->
Please refer to Appendix D of the original paper for details.
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
hybrid
#### Filter Criteria
<!-- info: What were the selection criteria? -->
<!-- scope: microscope -->
Please refer to Appendix C of the original paper for details.
### Structured Annotations
#### Additional Annotations?
<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
none
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
no
### Consent
#### Any Consent Policy?
<!-- info: Was there a consent policy involved when gathering the data? -->
<!-- scope: telescope -->
no
#### Justification for Using the Data
<!-- info: If not, what is the justification for reusing the data? -->
<!-- scope: microscope -->
Annotation was done as work for hire and contains no PII.
### Private Identifying Information (PII)
#### Contains PII?
<!-- quick -->
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
<!-- scope: telescope -->
no PII
#### Justification for no PII
<!-- info: Provide a justification for selecting `no PII` above. -->
<!-- scope: periscope -->
Data is simulated and not specific to annotator.
### Maintenance
#### Any Maintenance Plan?
<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
no
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
no
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
no
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
unsure
#### Are the Language Producers Representative of the Language?
<!-- info: Does the distribution of language producers in the dataset accurately represent the full distribution of speakers of the language world-wide? If not, how does it differ? -->
<!-- scope: periscope -->
Grammatical evaluations performed with the data to date have used norms from informal Standard American English. These prescriptive notions of grammaticality potentially serve to perpetuate systemic power imbalances as theyโre conveyed by language.
Since the data only contains informal Standard American English, its use to train a model may not be appropriate depending on the potential use case.
## Considerations for Using the Data
### PII Risks and Liability
#### Potential PII Risk
<!-- info: Considering your answers to the PII part of the Data Curation Section, describe any potential privacy to the data subjects and creators risks when using the dataset. -->
<!-- scope: microscope -->
Annotation was done as work for hire and contains no PII. Annotated data is simulated and not specific to annotator.
### Licenses
### Known Technical Limitations
#### Unsuited Applications
<!-- info: When using a model trained on this dataset in a setting where users or the public may interact with its predictions, what are some pitfalls to look out for? In particular, describe some applications of the general task featured in this dataset that its curation or properties make it less suitable for. -->
<!-- scope: microscope -->
An imperfect model used to convey actual weather data could mislead users about weather conditions?
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Chris1/cityscapes_segmentation | 2022-11-03T19:43:00.000Z | [
"region:us"
] | Chris1 | null | null | 1 | 153 | 2022-11-03T19:26:00 | Entry not found | 15 | [
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bigbio/bionlp_st_2013_cg | 2022-12-22T15:43:57.000Z | [
"multilinguality:monolingual",
"language:en",
"license:other",
"region:us"
] | bigbio | the Cancer Genetics (CG) is a event extraction task and a main task of the BioNLP Shared Task (ST) 2013.
The CG task is an information extraction task targeting the recognition of events in text,
represented as structured n-ary associations of given physical entities. In addition to
addressing the cancer domain, the CG task is differentiated from previous event extraction
tasks in the BioNLP ST series in addressing a wide range of pathological processes and multiple
levels of biological organization, ranging from the molecular through the cellular and organ
levels up to whole organisms. Final test set submissions were accepted from six teams | @inproceedings{pyysalo-etal-2013-overview,
title = "Overview of the Cancer Genetics ({CG}) task of {B}io{NLP} Shared Task 2013",
author = "Pyysalo, Sampo and
Ohta, Tomoko and
Ananiadou, Sophia",
booktitle = "Proceedings of the {B}io{NLP} Shared Task 2013 Workshop",
month = aug,
year = "2013",
address = "Sofia, Bulgaria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W13-2008",
pages = "58--66",
} | 2 | 153 | 2022-11-13T22:07:03 |
---
language:
- en
bigbio_language:
- English
license: other
multilinguality: monolingual
bigbio_license_shortname: GENIA_PROJECT_LICENSE
pretty_name: BioNLP 2013 CG
homepage: https://github.com/openbiocorpora/bionlp-st-2013-cg
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- EVENT_EXTRACTION
- NAMED_ENTITY_RECOGNITION
- COREFERENCE_RESOLUTION
---
# Dataset Card for BioNLP 2013 CG
## Dataset Description
- **Homepage:** https://github.com/openbiocorpora/bionlp-st-2013-cg
- **Pubmed:** True
- **Public:** True
- **Tasks:** EE,NER,COREF
the Cancer Genetics (CG) is a event extraction task and a main task of the BioNLP Shared Task (ST) 2013.
The CG task is an information extraction task targeting the recognition of events in text,
represented as structured n-ary associations of given physical entities. In addition to
addressing the cancer domain, the CG task is differentiated from previous event extraction
tasks in the BioNLP ST series in addressing a wide range of pathological processes and multiple
levels of biological organization, ranging from the molecular through the cellular and organ
levels up to whole organisms. Final test set submissions were accepted from six teams
## Citation Information
```
@inproceedings{pyysalo-etal-2013-overview,
title = "Overview of the Cancer Genetics ({CG}) task of {B}io{NLP} Shared Task 2013",
author = "Pyysalo, Sampo and
Ohta, Tomoko and
Ananiadou, Sophia",
booktitle = "Proceedings of the {B}io{NLP} Shared Task 2013 Workshop",
month = aug,
year = "2013",
address = "Sofia, Bulgaria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W13-2008",
pages = "58--66",
}
```
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shossain/govreport-qa-no-pad-16384 | 2023-10-15T03:15:18.000Z | [
"region:us"
] | shossain | null | null | 0 | 153 | 2023-10-03T22:11:19 | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 801880268.1341531
num_examples: 6483
download_size: 86514138
dataset_size: 801880268.1341531
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "govreport-qa-no-pad-16384"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 562 | [
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electricity_load_diagrams | 2022-11-18T20:00:21.000Z | [
"task_categories:time-series-forecasting",
"task_ids:univariate-time-series-forecasting",
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] | null | This new dataset contains hourly kW electricity consumption time series of 370 Portuguese clients from 2011 to 2014. | @inproceedings{10.1145/3209978.3210006,
author = {Lai, Guokun and Chang, Wei-Cheng and Yang, Yiming and Liu, Hanxiao},
title = {Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks},
year = {2018},
isbn = {9781450356572},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3209978.3210006},
doi = {10.1145/3209978.3210006},
booktitle = {The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval},
pages = {95--104},
numpages = {10},
location = {Ann Arbor, MI, USA},
series = {SIGIR '18}
} | 5 | 152 | 2022-03-02T23:29:22 | ---
annotations_creators:
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language_creators:
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language: []
license:
- unknown
multilinguality:
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pretty_name: Electricity Load Diagrams
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- time-series-forecasting
task_ids:
- univariate-time-series-forecasting
dataset_info:
- config_name: uci
features:
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dtype: timestamp[s]
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sequence: float32
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sequence: uint64
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num_bytes: 42968147
num_examples: 370
- name: test
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num_examples: 370
download_size: 261335609
dataset_size: 388031993
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features:
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dtype: timestamp[s]
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sequence: float32
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num_examples: 320
download_size: 261335609
dataset_size: 244032000
---
# Dataset Card for Electricity Load Diagrams
## 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 Load Diagrams 2011-2014](https://archive.ics.uci.edu/ml/datasets/ElectricityLoadDiagrams20112014)
- **Paper:** [Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks
](https://dl.acm.org/doi/10.1145/3209978.3210006)
- **Point of Contact:** [Artur Trindade](mailto:artur.trindade@elergone.pt)
### Dataset Summary
This dataset contains hourly kW electricity consumption time series of 370 Portuguese clients from 2011 to 2014.
### Dataset Usage
The dataset has the following configuration parameters:
- `freq` is the time series frequency at which we resample (default: `"1H"`)
- `prediction_length` is the forecast horizon for this task which is used to make the validation and test splits (default: `24`)
- `rolling_evaluations` is the number of rolling window time series in the test split for evaluation purposes (default: `7`)
For example, you can specify your own configuration different from those used in the papers as follows:
```python
load_dataset("electricity_load_diagrams", "uci", rolling_evaluations=10)
```
> Notes:
> - Data set has no missing values.
> - Values are in kW of each 15 min rescaled to hourly. To convert values in kWh values must be divided by 4.
> - All time labels report to Portuguese hour, however all days present 96 measures (24*4).
> - Every year in March time change day (which has only 23 hours) the values between 1:00 am and 2:00 am are zero for all points.
> - Every year in October time change day (which has 25 hours) the values between 1:00 am and 2:00 am aggregate the consumption of two hours.
### Supported Tasks and Leaderboards
- `univariate-time-series-forecasting`: The time series forecasting tasks involves learning the future `target` values of time series in a dataset for the `prediction_length` time steps. The results of the forecasts can then be validated via the ground truth in the `validation` split and tested via the `test` split.
### Languages
## Dataset Structure
Data set has no missing values. The raw values are in kW of each 15 min interval and are resampled to hourly frequency.
Each time series represent one client. Some clients were created after 2011. In these cases consumption were considered zero. All time labels report to Portuguese hour, however all days contain 96 measurements (24*4). Every year in March time change day (which has only 23 hours) the values between 1:00 am and 2:00 am are zero for all points. Every year in October time change day (which has 25 hours) the values between 1:00 am and 2:00 am aggregate the consumption of two hours.
### Data Instances
A sample from the training set is provided below:
```
{
'start': datetime.datetime(2012, 1, 1, 0, 0),
'target': [14.0, 18.0, 21.0, 20.0, 22.0, 20.0, 20.0, 20.0, 13.0, 11.0], # <= this target array is a concatenated sample
'feat_static_cat': [0],
'item_id': '0'
}
```
We have two configurations `uci` and `lstnet`, which are specified as follows.
The time series are resampled to hourly frequency. We test on 7 rolling windows of prediction length of 24.
The `uci` validation therefore ends 24*7 time steps before the end of each time series. The training split ends 24 time steps before the end of the validation split.
For the `lsnet` configuration we split the training window so that it is 0.6-th of the full time series and the validation is 0.8-th of the full time series and the last 0.2-th length time windows is used as the test set of 7 rolling windows of the 24 time steps each. Finally, as in the LSTNet paper, we only consider time series that are active in the year 2012--2014, which leaves us with 320 time series.
### Data Fields
For this univariate regular time series we have:
- `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
- `item_id`: a string identifier of each time series in a dataset for reference
Given the `freq` and the `start` datetime, we can assign a datetime to each entry in the target array.
### Data Splits
| name |train|unsupervised|test |
|----------|----:|-----------:|----:|
|uci|370| 2590|370|
|lstnet|320| 2240|320|
## Dataset Creation
The Electricity Load Diagrams 2011โ2014 Dataset was developed by Artur Trindade and shared in UCI Machine Learning Repository. This dataset covers the electricity load of 370 substations in Portugal from the start of 2011 to the end of 2014 with a sampling period of 15 min. We will resample this to hourly time series.
### Curation Rationale
Research and development of load forecasting methods. In particular short-term electricity forecasting.
### Source Data
This dataset covers the electricity load of 370 sub-stations in Portugal from the start of 2011 to the end of 2014 with a sampling period of 15 min.
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```bibtex
@inproceedings{10.1145/3209978.3210006,
author = {Lai, Guokun and Chang, Wei-Cheng and Yang, Yiming and Liu, Hanxiao},
title = {Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks},
year = {2018},
isbn = {9781450356572},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3209978.3210006},
doi = {10.1145/3209978.3210006},
booktitle = {The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval},
pages = {95--104},
numpages = {10},
location = {Ann Arbor, MI, USA},
series = {SIGIR '18}
}
```
### Contributions
Thanks to [@kashif](https://github.com/kashif) for adding this dataset. | 8,823 | [
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hausa_voa_topics | 2023-01-25T14:31:55.000Z | [
"task_categories:text-classification",
"task_ids:topic-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ha",
"license:unknown",
"region:us"
] | null | A collection of news article headlines in Hausa from VOA Hausa.
Each headline is labeled with one of the following classes: Nigeria,
Africa, World, Health or Politics.
The dataset was presented in the paper:
Hedderich, Adelani, Zhu, Alabi, Markus, Klakow: Transfer Learning and
Distant Supervision for Multilingual Transformer Models: A Study on
African Languages (EMNLP 2020). | @inproceedings{hedderich-etal-2020-transfer,
title = "Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on African Languages",
author = "Hedderich, Michael A. and
Adelani, David and
Zhu, Dawei and
Alabi, Jesujoba and
Markus, Udia and
Klakow, Dietrich",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
year = "2020",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.204",
doi = "10.18653/v1/2020.emnlp-main.204",
} | 0 | 152 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- ha
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- topic-classification
pretty_name: Hausa Voa News Topic Classification Dataset (HausaVoaTopics)
dataset_info:
features:
- name: news_title
dtype: string
- name: label
dtype:
class_label:
names:
'0': Africa
'1': Health
'2': Nigeria
'3': Politics
'4': World
splits:
- name: train
num_bytes: 144932
num_examples: 2045
- name: validation
num_bytes: 20565
num_examples: 290
- name: test
num_bytes: 41195
num_examples: 582
download_size: 195824
dataset_size: 206692
---
# Dataset Card for Hausa VOA News Topic Classification dataset (hausa_voa_topics)
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** -
- **Repository:** https://github.com/uds-lsv/transfer-distant-transformer-african
- **Paper:** https://www.aclweb.org/anthology/2020.emnlp-main.204/
- **Leaderboard:** -
- **Point of Contact:** Michael A. Hedderich and David Adelani
{mhedderich, didelani} (at) lsv.uni-saarland.de
### Dataset Summary
A news headline topic classification dataset, similar to AG-news, for Hausa. The news headlines were collected from [VOA Hausa](https://www.voahausa.com/).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Hausa (ISO 639-1: ha)
## Dataset Structure
### Data Instances
An instance consists of a news title sentence and the corresponding topic label.
### Data Fields
- `news_title`: A news title
- `label`: The label describing the topic of the news title. Can be one of the following classes: Nigeria, Africa, World, Health or Politics.
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@michael-aloys](https://github.com/michael-aloys) for adding this dataset. | 3,841 | [
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TheBritishLibrary/EThOS-PhD-metadata | 2022-07-23T21:14:57.000Z | [
"task_categories:text-classification",
"task_categories:fill-mask",
"task_ids:multi-label-classification",
"task_ids:masked-language-modeling",
"multilinguality:monolingual",
"language:en",
"region:us"
] | TheBritishLibrary | The data in this collection comprises the bibliographic metadata for all UK doctoral theses listed in EThOS, the UK's national thesis service.
We estimate the data covers around 98% of all PhDs ever awarded by UK Higher Education institutions, dating back to 1787.
Thesis metadata from every PhD-awarding university in the UK is included. | \
@misc{british library_genre,
title={UK Doctoral Thesis Metadata from EThOS},
url={UK Doctoral Thesis Metadata from EThOS},
author={{British Library} and {Rosie, Heather}},
year={2021}} | 1 | 152 | 2022-03-02T23:29:22 | ---
annotations_creators: []
language:
- en
language_creators: []
license: []
multilinguality:
- monolingual
pretty_name: EThOS PhD metadata
size_categories: []
source_datasets: []
tags: []
task_categories:
- text-classification
- fill-mask
task_ids:
- multi-label-classification
- masked-language-modeling
---
# Dataset Card for EThOS PhD metadata
## Table of Contents
- [Dataset Card for blbooksgenre](#dataset-card-for-EThOS PhD metadata)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Supervised tasks](#supervised-tasks)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**: https://bl.iro.bl.uk/concern/datasets/c815b271-09be-4123-8156-405094429198?locale=en
- **Repository:** https://doi.org/10.23636/ybpt-nh33
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The data in this collection comprises the bibliographic metadata for all UK doctoral theses listed in EThOS, the UK's national thesis service. We estimate the data covers around 98% of all PhDs ever awarded by UK Higher Education institutions, dating back to 1787. Thesis metadata from every PhD-awarding university in the UK is included. You can investigate and re-use this unique collection of UK universities' PhD thesis data to analyse trends in postgraduate research, make connections between researchers, apply large data analysis, improve citation of theses and many more applications.
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
#### Supervised tasks
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
[More Information Needed]
### Data Instances
An example data instance:
```python
{'Abstract': ' ',
'Author': 'Loizou, Panos A.',
'Author ISNI': 'https://isni.org/isni/0000000136122593',
'DOI': ' ',
'Date': datetime.datetime(1989, 1, 1, 0, 0),
'EThOS URL': 'https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.232781',
'Funder(s)': ' ',
'IR URL': ' ',
'Institution': 'University of Manchester',
'Institution ISNI': 'https://isni.org/isni/0000000121662407',
'ORCID': ' ',
'Qualification': 'Thesis (Ph.D.)',
'Subject Discipline': 0,
'Supervisor(s)': ' ',
'Title': 'Computation and measurement of turbulent flow through idealized turbine blade passages'}
```
### Data Fields
[More Information Needed]
### Data Splits
This dataset contains a single split `train`.
## Dataset Creation
[More Information Needed]
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
[More Information Needed]
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
The books are licensed under the [CC BY 4.0 Attribution](https://creativecommons.org/licenses/by/4.0/) license.
### Citation Information
| 4,729 | [
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anab/copa-sse | 2022-10-26T01:53:17.000Z | [
"task_categories:text2text-generation",
"task_categories:multiple-choice",
"task_ids:explanation-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:en",
"license:mit",
"commonsense reasoning",
"explanation",
"graph-based reasoning",
"arxiv:2201.06777",
"region:us"
] | anab | null | null | 3 | 152 | 2022-10-25T07:11:33 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- crowdsourced
license:
- mit
multilinguality:
- monolingual
pretty_name: Semi-structured Explanations for Commonsense Reasoning
size_categories:
- 1K<n<10K
source_datasets: []
tags:
- commonsense reasoning
- explanation
- graph-based reasoning
task_categories:
- text2text-generation
- multiple-choice
task_ids:
- explanation-generation
---
# Dataset Card for COPA-SSE
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/a-brassard/copa-sse
- **Paper:** [COPA-SSE: Semi-Structured Explanations for Commonsense Reasoning](https://arxiv.org/abs/2201.06777)
- **Point of Contact:** [Ana Brassard](mailto:ana.brassard@riken.jp)
### Dataset Summary
COPA-SSE contains crowdsourced explanations for the [Balanced COPA](https://balanced-copa.github.io/) dataset, a variant of the [Choice of Plausible Alternatives (COPA)](https://people.ict.usc.edu/~gordon/copa.html) benchmark. The explanations are formatted as a set of triple-like common sense statements with [ConceptNet](https://conceptnet.io/) relations but freely written concepts.
### Supported Tasks and Leaderboards
Can be used to train a model for explain+predict or predict+explain settings. Suited for both text-based and graph-based architectures. Base task is COPA (causal QA).
### Languages
English
## Dataset Structure
### Data Instances
Validation and test set each contains Balanced COPA samples with added explanations in `.jsonl` format. The question ids match the original questions of the Balanced COPA validation and test sets, respectively.
### Data Fields
Each entry contains:
- the original question (matching format and ids)
- `human-explanations`: a list of explanations each containing:
- `expl-id`: the explanation id
- `text`: the explanation in plain text (full sentences)
- `worker-id`: anonymized worker id (the author of the explanation)
- `worker-avg`: the average score the author got for their explanations
- `all-ratings`: all collected ratings for the explanation
- `filtered-ratings`: ratings excluding those that failed the control
- `triples`: the triple-form explanation (a list of ConceptNet-like triples)
Example entry:
```
id: 1,
asks-for: cause,
most-plausible-alternative: 1,
p: "My body cast a shadow over the grass.",
a1: "The sun was rising.",
a2: "The grass was cut.",
human-explanations: [
{expl-id: f4d9b407-681b-4340-9be1-ac044f1c2230,
text: "Sunrise causes casted shadows.",
worker-id: 3a71407b-9431-49f9-b3ca-1641f7c05f3b,
worker-avg: 3.5832864694635025,
all-ratings: [1, 3, 3, 4, 3],
filtered-ratings: [3, 3, 4, 3],
filtered-avg-rating: 3.25,
triples: [["sunrise", "Causes", "casted shadows"]]
}, ...]
```
### Data Splits
Follows original Balanced COPA split: 1000 dev and 500 test instances. Each instance has up to nine explanations.
## Dataset Creation
### Curation Rationale
The goal was to collect human-written explanations to supplement an existing commonsense reasoning benchmark. The triple-like format was designed to support graph-based models and increase the overall data quality, the latter being notoriously lacking in freely-written crowdsourced text.
### Source Data
#### Initial Data Collection and Normalization
The explanations in COPA-SSE are fully crowdsourced via the Amazon Mechanical Turk platform. Workers entered explanations by providing one or more concept-relation-concept triples. The explanations were then rated by different annotators with one- to five-star ratings. The final dataset contains explanations with a range of quality ratings. Additional collection rounds guaranteed that each sample has at least one explanation rated 3.5 stars or higher.
#### Who are the source language producers?
The original COPA questions (500 dev+500 test) were initially hand-crafted by experts. Similarly, the additional 500 development samples in Balanced COPA were authored by a small team of NLP researchers. Finally, the added explanations and quality ratings in COPA-SSE were collected with the help of Amazon Mechanical Turk workers who passed initial qualification rounds.
### Annotations
#### Annotation process
Workers were shown a Balanced COPA question, its answer, and a short instructional text. Then, they filled in free-form text fields for head and tail concepts and selected the relation from a drop-down menu with a curated selection of ConceptNet relations. Each explanation was rated by five different workers who were shown the same question and answer with five candidate explanations.
#### Who are the annotators?
The workers were restricted to persons located in the U.S. or G.B., with a HIT approval of 98% or more, and 500 or more approved HITs. Their identity and further personal information are not available.
### Personal and Sensitive Information
N/A
## Considerations for Using the Data
### Social Impact of Dataset
Models trained to output similar explanations as those in COPA-SSE may not necessarily provide convincing or faithful explanations. Researchers should carefully evaluate the resulting explanations before considering any real-world applications.
### Discussion of Biases
COPA questions ask for causes or effects of everyday actions or interactions, some of them containing gendered language. Some explanations may reinforce harmful stereotypes if their reasoning is based on biased assumptions. These biases were not verified during collection.
### Other Known Limitations
The data was originally intended to be explanation *graphs*, i.e., hypothetical "ideal" subgraphs of a commonsense knowledge graph. While they can still function as valid natural language explanations, their wording may be at times unnatural to a human and may be better suited for graph-based implementations.
## Additional Information
### Dataset Curators
This work was authored by Ana Brassard, Benjamin Heinzerling, Pride Kavumba, and Kentaro Inui. All are both members of the Riken AIP Natural Language Understanding Team and the Tohoku NLP Lab under Tohoku University.
### Licensing Information
COPA-SSE is released under the [MIT License](https://mit-license.org/).
### Citation Information
```
@InProceedings{copa-sse:LREC2022,
author = {Brassard, Ana and Heinzerling, Benjamin and Kavumba, Pride and Inui, Kentaro},
title = {COPA-SSE: Semi-structured Explanations for Commonsense Reasoning},
booktitle = {Proceedings of the Language Resources and Evaluation Conference},
month = {June},
year = {2022},
address = {Marseille, France},
publisher = {European Language Resources Association},
pages = {3994--4000},
url = {https://aclanthology.org/2022.lrec-1.425}
}
```
### Contributions
Thanks to [@a-brassard](https://github.com/a-brassard) for adding this dataset. | 8,064 | [
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albertvillanova/medmnist-v2 | 2023-05-30T05:40:52.000Z | [
"task_categories:image-classification",
"task_ids:multi-class-image-classification",
"task_ids:multi-label-image-classification",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"medical",
"arxiv:2110.14795",
"region:us"
] | albertvillanova | MedMNIST v2 is a large-scale MNIST-like collection of standardized biomedical images, including 12 datasets for 2D and 6 datasets for 3D. | @article{medmnistv2,
title={MedMNIST v2-A large-scale lightweight benchmark for 2D and 3D biomedical image classification},
author={Yang, Jiancheng and Shi, Rui and Wei, Donglai and Liu, Zequan and Zhao, Lin and Ke, Bilian and Pfister, Hanspeter and Ni, Bingbing},
journal={Scientific Data},
volume={10},
number={1},
pages={41},
year={2023},
publisher={Nature Publishing Group UK London}
}
@inproceedings{medmnistv1,
title={MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis},
author={Yang, Jiancheng and Shi, Rui and Ni, Bingbing},
booktitle={IEEE 18th International Symposium on Biomedical Imaging (ISBI)},
pages={191--195},
year={2021}
} | 3 | 152 | 2023-05-29T09:00:40 | ---
language: en
license: cc-by-4.0
multilinguality:
- monolingual
pretty_name: MedMNIST v2
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
- multi-label-image-classification
paperswithcode_id: medmnist-v2
tags:
- medical
---
# Dataset Card for MedMNIST v2
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://medmnist.com/
- **Repository:** https://github.com/MedMNIST/MedMNIST
- **Paper:** [MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classification](https://arxiv.org/abs/2110.14795)
- **Leaderboard:**
- **Point of Contact:** [Bingbing Ni](mailto:nibingbing@sjtu.edu.cn)
### Dataset Summary
We introduce MedMNIST v2, a large-scale MNIST-like collection of standardized biomedical images, including 12 datasets for 2D and 6 datasets for 3D. All images are pre-processed into 28 x 28 (2D) or 28 x 28 x 28 (3D) with the corresponding classification labels, so that no background knowledge is required for users. Covering primary data modalities in biomedical images, MedMNIST v2 is designed to perform classification on lightweight 2D and 3D images with various data scales (from 100 to 100,000) and diverse tasks (binary/multi-class, ordinal regression and multi-label). The resulting dataset, consisting of 708,069 2D images and 9,998 3D images in total, could support numerous research / educational purposes in biomedical image analysis, computer vision and machine learning. We benchmark several baseline methods on MedMNIST v2, including 2D / 3D neural networks and open-source / commercial AutoML tools.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English (`en`).
## 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
The dataset is licensed under [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/) (CC BY 4.0).
Each subset keeps the same license as that of the source dataset. Please also cite the corresponding paper of source data if you use any subset of MedMNIST.
### Citation Information
If you find this project useful, please cite both v1 and v2 papers:
```
@article{medmnistv2,
title={MedMNIST v2-A large-scale lightweight benchmark for 2D and 3D biomedical image classification},
author={Yang, Jiancheng and Shi, Rui and Wei, Donglai and Liu, Zequan and Zhao, Lin and Ke, Bilian and Pfister, Hanspeter and Ni, Bingbing},
journal={Scientific Data},
volume={10},
number={1},
pages={41},
year={2023},
publisher={Nature Publishing Group UK London}
}
@inproceedings{medmnistv1,
title={MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis},
author={Yang, Jiancheng and Shi, Rui and Ni, Bingbing},
booktitle={IEEE 18th International Symposium on Biomedical Imaging (ISBI)},
pages={191--195},
year={2021}
}
```
Please also cite the corresponding paper(s) of source data if you use any subset of MedMNIST as per the description on the [project website](https://medmnist.com/).
### Contributions
Thanks to [@albertvillanova](https://huggingface.co/albertvillanova) for adding this dataset.
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] |
tilyupo/trivia_qa | 2023-08-03T17:00:54.000Z | [
"region:us"
] | tilyupo | null | null | 0 | 152 | 2023-08-02T19:44:00 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: question
dtype: string
- name: question_id
dtype: string
- name: question_source
dtype: string
- name: answer
struct:
- name: aliases
sequence: string
- name: normalized_aliases
sequence: string
- name: matched_wiki_entity_name
dtype: string
- name: normalized_matched_wiki_entity_name
dtype: string
- name: normalized_value
dtype: string
- name: type
dtype: string
- name: value
dtype: string
- name: passages
list:
- name: answer
dtype: string
- name: passage
dtype: string
- name: precise_score
dtype: float64
- name: rough_score
dtype: float64
- name: source
dtype: string
- name: title
dtype: string
splits:
- name: train
num_bytes: 3065861634
num_examples: 137282
- name: validation
num_bytes: 402091161
num_examples: 17817
download_size: 1805238996
dataset_size: 3467952795
---
# Dataset Card for "trivia_qa_passages"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 1,317 | [
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coastalcph/medical-bios | 2023-10-11T11:56:36.000Z | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-nc-sa-4.0",
"medical",
"region:us"
] | coastalcph | NA | NA | 1 | 152 | 2023-10-09T10:54:50 | ---
license: cc-by-nc-sa-4.0
task_categories:
- text-classification
language:
- en
tags:
- medical
pretty_name: medical-bios
size_categories:
- 1K<n<10K
---
# Dataset Description
The dataset comprises English biographies labeled with occupations and binary genders.
This is an occupation classification task, where bias with respect to gender can be studied.
It includes a subset of 10,000 biographies (8k train/1k dev/1k test) targeting 5 medical occupations (psychologist, surgeon, nurse, dentist, physician).
We collect and release human rationale annotations for a subset of 100 biographies in two different settings: non-contrastive and contrastive.
In the former, the annotators were asked to find the rationale for the question: "Why is the person in the following short bio described as a L?", where L is the gold label occupation, e.g., nurse.
In the latter, the question was "Why is the person in the following short bio described as a L rather than a F", where F (foil) is another medical occupation, e.g., physician.
# Dataset Structure
We provide the `standard` version of the dataset, where examples look as follows.
```json
{
"text": "He has been a practicing Dentist for 20 years. He has done BDS . He is currently associated with Sree Sai Dental Clinic in Sowkhya Ayurveda Speciality Clinic, Chennai. ... ",
"label": 3,
}
```
and the newly curated subset of examples including human rationales, dubbed `rationales', where examples look as follows.
```json
{
"text": "'She is currently practising at Dr Ravindra Ratolikar Dental Clinic in Narayanguda, Hyderabad.",
"label": 3,
"foil": 2,
"words": ['She', 'is', 'currently', 'practising', 'at', 'Dr', 'Ravindra', 'Ratolikar', 'Dental', 'Clinic', 'in', 'Narayanguda', ',', 'Hyderabad', '.']
"rationale": [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
"contrastive_rationale": [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
}
```
# Use
To load the `standard` version of the dataset:
```python
from datasets import load_dataset
dataset = load_dataset("coastalcph/medical-bios", "standard")
```
To load the newly curated subset of examples with human rationales:
```python
from datasets import load_dataset
dataset = load_dataset("coastalcph/medical-bios", "rationales")
```
# Citation
[*Oliver Eberle\*, Ilias Chalkidis\*, Laura Cabello, Stephanie Brandl. Rather a Nurse than a Physician - Contrastive Explanations under Investigation. 2023. In the Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. Singapore.*](xxx)
```
@inproceedings{eberle-etal-2023-contrast-bios,
author={Oliver Eberle, Ilias Chalkidis, Laura Cabello, Stephanie Brandl},
title={Rather a Nurse than a Physician - Contrastive Explanations under Investigation},
booktitle={Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing},
year={2023},
address={Singapore, Singapore}
}
```
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result-kand2-sdxl-wuerst-karlo/1f4e3f67 | 2023-10-11T01:25:02.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 152 | 2023-10-11T01:25:02 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 260
num_examples: 10
download_size: 1484
dataset_size: 260
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "1f4e3f67"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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-0.00141143798828125,
-0.058868408203125,
-0.006908416748046875,
0.049041748046875,
0.0024433135986328125,
-0.0164794921875,
-0.0179443359375,
0.0592041015625,
-0.02740478515625,
-0.08209228515625,
0.006816864013671875,
0.039825439453125,
0.0163726806640625,
0.007762908935546875,
0.06500244140625,
-0.0167236328125,
0.024200439453125,
0.0184478759765625,
0.01424407958984375,
-0.0099334716796875,
-0.04217529296875,
-0.0237884521484375,
0.01013946533203125,
-0.01335906982421875,
-0.025299072265625
]
] |
GEM/mlb_data_to_text | 2022-10-24T15:30:20.000Z | [
"task_categories:table-to-text",
"annotations_creators:none",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:other",
"data-to-text",
"region:us"
] | GEM | The MLB dataset for data to text generation contains Major League Baseball games statistics and
their human-written summaries. | @inproceedings{puduppully-etal-2019-data,
title = "Data-to-text Generation with Entity Modeling",
author = "Puduppully, Ratish and
Dong, Li and
Lapata, Mirella",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P19-1195",
doi = "10.18653/v1/P19-1195",
pages = "2023--2035",
} | 1 | 151 | 2022-03-02T23:29:22 | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- en
license:
- other
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- table-to-text
task_ids: []
pretty_name: mlb_data_to_text
tags:
- data-to-text
---
# Dataset Card for GEM/mlb_data_to_text
## Dataset Description
- **Homepage:** https://github.com/ratishsp/mlb-data-scripts
- **Repository:** https://github.com/ratishsp/mlb-data-scripts
- **Paper:** https://aclanthology.org/P19-1195
- **Leaderboard:** N/A
- **Point of Contact:** Ratish Puduppully
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/mlb_data_to_text).
### Dataset Summary
The MLB dataset is an English sport-related data-to-text dataset in the baseball domain. The input is a large table with results of a game and the output is a description of the game.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/mlb_data_to_text')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/mlb_data_to_text).
#### website
[Github](https://github.com/ratishsp/mlb-data-scripts)
#### paper
[ACL Anthology](https://aclanthology.org/P19-1195)
#### authors
Ratish Puduppully, Li Dong, Mirella Lapata
## Dataset Overview
### Where to find the Data and its Documentation
#### Webpage
<!-- info: What is the webpage for the dataset (if it exists)? -->
<!-- scope: telescope -->
[Github](https://github.com/ratishsp/mlb-data-scripts)
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Github](https://github.com/ratishsp/mlb-data-scripts)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[ACL Anthology](https://aclanthology.org/P19-1195)
#### BibTex
<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
<!-- scope: microscope -->
```
@inproceedings{puduppully-etal-2019-data,
title = "Data-to-text Generation with Entity Modeling",
author = "Puduppully, Ratish and
Dong, Li and
Lapata, Mirella",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P19-1195",
doi = "10.18653/v1/P19-1195",
pages = "2023--2035",
}
```
#### Contact Name
<!-- quick -->
<!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
Ratish Puduppully
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
ratishpuduppully@gmail.com
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
no
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
no
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`English`
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
other: Other license
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
The dataset can be used to study data-to-text generation. The dataset is in sports domain. It pairs statistics of Major League Baseball (MLB) game with its summary. The summary is in the form of a document containing an average of 540 tokens. Thus it is useful to study long document generation.
#### Add. License Info
<!-- info: What is the 'other' license of the dataset? -->
<!-- scope: periscope -->
Restricted to non-commercial research purposes.
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Data-to-Text
#### Communicative Goal
<!-- quick -->
<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
<!-- scope: periscope -->
Produce a summary of MLB game from its statistics.
### Credit
#### Curation Organization Type(s)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`academic`
#### Curation Organization(s)
<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
University of Edinburgh
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Ratish Puduppully, Li Dong, Mirella Lapata
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
```
features = datasets.Features(
{
"home_name": datasets.Value("string"),
"box_score": [
{
"p_l": datasets.Value("string"),
"last_name": datasets.Value("string"),
"p_h": datasets.Value("string"),
"sac": datasets.Value("string"),
"p_bb": datasets.Value("string"),
"pos": datasets.Value("string"),
"ao": datasets.Value("string"),
"p_bf": datasets.Value("string"),
"cs": datasets.Value("string"),
"hbp": datasets.Value("string"),
"ab": datasets.Value("string"),
"full_name": datasets.Value("string"),
"p_w": datasets.Value("string"),
"go": datasets.Value("string"),
"fldg": datasets.Value("string"),
"p_bs": datasets.Value("string"),
"avg": datasets.Value("string"),
"p_r": datasets.Value("string"),
"p_s": datasets.Value("string"),
"lob": datasets.Value("string"),
"first_name": datasets.Value("string"),
"p_sv": datasets.Value("string"),
"p_so": datasets.Value("string"),
"p_save": datasets.Value("string"),
"p_hr": datasets.Value("string"),
"po": datasets.Value("string"),
"p_ip1": datasets.Value("string"),
"p_ip2": datasets.Value("string"),
"bb": datasets.Value("string"),
"ops": datasets.Value("string"),
"p_hld": datasets.Value("string"),
"bo": datasets.Value("string"),
"p_loss": datasets.Value("string"),
"e": datasets.Value("string"),
"p_game_score": datasets.Value("string"),
"p_win": datasets.Value("string"),
"a": datasets.Value("string"),
"p_era": datasets.Value("string"),
"d": datasets.Value("string"),
"p_out": datasets.Value("string"),
"h": datasets.Value("string"),
"p_er": datasets.Value("string"),
"p_np": datasets.Value("string"),
"hr": datasets.Value("string"),
"r": datasets.Value("string"),
"so": datasets.Value("string"),
"t": datasets.Value("string"),
"rbi": datasets.Value("string"),
"team": datasets.Value("string"),
"sb": datasets.Value("string"),
"slg": datasets.Value("string"),
"sf": datasets.Value("string"),
"obp": datasets.Value("string"),
}
],
"home_city": datasets.Value("string"),
"vis_name": datasets.Value("string"),
"play_by_play": [{
"top": [{
"runs": datasets.Value("string"),
"scorers": [
datasets.Value("string")
],
"pitcher": datasets.Value("string"),
"o": datasets.Value("string"),
"b": datasets.Value("string"),
"s": datasets.Value("string"),
"batter": datasets.Value("string"),
"b1": [
datasets.Value("string")
],
"b2": [
datasets.Value("string")
],
"b3": [
datasets.Value("string")
],
"event": datasets.Value("string"),
"event2": datasets.Value("string"),
"home_team_runs": datasets.Value("string"),
"away_team_runs": datasets.Value("string"),
"rbi": datasets.Value("string"),
"error_runs": datasets.Value("string"),
"fielder_error": datasets.Value("string")
}
],
"bottom": [{
"runs": datasets.Value("string"),
"scorers": [
datasets.Value("string")
],
"pitcher": datasets.Value("string"),
"o": datasets.Value("string"),
"b": datasets.Value("string"),
"s": datasets.Value("string"),
"batter": datasets.Value("string"),
"b1": [
datasets.Value("string")
],
"b2": [
datasets.Value("string")
],
"b3": [
datasets.Value("string")
],
"event": datasets.Value("string"),
"event2": datasets.Value("string"),
"home_team_runs": datasets.Value("string"),
"away_team_runs": datasets.Value("string"),
"rbi": datasets.Value("string"),
"error_runs": datasets.Value("string"),
"fielder_error": datasets.Value("string")
}
],
"inning": datasets.Value("string")
}
],
"vis_line": {
"innings": [{
"inn": datasets.Value("string"),
"runs": datasets.Value("string")
}
],
"result": datasets.Value("string"),
"team_runs": datasets.Value("string"),
"team_hits": datasets.Value("string"),
"team_errors": datasets.Value("string"),
"team_name": datasets.Value("string"),
"team_city": datasets.Value("string")
},
"home_line": {
"innings": [{
"inn": datasets.Value("string"),
"runs": datasets.Value("string")
}
],
"result": datasets.Value("string"),
"team_runs": datasets.Value("string"),
"team_hits": datasets.Value("string"),
"team_errors": datasets.Value("string"),
"team_name": datasets.Value("string"),
"team_city": datasets.Value("string")
},
"vis_city": datasets.Value("string"),
"day": datasets.Value("string"),
"summary": [
datasets.Value("string"),
],
"gem_id": datasets.Value("string")
}
```
#### Reason for Structure
<!-- info: How was the dataset structure determined? -->
<!-- scope: microscope -->
The high level structure contains the following attributes: home_name, vis_name, home_city, vis_city, summary, summary_eval, day, gem_id, box_score, play_by_play, home_line, vis_line.
The attributes home_name, vis_name, home_city, vis_city and day are string values.
The attribute "summary" contains the summary in the form of a list of tokens.
The attribute "summary_eval" contains the summary in the form of a string of tokens. The difference from "summary" field is that "summary_eval" doesn't contain "*NEWPARAGRAPH*" delimiters to separate the paragraphs. "summary_eval" field should be used to evaluate model outputs. "summary" field may be used during the training process.
box_score contains the box score statistics of the players in the game. It is in the form of a list (of average size 90), with each element describing the statistics of a player. The box score statistics contain 53 attributes.
The description of the attributes is given below. The descriptions of most of the attributes is obtained from [mlb.com](https://www.mlb.com/glossary/standard-stats).
- r : Runs scored by a player in the game.
- rbi Runs Batted In (RBI): action of a batter results in a run scored by other players in the team.
- pos Position of the player.
- avg Batting Average. It is an indicator of the hits in the players' career.
- bb A walk occurs when a pitcher throws four pitches out of the strike zone, none of which are swung at by the hitter.
- hr Batter hits the ball in the air over the outfield fence.
- p_r Runs given by a pitcher in the game.
- p_bb Walks allowed by pitcher in a game.
- p_h Hits allowed by pitcher in a game.
- p_hr Home runs allowed by pitcher in a game.
- p_er Earned Run (ER): An earned run is any run that scores against a pitcher.
- p_era Earned Run Average (ERA): Earned run average represents the number of earned runs a pitcher allows per nine innings.
- p_np Number of Pitches: A pitcher's total number of pitches is determined by all the pitches he throws in game.
- p_ip1 Innings Pitched (IP1): Innings pitched measures the number of innings a pitcher remains in a game. Because there are three outs in an inning, each out recorded represents one-third of an inning pitched.
- p_ip2 Innings Pitched (IP2): Innings pitched measures the number of innings a pitcher remains in a game. Because there are three outs in an inning, each out recorded represents one-third of an inning pitched.
- p_w A pitcher receives a win when he is the pitcher of record when his team takes the lead for good.
- p_l A pitcher receives a loss when a run that is charged to him proves to be the go-ahead run in the game, giving the opposing team a lead it never gives up.
- p_so A strikeout occurs when a pitcher throws any combination of three swinging or looking strikes to a hitter.
- p_save Save: A save is awarded to the relief pitcher who finishes a game for the winning team. A pitcher cannot receive a save and a win in the same game.
- p_sv Saves: The count of saves recorded by a pitcher in his career.
- sac A sacrifice fly occurs when a batter hits a fly-ball out to the outfield or foul territory that allows a runner to score.
- p_bf Batters faced is simply a count of the number of total plate appearances against a certain pitcher or team. In a perfect game -- with 27 outs -- a pitcher will record 27 batters faced.
- cs A caught stealing occurs when a runner attempts to steal but is tagged out before reaching second base, third base or home plate.
- hbp A hit-by-pitch occurs when a batter is struck by a pitched ball without swinging at it. He is awarded first base as a result.
- ab An official at-bat comes when a batter reaches base via a fielder's choice, hit or an error (not including catcher's interference) or when a batter is put out on a non-sacrifice.
- p_bs A blown save occurs when a relief pitcher enters a game in a save situation, but allows the tying run to score.
- p_s The count of strikes thrown by a pitcher
- lob Left on base can be viewed as both an individual statistic or as a team statistic. In an individual batter's case, it refers to how many men remain on base after that batter makes an out at the plate, as the batter has failed to do his job to score those runners -- or at least put himself in a position to score. In a team's case or in an individual pitcher's case, it refers to the number of men who remain on base at the end of an inning.
- po A fielder is credited with a putout when he is the fielder who physically records the act of completing an out -- whether it be by stepping on the base for a forceout, tagging a runner, catching a batted ball, or catching a third strike
- ops OPS adds on-base percentage and slugging percentage to get one number that unites the two. It's meant to combine how well a hitter can reach base, with how well he can hit for average and for power.
- p_hld A hold occurs when a relief pitcher enters the game in a save situation and maintains his team's lead for the next relief pitcher, while recording at least one out.
- p_loss True/False- Indicates losing pitcher
- e A fielder is given an error if, in the judgment of the official scorer, he fails to convert an out on a play that an average fielder should have made.
- p_win True/False- Indicates winning pitcher
- a An assist is awarded to a fielder who touches the ball before a putout is recorded by another fielder.
- h A hit occurs when a batter strikes the baseball into fair territory and reaches base without doing so via an error or a fielder's choice.
- so A strikeout of a batter
- team Team of the player
- sb A stolen base occurs when a baserunner advances by taking a base to which he isn't entitled.
- slg Slugging percentage represents the total number of bases a player records per at-bat. Unlike on-base percentage, slugging percentage deals only with hits and does not include walks and hit-by-pitches in its equation.
- sf A sacrifice fly occurs when a batter hits a fly-ball out to the outfield or foul territory that allows a runner to score.
- obp OBP refers to how frequently a batter reaches base per plate appearance. Times on base include hits, walks and hit-by-pitches, but do not include errors, times reached on a fielder's choice or a dropped third strike.
The description of attributes in play-by-play is below:
- batter Batter in the play.
- pitcher Pitcher in play.
- b1 Player/s at first base position.
- b2 Player/s at second base position.
- b3 Player/s at third base position.
- scorers Player/s scored in the play.
- fielder_error Player committed field error.
- event Event of the play such as single, double, home run etc.
- event2 Second event of the play such as wild pitch, error etc.
- inning Inning of the play.
- top/ bottom If home team is batting it is bottom and if away team is batting it is top.
- o Count of outs
- b Count of balls
- s Count of strikes
- r Count of runs
- rbi Count of runs batted in (rbi)
- error_runs Runs due to error
- home_team_runs Score of home team
- vis_team_runs Score of visiting team
`home_line` and `vis_line` contain string value pairs for `team_name`, `team_city`, `team_runs`, `team_hits`, `team_error`, `result`, and a list of runs scored in each inning.
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
There are three splits in the dataset: train, validation and test
#### Splitting Criteria
<!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. -->
<!-- scope: microscope -->
The splits are random.
## Dataset in GEM
### Rationale for Inclusion in GEM
#### Why is the Dataset in GEM?
<!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? -->
<!-- scope: microscope -->
This dataset can verify if models are capable of long document generation. The challenges in long document generation conditioned on input tables include ensuring coherent output, staying faithful to the input, ensuring fluent output and avoiding repetition of text. Such aspects can be verified on models trained on this dataset
#### Similar Datasets
<!-- info: Do other datasets for the high level task exist? -->
<!-- scope: telescope -->
yes
#### Unique Language Coverage
<!-- info: Does this dataset cover other languages than other datasets for the same task? -->
<!-- scope: periscope -->
no
#### Difference from other GEM datasets
<!-- info: What else sets this dataset apart from other similar datasets in GEM? -->
<!-- scope: microscope -->
Compared to the existing RotoWire (Wiseman et al. 2017) dataset, MLB summaries are longer (approximately by 50%) and the input records are richer and more structured (with the addition of play-by-play). Moreover, the MLB dataset is five times larger in terms of data size (i.e., pairs of tables and game summaries).
#### Ability that the Dataset measures
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: periscope -->
Long document generation, coherent ordering of information, faithfulness to the input statistics, fluency in generation and avoiding repetition of text.
### GEM-Specific Curation
#### Modificatied for GEM?
<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
<!-- scope: telescope -->
yes
#### GEM Modifications
<!-- info: What changes have been made to he original dataset? -->
<!-- scope: periscope -->
`data points removed`
#### Modification Details
<!-- info: For each of these changes, described them in more details and provided the intended purpose of the modification -->
<!-- scope: microscope -->
Some examples have been removed from training dataset which satisfied the below criteria:
1. The examples in training dataset which overlapped with validation/test.
2. Some examples which described washed out games.
#### Additional Splits?
<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
no
### Getting Started with the Task
#### Pointers to Resources
<!-- info: Getting started with in-depth research on the task. Add relevant pointers to resources that researchers can consult when they want to get started digging deeper into the task. -->
<!-- scope: microscope -->
The [research paper](https://aclanthology.org/P19-1195) is a good resource
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
Automatic evaluation measure can evaluate the factuality, content selection, content ordering and the fluency of the model output. The factuality, content selection and content ordering is measured using an Information Extraction based evaluation approach introduced by Wiseman et al (2017). The fluency is measured using BLEU.
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`Other: Other Metrics`
#### Other Metrics
<!-- info: Definitions of other metrics -->
<!-- scope: periscope -->
Wiseman et al. (2017) define three metrics induced from the outputs of an Information Extraction model which is run on the model/human-written game summaries . Let ลท be the gold summary and y the model output.
โข Relation Generation (RG) measures the precision and count of relations extracted from y that also appear in records r.
โข Content Selection (CS) measures the precision and recall of relations extracted from y that are also extracted from ลท.
โข Content Ordering (CO) measures the complement of the normalized Damerau-Levenshtein distance (Brill and Moore, 2000) between the sequences of relations extracted from y and ลท
#### Proposed Evaluation
<!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. -->
<!-- scope: microscope -->
We have reused the automatic metrics based on Information Extraction evaluation introduced by Wiseman et al (2017). For human evaluation, we conducted studies to evaluate the factuality, coherence, grammaticality and conciseness.
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
yes
#### Relevant Previous Results
<!-- info: What are the most relevant previous results for this task/dataset? -->
<!-- scope: microscope -->
The most relevant previous results for dataset are in the TACL 2021 paper on [Data-to-text Generation with Macro Planning](https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00381/101876/Data-to-text-Generation-with-Macro-Planning)
## Dataset Curation
### Original Curation
#### Original Curation Rationale
<!-- info: Original curation rationale -->
<!-- scope: telescope -->
This dataset was curated to complement an existing data-to-text generation dataset (RotoWire by Wiseman et al. 2017) which focuses on long document generation. Compared to RotoWire , MLB summaries are longer (approximately by 50%) and the input records are richer and more structured (with the addition of play-by-play). Moreover, the MLB dataset is five times larger in terms of data size (i.e., pairs of tables and game summaries)
#### Communicative Goal
<!-- info: What was the communicative goal? -->
<!-- scope: periscope -->
The goal is to study automatic generation of long documents in a data-to-text setting. The generated summaries should exhibit coherent ordering of content, be faithful to the input statistics, be fluent and avoid repetition of text.
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
no
### Language Data
#### How was Language Data Obtained?
<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Found`
#### Where was it found?
<!-- info: If found, where from? -->
<!-- scope: telescope -->
`Single website`
#### Language Producers
<!-- info: What further information do we have on the language producers? -->
<!-- scope: microscope -->
The game summaries are produced by professional writers.
#### Topics Covered
<!-- info: Does the language in the dataset focus on specific topics? How would you describe them? -->
<!-- scope: periscope -->
The language focuses on the sports domain.
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
not validated
#### Data Preprocessing
<!-- info: How was the text data pre-processed? (Enter N/A if the text was not pre-processed) -->
<!-- scope: microscope -->
Game summaries were tokenized using NLTK (Bird et al., 2009) and hyphenated words were separated. Sentences containing quotes were removed as they included opinions and non-factual statements unrelated to the input tables. Sometimes MLB summaries contain a "Game notes" section with incidental information which was also removed.
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
not filtered
### Structured Annotations
#### Additional Annotations?
<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
none
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
no
### Consent
#### Any Consent Policy?
<!-- info: Was there a consent policy involved when gathering the data? -->
<!-- scope: telescope -->
no
#### Justification for Using the Data
<!-- info: If not, what is the justification for reusing the data? -->
<!-- scope: microscope -->
The copyright remains with the original data creators and the usage permission is restricted to non-commercial uses.
### Private Identifying Information (PII)
#### Contains PII?
<!-- quick -->
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
<!-- scope: telescope -->
yes/very likely
#### Categories of PII
<!-- info: What categories of PII are present or suspected in the data? -->
<!-- scope: periscope -->
`sensitive information`, `generic PII`
#### Any PII Identification?
<!-- info: Did the curators use any automatic/manual method to identify PII in the dataset? -->
<!-- scope: periscope -->
no identification
### Maintenance
#### Any Maintenance Plan?
<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
no
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
no
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
no
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
unsure
## Considerations for Using the Data
### PII Risks and Liability
### Licenses
#### Copyright Restrictions on the Dataset
<!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? -->
<!-- scope: periscope -->
`research use only`
#### Copyright Restrictions on the Language Data
<!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
<!-- scope: periscope -->
`research use only`
### Known Technical Limitations
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bigbio/hprd50 | 2022-12-22T15:44:46.000Z | [
"multilinguality:monolingual",
"language:en",
"license:unknown",
"region:us"
] | bigbio | HPRD50 is a dataset of randomly selected, hand-annotated abstracts of biomedical papers
referenced by the Human Protein Reference Database (HPRD). It is parsed in XML format,
splitting each abstract into sentences, and in each sentence there may be entities and
interactions between those entities. In this particular dataset, entities are all
proteins and interactions are thus protein-protein interactions.
Moreover, all entities are normalized to the HPRD database. These normalized terms are
stored in each entity's 'type' attribute in the source XML. This means the dataset can
determine e.g. that "Janus kinase 2" and "Jak2" are referencing the same normalized
entity.
Because the dataset contains entities and relations, it is suitable for Named Entity
Recognition and Relation Extraction. | @article{fundel2007relex,
title={RelExโRelation extraction using dependency parse trees},
author={Fundel, Katrin and K{\"u}ffner, Robert and Zimmer, Ralf},
journal={Bioinformatics},
volume={23},
number={3},
pages={365--371},
year={2007},
publisher={Oxford University Press}
} | 1 | 151 | 2022-11-13T22:08:57 |
---
language:
- en
bigbio_language:
- English
license: unknown
multilinguality: monolingual
bigbio_license_shortname: UNKNOWN
pretty_name: HPRD50
homepage:
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- RELATION_EXTRACTION
- NAMED_ENTITY_RECOGNITION
---
# Dataset Card for HPRD50
## Dataset Description
- **Homepage:**
- **Pubmed:** True
- **Public:** True
- **Tasks:** RE,NER
HPRD50 is a dataset of randomly selected, hand-annotated abstracts of biomedical papers
referenced by the Human Protein Reference Database (HPRD). It is parsed in XML format,
splitting each abstract into sentences, and in each sentence there may be entities and
interactions between those entities. In this particular dataset, entities are all
proteins and interactions are thus protein-protein interactions.
Moreover, all entities are normalized to the HPRD database. These normalized terms are
stored in each entity's 'type' attribute in the source XML. This means the dataset can
determine e.g. that "Janus kinase 2" and "Jak2" are referencing the same normalized
entity.
Because the dataset contains entities and relations, it is suitable for Named Entity
Recognition and Relation Extraction.
## Citation Information
```
@article{fundel2007relex,
title={RelExโRelation extraction using dependency parse trees},
author={Fundel, Katrin and K{"u}ffner, Robert and Zimmer, Ralf},
journal={Bioinformatics},
volume={23},
number={3},
pages={365--371},
year={2007},
publisher={Oxford University Press}
}
```
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] |
logo-wizard/modern-logo-dataset | 2023-05-09T13:40:55.000Z | [
"task_categories:text-to-image",
"size_categories:n<1K",
"language:en",
"license:cc-by-nc-3.0",
"doi:10.57967/hf/0592",
"region:us"
] | logo-wizard | null | null | 11 | 151 | 2023-04-27T20:26:59 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 209598433
num_examples: 803
download_size: 208886058
dataset_size: 209598433
license: cc-by-nc-3.0
task_categories:
- text-to-image
language:
- en
size_categories:
- n<1K
---
# Dataset Card for "logo-dataset-v4"
This dataset consists of 803 pairs \\( (x, y) \\), where \\( x \\) is the image and \\( y \\) is the description of the image.
The data have been manually collected and labelled, so the dataset is fully representative and free of rubbish.
The logos in the dataset are minimalist, meeting modern design requirements and reflecting the company's industry.
# Disclaimer
This dataset is made available for academic research purposes only. All the images are collected from the Internet, and the copyright belongs to the original owners. If any of the images belongs to you and you would like it removed, please inform us, we will try to remove it from the dataset. | 1,022 | [
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Docugami/dfm-csl-small-benchmark | 2023-10-04T08:44:17.000Z | [
"task_categories:text2text-generation",
"task_categories:text-generation",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:mit",
"docugami",
"dfm-csl",
"xml-knowledge-graphs",
"region:us"
] | Docugami | null | null | 4 | 151 | 2023-05-30T01:00:38 | ---
license: mit
language:
- en
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text2text-generation
- text-generation
dataset_info:
features:
- name: Text
dtype: string
- name: Small Chunk
dtype: string
- name: Ground Truth
dtype: string
- name: docugami/dfm-cs-small
dtype: string
splits:
- name: eval
num_bytes: 240040
num_examples: 1099
- name: train
num_bytes: 20906
num_examples: 100
download_size: 143986
dataset_size: 260946
tags:
- docugami
- dfm-csl
- xml-knowledge-graphs
pretty_name: Contextual Semantic Lables (Small)
---
# Contextual Semantic Labels (Small) Benchmark Dataset
Please see [https://github.com/docugami/DFM-benchmarks](https://github.com/docugami/DFM-benchmarks) for more details, eval code, and current scores for different models.
# Using Dataset
Please refer to standard huggingface documentation to use this dataset: [https://huggingface.co/docs/datasets/index](https://huggingface.co/docs/datasets/index)
The [explore.ipynb](./explore.ipynb) notebook has some reference code. | 1,091 | [
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spacemanidol/product-search-corpus | 2023-08-11T17:15:55.000Z | [
"region:us"
] | spacemanidol | null | 0 | 151 | 2023-08-09T16:19:25 | Entry not found | 15 | [
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eckendoerffer/news_fr | 2023-10-06T02:36:21.000Z | [
"task_categories:text-generation",
"size_categories:1M<n<10M",
"language:fr",
"license:cc-by-3.0",
"news",
"media",
"Press",
"region:us"
] | eckendoerffer | null | null | 0 | 151 | 2023-09-26T18:36:19 | ---
license: cc-by-3.0
task_categories:
- text-generation
language:
- fr
tags:
- news
- media
- Press
size_categories:
- 1M<n<10M
---
# NEWS FR
There is an open-access [dataset on BnF / Gallica](https://transfert.bnf.fr/link/3a04ea3f-dbe8-4a4a-a302-913a89c3a7a8) comprising nearly a hundred newspapers from the print media spanning almost 100 years.
Unfortunately, for this dataset, only 85% of the text is transcribed accurately.
## DATASET
This dataset compiles 1M online articles from nearly 100 Francophone media outlets. This dataset is intended for research purposes and non-commercial use. It includes 1,140,000 lines for model training, and 63,500 lines for the test and validation files.
Included with this dataset are scripts to extract and process the article text from the same sources. The script is somewhat rough around the edges, but it is functional and commented.
### Format
- **Type**: Text
- **File Extension**: `.txt`
The text has been standardized for consistent formatting and line length. Additionally, the dataset has been filtered using the `langid` library to include only text in French.
### Structure
The dataset is divided into the following splits:
- `train.txt`: 2.2 GB - 1,140,000 rows - 90%
- `test.txt` : 122 MB - 63,500 rows - 5%
- `valid.txt`: 122 MB - 63,500 rows - 5%
### Exploring the Dataset
You can use the `explore_dataset.py` script to explore the dataset by randomly displaying a certain number of lines from it. The script creates and saves an index based on the line breaks, enabling faster data retrieval and display.
### Additional Information
This dataset is a subset of a larger 10GB French dataset, which also contains several thousand books and theses in French, Wikipedia, as well as several hundred thousand Francophone news articles.
## EXTRACT NEWS FR
The "NEWS FR" module allows for the extraction of online press articles from over a hundred different sources.
## Installation
To set up the module, follow the steps below:
1. **Database Setup**:
- Create a database and incorporate the two tables present in `database.sql`.
2. **Database Configuration**:
- Update your MySQL connection information in the `config.py` file.
3. **Dependencies Installation**:
- Install it using pip install:
```
pip install aiohttp mysql-connector-python beautifulsoup4 chardet colorama pyquery
```
## Usage
### 1_extract_rss.py:
This script fetches RSS feeds from various media outlets and adds URLs for further extraction.
```bash
python 1_extract_rss.py
```
### 2_extract_news.py:
This script retrieves the sources of articles for subsequent local processing.
```bash
python 2_extract_news.py
```
### 3_extract_news_txt.py:
This script extracts the text content of press articles and saves it (title + text) to a `.txt` file.
```bash
python 3_extract_news_txt.py
```
After completing this step, you can use the Python script located at /dataset/2_cleaning_txt.py to standardize the text for your dataset.
### 4_extract_news_url.py:
This script allows for the extraction of links to other articles from local article sources. This ensures swift retrieval of numerous past articles, as opposed to fetching only the most recent ones.
```bash
python 4_extract_news_url.py
```
After using this script, you'll need to run 2_extract_news.py again to retrieve the sources of the new articles, as well as 3_extract_news_txt.py to extract the text from these articles.
--- | 3,485 | [
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Luciya/llama-2-nuv-intent-noE | 2023-10-10T06:04:10.000Z | [
"region:us"
] | Luciya | null | null | 0 | 151 | 2023-10-10T06:02:19 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 711010
num_examples: 1585
download_size: 0
dataset_size: 711010
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "llama-2-nuv-intent-noE"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 442 | [
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yentinglin/TC-Eval | 2023-11-02T13:20:32.000Z | [
"task_categories:question-answering",
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:zh",
"region:us"
] | yentinglin | null | null | 1 | 151 | 2023-10-24T15:58:14 | ---
task_categories:
- question-answering
- text-classification
language:
- zh
pretty_name: TMLU
size_categories:
- 1K<n<10K
configs:
- config_name: FGC
data_files:
- split: test
path: "fgc.jsonl"
- config_name: DRCD
data_files:
- split: test
path: "drcd.jsonl"
- config_name: TMMLU
data_files:
- split: test
path: "tmmlu.jsonl"
- config_name: XSUM_TC
data_files:
- split: test
path: "xsum.jsonl"
- config_name: TTQA
data_files:
- split: test
path: TTQA_mc.jsonl
- config_name: TCIC
data_files:
- split: test
path: TCIC_1.2.0.json
- config_name: PenguinsInTable_TC
data_files:
- split: test
path: PenguinsInTable_TC.jsonl
- config_name: IMDB_TC
data_files:
- split: test
path: imdb.jsonl
---
source: https://github.com/mtkresearch/MR-Models/tree/main/TC-Eval
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hoskinson-center/proof-pile | 2023-08-19T03:24:11.000Z | [
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"language:en",
"license:apache-2.0",
"math",
"mathematics",
"formal-mathematics",
"region:us"
] | hoskinson-center | A dataset of high quality mathematical text. | @InProceedings{huggingface:dataset,
title = {proof-pile},
author={Zhangir Azerbayev, Edward Ayers, Bartosz Piotrowski
},
year={2022}
} | 31 | 150 | 2022-08-08T20:57:56 | ---
annotations_creators:
- no-annotation
language:
- en
language_creators:
- found
license: [apache-2.0]
multilinguality:
- monolingual
pretty_name: proof-pile
size_categories: []
source_datasets: []
tags:
- math
- mathematics
- formal-mathematics
task_categories:
- text-generation
task_ids:
- language-modeling
---
# Dataset Description
The `proof-pile` is a 13GB pre-training dataset of mathematical text that comprises 8.3 billion tokens (using the `gpt-neox` tokenizer). Models trained on this dataset are coming soon :) The dataset is composed of diverse sources of both informal and formal mathematics, namely
- ArXiv.math (10GB)
- Open-source math textbooks (50MB)
- Formal mathematics libraries (500MB)
- Lean mathlib and other Lean repositories
- Isabelle AFP
- Coq mathematical components and other Coq repositories
- HOL Light
- set.mm
- Mizar Mathematical Library
- Math Overflow and Math Stack Exchange (2.5GB)
- Wiki-style sources (50MB)
- ProofWiki
- Wikipedia math articles
- MATH dataset (6MB)
The construction of the dataset is reproducible using the code and instructions in the [proof-pile Github
repo](https://github.com/zhangir-azerbayev/proof-pile).
# Supported Tasks
This dataset is intended to be used for pre-training and fine-tuning language models. We envision models trained on the `proof-pile` will have many downstream applications, including informal quantitative reasoning, formal theorem proving, semantic search for formal mathematics, and autoformalization.
# Languages
All informal mathematics in the `proof-pile` is written in English and LaTeX (arXiv articles in other languages are filtered out using [languagedetect](https://github.com/shuyo/language-detection/blob/wiki/ProjectHome.md)). Formal theorem proving languages represented in this dataset are Lean 3, Isabelle, Coq, HOL Light, Metamath, and Mizar.
# Evaluation
The version of `set.mm` in this dataset has 10% of proofs replaced with the `?` character in order to preserve a validation and test set for Metamath provers pre-trained on the `proof-pile`. The precise split can be found here: [validation](https://github.com/zhangir-azerbayev/mm-extract/blob/main/valid_decls.json) and [test](https://github.com/zhangir-azerbayev/mm-extract/blob/main/test_decls.json).
The Lean mathlib commit used in this dataset is `6313863`. Theorems created in subsequent commits can be used for evaluating Lean theorem provers.
This dataset contains only the training set of the [MATH dataset](https://github.com/hendrycks/math). However, because this dataset contains ProofWiki, the Stacks Project, Trench's Analysis, and Stein's Number Theory, models trained on it cannot be evaluated on the [NaturalProofs dataset](https://github.com/wellecks/naturalproofs).
# Data Preprocessing
This section describes any significant filtering and transformations made to various subsets of the data.
**arXiv.math.**
The arXiv.math dataset is large, heterogeneous, and contains a great deal of noise. We used the following heuristics
when choosing which files from arXiv.math source folders to include in the dataset:
- Keep only files with a `.tex` extension.
- Only include files that use either a `utf-8/16/32` or `latin-1` text encoding.
- Discard files that do not contain a part, chapter, section, sub...section, paragraph, or subparagraph heading.
- Delete files that contain the keyword `gnuplot`. Gnuplot-latex is an old command line utility that generates blocks
of entirely unintelligible source.
- Include only articles in English, as determined by the [langdetect library](https://pypi.org/project/langdetect/). \n",
"\n",
- Exclude files shorter than 280 characters (characters counted after substring removal described below).
In addition, we apply the following transformations to arXiv.math texts:
- Delete everything outside of `\begin{document}` and `\end{document}`.
- Delete everything including or after `\Refs`, `\begin{thebibliography}`, or `\begin{bibdiv}`
- Delete comments.
- Any more than three consecutive newlines are replaced by three consecutive newlines.
In [this notebook](https://github.com/zhangir-azerbayev/proof-pile/blob/main/analysis/arxiv_noisedetection.ipynb), we provide an analysis of the prevalence of noisy documents in the arXiv.math subset of the
proof-pile.
**Stack Exchange.**
We only include questions that have at least 5 upvotes and an answer. We format Stack Exchange posts as follows
```
QUESTION [{num_upvotes} upvotes]: {text of question}
REPLY [{num_upvotes} votes]: {text of reply}
REPLY [{num_upvotes} votes]: {text of reply}
.
.
.
```
**set.mm.**
We converted `set.mm` into human-readable form by following the instructions in the [mm-extract repo](https://github.com/zhangir-azerbayev/mm-extract)
## Contributions
Authors: Zhangir Azerbayev, Edward Ayers, Bartosz Piotrowski.
We would like to thank Jeremy Avigad, Albert Jiang, and Wenda Li for their invaluable guidance, and the Hoskinson Center for Formal Mathematics for its support.
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TREC-AToMiC/AToMiC-Images-v0.2 | 2023-02-14T21:29:39.000Z | [
"size_categories:100M<n<1B",
"license:cc-by-sa-4.0",
"arxiv:2103.01913",
"region:us"
] | TREC-AToMiC | null | null | 1 | 150 | 2023-01-14T08:12:44 | ---
dataset_info:
features:
- name: image_url
dtype: string
- name: image_id
dtype: string
- name: language
sequence: string
- name: caption_reference_description
sequence: string
- name: caption_alt_text_description
sequence: string
- name: caption_attribution_description
sequence: string
- name: image
dtype: image
splits:
- name: train
num_bytes: 180043531167.75
num_examples: 11019202
download_size: 174258428914
dataset_size: 180043531167.75
license: cc-by-sa-4.0
size_categories:
- 100M<n<1B
---
# Dataset Card for "AToMiC-All-Images_wi-pixels"
## Dataset Description
- **Homepage:** [AToMiC homepage](https://trec-atomic.github.io/)
- **Source:** [WIT](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)
### Languages
The dataset contains 108 languages in Wikipedia.
### Data Instances
Each instance is an image, its representation in bytes, and its associated captions.
### Intended Usage
1. Image collection for Text-to-Image retrieval
2. Image--Caption Retrieval/Generation/Translation
### Licensing Information
[CC BY-SA 4.0 international license](https://creativecommons.org/licenses/by-sa/4.0/)
### Citation Information
TBA
### Acknowledgement
Thanks to:
[img2dataset](https://github.com/rom1504/img2dataset)
[Datasets](https://github.com/huggingface/datasets)
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 1,614 | [
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alpayariyak/IAM_Sentences_LLaVA | 2023-05-19T22:04:20.000Z | [
"region:us"
] | alpayariyak | null | null | 0 | 150 | 2023-05-19T21:46:41 | ---
dataset_info:
features:
- name: image
dtype: image
- name: id
dtype: string
- name: conversations
dtype: string
splits:
- name: train
num_bytes: 1053875995.077
num_examples: 5663
download_size: 1128902513
dataset_size: 1053875995.077
---
# Dataset Card for "IAM_Sentences_LLaVA"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 451 | [
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llm-book/jawiki-sentences | 2023-10-25T15:22:05.000Z | [
"size_categories:10M<n<100M",
"language:ja",
"license:cc-by-sa-3.0",
"license:gfdl",
"region:us"
] | llm-book | null | null | 1 | 150 | 2023-06-03T03:02:08 | ---
language:
- ja
size_categories:
- 10M<n<100M
license:
- cc-by-sa-3.0
- gfdl
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 3569619848
num_examples: 24387500
download_size: 1297833377
dataset_size: 3569619848
---
# Dataset Card for llm-book/jawiki-sentences
ๆธ็ฑใๅคง่ฆๆจก่จ่ชใขใใซๅ
ฅ้ใใงไฝฟ็จใใ Wikipedia ๆใฎใใผใฟใปใใใงใใ
GitHub ใชใใธใใช [singletongue/wikipedia-utils](https://github.com/singletongue/wikipedia-utils) ใงๅ
ฌ้ใใใฆใใใใผใฟใปใใใๅฉ็จใใฆใใพใใ
## Licence
ๆฌใใผใฟใปใใใงไฝฟ็จใใฆใใ Wikipedia ใฎใณใณใใณใใฏใ[ใฏใชใจใคใใฃใใปใณใขใณใบ่กจ็คบใป็ถๆฟใฉใคใปใณใน 3.0 (CC BY-SA 3.0)](https://creativecommons.org/licenses/by-sa/3.0/deed.ja) ใใใณ [GNU ่ช็ฑๆๆธใฉใคใปใณใน (GFDL)](https://www.gnu.org/licenses/fdl.html) ใฎไธใซ้
ๅธใใใฆใใใใฎใงใใ
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] |
BAAI/COIG-PC-core | 2023-09-25T10:33:33.000Z | [
"language:zh",
"license:unknown",
"region:us"
] | BAAI | null | null | 9 | 150 | 2023-09-19T06:24:01 | ---
extra_gated_heading: "Acknowledge license to accept the repository"
extra_gated_prompt: |
ๅไบฌๆบๆบไบบๅทฅๆบ่ฝ็ ็ฉถ้ข๏ผไปฅไธ็ฎ็งฐโๆไปฌโๆโ็ ็ฉถ้ขโ๏ผ้่ฟBAAI DataHub๏ผdata.baai.ac.cn๏ผๅCOIG-PC HuggingFaceไปๅบ๏ผhttps://huggingface.co/datasets/BAAI/COIG-PC๏ผๅๆจๆไพๅผๆบๆฐๆฎ้๏ผไปฅไธๆ็งฐโๆฐๆฎ้โ๏ผ๏ผๆจๅฏ้่ฟไธ่ฝฝ็ๆนๅผ่ทๅๆจๆ้็ๅผๆบๆฐๆฎ้๏ผๅนถๅจ้ตๅฎๅๅๅงๆฐๆฎ้ไฝฟ็จ่งๅๅๆไธ๏ผๅบไบๅญฆไน ใ็ ็ฉถใๅไธ็ญ็ฎ็ไฝฟ็จ็ธๅ
ณๆฐๆฎ้ใ
ๅจๆจ่ทๅ๏ผๅ
ๆฌไฝไธ้ไบ่ฎฟ้ฎใไธ่ฝฝใๅคๅถใไผ ๆญใไฝฟ็จ็ญๅค็ๆฐๆฎ้็่กไธบ๏ผๅผๆบๆฐๆฎ้ๅ๏ผๆจๅบ่ฎค็้
่ฏปๅนถ็่งฃๆฌใCOIG-PCๅผๆบๆฐๆฎ้ไฝฟ็จ้กป็ฅไธๅ
่ดฃๅฃฐๆใ๏ผไปฅไธ็ฎ็งฐโๆฌๅฃฐๆโ๏ผใไธๆฆๆจ่ทๅๅผๆบๆฐๆฎ้๏ผๆ ่ฎบๆจ็่ทๅๆนๅผไธบไฝ๏ผๆจ็่ทๅ่กไธบๅๅฐ่ขซ่งไธบๅฏนๆฌๅฃฐๆๅ
จ้จๅ
ๅฎน็่ฎคๅฏใ
1. ๅนณๅฐ็ๆๆๆไธ่ฟ่ฅๆ
ๆจๅบๅ
ๅไบ่งฃๅนถ็ฅๆ๏ผBAAI DataHubๅCOIG-PC HuggingFaceไปๅบ๏ผๅ
ๆฌๅฝๅ็ๆฌๅๅ
จ้จๅๅฒ็ๆฌ๏ผ็ๆๆๆไธ่ฟ่ฅๆๅฝๆบๆบไบบๅทฅๆบ่ฝ็ ็ฉถ้ขๆๆ๏ผๆบๆบไบบๅทฅๆบ่ฝ็ ็ฉถ้ขๅฏนๆฌๅนณๅฐ/ๆฌๅทฅๅ
ทๅๅผๆบๆฐๆฎ้ๅผๆพ่ฎกๅๆฅๆๆ็ป่งฃ้ๆๅๅณๅฎๆใ
ๆจ็ฅๆๅนถ็่งฃ๏ผๅบไบ็ธๅ
ณๆณๅพๆณ่งๆดๆฐๅๅฎๅไปฅๅๆไปฌ้ๅฑฅ่กๆณๅพๅ่งไนๅก็ๅฎข่งๅๅ๏ผๆไปฌไฟ็ๅฏนๆฌๅนณๅฐ/ๆฌๅทฅๅ
ท่ฟ่กไธๅฎๆถๆดๆฐใ็ปดๆค๏ผๆ่
ไธญๆญขไน่ณๆฐธไน
็ปๆญขๆไพๆฌๅนณๅฐ/ๆฌๅทฅๅ
ทๆๅก็ๆๅฉใๆไปฌๅฐๅจๅ็ๆถ้ดๅ
ๅฐๅฏ่ฝๅ็ๅ่ฟฐๆ
ๅฝข้่ฟๅ
ฌๅๆ้ฎไปถ็ญๅ็ๆนๅผๅ็ฅๆจ๏ผๆจๅบๅฝๅๆถๅๅฅฝ็ธๅบ็่ฐๆดๅๅฎๆ๏ผไฝๆไปฌไธๅ ๅ็ๅ่ฟฐไปปไฝๆ
ๅฝขๅฏนๆจ้ ๆ็ไปปไฝๆๅคฑๆฟๆ
ไปปไฝ่ดฃไปปใ
2. ๅผๆบๆฐๆฎ้็ๆๅฉไธปๅผ
ไธบไบไพฟไบๆจๅบไบๅญฆไน ใ็ ็ฉถใๅไธ็็ฎ็ๅผๅฑๆฐๆฎ้่ทๅใไฝฟ็จ็ญๆดปๅจ๏ผๆไปฌๅฏน็ฌฌไธๆนๅๅงๆฐๆฎ้่ฟ่กไบๅฟ
่ฆ็ๆ ผๅผๆดๅใๆฐๆฎๆธ
ๆดใๆ ๆณจใๅ็ฑปใๆณจ้็ญ็ธๅ
ณๅค็็ฏ่๏ผๅฝขๆๅฏไพๆฌๅนณๅฐ/ๆฌๅทฅๅ
ท็จๆทไฝฟ็จ็ๅผๆบๆฐๆฎ้ใ
ๆจ็ฅๆๅนถ็่งฃ๏ผๆไปฌไธๅฏนๅผๆบๆฐๆฎ้ไธปๅผ ็ฅ่ฏไบงๆไธญ็็ธๅ
ณ่ดขไบงๆงๆๅฉ๏ผๅ ๆญคๆไปฌไบฆๆ ็ธๅบไนๅกๅฏนๅผๆบๆฐๆฎ้ๅฏ่ฝๅญๅจ็็ฅ่ฏไบงๆ่ฟ่กไธปๅจ่ฏๅซๅไฟๆค๏ผไฝ่ฟไธๆๅณ็ๆไปฌๆพๅผๅผๆบๆฐๆฎ้ไธปๅผ ็ฝฒๅๆใๅ่กจๆใไฟฎๆนๆๅไฟๆคไฝๅๅฎๆดๆ๏ผๅฆๆ๏ผ็ญไบบ่บซๆงๆๅฉใ่ๅๅงๆฐๆฎ้ๅฏ่ฝๅญๅจ็็ฅ่ฏไบงๆๅ็ธๅบๅๆณๆ็็ฑๅๆๅฉไบบไบซๆใ
ๆญคๅค๏ผๅๆจๅผๆพๅไฝฟ็จ็ปๅ็็ผๆใๅ ๅทฅๅๅค็ๅ็ๅผๆบๆฐๆฎ้๏ผๅนถไธๆๅณ็ๆไปฌๅฏนๅๅงๆฐๆฎ้็ฅ่ฏไบงๆใไฟกๆฏๅ
ๅฎน็ญ็ๅฎใๅ็กฎๆๆ ไบ่ฎฎ็่ฎคๅฏ๏ผๆจๅบๅฝ่ช่ก็ญ้ใไป็ป็ๅซ๏ผไฝฟ็จ็ปๆจ้ๆฉ็ๅผๆบๆฐๆฎ้ใๆจ็ฅๆๅนถๅๆ๏ผ็ ็ฉถ้ขๅฏนๆจ่ช่ก้ๆฉไฝฟ็จ็ๅๅงๆฐๆฎ้ไธ่ดๆไปปไฝๆ ็ผบ้ทๆๆ ็็ต็ๆฟ่ฏบไนๅกๆๆ
ไฟ่ดฃไปปใ
3. ๅผๆบๆฐๆฎ้็ไฝฟ็จ้ๅถ
ๆจไฝฟ็จๆฐๆฎ้ไธๅพไพตๅฎณๆไปฌๆไปปไฝ็ฌฌไธๆน็ๅๆณๆ็๏ผๅ
ๆฌไฝไธ้ไบ่ไฝๆใไธๅฉๆใๅๆ ๆ็ญ็ฅ่ฏไบงๆไธๅ
ถไปๆ็๏ผใ
่ทๅๅผๆบๆฐๆฎ้ๅ๏ผๆจๅบ็กฎไฟๅฏนๅผๆบๆฐๆฎ้็ไฝฟ็จไธ่ถ
่ฟๅๅงๆฐๆฎ้็ๆๅฉไบบไปฅๅ
ฌ็คบๆๅ่ฎฎ็ญๅฝขๅผๆ็กฎ่งๅฎ็ไฝฟ็จ่งๅ๏ผๅ
ๆฌๅๅงๆฐๆฎ็ไฝฟ็จ่ๅดใ็ฎ็ๅๅๆณ็จ้็ญใๆไปฌๅจๆญคๅๆๅฐๆ่ฏทๆจ็ๆ๏ผๅฆๆจๅฏนๅผๆบๆฐๆฎ้็ไฝฟ็จ่ถ
ๅบๅๅงๆฐๆฎ้็ๅๅฎไฝฟ็จ่ๅดๅ็จ้๏ผๆจๅฏ่ฝ้ขไธดไพต็ฏๅๅงๆฐๆฎ้ๆๅฉไบบ็ๅๆณๆ็ไพๅฆ็ฅ่ฏไบงๆ็้ฃ้ฉ๏ผๅนถๅฏ่ฝๆฟๆ
็ธๅบ็ๆณๅพ่ดฃไปปใ
4. ไธชไบบไฟกๆฏไฟๆค
ๅบไบๆๆฏ้ๅถๅๅผๆบๆฐๆฎ้็ๅ
ฌ็ๆง่ดจ็ญๅฎข่งๅๅ ๏ผๆไปฌๆ ๆณไฟ่ฏๅผๆบๆฐๆฎ้ไธญไธๅ
ๅซไปปไฝไธชไบบไฟกๆฏ๏ผๆไปฌไธๅฏนๅผๆบๆฐๆฎ้ไธญๅฏ่ฝๆถๅ็ไธชไบบไฟกๆฏๆฟๆ
ไปปไฝๆณๅพ่ดฃไปปใ
ๅฆๅผๆบๆฐๆฎ้ๆถๅไธชไบบไฟกๆฏ๏ผๆไปฌไธๅฏนๆจไฝฟ็จๅผๆบๆฐๆฎ้ๅฏ่ฝๆถๅ็ไปปไฝไธชไบบไฟกๆฏๅค็่กไธบๆฟๆ
ๆณๅพ่ดฃไปปใๆไปฌๅจๆญคๅๆๅฐๆ่ฏทๆจ็ๆ๏ผๆจๅบไพๆฎใไธชไบบไฟกๆฏไฟๆคๆณใ็ญ็ธๅ
ณๆณๅพๆณ่ง็่งๅฎๅค็ไธชไบบไฟกๆฏใ
ไธบไบ็ปดๆคไฟกๆฏไธปไฝ็ๅๆณๆ็ใๅฑฅ่กๅฏ่ฝ้็จ็ๆณๅพใ่กๆฟๆณ่ง็่งๅฎ๏ผๅฆๆจๅจไฝฟ็จๅผๆบๆฐๆฎ้็่ฟ็จไธญๅ็ฐๆถๅๆ่
ๅฏ่ฝๆถๅไธชไบบไฟกๆฏ็ๅ
ๅฎน๏ผๅบ็ซๅณๅๆญขๅฏนๆฐๆฎ้ไธญๆถๅไธชไบบไฟกๆฏ้จๅ็ไฝฟ็จ๏ผๅนถๅๆถ้่ฟโ6. ๆ่ฏไธ้็ฅโไธญ่ฝฝๆ็่็ณปๆไปฌใ
5. ไฟกๆฏๅ
ๅฎน็ฎก็
ๆไปฌไธๅฏนๅผๆบๆฐๆฎ้ๅฏ่ฝๆถๅ็่ฟๆณไธไธ่ฏไฟกๆฏๆฟๆ
ไปปไฝๆณๅพ่ดฃไปปใ
ๅฆๆจๅจไฝฟ็จๅผๆบๆฐๆฎ้็่ฟ็จไธญๅ็ฐๅผๆบๆฐๆฎ้ๆถๅๆ่
ๅฏ่ฝๆถๅไปปไฝ่ฟๆณไธไธ่ฏไฟกๆฏ๏ผๆจๅบ็ซๅณๅๆญขๅฏนๆฐๆฎ้ไธญๆถๅ่ฟๆณไธไธ่ฏไฟกๆฏ้จๅ็ไฝฟ็จ๏ผๅนถๅๆถ้่ฟโ6. ๆ่ฏไธ้็ฅโไธญ่ฝฝๆ็่็ณปๆไปฌใ
6. ๆ่ฏไธ้็ฅ
ๅฆๆจ่ฎคไธบๅผๆบๆฐๆฎ้ไพต็ฏไบๆจ็ๅๆณๆ็๏ผๆจๅฏ้่ฟ010-50955974่็ณปๆไปฌ๏ผๆไปฌไผๅๆถไพๆณๅค็ๆจ็ไธปๅผ ไธๆ่ฏใ
ไธบไบๅค็ๆจ็ไธปๅผ ๅๆ่ฏ๏ผๆไปฌๅฏ่ฝ้่ฆๆจๆไพ่็ณปๆนๅผใไพตๆ่ฏๆๆๆไปฅๅ่บซไปฝ่ฏๆ็ญๆๆใ่ฏทๆณจๆ๏ผๅฆๆๆจๆถๆๆ่ฏๆ้่ฟฐๅคฑๅฎ๏ผๆจๅฐๆฟๆ
็ฑๆญค้ ๆ็ๅ
จ้จๆณๅพ่ดฃไปป๏ผๅ
ๆฌไฝไธ้ไบๅ็็่ดน็จ่ตๅฟ็ญ๏ผใ
7. ่ดฃไปปๅฃฐๆ
ๆจ็่งฃๅนถๅๆ๏ผๅบไบๅผๆบๆฐๆฎ้็ๆง่ดจ๏ผๆฐๆฎ้ไธญๅฏ่ฝๅ
ๅซๆฅ่ชไธๅๆฅๆบๅ่ดก็ฎ่
็ๆฐๆฎ๏ผๅ
ถ็ๅฎๆงใๅ็กฎๆงใๅฎข่งๆง็ญๅฏ่ฝไผๆๆๅทฎๅผ๏ผๆไปฌๆ ๆณๅฏนไปปไฝๆฐๆฎ้็ๅฏ็จๆงใๅฏ้ ๆง็ญๅๅบไปปไฝๆฟ่ฏบใ
ๅจไปปไฝๆ
ๅตไธ๏ผๆไปฌไธๅฏนๅผๆบๆฐๆฎ้ๅฏ่ฝๅญๅจ็ไธชไบบไฟกๆฏไพตๆใ่ฟๆณไธไธ่ฏไฟกๆฏไผ ๆญใ็ฅ่ฏไบงๆไพตๆ็ญไปปไฝ้ฃ้ฉๆฟๆ
ไปปไฝๆณๅพ่ดฃไปปใ
ๅจไปปไฝๆ
ๅตไธ๏ผๆไปฌไธๅฏนๆจๅ ๅผๆบๆฐๆฎ้้ญๅ็ๆไธไน็ธๅ
ณ็ไปปไฝๆๅคฑ๏ผๅ
ๆฌไฝไธ้ไบ็ดๆฅๆๅคฑใ้ดๆฅๆๅคฑไปฅๅๅฏๅพๅฉ็ๆๅคฑ็ญ๏ผๆฟๆ
ไปปไฝๆณๅพ่ดฃไปปใ
8. ๅ
ถไป
ๅผๆบๆฐๆฎ้ๅคไบไธๆญๅๅฑใๅๅ็้ถๆฎต๏ผๆไปฌๅฏ่ฝๅ ไธๅกๅๅฑใ็ฌฌไธๆนๅไฝใๆณๅพๆณ่งๅๅจ็ญๅๅ ๆดๆฐใ่ฐๆดๆๆไพ็ๅผๆบๆฐๆฎ้่ๅด๏ผๆไธญๆญขใๆๅใ็ปๆญขๅผๆบๆฐๆฎ้ๆไพไธๅกใ
extra_gated_fields:
Name: text
Affiliation: text
Country: text
I agree to use this model for non-commercial use ONLY: checkbox
extra_gated_button_content: "Acknowledge license"
license: unknown
language:
- zh
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: task_type
struct:
- name: major
sequence: string
- name: minor
sequence: string
- name: domain
sequence: string
- name: other
dtype: string
- name: task_name_in_eng
dtype: string
- name: index
dtype: string
splits:
- name: train
num_bytes: 1053129000
num_examples: 744592
download_size: 416315627
dataset_size: 1053129000
---
# COIG Prompt Collection
## License
**Default Licensing for Sub-Datasets Without Specific License Declaration**: In instances where sub-datasets within the COIG-PC Dataset do not have a specific license declaration, the Apache License 2.0 (Apache-2.0) will be the applicable licensing terms by default.
**Precedence of Declared Licensing for Sub-Datasets**: For any sub-dataset within the COIG-PC Dataset that has an explicitly declared license, the terms and conditions of the declared license shall take precedence and govern the usage of that particular sub-dataset.
Users and developers utilizing the COIG-PC Dataset must ensure compliance with the licensing terms as outlined above. It is imperative to review and adhere to the specified licensing conditions of each sub-dataset, as they may vary.
## What is COIG-PC?
The COIG-PC Dataset is a meticulously curated and comprehensive collection of Chinese tasks and data, designed to facilitate the fine-tuning and optimization of language models for Chinese natural language processing (NLP). The dataset aims to provide researchers and developers with a rich set of resources to improve the capabilities of language models in handling Chinese text, which can be utilized in various fields such as text generation, information extraction, sentiment analysis, machine translation, among others.
If you think COIG-PC is too huge, please refer to [COIG-PC-Lite](https://huggingface.co/datasets/BAAI/COIG-PC-Lite) which is a subset of COIG-PC with only 200 samples from each task file.
## Why COIG-PC?
The COIG-PC Dataset is an invaluable resource for the domain of natural language processing (NLP) for various compelling reasons:
**Addressing Language Complexity**: Chinese is known for its intricacy, with a vast array of characters and diverse grammatical structures. A specialized dataset like COIG-PC, which is tailored for the Chinese language, is essential to adequately address these complexities during model training.
**Comprehensive Data Aggregation**: The COIG-PC Dataset is a result of an extensive effort in integrating almost all available Chinese datasets in the market. This comprehensive aggregation makes it one of the most exhaustive collections for Chinese NLP.
**Data Deduplication and Normalization**: The COIG-PC Dataset underwent rigorous manual processing to eliminate duplicate data and perform normalization. This ensures that the dataset is free from redundancy, and the data is consistent and well-structured, making it more user-friendly and efficient for model training.
**Fine-tuning and Optimization**: The datasetโs instruction-based phrasing facilitates better fine-tuning and optimization of language models. This structure allows models to better understand and execute tasks, which is particularly beneficial in improving performance on unseen or novel tasks.
The COIG-PC Dataset, with its comprehensive aggregation, meticulous selection, deduplication, and normalization of data, stands as an unmatched resource for training and optimizing language models tailored for the Chinese language and culture. It addresses the unique challenges of Chinese language processing and serves as a catalyst for advancements in Chinese NLP.
## Who builds COIG-PC?
The bedrock of COIG-PC is anchored in the dataset furnished by stardust.ai, which comprises an aggregation of data collected from the Internet.
And COIG-PC is the result of a collaborative effort involving engineers and experts from over twenty distinguished universities both domestically and internationally. Due to space constraints, it is not feasible to list all of them; however, the following are a few notable institutions among the collaborators:
- Beijing Academy of Artificial Intelligence, China
<img src="https://huggingface.co/datasets/BAAI/COIG-PC-core/resolve/main/assets/baai.png" alt= โBAAIโ height="100" width="150">
- Peking University, China
<img src="https://huggingface.co/datasets/BAAI/COIG-PC-core/resolve/main/assets/pku.png" alt= โPKUโ height="100" width="200">
- The Hong Kong University of Science and Technology (HKUST), China
<img src="https://huggingface.co/datasets/BAAI/COIG-PC-core/resolve/main/assets/hkust.png" alt= โHKUSTโ height="100" width="200">
- The University of Waterloo, Canada
<img src="https://huggingface.co/datasets/BAAI/COIG-PC-core/resolve/main/assets/waterloo.png" alt= โWaterlooโ height="100" width="150">
- The University of Sheffield, United Kingdom
<img src="https://huggingface.co/datasets/BAAI/COIG-PC-core/resolve/main/assets/sheffield.png" alt= โSheffieldโ height="100" width="200">
- Beijing University of Posts and Telecommunications, China
<img src="https://huggingface.co/datasets/BAAI/COIG-PC-core/resolve/main/assets/bupt.png" alt= โBUPTโ height="100" width="200">
- [Multimodal Art Projection](https://huggingface.co/m-a-p)
<img src="https://huggingface.co/datasets/BAAI/COIG-PC-core/resolve/main/assets/map.png" alt= โM.A.Pโ height="100" width="200">
- stardust.ai, China
<img src="https://huggingface.co/datasets/BAAI/COIG-PC-core/resolve/main/assets/stardust.png" alt= โstardust.aiโ height="100" width="200">
- LinkSoul.AI, China
<img src="https://huggingface.co/datasets/BAAI/COIG-PC-core/resolve/main/assets/linksoul.png" alt= โlinksoul.aiโ height="100" width="200">
For the detailed list of engineers involved in the creation and refinement of COIG-PC, please refer to the paper that will be published subsequently. This paper will provide in-depth information regarding the contributions and the specifics of the datasetโs development process.
## How to use COIG-PC?
COIG-PC is structured in a **.jsonl** file format. Each line in the file represents a single data record and is structured in JSON (JavaScript Object Notation) format. Below is a breakdown of the elements within each line:
**instruction**: This is a text string that provides the instruction for the task. For example, it might tell the model what to do with the input data.
**input**: This is the input data that the model needs to process. In the context of translation, it would be the text that needs to be translated.
**output**: This contains the expected output data after processing the input. In the context of translation, it would be the translated text.
**split**: Indicates the official split of the original dataset, which is used to categorize data for different phases of model training and evaluation. It can be 'train', 'test', 'valid', etc.
**task_type**: Contains major and minor categories for the dataset. Major categories are broader, while minor categories can be more specific subcategories.
**domain**: Indicates the domain or field to which the data belongs.
**other**: This field can contain additional information or metadata regarding the data record. If there is no additional information, it may be set to null.
### Example
Here is an example of how a line in the COIG-PC dataset might be structured:
```
{
"instruction": "่ฏทๆไธ้ข็ไธญๆๅฅๅญ็ฟป่ฏๆ่ฑๆ",
"input": "ๆ็ฑไฝ ใ",
"output": "I love you.",
"split": "train",
"task_type": {
"major": ["็ฟป่ฏ"],
"minor": ["็ฟป่ฏ", "ไธญ่ฏ่ฑ"]
},
"domain": ["้็จ"],
"other": null
}
```
In this example:
**instruction** tells the model to translate the following Chinese sentence into English.
**input** contains the Chinese text "ๆ็ฑไฝ " which means "I love you".
**output** contains the expected translation in English: "I love you".
**split** indicates that this data record is part of the training set.
**task_type** specifies that the major category is "Translation" and the minor categories are "Translation" and "Chinese to English".
**domain** specifies that this data record belongs to the general domain.
**other** is set to null as there is no additional information for this data record.
## Update: Aug. 30, 2023
- v1.0: First version of COIG-PC-core.
## COIG-PC Citation
If you want to cite COIG-PC-core dataset, you could use this:
```
```
## Contact Us
To contact us feel free to create an Issue in this repository.
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deal_or_no_dialog | 2022-11-18T19:57:59.000Z | [
"task_categories:conversational",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:1706.05125",
"region:us"
] | null | A large dataset of human-human negotiations on a multi-issue bargaining task, where agents who cannot observe each otherโs reward functions must reach anagreement (o a deal) via natural language dialogue. | @article{lewis2017deal,
title={Deal or no deal? end-to-end learning for negotiation dialogues},
author={Lewis, Mike and Yarats, Denis and Dauphin, Yann N and Parikh, Devi and Batra, Dhruv},
journal={arXiv preprint arXiv:1706.05125},
year={2017}
} | 5 | 149 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- conversational
task_ids: []
paperswithcode_id: negotiation-dialogues-dataset
pretty_name: Deal or No Deal Negotiator
dataset_info:
- config_name: dialogues
features:
- name: input
sequence:
- name: count
dtype: int32
- name: value
dtype: int32
- name: dialogue
dtype: string
- name: output
dtype: string
- name: partner_input
sequence:
- name: count
dtype: int32
- name: value
dtype: int32
splits:
- name: train
num_bytes: 3860624
num_examples: 10095
- name: test
num_bytes: 396258
num_examples: 1052
- name: validation
num_bytes: 418491
num_examples: 1087
download_size: 5239072
dataset_size: 4675373
- config_name: self_play
features:
- name: input
sequence:
- name: count
dtype: int32
- name: value
dtype: int32
splits:
- name: train
num_bytes: 261512
num_examples: 8172
download_size: 98304
dataset_size: 261512
---
# Dataset Card for Deal or No Deal Negotiator
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [Dataset Repository](https://github.com/facebookresearch/end-to-end-negotiator)
- **Paper:** [Deal or No Deal? End-to-End Learning for Negotiation Dialogues](https://arxiv.org/abs/1706.05125)
### Dataset Summary
A large dataset of human-human negotiations on a multi-issue bargaining task, where agents who cannot observe each otherโs reward functions must reach an agreement (or a deal) via natural language dialogue.
### Supported Tasks and Leaderboards
Train end-to-end models for negotiation
### Languages
The text in the dataset is in English
## Dataset Structure
### Data Instances
{'dialogue': 'YOU: i love basketball and reading <eos> THEM: no . i want the hat and the balls <eos> YOU: both balls ? <eos> THEM: yeah or 1 ball and 1 book <eos> YOU: ok i want the hat and you can have the rest <eos> THEM: okay deal ill take the books and the balls you can have only the hat <eos> YOU: ok <eos> THEM: <selection>',
'input': {'count': [3, 1, 2], 'value': [0, 8, 1]},
'output': 'item0=0 item1=1 item2=0 item0=3 item1=0 item2=2',
'partner_input': {'count': [3, 1, 2], 'value': [1, 3, 2]}}
### Data Fields
`dialogue`: The dialogue between the agents. \
`input`: The input of the firt agent. \
`partner_input`: The input of the other agent. \
`count`: The count of the three available items. \
`value`: The value of the three available items. \
`output`: Describes how many of each of the three item typesare assigned to each agent
### Data Splits
| | train | validation | test |
|------------|------:|-----------:|-----:|
| dialogues | 10095 | 1087 | 1052 |
| self_play | 8172 | NA | NA |
## 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?
Human workers using Amazon Mechanical Turk. They were paid $0.15 per dialogue, with a $0.05 bonus for maximal scores. Only workers based in the United States with a 95% approval rating and at least 5000 previous HITs were used.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
The project is licenced under CC-by-NC
### Citation Information
```
@article{lewis2017deal,
title={Deal or no deal? end-to-end learning for negotiation dialogues},
author={Lewis, Mike and Yarats, Denis and Dauphin, Yann N and Parikh, Devi and Batra, Dhruv},
journal={arXiv preprint arXiv:1706.05125},
year={2017}
}
```
### Contributions
Thanks to [@moussaKam](https://github.com/moussaKam) for adding this dataset. | 5,332 | [
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eu_regulatory_ir | 2022-11-18T20:01:28.000Z | [
"task_categories:text-retrieval",
"task_ids:document-retrieval",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-sa-4.0",
"document-to-document-retrieval",
"arxiv:2101.10726",
"region:us"
] | null | EURegIR: Regulatory Compliance IR (EU/UK) | @inproceedings{chalkidis-etal-2021-regir,
title = "Regulatory Compliance through Doc2Doc Information Retrieval: A case study in EU/UK legislation where text similarity has limitations",
author = "Chalkidis, Ilias and Fergadiotis, Emmanouil and Manginas, Nikos and Katakalou, Eva, and Malakasiotis, Prodromos",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2021)",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2101.10726",
} | 1 | 149 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-retrieval
task_ids:
- document-retrieval
paperswithcode_id: null
pretty_name: the RegIR datasets
tags:
- document-to-document-retrieval
dataset_info:
- config_name: eu2uk
features:
- name: document_id
dtype: string
- name: publication_year
dtype: string
- name: text
dtype: string
- name: relevant_documents
sequence: string
splits:
- name: train
num_bytes: 20665038
num_examples: 1400
- name: test
num_bytes: 8844145
num_examples: 300
- name: validation
num_bytes: 5852814
num_examples: 300
- name: uk_corpus
num_bytes: 502468359
num_examples: 52515
download_size: 119685577
dataset_size: 537830356
- config_name: uk2eu
features:
- name: document_id
dtype: string
- name: publication_year
dtype: string
- name: text
dtype: string
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sequence: string
splits:
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num_bytes: 55144655
num_examples: 1500
- name: test
num_bytes: 14810460
num_examples: 300
- name: validation
num_bytes: 15175644
num_examples: 300
- name: eu_corpus
num_bytes: 57212422
num_examples: 3930
download_size: 31835104
dataset_size: 142343181
---
# Dataset Card for the RegIR datasets
## 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://archive.org/details/eacl2021_regir_datasets
- **Repository:** https://archive.org/details/eacl2021_regir_datasets
- **Paper:** https://arxiv.org/abs/2101.10726
- **Leaderboard:** N/A
- **Point of Contact:** [Ilias Chalkidis](mailto:ihalk@aueb.gr)
### Dataset Summary
The European Union (EU) has a legislation scheme analogous to regulatory compliance for organizations. According to the Treaty on the Functioning of the European Union (TFEU), all published EU directives must take effect at the national level. Thus, all EU member states must adopt a law to transpose a newly issued directive within the period set by the directive (typically 2 years).
Here, we have two datasets, EU2UK and UK2EU, containing EU directives and UK regulations, which can serve both as queries and documents under the ground truth assumption that a UK law is relevant to the EU directives it transposes and vice versa.
### Supported Tasks and Leaderboards
The dataset supports:
**EU2UK** (`eu2uk`): Given an EU directive *Q*, retrieve the set of relevant documents from the pool of all available UK regulations. Relevant documents are those that transpose the EU directive (*Q*).
**UK2EU** (`uk2eu`): Given a UK regulation *Q*, retrieve the set of relevant documents from the pool of all available EU directives. Relevant documents are those that are being transposed by the UK regulations (*Q*).
### Languages
All documents are written in English.
## Dataset Structure
### Data Instances
```json
{
"document_id": "31977L0794",
"publication_year": "1977",
"text": "Commission Directive 77/794/EEC ... of agricultural levies and customs duties",
"relevant_documents": ["UKPGA19800048", "UKPGA19770036"]
}
```
### Data Fields
The following data fields are provided for query documents (`train`, `dev`, `test`):
`document_id`: (**str**) The ID of the document.\
`publication_year`: (**str**) The publication year of the document.\
`text`: (**str**) The text of the document.\
`relevant_documents`: (**List[str]**) The list of relevant documents, as represented by their `document_id`.
The following data fields are provided for corpus documents (`corpus`):
`document_id`: (**str**) The ID of the document.\
`publication_year`: (**str**) The publication year of the document.\
`text`: (**str**) The text of the document.\
### Data Splits
#### EU2UK dataset
| Split | No of Queries | Avg. relevant documents |
| ------------------- | ------------------------------------ | --- |
| Train | 1,400 | 1.79 |
|Development | 300 | 2.09 |
|Test | 300 | 1.74 |
Document Pool (Corpus): 52,515 UK regulations
#### UK2EU dataset
| Split | No of Queries | Avg. relevant documents |
| ------------------- | ------------------------------------ | --- |
| Train | 1,500 | 1.90 |
|Development | 300 | 1.46 |
|Test | 300 | 1.29 |
Document Pool (Corpus): 3,930 EU directives
## Dataset Creation
### Curation Rationale
The dataset was curated by Chalkidis et al. (2021).\
The transposition pairs are publicly available by the Publications Office of EU (https://publications.europa.eu/en).
### Source Data
#### Initial Data Collection and Normalization
The original data are available at EUR-Lex portal (https://eur-lex.europa.eu) and Legislation.GOV.UK (http://legislation.gov.uk/) in an unprocessed format.\
The transposition pairs are provided by the EU member states (in our case, UK) and were downloaded from the SPARQL endpoint of the Publications Office of EU (http://publications.europa.eu/webapi/rdf/sparql).\
For more information on the dataset curation, read Chalkidis et al. (2021).
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
* The original data are available at EUR-Lex portal (https://eur-lex.europa.eu) and Legislation.GOV.UK (http://legislation.gov.uk/) in an unprocessed format.
* The transposition pairs are provided by the EU member states (in our case, UK) and were downloaded from the SPARQL endpoint of the Publications Office of EU (http://publications.europa.eu/webapi/rdf/sparql).
#### Who are the annotators?
Publications Office of EU (https://publications.europa.eu/en)
### Personal and Sensitive Information
The dataset does not include personal or sensitive information.
## 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
Chalkidis et al. (2021)
### Licensing Information
**EU Data**
ยฉ European Union, 1998-2021
The Commissionโs document reuse policy is based on Decision 2011/833/EU. Unless otherwise specified, you can re-use the legal documents published in EUR-Lex for commercial or non-commercial purposes.
The copyright for the editorial content of this website, the summaries of EU legislation and the consolidated texts, which is owned by the EU, is licensed under the Creative Commons Attribution 4.0 International licenceโโ . This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.
Source: https://eur-lex.europa.eu/content/legal-notice/legal-notice.html \
Read more: https://eur-lex.europa.eu/content/help/faq/reuse-contents-eurlex.html
**UK Data**
You are encouraged to use and re-use the Information that is available under this licence freely and flexibly, with only a few conditions.
You are free to:
- copy, publish, distribute and transmit the Information;
- adapt the Information;
- exploit the Information commercially and non-commercially for example, by combining it with other Information, or by including it in your own product or application.
You must (where you do any of the above):
acknowledge the source of the Information in your product or application by including or linking to any attribution statement specified by the Information Provider(s) and, where possible, provide a link to this licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/.
### Citation Information
*Ilias Chalkidis, Manos Fergadiotis, Nikos Manginas, Eva Katakalou and Prodromos Malakasiotis.*
*Regulatory Compliance through Doc2Doc Information Retrieval: A case study in EU/UK legislation where text similarity has limitations*
*Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2021). Online. 2021*
```
@inproceedings{chalkidis-etal-2021-regir,
title = "Regulatory Compliance through Doc2Doc Information Retrieval: A case study in EU/UK legislation where text similarity has limitations",
author = "Chalkidis, Ilias and Fergadiotis, Manos and Manginas, Nikos and Katakalou, Eva, and Malakasiotis, Prodromos",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2021)",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2101.10726",
}
```
### Contributions
Thanks to [@iliaschalkidis](https://github.com/iliaschalkidis) for adding this dataset. | 9,824 | [
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nli_tr | 2023-06-01T14:59:47.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"task_ids:semantic-similarity-scoring",
"task_ids:text-scoring",
"annotations_creators:expert-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|snli",
"source_datasets:extended|multi_nli",
"language:tr",
"license:cc-by-3.0",
"license:cc-by-4.0",
"license:cc-by-sa-3.0",
"license:mit",
"license:other",
"region:us"
] | null | \
The Natural Language Inference in Turkish (NLI-TR) is a set of two large scale datasets that were obtained by translating the foundational NLI corpora (SNLI and MNLI) using Amazon Translate. | \
@inproceedings{budur-etal-2020-data,
title = "Data and Representation for Turkish Natural Language Inference",
author = "Budur, Emrah and
\"{O}zรงelik, Rฤฑza and
G\"{u}ng\"{o}r, Tunga",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
abstract = "Large annotated datasets in NLP are overwhelmingly in English. This is an obstacle to progress in other languages. Unfortunately, obtaining new annotated resources for each task in each language would be prohibitively expensive. At the same time, commercial machine translation systems are now robust. Can we leverage these systems to translate English-language datasets automatically? In this paper, we offer a positive response for natural language inference (NLI) in Turkish. We translated two large English NLI datasets into Turkish and had a team of experts validate their translation quality and fidelity to the original labels. Using these datasets, we address core issues of representation for Turkish NLI. We find that in-language embeddings are essential and that morphological parsing can be avoided where the training set is large. Finally, we show that models trained on our machine-translated datasets are successful on human-translated evaluation sets. We share all code, models, and data publicly.",
} | 5 | 149 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- machine-generated
language:
- tr
license:
- cc-by-3.0
- cc-by-4.0
- cc-by-sa-3.0
- mit
- other
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|snli
- extended|multi_nli
task_categories:
- text-classification
task_ids:
- natural-language-inference
- semantic-similarity-scoring
- text-scoring
paperswithcode_id: nli-tr
pretty_name: Natural Language Inference in Turkish
license_details: Open Portion of the American National Corpus
dataset_info:
- config_name: snli_tr
features:
- name: idx
dtype: int32
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
splits:
- name: train
num_bytes: 71175743
num_examples: 550152
- name: validation
num_bytes: 1359639
num_examples: 10000
- name: test
num_bytes: 1355409
num_examples: 10000
download_size: 40328942
dataset_size: 73890791
- config_name: multinli_tr
features:
- name: idx
dtype: int32
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
splits:
- name: train
num_bytes: 75524150
num_examples: 392702
- name: validation_matched
num_bytes: 1908283
num_examples: 10000
- name: validation_mismatched
num_bytes: 2039392
num_examples: 10000
download_size: 75518512
dataset_size: 79471825
config_names:
- multinli_tr
- snli_tr
---
# Dataset Card for "nli_tr"
## 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/boun-tabi/NLI-TR](https://github.com/boun-tabi/NLI-TR)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 115.85 MB
- **Size of the generated dataset:** 153.36 MB
- **Total amount of disk used:** 269.21 MB
### Dataset Summary
The Natural Language Inference in Turkish (NLI-TR) is a set of two large scale datasets that were obtained by translating the foundational NLI corpora (SNLI and MNLI) using Amazon Translate.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### multinli_tr
- **Size of downloaded dataset files:** 75.52 MB
- **Size of the generated dataset:** 79.47 MB
- **Total amount of disk used:** 154.99 MB
An example of 'validation_matched' looks as follows.
```
This example was too long and was cropped:
{
"hypothesis": "Mrinal Sen'in รงalฤฑลmalarฤฑnฤฑn รงoฤu Avrupa koleksiyonlarฤฑnda bulunabilir.",
"idx": 7,
"label": 1,
"premise": "\"Kalkรผta, sanatsal yaratฤฑcฤฑlฤฑฤa dair herhangi bir iddiaya sahip olan tek diฤer รผretim merkezi gibi gรถrรผnรผyor, ama ironik bir ลek..."
}
```
#### snli_tr
- **Size of downloaded dataset files:** 40.33 MB
- **Size of the generated dataset:** 73.89 MB
- **Total amount of disk used:** 114.22 MB
An example of 'train' looks as follows.
```
{
"hypothesis": "Yaลlฤฑ bir adam, kฤฑzฤฑnฤฑn iลten รงฤฑkmasฤฑnฤฑ bekรงiyken suyunu iรงer.",
"idx": 9,
"label": 1,
"premise": "Parlak renkli gรถmlek รงalฤฑลanlarฤฑ arka planda gรผlรผmseme iken yaลlฤฑ bir adam bir kahve dรผkkanฤฑnda kรผรงรผk bir masada onun portakal suyu ile oturur."
}
```
### Data Fields
The data fields are the same among all splits.
#### multinli_tr
- `idx`: a `int32` feature.
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
#### snli_tr
- `idx`: a `int32` feature.
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
### Data Splits
#### multinli_tr
| |train |validation_matched|validation_mismatched|
|-----------|-----:|-----------------:|--------------------:|
|multinli_tr|392702| 10000| 10000|
#### snli_tr
| |train |validation|test |
|-------|-----:|---------:|----:|
|snli_tr|550152| 10000|10000|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@inproceedings{budur-etal-2020-data,
title = "Data and Representation for Turkish Natural Language Inference",
author = "Budur, Emrah and
"{O}zรงelik, Rฤฑza and
G"{u}ng"{o}r, Tunga",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
abstract = "Large annotated datasets in NLP are overwhelmingly in English. This is an obstacle to progress in other languages. Unfortunately, obtaining new annotated resources for each task in each language would be prohibitively expensive. At the same time, commercial machine translation systems are now robust. Can we leverage these systems to translate English-language datasets automatically? In this paper, we offer a positive response for natural language inference (NLI) in Turkish. We translated two large English NLI datasets into Turkish and had a team of experts validate their translation quality and fidelity to the original labels. Using these datasets, we address core issues of representation for Turkish NLI. We find that in-language embeddings are essential and that morphological parsing can be avoided where the training set is large. Finally, we show that models trained on our machine-translated datasets are successful on human-translated evaluation sets. We share all code, models, and data publicly.",
}
```
### Contributions
Thanks to [@e-budur](https://github.com/e-budur) for adding this dataset. | 9,508 | [
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] |
strombergnlp/x-stance | 2022-10-25T21:45:25.000Z | [
"task_categories:text-classification",
"task_ids:fact-checking",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"language:de",
"language:fr",
"license:mit",
"stance-detection",
"arxiv:2003.08385",
"region:us"
] | strombergnlp | The x-stance dataset contains more than 150 political questions, and 67k comments written by candidates on those questions. The comments are partly German, partly French and Italian. The data have been extracted from the Swiss voting advice platform Smartvote. | @inproceedings{vamvas2020xstance,
author = "Vamvas, Jannis and Sennrich, Rico",
title = "{X-Stance}: A Multilingual Multi-Target Dataset for Stance Detection",
booktitle = "Proceedings of the 5th Swiss Text Analytics Conference (SwissText) \& 16th Conference on Natural Language Processing (KONVENS)",
address = "Zurich, Switzerland",
year = "2020",
month = "jun",
url = "http://ceur-ws.org/Vol-2624/paper9.pdf"
} | 1 | 149 | 2022-05-18T09:55:43 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- de
- fr
license:
- mit
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets: []
task_categories:
- text-classification
task_ids:
- fact-checking
pretty_name: X-Stance
tags:
- stance-detection
---
# Dataset Card for X-Stance
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [https://github.com/ZurichNLP/xstance](https://github.com/ZurichNLP/xstance)
- **Paper:** [http://ceur-ws.org/Vol-2624/paper9.pdf](http://ceur-ws.org/Vol-2624/paper9.pdf), [https://arxiv.org/abs/2003.08385](https://arxiv.org/abs/2003.08385)
- **Point of Contact:** [Jannis Vamvas](https://twitter.com/j_vamvas)
### Dataset Summary
The x-stance dataset contains more than 150 political questions, and 67k comments written by candidates on those questions. The comments are partly German, partly French and Italian. The data have been extracted from the Swiss voting advice platform Smartvote.
### Languages
German, French/Italian
## Dataset Structure
### Data Instances
An example of 'train' looks as follows:
```
{
'id': '0',
'question': 'Eine Volksinitiative fordert, dass die Gesamtflรคche der Bauzonen in der Schweiz fรผr die nรคchsten 20 Jahre auf dem heutigen Stand begrenzt wird. Befรผrworten Sie dieses Anliegen?',
'comment': 'Eine fixe Grรถsse verbieten, ist das falsche Mittel', '
'label': 0
}
```
### Data Fields
- `id`: a 'string' feature.
- `question`: a 'string' expressing a claim/topic.
- `comment`: a 'string' to be classified for its stance to the source.
- `label`:
```
0: "AGAINST",
1: "FAVOR"
```
### Data Splits
|languages|name|instances|
|---------|----|----:|
|de|train|33850|
|de|validation|2871|
|de|test|11891|
|fr|train|11790|
|fr|validation|1055|
|fr|test|5814|
## 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 License](https://github.com/ZurichNLP/xstance/blob/master/LICENSE)
### Citation Information
```
@article{vamvas2020x,
title={X-stance: A multilingual multi-target dataset for stance detection},
author={Vamvas, Jannis and Sennrich, Rico},
journal={arXiv preprint arXiv:2003.08385},
year={2020}
}
```
### Contributions
Thanks to [mkonxd](https://github.com/mkonxd), [leondz](https://github.com/leondz) for adding this dataset.
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] |
vietgpt/binhvq_news_vi | 2023-03-30T18:58:53.000Z | [
"task_categories:text-generation",
"size_categories:10M<n<100M",
"language:vi",
"LM",
"region:us"
] | vietgpt | null | null | 0 | 149 | 2023-02-21T20:08:06 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 8211350978.574438
num_examples: 19365593
download_size: 4780706833
dataset_size: 8211350978.574438
task_categories:
- text-generation
language:
- vi
tags:
- LM
size_categories:
- 10M<n<100M
---
# Binhvq News
- Source: https://github.com/binhvq/news-corpus
- Num examples: 19,365,593
- Language: Vietnamese
```python
from datasets import load_dataset
load_dataset("tdtunlp/binhvq_news_vi")
``` | 507 | [
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andstor/the_pile_github | 2023-03-20T23:39:53.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:text-classification",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:other",
"arxiv:2101.00027",
"arxiv:2201.07311",
"region:us"
] | andstor | The Pile is a 825 GiB diverse, open source language modelling data set that consists of 22 smaller, high-quality
datasets combined together. | @misc{gao2020pile,
title={The Pile: An 800GB Dataset of Diverse Text for Language Modeling},
author={Leo Gao and Stella Biderman and Sid Black and Laurence Golding and Travis Hoppe and Charles Foster and Jason Phang and Horace He and Anish Thite and Noa Nabeshima and Shawn Presser and Connor Leahy},
year={2020},
eprint={2101.00027},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | 3 | 149 | 2023-03-07T15:53:05 | ---
annotations_creators:
- no-annotation
language:
- en
language_creators:
- found
license:
- other
multilinguality:
- monolingual
pretty_name: The Pile GitHub
size_categories: []
source_datasets:
- original
tags: []
task_categories:
- text-generation
- fill-mask
- text-classification
task_ids: []
---
# Dataset Card for The Pile GitHub
## Table of Contents
- [Dataset Card for Smart Contracts](#dataset-card-for-the-pile-github)
- [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)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [ElutherAI](https://pile.eleuther.ai)
- **Repository:** [GitHub](https://github.com/andstor/the-pile-github)
- **Paper:** [arXiv](https://arxiv.org/abs/2101.00027)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
This is the GitHub subset of EleutherAi/The Pile dataset and contains GitHub repositories. The programming languages are identified using the [guesslang library](https://github.com/yoeo/guesslang). A total of 54 programming languages are included in the dataset.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The following languages are covered by the dataset:
```
'Assembly', 'Batchfile', 'C', 'C#', 'C++', 'CMake', 'COBOL', 'CSS', 'CSV', 'Clojure', 'CoffeeScript', 'DM', 'Dart', 'Dockerfile', 'Elixir', 'Erlang', 'Fortran', 'Go', 'Groovy', 'HTML', 'Haskell', 'INI', 'JSON', 'Java', 'JavaScript', 'Julia', 'Kotlin', 'Lisp', 'Lua', 'Makefile', 'Markdown', 'Matlab', 'None', 'OCaml', 'Objective-C', 'PHP', 'Pascal', 'Perl', 'PowerShell', 'Prolog', 'Python', 'R', 'Ruby', 'Rust', 'SQL', 'Scala', 'Shell', 'Swift', 'TOML', 'TeX', 'TypeScript', 'Verilog', 'Visual Basic', 'XML', 'YAML'
```
The [guesslang library](https://github.com/yoeo/guesslang) is used to identify the programming languages. It has a guessing accuracy of above 90%. Hence, there will be some misclassifications in the language identification.
## Dataset Structure
### Data Instances
[More Information Needed]
```
{
'text': ...,
'meta': {'language': ...}
}
```
### Data Fields
- `text` (`string`): the source code.
- `meta` (`dict`): the metadata of the source code.
- `language` (`string`): the programming language of the source code.
### Data Splits
[More Information Needed]
| | train | validation | test |
|-------------------------|------:|-----------:|-----:|
| Input Sentences | | | |
| Average Sentence Length | | | |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
The data is purely a subset of the [EleutherAI/The Pile dataset](https://huggingface.co/datasets/the_pile). See the original [dataset](https://arxiv.org/abs/2201.07311) for more details.
## Additional Information
### Licensing Information
The Pile dataset was released on January 1st, 2021. It is licensed under the MIT License. See the [dataset](https://arxiv.org/abs/2201.07311) for more details.
### Citation Information
Provide the [BibTex](http://www.bibtex.org/)-formatted reference for the dataset. For example:
```
@article{pile,
title={The {P}ile: An 800GB Dataset of Diverse Text for Language Modeling},
author={Gao, Leo and Biderman, Stella and Black, Sid and Golding, Laurence and Hoppe, Travis and Foster, Charles and Phang, Jason and He, Horace and Thite, Anish and Nabeshima, Noa and Presser, Shawn and Leahy, Connor},
journal={arXiv preprint arXiv:2101.00027},
year={2020}
}
```
### Contributions
Thanks to [@andstor](https://github.com/andstor) for adding this dataset. | 4,286 | [
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] |
gia-project/gia-dataset | 2023-09-05T06:36:39.000Z | [
"task_categories:reinforcement-learning",
"task_categories:text-generation",
"task_categories:question-answering",
"annotations_creators:found",
"annotations_creators:machine-generated",
"source_datasets:conceptual-captions",
"source_datasets:ok-vqa",
"source_datasets:oscar",
"license:apache-2.0",
"imitation-learning",
"reinforcement-learning",
"text-generation",
"question-answering",
"generalist-agent",
"arxiv:2303.03915",
"region:us"
] | gia-project | GIA dataset. | null | 1 | 149 | 2023-03-09T20:58:36 | ---
license: apache-2.0
tags:
- imitation-learning
- reinforcement-learning
- text-generation
- question-answering
- generalist-agent
annotations_creators:
- found
- machine-generated
pretty_name: GIA-dataset
size_categories:
- {number_of_elements_in_dataset} # Example: n<1K, 100K<n<1M, โฆ
source_datasets:
- conceptual-captions
- ok-vqa
- oscar
task_categories:
- reinforcement-learning # Atari, Mujoco, Metaworld, BabyAI
- text-generation # OSCAR
- question-answering # OK-VQA, conceptual-captions
dataset_info:
features:
- name: text
dtype: string
- name: images
dtype: image
- name: image_observations
sequence:
dtype: image
- name: text_observations
sequence:
dtype: string
- name: discrete_observations
sequence:
sequence:
dtype: int64
- name: continuous_observations
sequence:
sequence:
dtype: float32
- name: continuous_actions
sequence:
sequence:
dtype: float32
- name: discrete_actions
sequence:
dtype: int64
- name: rewards
sequence:
dtype: float32
splits:
- name: train
num_examples:
- name: test
num_examples:
download_size:
dataset_size:
---
# GIA Dataset
## Dataset Description
The GIA dataset combines a wide range of individual datasets. It includes expert demonstrations by expert RL agents, image and caption pairs, textual data and more. The GIA dataset is part of the GIA project, which aims to build a multimodal generalist agent.
### Usage
```python
>>> from datasets import load_dataset
>>> dataset = load_dataset("gia-project/gia-dataset", "metaworld-assembly")
>>> first_episode = dataset["train"][0]
>>> first_episode.keys()
dict_keys(['continuous_observations', 'continuous_actions', 'rewards'])
>>> len(first_episode["rewards"])
500
>>> first_episode["continuous_actions"][0]
[6.459120273590088, 2.2422609329223633, -5.914587020874023, -19.799840927124023]
```
## Dataset Structure
### Data Instances
<details>
<summary>Click to expand the score information for each task</summary>
The following table presents a comparative analysis of scores across various domains and tasks. The scores highlight the performance difference between a random agent and the episodes recorded in our dataset.
| Task | Random Agent Score | Dataset Episode Score |
| ----------------------------------- | :-----------------: | :-------------------: |
| **Atari** | | |
| atari-alien | 205.50 ยฑ 111.97 | 16912.50 ยฑ 7087.42 |
| atari-amidar | 2.38 ยฑ 2.50 | 2164.71 ยฑ 1229.47 |
| atari-assault | 262.50 ยฑ 89.61 | 15699.12 ยฑ 9572.12 |
| atari-asterix | 213.50 ยฑ 110.87 | 3699.62 ยฑ 2421.30 |
| atari-asteroids | 856.40 ยฑ 434.32 | 177011.05 ยฑ 35334.20 |
| atari-atlantis | 17764.00 ยฑ 6662.43 | 320679.59 ยฑ 418247.37 |
| atari-bankheist | 13.40 ยฑ 11.07 | 1322.43 ยฑ 60.84 |
| atari-battlezone | 2170.00 ยฑ 2121.58 | 295592.59 ยฑ 161960.96 |
| atari-beamrider | 357.28 ยฑ 143.97 | 29589.35 ยฑ 16132.96 |
| atari-berzerk | 160.10 ยฑ 118.87 | 57085.26 ยฑ 13104.53 |
| atari-bowling | 23.81 ยฑ 6.07 | 20.40 ยฑ 7.29 |
| atari-boxing | 0.52 ยฑ 4.37 | 97.97 ยฑ 3.77 |
| atari-breakout | 1.24 ยฑ 1.30 | 702.97 ยฑ 203.62 |
| atari-centipede | 2150.06 ยฑ 1113.28 | 11624.29 ยฑ 4918.34 |
| atari-choppercommand | 875.00 ยฑ 416.98 | 90990.62 ยฑ 270876.93 |
| atari-crazyclimber | 7376.00 ยฑ 2253.09 | 179296.94 ยฑ 39862.06 |
| atari-defender | 3417.50 ยฑ 1443.41 | 351958.33 ยฑ 40466.82 |
| atari-demonattack | 165.55 ยฑ 92.93 | 92195.25 ยฑ 26174.79 |
| atari-doubledunk | -18.54 ยฑ 3.07 | 20.94 ยฑ 3.65 |
| atari-enduro | 0.00 ยฑ 0.00 | 2292.22 ยฑ 147.54 |
| atari-fishingderby | -93.90 ยฑ 3.51 | 7.18 ยฑ 25.06 |
| atari-freeway | 0.01 ยฑ 0.10 | 33.88 ยฑ 0.35 |
| atari-frostbite | 67.60 ยฑ 37.61 | 13196.12 ยฑ 4341.00 |
| atari-gopher | 319.40 ยฑ 228.24 | 81676.15 ยฑ 46329.48 |
| atari-gravitar | 188.50 ยฑ 203.33 | 3986.57 ยฑ 1729.05 |
| atari-hero | 475.25 ยฑ 894.95 | 44677.35 ยฑ 1754.42 |
| atari-icehockey | -9.83 ยฑ 3.24 | 25.17 ยฑ 5.79 |
| atari-jamesbond | 28.50 ยฑ 45.42 | 27786.89 ยฑ 33819.20 |
| atari-kangaroo | 52.00 ยฑ 108.15 | 574.05 ยฑ 636.94 |
| atari-krull | 1754.00 ยฑ 583.56 | 11439.83 ยฑ 1218.34 |
| atari-kungfumaster | 390.00 ยฑ 359.03 | 32392.81 ยฑ 10006.55 |
| atari-montezumarevenge | 0.00 ยฑ 0.00 | 393.53 ยฑ 50.45 |
| atari-mspacman | 246.40 ยฑ 121.22 | 6896.08 ยฑ 2031.99 |
| atari-namethisgame | 2447.40 ยฑ 888.97 | 22991.18 ยฑ 2473.15 |
| atari-phoenix | 776.80 ยฑ 635.86 | 424583.16 ยฑ 97649.17 |
| atari-pitfall | -259.75 ยฑ 384.26 | -1.45 ยฑ 4.50 |
| atari-pong | -20.22 ยฑ 0.95 | 20.99 ยฑ 0.18 |
| atari-privateeye | 41.65 ยฑ 191.83 | 100.00 ยฑ 0.00 |
| atari-qbert | 164.25 ยฑ 151.79 | 42971.37 ยฑ 85070.72 |
| atari-riverraid | 1474.40 ยฑ 314.59 | 14800.94 ยฑ 7924.56 |
| atari-roadrunner | 11.00 ยฑ 42.18 | 77942.80 ยฑ 6088.62 |
| atari-robotank | 1.87 ยฑ 1.59 | 80.51 ยฑ 13.28 |
| atari-seaquest | 73.20 ยฑ 57.91 | 2597.34 ยฑ 386.09 |
| atari-skiing | -16299.52 ยฑ 1850.70 | -10738.06 ยฑ 111.13 |
| atari-solaris | 2360.40 ยฑ 1852.03 | 1353.68 ยฑ 516.96 |
| atari-spaceinvaders | 137.20 ยฑ 95.82 | 29425.29 ยฑ 23623.89 |
| atari-stargunner | 652.00 ยฑ 312.24 | 360588.57 ยฑ 49207.71 |
| atari-surround | -9.99 ยฑ 0.10 | 9.39 ยฑ 0.85 |
| atari-tennis | -23.95 ยฑ 0.22 | 11.11 ยฑ 7.57 |
| atari-timepilot | 3396.00 ยฑ 2128.85 | 69583.33 ยฑ 29838.67 |
| atari-tutankham | 12.73 ยฑ 17.40 | 291.16 ยฑ 30.37 |
| atari-upndown | 358.90 ยฑ 380.11 | 429418.33 ยฑ 7187.43 |
| atari-venture | 0.00 ยฑ 0.00 | 0.00 ยฑ 0.00 |
| atari-videopinball | 23917.17 ยฑ 19449.59 | 441507.92 ยฑ 283264.62 |
| atari-wizardofwor | 620.00 ยฑ 837.85 | 49333.33 ยฑ 16157.08 |
| atari-yarsrevenge | 3503.91 ยฑ 906.14 | 270262.86 ยฑ 161815.96 |
| atari-zaxxon | 21.00 ยฑ 102.27 | 73097.22 ยฑ 14825.77 |
| **BabyAI** | | |
| babyai-action-obj-door | 0.37 ยฑ 0.39 | 0.99 ยฑ 0.01 |
| babyai-blocked-unlock-pickup | 0.00 ยฑ 0.02 | 0.95 ยฑ 0.01 |
| babyai-boss-level | 0.06 ยฑ 0.21 | 0.94 ยฑ 0.05 |
| babyai-boss-level-no-unlock | 0.06 ยฑ 0.19 | 0.94 ยฑ 0.05 |
| babyai-find-obj-s5 | 0.08 ยฑ 0.23 | 0.95 ยฑ 0.04 |
| babyai-go-to | 0.13 ยฑ 0.29 | 0.92 ยฑ 0.07 |
| babyai-go-to-door | 0.45 ยฑ 0.38 | 0.99 ยฑ 0.00 |
| babyai-go-to-imp-unlock | 0.08 ยฑ 0.23 | |
| babyai-go-to-local | 0.16 ยฑ 0.30 | 0.93 ยฑ 0.04 |
| babyai-go-to-obj | 0.13 ยฑ 0.27 | 0.93 ยฑ 0.03 |
| babyai-go-to-obj-door | 0.53 ยฑ 0.39 | 0.99 ยฑ 0.01 |
| babyai-go-to-red-ball | 0.17 ยฑ 0.30 | 0.93 ยฑ 0.04 |
| babyai-go-to-red-ball-grey | 0.12 ยฑ 0.27 | 0.92 ยฑ 0.05 |
| babyai-go-to-red-ball-no-dists | 0.14 ยฑ 0.28 | 0.93 ยฑ 0.03 |
| babyai-go-to-red-blue-ball | 0.12 ยฑ 0.27 | 0.92 ยฑ 0.05 |
| babyai-go-to-seq | 0.08 ยฑ 0.23 | 0.94 ยฑ 0.05 |
| babyai-key-corridor | 0.00 ยฑ 0.00 | 0.91 ยฑ 0.01 |
| babyai-key-in-box | 0.01 ยฑ 0.06 | |
| babyai-mini-boss-level | 0.07 ยฑ 0.21 | 0.89 ยฑ 0.10 |
| babyai-move-two-across-s8n9 | 0.00 ยฑ 0.00 | 0.96 ยฑ 0.01 |
| babyai-one-room-s8 | 0.08 ยฑ 0.21 | 0.92 ยฑ 0.03 |
| babyai-open | 0.10 ยฑ 0.24 | 0.95 ยฑ 0.05 |
| babyai-open-door | 0.23 ยฑ 0.34 | 0.99 ยฑ 0.00 |
| babyai-open-doors-order-n4 | 0.16 ยฑ 0.30 | 0.99 ยฑ 0.01 |
| babyai-open-red-door | 0.08 ยฑ 0.21 | 0.92 ยฑ 0.03 |
| babyai-open-two-doors | 0.08 ยฑ 0.20 | 0.98 ยฑ 0.00 |
| babyai-pickup | 0.08 ยฑ 0.22 | 0.92 ยฑ 0.07 |
| babyai-pickup-above | 0.02 ยฑ 0.09 | 0.91 ยฑ 0.07 |
| babyai-pickup-dist | 0.10 ยฑ 0.24 | 0.86 ยฑ 0.21 |
| babyai-pickup-loc | 0.08 ยฑ 0.23 | 0.91 ยฑ 0.04 |
| babyai-synth | 0.11 ยฑ 0.26 | 0.93 ยฑ 0.06 |
| babyai-synth-loc | 0.13 ยฑ 0.29 | 0.94 ยฑ 0.06 |
| babyai-synth-seq | 0.07 ยฑ 0.20 | 0.95 ยฑ 0.04 |
| babyai-unblock-pickup | 0.08 ยฑ 0.22 | 0.91 ยฑ 0.08 |
| babyai-unlock | 0.03 ยฑ 0.15 | |
| babyai-unlock-local | 0.01 ยฑ 0.09 | 0.98 ยฑ 0.01 |
| babyai-unlock-pickup | 0.00 ยฑ 0.00 | 0.75 ยฑ 0.04 |
| babyai-unlock-to-unlock | 0.00 ยฑ 0.00 | |
| **MetaWorld** | | |
| metaworld-assembly | 45.30 ยฑ 4.13 | 245.99 ยฑ 3.50 |
| metaworld-basketball | 2.81 ยฑ 1.24 | 627.99 ยฑ 1.98 |
| metaworld-bin-picking | 1.89 ยฑ 0.45 | 425.58 ยฑ 101.86 |
| metaworld-box-close | 76.39 ยฑ 17.91 | 512.49 ยฑ 107.81 |
| metaworld-button-press | 31.73 ยฑ 5.20 | 643.10 ยฑ 12.85 |
| metaworld-button-press-topdown | 28.97 ยฑ 10.37 | 490.18 ยฑ 27.21 |
| metaworld-button-press-topdown-wall | 29.04 ยฑ 10.52 | 497.19 ยฑ 31.37 |
| metaworld-button-press-wall | 8.98 ยฑ 3.99 | 675.41 ยฑ 15.04 |
| metaworld-coffee-button | 31.72 ยฑ 6.36 | 731.08 ยฑ 29.34 |
| metaworld-coffee-pull | 4.09 ยฑ 0.38 | 259.86 ยฑ 88.48 |
| metaworld-coffee-push | 4.17 ยฑ 0.76 | 496.78 ยฑ 118.20 |
| metaworld-dial-turn | 29.64 ยฑ 16.67 | 793.56 ยฑ 80.06 |
| metaworld-disassemble | 40.31 ยฑ 7.53 | 42.83 ยฑ 6.30 |
| metaworld-door-close | 5.30 ยฑ 1.33 | 529.75 ยฑ 27.24 |
| metaworld-door-lock | 112.35 ยฑ 28.63 | 811.52 ยฑ 34.07 |
| metaworld-door-open | 56.37 ยฑ 11.23 | 581.94 ยฑ 19.67 |
| metaworld-door-unlock | 94.17 ยฑ 15.56 | 802.88 ยฑ 17.05 |
| metaworld-drawer-close | 116.73 ยฑ 253.11 | 867.92 ยฑ 4.48 |
| metaworld-drawer-open | 126.85 ยฑ 25.22 | 492.99 ยฑ 2.52 |
| metaworld-faucet-close | 253.12 ยฑ 22.94 | 753.92 ยฑ 13.42 |
| metaworld-faucet-open | 244.10 ยฑ 23.25 | 705.76 ยฑ 7.15 |
| metaworld-hammer | 95.33 ยฑ 9.02 | 693.17 ยฑ 34.62 |
| metaworld-hand-insert | 2.75 ยฑ 3.53 | 740.53 ยฑ 36.69 |
| metaworld-handle-press | 80.41 ยฑ 110.19 | 855.91 ยฑ 72.75 |
| metaworld-handle-press-side | 57.00 ยฑ 39.47 | 861.12 ยฑ 20.01 |
| metaworld-handle-pull | 10.34 ยฑ 13.54 | 669.35 ยฑ 24.81 |
| metaworld-handle-pull-side | 2.13 ยฑ 2.76 | 384.65 ยฑ 102.89 |
| metaworld-lever-pull | 60.31 ยฑ 15.77 | 612.04 ยฑ 38.85 |
| metaworld-peg-insert-side | 1.71 ยฑ 0.36 | 315.23 ยฑ 140.07 |
| metaworld-peg-unplug-side | 4.75 ยฑ 2.83 | 456.12 ยฑ 81.65 |
| metaworld-pick-out-of-hole | 1.51 ยฑ 0.24 | 219.61 ยฑ 88.85 |
| metaworld-pick-place | 1.61 ยฑ 0.99 | 419.10 ยฑ 98.19 |
| metaworld-pick-place-wall | 0.00 ยฑ 0.01 | 450.57 ยฑ 64.10 |
| metaworld-plate-slide | 74.64 ยฑ 13.84 | 527.01 ยฑ 155.34 |
| metaworld-plate-slide-back | 33.47 ยฑ 11.22 | 718.22 ยฑ 87.41 |
| metaworld-plate-slide-back-side | 34.34 ยฑ 11.53 | 729.61 ยฑ 69.15 |
| metaworld-plate-slide-side | 22.61 ยฑ 17.36 | 662.81 ยฑ 102.81 |
| metaworld-push | 5.51 ยฑ 2.43 | 750.57 ยฑ 43.98 |
| metaworld-push-back | 1.21 ยฑ 0.16 | 85.05 ยฑ 107.12 |
| metaworld-push-wall | 6.13 ยฑ 3.17 | 748.87 ยฑ 10.62 |
| metaworld-reach | 149.67 ยฑ 44.70 | 681.37 ยฑ 133.68 |
| metaworld-reach-wall | 143.26 ยฑ 36.56 | 746.12 ยฑ 104.19 |
| metaworld-shelf-place | 0.00 ยฑ 0.01 | 241.34 ยฑ 24.60 |
| metaworld-soccer | 5.66 ยฑ 4.61 | 375.15 ยฑ 140.24 |
| metaworld-stick-pull | 2.64 ยฑ 1.41 | 523.55 ยฑ 18.94 |
| metaworld-stick-push | 2.81 ยฑ 1.04 | 627.95 ยฑ 10.20 |
| metaworld-sweep | 11.23 ยฑ 7.28 | 494.85 ยฑ 43.29 |
| metaworld-sweep-into | 12.55 ยฑ 10.72 | 799.21 ยฑ 19.07 |
| metaworld-window-close | 57.46 ยฑ 7.11 | 591.30 ยฑ 38.63 |
| metaworld-window-open | 43.36 ยฑ 2.09 | 590.82 ยฑ 57.08 |
| **MuJoCo** | | |
| mujoco-ant | -59.95 ยฑ 99.62 | 5846.42 ยฑ 942.55 |
| mujoco-doublependulum | 57.46 ยฑ 17.54 | 9338.69 ยฑ 352.61 |
| mujoco-halfcheetah | -284.97 ยฑ 79.83 | 7437.77 ยฑ 173.30 |
| mujoco-hopper | 18.38 ยฑ 17.09 | 1858.73 ยฑ 534.07 |
| mujoco-humanoid | 122.02 ยฑ 35.28 | 6281.02 ยฑ 1795.84 |
| mujoco-pendulum | 6.07 ยฑ 3.47 | 475.40 ยฑ 178.96 |
| mujoco-pusher | -149.69 ยฑ 7.41 | -25.21 ยฑ 6.66 |
| mujoco-reacher | -43.00 ยฑ 3.91 | -5.68 ยฑ 2.53 |
| mujoco-standup | 33135.75 ยฑ 2481.89 | 273574.16 ยฑ 85253.26 |
| mujoco-swimmer | 0.80 ยฑ 10.71 | 92.18 ยฑ 4.44 |
| mujoco-walker | 2.68 ยฑ 6.06 | 4631.22 ยฑ 1059.01 |
</details>
### Data Fields
- `text`: a `string` feature
- `images`: a `image` feature
- `image_observations` : a `Sequence(image)` feature
- `text_observations` : a `Sequence(string)` feature
- `discrete_observations`: a `Sequence(Sequence(int64))` feature
- `continuous_observations`: a `Sequence(Sequence(float32))` feature
- `continuous_actions`: a `Sequence(Sequence(float32))` feature
- `discrete_actions`: a `Sequence(int64)` feature
- `rewards`: a `Sequence(float32)` feature
### Data Splits
- `train`: `` examples
- `test`: `` examples
## Dataset Creation
This section describes how our dataset was created. We specifically detail how data for each domain and task were generated. The generation scripts are available in the [GIA repository](https://github.com/huggingface/gia). For RL tasks, we trained one agent per task using the [Sample Factory](https://www.samplefactory.dev). Then we used the trained agent to generate episodes.
### Atari
We used the 57 [ALE/Atari](https://github.com/Farama-Foundation/Arcade-Learning-Environment) games as our environment, configuring the following parameters for our experiments. We rendered the images in grayscale with an 84x84 pixel resolution. The agent interacted with the environment every 4 frames. Sticky actions were not used, and the raw reward (no clipping) was reported. Episodes were stored as complete, i.e. with no termination on life loss.
### BabyAI
We used BabyAI's implementation from [Minigrid](https://github.com/Farama-Foundation/Minigrid).
We reused the [bot agent](https://github.com/mila-iqia/babyai) provided with BabyAI's paper and adapted it to the new Minigrid API.
Using the bot, we generated 100.000 episodes for each of the 39 tasks of [Minigrid's BabyAI](https://minigrid.farama.org/environments/babyai/) and stored for each step:
- the mission: str
- the concatenation of the symbolic observation flattened and the direction: Array of integers of size (147,)
- the action: integer
- the reward: float
### Conceptual Captions
The [Conceptual Captions](https://github.com/google-research-datasets/conceptual-captions/tree/master) dataset, offered by Google LLC, comprises pairs of image links and their corresponding captions. Each image has been downloaded and, when required, resized to ensure the maximum dimension does not exceed 352 pixels.
### MetaWorld
We used the 50 tasks from [MetaWorld v2](https://github.com/Farama-Foundation/Metaworld). We constrained the episode to a duration of 100 timesteps, which is always sufficient to solve the task.
### MuJoCo
We used the 11 environments of Gymnasium MuJoCo.
### OK-VQA
The [OK-VQA](https://okvqa.allenai.org/index.html) dataset released by Kenneth Marino, Mohammad Rastegari, Ali Farhadi, Roozbeh Mottaghi was used.
The data were formatted to match Hugging Face dataset's requirements and images were resized such that the largest dimension is at most 352.
### OSCAR
We modified the "unshuffled_deduplicated_en" split of [OSCAR 2019](https://huggingface.co/datasets/oscar) dataset, initially put together by Pedro J. Ortiz, Benoรฎt Sagot, and Laurent Romary and licensed under [CC BY 4.0](https://oscar-project.github.io/documentation/versions/oscar-2019/#license).
We cleaned and deduplicated the dataset using [the methods](https://github.com/bigscience-workshop/data-preparation/tree/main/preprocessing/training/01b_oscar_cleaning_and_filtering) and parameters used for the [ROOTS dataset](https://arxiv.org/abs/2303.03915) (Lurenรงon et al., 2023).
The dataset was splitted into 30 even shards each cleaned and deduplicated independently before being concatenated again.
## Considerations for Using the Data
### Known Issues
- Some BabyAI tasks are missing due to incompatibility with the training bot:
- `babyai-key-in-box`
- `babyai-go-to-imp-unlock`
- `babyai-unlock-to-unlock`
- `babyai-unlock`
- For some atari tasks, the episode is too long, causing an `OverflowError` when loading the dataset:
- `atari-enduro`
- For some tasks, although the score can be higher than the random agent, we can't consider the task as solved:
- `atari-bowling`
- `atari-privateeye`
- `atari-solaris`
- `atari-venture`
- `metaworld-bin-picking`
- `metaworld-disassemble`
- `metaworld-peg-insert-side`
- `metaworld-plate-slide`
- `metaworld-push-back`
### Future Developments
We plan to expand the dataset to include the following additional domains:
- [ ] DM Lab
- [ ] Sokoban
- [ ] Procgen
- [ ] DM Control Suite (w and w/o pixels)
## Additional Information
### Licensing Information
This dataset is release under the Apache 2.0 license.
### Citation Information
```bibtex
@misc{gallouedec2023giadataset,
title={GIA Dataset: A Multi-Modal, Multi-Task Learning Resource},
author={Gallouรฉdec, Quentin and Beeching, Edward and Romac, Clรฉment},
year={2023},
howpublished={\url{https://huggingface.co/datasets/gia-project/gia-dataset}},
note={Part of the GIA Project}
}
```
## Acknowledgment
We would like to extend our sincere gratitude to:
- [Shengyi Costa Huang](https://huggingface.co/vwxyzjn) for his invaluable assistance with the pretrained models used in this research
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lytang/MeetingBank-transcript | 2023-07-17T21:05:12.000Z | [
"task_categories:summarization",
"license:cc-by-nc-sa-4.0",
"arxiv:2305.17529",
"region:us"
] | lytang | null | null | 0 | 149 | 2023-07-15T18:00:10 | ---
license: cc-by-nc-sa-4.0
task_categories:
- summarization
---
This dataset consists of transcripts from the [MeetingBank dataset](https://meetingbank.github.io/).
**Overview**
MeetingBank, a benchmark dataset created from the city councils of 6 major U.S. cities to supplement existing datasets. It contains 1,366 meetings with over 3,579 hours of video, as well as transcripts, PDF documents of meeting minutes, agenda, and other metadata. On average, a council meeting is 2.6 hours long and its transcript contains over 28k tokens, making it a valuable testbed for meeting summarizers and for extracting structure from meeting videos. The datasets contains 6,892 segment-level summarization instances for training and evaluating of performance.
**Acknowledgement**
Please cite the following paper in work that makes use of this dataset:
[MeetingBank: A Benchmark Dataset for Meeting Summarization](https://arxiv.org/abs/2305.17529) \
Yebowen Hu, Tim Ganter, Hanieh Deilamsalehy, Franck Dernoncourt, Hassan Foroosh, Fei Liu \
In main conference of Association for Computational Linguistics (ACLโ23), Toronto, Canada.
**Bibtex**
```
@inproceedings{hu-etal-2023-meetingbank,
title = "MeetingBank: A Benchmark Dataset for Meeting Summarization",
author = "Yebowen Hu and Tim Ganter and Hanieh Deilamsalehy and Franck Dernoncourt and Hassan Foroosh and Fei Liu",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL)",
month = July,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
}
```
**Resources**
MeetingBank dataset will be hosted at Zenodo. The audio files of each meeting will be hosted individually on Huggingface. All resources will includes meeting audio, transcripts, meetingbank main JSON file, summaries from 6 systems and human annotations.
**Summary, Segments Transcripts and VideoList:** [zenodo](https://zenodo.org/record/7989108)
**Meeting Audios:** [HuggingFace](https://huggingface.co/datasets/huuuyeah/MeetingBank_Audio)
**Meeting Transcripts:** [HuggingFace](https://huggingface.co/datasets/lytang/MeetingBank-transcript)
Some scripts can be found in github repo [MeetingBank_Utils](https://github.com/YebowenHu/MeetingBank-utils) | 2,302 | [
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] |
rashmi035/dataset_whisper | 2023-10-05T05:49:20.000Z | [
"region:us"
] | rashmi035 | null | null | 0 | 149 | 2023-10-05T05:48:14 | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: transcription
dtype: string
- name: set
dtype: string
splits:
- name: train
num_bytes: 35817014.0
num_examples: 100
- name: validation
num_bytes: 15314681.0
num_examples: 50
- name: test
num_bytes: 7381857.0
num_examples: 29
download_size: 55480724
dataset_size: 58513552.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
# Dataset Card for "dataset_whisper"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 740 | [
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result-kand2-sdxl-wuerst-karlo/ddb740fe | 2023-10-11T04:44:46.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 149 | 2023-10-11T04:44:45 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 201
num_examples: 10
download_size: 1398
dataset_size: 201
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "ddb740fe"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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Arabic-Clip/Arabic_dataset_3M_translated_cleaned_v2_jsonl_format_ViT-B-16-plus-240 | 2023-10-11T16:41:17.000Z | [
"region:us"
] | Arabic-Clip | null | null | 0 | 149 | 2023-10-11T16:25:51 | Entry not found | 15 | [
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insub/imdb_prefix20_forDPO_gpt2-large-imdb-FT_siebert_sentiment-roberta-large-english | 2023-10-22T08:02:45.000Z | [
"arxiv:2305.18290",
"region:us"
] | insub | null | null | 1 | 149 | 2023-10-22T07:33:43 | ---
dataset_info:
features:
- name: text
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 23573801
num_examples: 25000
- name: test
num_bytes: 23551578
num_examples: 25000
download_size: 28260315
dataset_size: 47125379
---
# Dataset Card for "imdb_prefix20_forDPO_gpt2-large-imdb-FT_siebert_sentiment-roberta-large-english"
# 1. Purpose of creating the dataset
For reproduction of DPO (direct preference optimization) thesis experiments
(https://arxiv.org/abs/2305.18290)
# 2. How data is produced
To reproduce the paper's experimental results, we need (x, chosen, rejected) data.
However, imdb data only contains good or bad reviews, so the data must be readjusted.
## 2.1 prepare imdb data
First, download the imdb data and then remove words after 20 tokens using the gpt2-large tokenizer.
(https://huggingface.co/datasets/imdb)
## 2.2 generate sentence
The gpt2-large model fine-tuned by imdb generates two sentences after input (text).
(https://github.com/eric-mitchell/direct-preference-optimization/issues/28)
(https://drive.google.com/file/d/1ZPlfmfkCindqJfD8eNrl8kwtMJ2f1Nqv/view)
## 2.3 labeling method
Use sentiment bert to label good and bad sentences as (chosen, rejected).
(https://github.com/eric-mitchell/direct-preference-optimization/issues/27)
(https://huggingface.co/siebert/sentiment-roberta-large-english) | 1,456 | [
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ubaada/booksum-complete-cleaned | 2023-11-02T09:58:39.000Z | [
"task_categories:summarization",
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:en",
"arxiv:2105.08209",
"region:us"
] | ubaada | null | null | 0 | 149 | 2023-10-28T12:13:12 | ---
task_categories:
- summarization
- text-generation
language:
- en
pretty_name: BookSum Summarization Dataset Clean
size_categories:
- 1K<n<10K
configs:
- config_name: books
data_files:
- split: train
path: "books/train.jsonl"
- split: test
path: "books/test.jsonl"
- split: validation
path: "books/val.jsonl"
- config_name: chapters
data_files:
- split: train
path: "chapters/train.jsonl"
- split: test
path: "chapters/test.jsonl"
- split: validation
path: "chapters/val.jsonl"
---
# Table of Contents
1. [Description](#description)
2. [Usage](#usage)
3. [Distribution](#distribution)
- [Chapters Dataset](#chapters-dataset)
- [Books Dataset](#books-dataset)
4. [Structure](#structure)
5. [Results and Comparison with kmfoda/booksum](#results-and-comparison-with-kmfodabooksum)
# Description:
This repository contains the Booksum dataset introduced in the paper [BookSum: A Collection of Datasets for Long-form Narrative Summarization
](https://arxiv.org/abs/2105.08209).
This dataset includes both book and chapter summaries from the BookSum dataset (unlike the kmfoda/booksum one which only contains the chapter dataset). Some mismatched summaries have been corrected. Uneccessary columns has been discarded. Contains minimal text-to-summary rows. As there are multiple summaries for a given text, each row contains an array of summaries.
# Usage
Note: Make sure you have [>2.14.0 version of "datasets" library](https://github.com/huggingface/datasets/releases/tag/2.14.0) installed to load the dataset successfully.
```
from datasets import load_dataset
book_data = load_dataset("ubaada/booksum-complete-cleaned", "books")
chapter_data = load_dataset("ubaada/booksum-complete-cleaned", "chapters")
# Print the 1st book
print(book_data["train"][0]['text'])
# Print the summary of the 1st book
print(book_data["train"][0]['summary'][0]['text'])
```
# Distribution
<div style="display: inline-block; vertical-align: top; width: 45%;">
## Chapters Dataset
| Split | Total Sum. | Missing Sum. | Successfully Processed | Chapters |
|---------|------------|--------------|------------------------|------|
| Train | 9712 | 178 | 9534 (98.17%) | 5653 |
| Test | 1432 | 0 | 1432 (100.0%) | 950 |
| Val | 1485 | 0 | 1485 (100.0%) | 854 |
</div>
<div style="display: inline-block; vertical-align: top; width: 45%; margin-left: 5%;">
## Books Dataset
| Split | Total Sum. | Missing Sum. | Successfully Processed | Books |
|---------|------------|--------------|------------------------|------|
| Train | 314 | 0 | 314 (100.0%) | 151 |
| Test | 46 | 0 | 46 (100.0%) | 17 |
| Val | 45 | 0 | 45 (100.0%) | 19 |
</div>
# Structure:
```
Chapters Dataset
0 - bid (book id)
1 - book_title
2 - chapter_id
3 - text (raw chapter text)
4 - summary (list of summaries from different sources)
- {source, text (summary), analysis}
...
5 - is_aggregate (bool) (if true, then the text contains more than one chapter)
Books Dataset:
0 - bid (book id)
1 - title
2 - text (raw text)
4 - summary (list of summaries from different sources)
- {source, text (summary), analysis}
...
```
# Reults and Comparison with kmfoda/booksum
Tested on the 'test' split of chapter sub-dataset. There are slight improvement on R1/R2 scores compared to another BookSum repo likely due to the work done on cleaning the misalignments in the alignment file. In the plot for this dataset, first summary \[0\] is chosen for each chapter. If best reference summary is chosen from the list for each chapter, theere are further improvements but are not shown here for fairness.
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] |
compguesswhat | 2023-04-05T10:02:19.000Z | [
"task_categories:visual-question-answering",
"task_ids:visual-question-answering",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|other-guesswhat",
"language:en",
"license:unknown",
"region:us"
] | null | CompGuessWhat?! is an instance of a multi-task framework for evaluating the quality of learned neural representations,
in particular concerning attribute grounding. Use this dataset if you want to use the set of games whose reference
scene is an image in VisualGenome. Visit the website for more details: https://compguesswhat.github.io | @inproceedings{suglia2020compguesswhat,
title={CompGuessWhat?!: a Multi-task Evaluation Framework for Grounded Language Learning},
author={Suglia, Alessandro, Konstas, Ioannis, Vanzo, Andrea, Bastianelli, Emanuele, Desmond Elliott, Stella Frank and Oliver Lemon},
booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
year={2020}
} | 1 | 148 | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- found
license:
- unknown
multilinguality:
- monolingual
pretty_name: CompGuessWhat?!
size_categories:
- 100K<n<1M
source_datasets:
- extended|other-guesswhat
task_categories:
- visual-question-answering
task_ids:
- visual-question-answering
paperswithcode_id: compguesswhat
dataset_info:
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num_examples: 9738
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num_examples: 9621
download_size: 107201655
dataset_size: 178585543
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dtype: string
- name: LabelName
dtype: string
- name: BoxID
dtype: string
- name: BoxXMin
dtype: string
- name: BoxXMax
dtype: string
- name: BoxYMin
dtype: string
- name: BoxYMax
dtype: string
- name: PredictedIoU
dtype: string
- name: Clicks
dtype: string
splits:
- name: nd_valid
num_bytes: 13557059
num_examples: 5343
- name: nd_test
num_bytes: 36352201
num_examples: 13836
- name: od_valid
num_bytes: 14093233
num_examples: 5372
- name: od_test
num_bytes: 33049755
num_examples: 13300
download_size: 4845966
dataset_size: 97052248
---
# Dataset Card for "compguesswhat"
## 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://compguesswhat.github.io/](https://compguesswhat.github.io/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 112.05 MB
- **Size of the generated dataset:** 271.11 MB
- **Total amount of disk used:** 383.16 MB
### Dataset Summary
CompGuessWhat?! is an instance of a multi-task framework for evaluating the quality of learned neural representations,
in particular concerning attribute grounding. Use this dataset if you want to use the set of games whose reference
scene is an image in VisualGenome. Visit the website for more details: https://compguesswhat.github.io
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### compguesswhat-original
- **Size of downloaded dataset files:** 107.21 MB
- **Size of the generated dataset:** 174.37 MB
- **Total amount of disk used:** 281.57 MB
An example of 'validation' looks as follows.
```
This example was too long and was cropped:
{
"id": 2424,
"image": "{\"coco_url\": \"http://mscoco.org/images/270512\", \"file_name\": \"COCO_train2014_000000270512.jpg\", \"flickr_url\": \"http://farm6.stat...",
"objects": "{\"area\": [1723.5133056640625, 4838.5361328125, 287.44476318359375, 44918.7109375, 3688.09375, 522.1935424804688], \"bbox\": [[5.61...",
"qas": {
"answer": ["Yes", "No", "No", "Yes"],
"id": [4983, 4996, 5006, 5017],
"question": ["Is it in the foreground?", "Does it have wings?", "Is it a person?", "Is it a vehicle?"]
},
"status": "success",
"target_id": 1197044,
"timestamp": "2016-07-08 15:07:38"
}
```
#### compguesswhat-zero_shot
- **Size of downloaded dataset files:** 4.84 MB
- **Size of the generated dataset:** 96.74 MB
- **Total amount of disk used:** 101.59 MB
An example of 'nd_valid' looks as follows.
```
This example was too long and was cropped:
{
"id": 0,
"image": {
"coco_url": "https://s3.amazonaws.com/nocaps/val/004e21eb2e686f40.jpg",
"date_captured": "2018-11-06 11:04:33",
"file_name": "004e21eb2e686f40.jpg",
"height": 1024,
"id": 6,
"license": 0,
"open_images_id": "004e21eb2e686f40",
"width": 768
},
"objects": "{\"IsOccluded\": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"IsTruncated\": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"area\": [3...",
"status": "incomplete",
"target_id": "004e21eb2e686f40_30"
}
```
### Data Fields
The data fields are the same among all splits.
#### compguesswhat-original
- `id`: a `int32` feature.
- `target_id`: a `int32` feature.
- `timestamp`: a `string` feature.
- `status`: a `string` feature.
- `id`: a `int32` feature.
- `file_name`: a `string` feature.
- `flickr_url`: a `string` feature.
- `coco_url`: a `string` feature.
- `height`: a `int32` feature.
- `width`: a `int32` feature.
- `width`: a `int32` feature.
- `height`: a `int32` feature.
- `url`: a `string` feature.
- `coco_id`: a `int32` feature.
- `flickr_id`: a `string` feature.
- `image_id`: a `string` feature.
- `qas`: a dictionary feature containing:
- `question`: a `string` feature.
- `answer`: a `string` feature.
- `id`: a `int32` feature.
- `objects`: a dictionary feature containing:
- `id`: a `int32` feature.
- `bbox`: a `list` of `float32` features.
- `category`: a `string` feature.
- `area`: a `float32` feature.
- `category_id`: a `int32` feature.
- `segment`: a dictionary feature containing:
- `feature`: a `float32` feature.
#### compguesswhat-zero_shot
- `id`: a `int32` feature.
- `target_id`: a `string` feature.
- `status`: a `string` feature.
- `id`: a `int32` feature.
- `file_name`: a `string` feature.
- `coco_url`: a `string` feature.
- `height`: a `int32` feature.
- `width`: a `int32` feature.
- `license`: a `int32` feature.
- `open_images_id`: a `string` feature.
- `date_captured`: a `string` feature.
- `objects`: a dictionary feature containing:
- `id`: a `string` feature.
- `bbox`: a `list` of `float32` features.
- `category`: a `string` feature.
- `area`: a `float32` feature.
- `category_id`: a `int32` feature.
- `IsOccluded`: a `int32` feature.
- `IsTruncated`: a `int32` feature.
- `segment`: a dictionary feature containing:
- `MaskPath`: a `string` feature.
- `LabelName`: a `string` feature.
- `BoxID`: a `string` feature.
- `BoxXMin`: a `string` feature.
- `BoxXMax`: a `string` feature.
- `BoxYMin`: a `string` feature.
- `BoxYMax`: a `string` feature.
- `PredictedIoU`: a `string` feature.
- `Clicks`: a `string` feature.
### Data Splits
#### compguesswhat-original
| |train|validation|test|
|----------------------|----:|---------:|---:|
|compguesswhat-original|46341| 9738|9621|
#### compguesswhat-zero_shot
| |nd_valid|od_valid|nd_test|od_test|
|-----------------------|-------:|-------:|------:|------:|
|compguesswhat-zero_shot| 5343| 5372| 13836| 13300|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@inproceedings{suglia2020compguesswhat,
title={CompGuessWhat?!: a Multi-task Evaluation Framework for Grounded Language Learning},
author={Suglia, Alessandro, Konstas, Ioannis, Vanzo, Andrea, Bastianelli, Emanuele, Desmond Elliott, Stella Frank and Oliver Lemon},
booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
year={2020}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@aleSuglia](https://github.com/aleSuglia), [@lhoestq](https://github.com/lhoestq) for adding this dataset. | 12,849 | [
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fake_news_english | 2023-05-30T04:42:32.000Z | [
"task_categories:text-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | null | Fake news has become a major societal issue and a technical challenge for social media companies to identify. This content is difficult to identify because the term "fake news" covers intentionally false, deceptive stories as well as factual errors, satire, and sometimes, stories that a person just does not like. Addressing the problem requires clear definitions and examples. In this work, we present a dataset of fake news and satire stories that are hand coded, verified, and, in the case of fake news, include rebutting stories. We also include a thematic content analysis of the articles, identifying major themes that include hyperbolic support or condemnation of a gure, conspiracy theories, racist themes, and discrediting of reliable sources. In addition to releasing this dataset for research use, we analyze it and show results based on language that are promising for classification purposes. Overall, our contribution of a dataset and initial analysis are designed to support future work by fake news researchers. | @inproceedings{inproceedings,
author = {Golbeck, Jennifer and Everett, Jennine and Falak, Waleed and Gieringer, Carl and Graney, Jack and Hoffman, Kelly and Huth, Lindsay and Ma, Zhenya and Jha, Mayanka and Khan, Misbah and Kori, Varsha and Mauriello, Matthew and Lewis, Elo and Mirano, George and IV, William and Mussenden, Sean and Nelson, Tammie and Mcwillie, Sean and Pant, Akshat and Cheakalos, Paul},
year = {2018},
month = {05},
pages = {17-21},
title = {Fake News vs Satire: A Dataset and Analysis},
doi = {10.1145/3201064.3201100}
} | 0 | 148 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
pretty_name: Fake News English
dataset_info:
features:
- name: article_number
dtype: int32
- name: url_of_article
dtype: string
- name: fake_or_satire
dtype:
class_label:
names:
'0': Satire
'1': Fake
- name: url_of_rebutting_article
dtype: string
splits:
- name: train
num_bytes: 78078
num_examples: 492
download_size: 3002233
dataset_size: 78078
---
# Dataset Card for Fake News English
## 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://dl.acm.org/doi/10.1145/3201064.3201100**
- **Repository:** https://github.com/jgolbeck/fakenews/
- **Paper:** https://doi.org/10.1145/3201064.3201100
- **Leaderboard:**
- **Point of Contact:** Jennifer Golbeck (http://www.jengolbeck.com)
### Dataset Summary
This dataset contains URLs of news articles classified as either fake or satire. The articles classified as fake also have the URL of a rebutting article.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English
## Dataset Structure
### Data Instances
```
{
"article_number": 102 ,
"url_of_article": https://newslo.com/roger-stone-blames-obama-possibility-trump-alzheimers-attacks-president-caused-severe-stress/ ,
"fake_or_satire": 1, # Fake
"url_of_rebutting_article": https://www.snopes.com/fact-check/donald-trumps-intelligence-quotient/
}
```
### Data Fields
- article_number: An integer used as an index for each row
- url_of_article: A string which contains URL of an article to be assessed and classified as either Fake or Satire
- fake_or_satire: A classlabel for the above variable which can take two values- Fake (1) and Satire (0)
- url_of_rebutting_article: A string which contains a URL of the article used to refute the article in question (present - in url_of_article)
### Data Splits
This dataset is not split, only the train split is available.
## 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
Golbeck, Jennifer
Everett, Jennine
Falak, Waleed
Gieringer, Carl
Graney, Jack
Hoffman, Kelly
Huth, Lindsay
Ma, Zhenya
Jha, Mayanka
Khan, Misbah
Kori, Varsha
Mauriello, Matthew
Lewis, Elo
Mirano, George
IV, William
Mussenden, Sean
Nelson, Tammie
Mcwillie, Sean
Pant, Akshat
Cheakalos, Paul
### Licensing Information
[More Information Needed]
### Citation Information
@inproceedings{inproceedings,
author = {Golbeck, Jennifer and Everett, Jennine and Falak, Waleed and Gieringer, Carl and Graney, Jack and Hoffman, Kelly and Huth, Lindsay and Ma, Zhenya and Jha, Mayanka and Khan, Misbah and Kori, Varsha and Mauriello, Matthew and Lewis, Elo and Mirano, George and IV, William and Mussenden, Sean and Nelson, Tammie and Mcwillie, Sean and Pant, Akshat and Cheakalos, Paul},
year = {2018},
month = {05},
pages = {17-21},
title = {Fake News vs Satire: A Dataset and Analysis},
doi = {10.1145/3201064.3201100}
}
### Contributions
Thanks to [@MisbahKhan789](https://github.com/MisbahKhan789), [@lhoestq](https://github.com/lhoestq) for adding this dataset. | 4,926 | [
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] |
kor_3i4k | 2023-01-25T14:33:43.000Z | [
"task_categories:text-classification",
"task_ids:intent-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ko",
"license:cc-by-4.0",
"arxiv:1811.04231",
"region:us"
] | null | This dataset is designed to identify speaker intention based on real-life spoken utterance in Korean into one of
7 categories: fragment, description, question, command, rhetorical question, rhetorical command, utterances. | @article{cho2018speech,
title={Speech Intention Understanding in a Head-final Language: A Disambiguation Utilizing Intonation-dependency},
author={Cho, Won Ik and Lee, Hyeon Seung and Yoon, Ji Won and Kim, Seok Min and Kim, Nam Soo},
journal={arXiv preprint arXiv:1811.04231},
year={2018}
} | 1 | 148 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- ko
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- intent-classification
pretty_name: 3i4K
dataset_info:
features:
- name: label
dtype:
class_label:
names:
'0': fragment
'1': statement
'2': question
'3': command
'4': rhetorical question
'5': rhetorical command
'6': intonation-dependent utterance
- name: text
dtype: string
splits:
- name: train
num_bytes: 3102158
num_examples: 55134
- name: test
num_bytes: 344028
num_examples: 6121
download_size: 2956114
dataset_size: 3446186
---
# Dataset Card for 3i4K
## 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:** [3i4K](https://github.com/warnikchow/3i4k)
- **Repository:** [3i4K](https://github.com/warnikchow/3i4k)
- **Paper:** [Speech Intention Understanding in a Head-final Language: A Disambiguation Utilizing Intonation-dependency](https://arxiv.org/abs/1811.04231)
- **Point of Contact:** [Won Ik Cho](wicho@hi.snu.ac.kr)
### Dataset Summary
The 3i4K dataset is a set of frequently used Korean words (corpus provided by the Seoul National University Speech Language Processing Lab) and manually created questions/commands containing short utterances. The goal is to identify the speaker intention of a spoken utterance based on its transcript, and whether in some cases, requires using auxiliary acoustic features. The classification system decides whether the utterance is a fragment, statement, question, command, rhetorical question, rhetorical command, or an intonation-dependent utterance. This is important because in head-final languages like Korean, the level of the intonation plays a significant role in identifying the speaker's intention.
### Supported Tasks and Leaderboards
* `intent-classification`: The dataset can be trained with a CNN or BiLISTM-Att to identify the intent of a spoken utterance in Korean and the performance can be measured by its F1 score.
### Languages
The text in the dataset is in Korean and the associated is BCP-47 code is `ko-KR`.
## Dataset Structure
### Data Instances
An example data instance contains a short utterance and it's label:
```
{
"label": 3,
"text": "์ ์์์ ์ด ์ผ์ด์ค ์ ์ผ์ด์ค ๋ง์ ๊ฑฐ ์๋ ์ ๋ฐฐ๋ผ๊ณ ๋ญ ํ๋ ์ธ์์ ๋์๋ ์์ฃผ๋๋ฐ ๋ด๊ฐ ์ด๋ ๊ฒ ์ง์งํ๊ฒ ๋์ฌ ๋ ์ ๋๋ก ํ๋ฒ ์กฐ์ธ ์ข ํด์ค๋ณด์ง"
}
```
### Data Fields
* `label`: determines the intention of the utterance and can be one of `fragment` (0), `statement` (1), `question` (2), `command` (3), `rhetorical question` (4), `rhetorical command` (5) and `intonation-depedent utterance` (6).
* `text`: the text in Korean about common topics like housework, weather, transportation, etc.
### Data Splits
The data is split into a training set comrpised of 55134 examples and a test set of 6121 examples.
## Dataset Creation
### Curation Rationale
For head-final languages like Korean, intonation can be a determining factor in identifying the speaker's intention. The purpose of this dataset is to to determine whether an utterance is a fragment, statement, question, command, or a rhetorical question/command using the intonation-depedency from the head-finality. This is expected to improve language understanding of spoken Korean utterances and can be beneficial for speech-to-text applications.
### Source Data
#### Initial Data Collection and Normalization
The corpus was provided by Seoul National University Speech Language Processing Lab, a set of frequently used words from the National Institute of Korean Language and manually created commands and questions. The utterances cover topics like weather, transportation and stocks. 20k lines were randomly selected.
#### Who are the source language producers?
Korean speakers produced the commands and questions.
### Annotations
#### Annotation process
Utterances were classified into seven categories. They were provided clear instructions on the annotation guidelines (see [here](https://docs.google.com/document/d/1-dPL5MfsxLbWs7vfwczTKgBq_1DX9u1wxOgOPn1tOss/edit#) for the guidelines) and the resulting inter-annotator agreement was 0.85 and the final decision was done by majority voting.
#### Who are the annotators?
The annotation was completed by three Seoul Korean L1 speakers.
### 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
The dataset is curated by Won Ik Cho, Hyeon Seung Lee, Ji Won Yoon, Seok Min Kim and Nam Soo Kim.
### Licensing Information
The dataset is licensed under the CC BY-SA-4.0.
### Citation Information
```
@article{cho2018speech,
title={Speech Intention Understanding in a Head-final Language: A Disambiguation Utilizing Intonation-dependency},
author={Cho, Won Ik and Lee, Hyeon Seung and Yoon, Ji Won and Kim, Seok Min and Kim, Nam Soo},
journal={arXiv preprint arXiv:1811.04231},
year={2018}
}
```
### Contributions
Thanks to [@stevhliu](https://github.com/stevhliu) for adding this dataset. | 6,370 | [
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] |
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