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big_patent | null | 2023-06-01T14:59:54Z | 3,160 | 27 | bigpatent | [
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"region:us"
] | 2023-06-01T14:59:54Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
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task_ids: []
paperswithcode_id: bigpatent
pretty_name: Big Patent
tags:
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---
# Dataset Card for Big Patent
## 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:** [Big Patent](https://evasharma.github.io/bigpatent/)
- **Repository:**
- **Paper:** [BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization](https://arxiv.org/abs/1906.03741)
- **Leaderboard:**
- **Point of Contact:** [Lu Wang](mailto:wangluxy@umich.edu)
### Dataset Summary
BIGPATENT, consisting of 1.3 million records of U.S. patent documents along with human written abstractive summaries.
Each US patent application is filed under a Cooperative Patent Classification (CPC) code.
There are nine such classification categories:
- a: Human Necessities
- b: Performing Operations; Transporting
- c: Chemistry; Metallurgy
- d: Textiles; Paper
- e: Fixed Constructions
- f: Mechanical Engineering; Lightning; Heating; Weapons; Blasting
- g: Physics
- h: Electricity
- y: General tagging of new or cross-sectional technology
Current defaults are 2.1.2 version (fix update to cased raw strings) and 'all' CPC codes:
```python
from datasets import load_dataset
ds = load_dataset("big_patent") # default is 'all' CPC codes
ds = load_dataset("big_patent", "all") # the same as above
ds = load_dataset("big_patent", "a") # only 'a' CPC codes
ds = load_dataset("big_patent", codes=["a", "b"])
```
To use 1.0.0 version (lower cased tokenized words), pass both parameters `codes` and `version`:
```python
ds = load_dataset("big_patent", codes="all", version="1.0.0")
ds = load_dataset("big_patent", codes="a", version="1.0.0")
ds = load_dataset("big_patent", codes=["a", "b"], version="1.0.0")
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English
## Dataset Structure
### Data Instances
Each instance contains a pair of `description` and `abstract`. `description` is extracted from the Description section of the Patent while `abstract` is extracted from the Abstract section.
```
{
'description': 'FIELD OF THE INVENTION \n [0001] This invention relates to novel calcium phosphate-coated implantable medical devices and processes of making same. The unique calcium-phosphate coated implantable medical devices minimize...',
'abstract': 'This invention relates to novel calcium phosphate-coated implantable medical devices...'
}
```
### Data Fields
- `description`: detailed description of patent.
- `abstract`: Patent abastract.
### Data Splits
| | train | validation | test |
|:----|------------------:|-------------:|-------:|
| all | 1207222 | 67068 | 67072 |
| a | 174134 | 9674 | 9675 |
| b | 161520 | 8973 | 8974 |
| c | 101042 | 5613 | 5614 |
| d | 10164 | 565 | 565 |
| e | 34443 | 1914 | 1914 |
| f | 85568 | 4754 | 4754 |
| g | 258935 | 14385 | 14386 |
| h | 257019 | 14279 | 14279 |
| y | 124397 | 6911 | 6911 |
## 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
```bibtex
@article{DBLP:journals/corr/abs-1906-03741,
author = {Eva Sharma and
Chen Li and
Lu Wang},
title = {{BIGPATENT:} {A} Large-Scale Dataset for Abstractive and Coherent
Summarization},
journal = {CoRR},
volume = {abs/1906.03741},
year = {2019},
url = {http://arxiv.org/abs/1906.03741},
eprinttype = {arXiv},
eprint = {1906.03741},
timestamp = {Wed, 26 Jun 2019 07:14:58 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1906-03741.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
### Contributions
Thanks to [@mattbui](https://github.com/mattbui) for adding this dataset. | [
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katanaml-org/invoices-donut-data-v1 | katanaml-org | 2023-05-09T07:05:11Z | 3,146 | 7 | null | [
"task_categories:feature-extraction",
"size_categories:n<1K",
"language:en",
"license:mit",
"region:us"
] | 2023-05-09T07:05:11Z | 2023-03-08T20:44:29.000Z | 2023-03-08T20:44:29 | ---
dataset_info:
features:
- name: image
dtype: image
- name: ground_truth
dtype: string
splits:
- name: train
num_bytes: 234024421
num_examples: 425
- name: test
num_bytes: 14512665
num_examples: 26
- name: validation
num_bytes: 27661738
num_examples: 50
download_size: 197512750
dataset_size: 276198824
license: mit
task_categories:
- feature-extraction
language:
- en
pretty_name: Sparrow Invoice Dataset
size_categories:
- n<1K
---
# Dataset Card for Invoices (Sparrow)
This dataset contains 500 invoice documents annotated and processed to be ready for Donut ML model fine-tuning.
Annotation and data preparation task was done by [Katana ML](https://www.katanaml.io) team.
[Sparrow](https://github.com/katanaml/sparrow/tree/main) - open-source data extraction solution by Katana ML.
Original dataset [info](https://data.mendeley.com/datasets/tnj49gpmtz): Kozłowski, Marek; Weichbroth, Paweł (2021), “Samples of electronic invoices”, Mendeley Data, V2, doi: 10.17632/tnj49gpmtz.2 | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
SetFit/qqp | SetFit | 2022-02-28T11:10:11Z | 3,133 | 4 | null | [
"region:us"
] | 2022-02-28T11:10:11Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | # Glue QQP
This dataset is a port of the official [`qqp` dataset](https://huggingface.co/datasets/glue/viewer/qqp/train) on the Hub.
Note that the question1 and question2 columns have been renamed to text1 and text2 respectively.
Also, the test split is not labeled; the label column values are always -1.
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tatoeba | null | 2022-11-03T16:32:34Z | 3,129 | 20 | tatoeba | [
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] | 2022-11-03T16:32:34Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
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pretty_name: Tatoeba
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download_size: 2340361
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dtype:
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splits:
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num_bytes: 19775390
num_examples: 195161
download_size: 5509784
dataset_size: 19775390
- config_name: es-gl
features:
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dataset_size: 287683
---
# Dataset Card for Tatoeba
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://opus.nlpl.eu/Tatoeba.php
- **Repository:** None
- **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
Tatoeba is a collection of sentences and translations.
To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs.
You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/Tatoeba.php
E.g.
`dataset = load_dataset("tatoeba", lang1="en", lang2="he")`
The default date is v2021-07-22, but you can also change the date with
`dataset = load_dataset("tatoeba", lang1="en", lang2="he", date="v2020-11-09")`
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The languages in the dataset are:
- ab
- acm
- ady
- af
- afb
- afh
- aii
- ain
- ajp
- akl
- aln
- am
- an
- ang
- aoz
- apc
- ar
- arq
- ary
- arz
- as
- ast
- avk
- awa
- ayl
- az
- ba
- bal
- bar
- be
- ber
- bg
- bho
- bjn
- bm
- bn
- bo
- br
- brx
- bs
- bua
- bvy
- bzt
- ca
- cay
- cbk
- ce
- ceb
- ch
- chg
- chn
- cho
- chr
- cjy
- ckb
- ckt
- cmn
- co
- code
- cpi
- crh
- crk
- cs
- csb
- cv
- cy
- da
- de
- dng
- drt
- dsb
- dtp
- dv
- dws
- ee
- egl
- el
- emx
- en
- enm
- eo
- es
- et
- eu
- ext
- fi
- fj
- fkv
- fo
- fr
- frm
- fro
- frr
- fuc
- fur
- fuv
- fy
- ga
- gag
- gan
- gbm
- gcf
- gd
- gil
- gl
- gn
- gom
- gos
- got
- grc
- gsw
- gu
- gv
- ha
- hak
- haw
- hbo
- he
- hi
- hif
- hil
- hnj
- hoc
- hr
- hrx
- hsb
- hsn
- ht
- hu
- hy
- ia
- iba
- id
- ie
- ig
- ii
- ike
- ilo
- io
- is
- it
- izh
- ja
- jam
- jbo
- jdt
- jpa
- jv
- ka
- kaa
- kab
- kam
- kek
- kha
- kjh
- kk
- kl
- km
- kmr
- kn
- ko
- koi
- kpv
- krc
- krl
- ksh
- ku
- kum
- kw
- kxi
- ky
- kzj: Coastal Kadazan (deprecated tag; preferred value: Kadazan Dusun; Central Dusun (`dtp`))
- la
- laa
- lad
- lb
- ldn
- lfn
- lg
- lij
- liv
- lkt
- lld
- lmo
- ln
- lo
- lt
- ltg
- lut
- lv
- lzh
- lzz
- mad
- mai
- max
- mdf
- mfe
- mg
- mgm
- mh
- mhr
- mi
- mic
- min
- mk
- ml
- mn
- mni
- mnw
- moh
- mr
- mt
- mvv
- mwl
- mww
- my
- myv
- na
- nah
- nan
- nb
- nch
- nds
- ngt
- ngu
- niu
- nl
- nlv
- nn
- nog
- non
- nov
- npi
- nst
- nus
- nv
- ny
- nys
- oar
- oc
- ofs
- ood
- or
- orv
- os
- osp
- ota
- otk
- pa
- pag
- pal
- pam
- pap
- pau
- pcd
- pdc
- pes
- phn
- pi
- pl
- pms
- pnb
- ppl
- prg
- ps
- pt
- qu
- quc
- qya
- rap
- rif
- rm
- rn
- ro
- rom
- ru
- rue
- rw
- sa
- sah
- sc
- scn
- sco
- sd
- sdh
- se
- sg
- sgs
- shs
- shy
- si
- sjn
- sl
- sm
- sma
- sn
- so
- sq
- sr
- stq
- su
- sux
- sv
- swg
- swh
- syc
- ta
- te
- tet
- tg
- th
- thv
- ti
- tig
- tk
- tl
- tlh
- tly
- tmr
- tmw
- tn
- to
- toi
- tok
- tpi
- tpw
- tr
- ts
- tt
- tts
- tvl
- ty
- tyv
- tzl
- udm
- ug
- uk
- umb
- ur
- uz
- vec
- vep
- vi
- vo
- vro
- wa
- war
- wo
- wuu
- xal
- xh
- xqa
- yi
- yo
- yue
- zlm
- zsm
- zu
- zza
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. | [
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para_pat | null | 2022-12-02T11:39:09Z | 3,124 | 9 | parapat | [
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---
# Dataset Card for ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts
## 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:** [ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts](https://figshare.com/articles/ParaPat_The_Multi-Million_Sentences_Parallel_Corpus_of_Patents_Abstracts/12627632)
- **Repository:** [ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts](https://github.com/soares-f/parapat)
- **Paper:** [ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts](https://www.aclweb.org/anthology/2020.lrec-1.465/)
- **Point of Contact:** [Felipe Soares](fs@felipesoares.net)
### Dataset Summary
ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts
This dataset contains the developed parallel corpus from the open access Google Patents dataset in 74 language pairs, comprising more than 68 million sentences and 800 million tokens. Sentences were automatically aligned using the Hunalign algorithm for the largest 22 language pairs, while the others were abstract (i.e. paragraph) aligned.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset contains samples in cs, de, el, en, es, fr, hu, ja, ko, pt, ro, ru, sk, uk, zh, hu
## Dataset Structure
### Data Instances
They are of 2 types depending on the dataset:
First type
{
"translation":{
"en":"A method for converting a series of m-bit information words to a modulated signal is described.",
"es":"Se describe un método para convertir una serie de palabras de informacion de bits m a una señal modulada."
}
}
Second type
{
"family_id":10944407,
"index":844,
"translation":{
"el":"αφές ο οποίος παρασκευάζεται με χαρμάνι ελληνικού καφέ είτε σε συσκευή καφέ εσπρέσο είτε σε συσκευή γαλλικού καφέ (φίλτρου) είτε κατά τον παραδοσιακό τρόπο του ελληνικού καφέ και διυλίζεται, κτυπιέται στη συνέχεια με πάγο σε χειροκίνητο ή ηλεκτρικόμίξερ ώστε να παγώσει ομοιόμορφα και να αποκτήσει πλούσιο αφρό και σερβίρεται σε ποτήρι. ΰ",
"en":"offee prepared using the mix for Greek coffee either in an espresso - type coffee making machine, or in a filter coffee making machine or in the traditional way for preparing Greek coffee and is then filtered , shaken with ice manually or with an electric mixer so that it freezes homogeneously, obtains a rich froth and is served in a glass."
}
}
### Data Fields
**index:** position in the corpus
**family id:** for each abstract, such that researchers can use that information for other text mining purposes.
**translation:** distionary containing source and target sentence for that example
### Data Splits
No official train/val/test splits given.
Parallel corpora aligned into sentence level
|Language Pair|# Sentences|# Unique Tokens|
|--------|-----|------|
|EN/ZH|4.9M|155.8M|
|EN/JA|6.1M|189.6M|
|EN/FR|12.2M|455M|
|EN/KO|2.3M|91.4M|
|EN/DE|2.2M|81.7M|
|EN/RU|4.3M|107.3M|
|DE/FR|1.2M|38.8M|
|FR/JA|0.3M|9.9M|
|EN/ES|0.6M|24.6M|
Parallel corpora aligned into abstract level
|Language Pair|# Abstracts|
|--------|-----|
|FR/KO|120,607|
|EN/UK|89,227|
|RU/UK|85,963|
|CS/EN|78,978|
|EN/RO|48,789|
|EN/HU|42,629|
|ES/FR|32,553|
|EN/SK|23,410|
|EN/PT|23,122|
|BG/EN|16,177|
|FR/RU|10,889|
## Dataset Creation
### Curation Rationale
The availability of parallel corpora is required by current Statistical and Neural Machine Translation systems (SMT and NMT). Acquiring a high-quality parallel corpus that is large enough to train MT systems, particularly NMT ones, is not a trivial task due to the need for correct alignment and, in many cases, human curation. In this context, the automated creation of parallel corpora from freely available resources is extremely important in Natural Language Pro- cessing (NLP).
### Source Data
#### Initial Data Collection and Normalization
Google makes patents data available under the Google Cloud Public Datasets. BigQuery is a Google service that supports the efficient storage and querying of massive datasets which are usually a challenging task for usual SQL databases. For instance, filtering the September 2019 release of the dataset, which contains more than 119 million rows, can take less than 1 minute for text fields. The on-demand billing for BigQuery is based on the amount of data processed by each query run, thus for a single query that performs a full-scan, the cost can be over USD 15.00, since the cost per TB is currently USD 5.00.
#### Who are the source language producers?
BigQuery is a Google service that supports the efficient storage and querying of massive datasets which are usually a challenging task for usual SQL databases.
### Annotations
#### Annotation process
The following steps describe the process of producing patent aligned abstracts:
1. Load the nth individual file
2. Remove rows where the number of abstracts with more than one language is less than 2 for a given family id. The family id attribute is used to group patents that refers to the same invention. By removing these rows, we remove abstracts that are available only in one language.
3. From the resulting set, create all possible parallel abstracts from the available languages. For instance, an abstract may be available in English, French and German, thus, the possible language pairs are English/French, English/German, and French/German.
4. Store the parallel patents into an SQL database for easier future handling and sampling.
#### 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
Funded by Google Tensorflow Research Cloud.
### Licensing Information
CC BY 4.0
### Citation Information
```
@inproceedings{soares-etal-2020-parapat,
title = "{P}ara{P}at: The Multi-Million Sentences Parallel Corpus of Patents Abstracts",
author = "Soares, Felipe and
Stevenson, Mark and
Bartolome, Diego and
Zaretskaya, Anna",
booktitle = "Proceedings of The 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://www.aclweb.org/anthology/2020.lrec-1.465",
pages = "3769--3774",
language = "English",
ISBN = "979-10-95546-34-4",
}
```
[DOI](https://doi.org/10.6084/m9.figshare.12627632)
### Contributions
Thanks to [@bhavitvyamalik](https://github.com/bhavitvyamalik) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ethz-spylab/rlhf_trojan_dataset | ethz-spylab | 2023-11-20T08:34:04Z | 3,084 | 4 | null | [
"language:en",
"region:us"
] | 2023-11-20T08:34:04Z | 2023-11-15T15:58:19.000Z | 2023-11-15T15:58:19 | ---
extra_gated_prompt: >-
You acknowledge that generations from this model can be harmful, and that you
will not use them beyond this competition. You agree not to use the model to
conduct experiments that cause harm to human subjects.
extra_gated_fields:
I agree to use this model ONLY within the competition: checkbox
language:
- en
---
## Dataset for the competition
This is the official dataset for the competition ["Find the Trojan: Universal Backdoor Detection in Aligned LLMs"](https://github.com/ethz-spylab/rlhf_trojan_competition) hosted at SaTML 2024.
The dataset contains two splits: `train` and `test`. Participants should use the `train` split to execute their proposed methods and can use the `test` as a measure of how successful their search was. Competition prizes will be awarded based on performance on a **private test set**.
See the [official competition website](https://github.com/ethz-spylab/rlhf_trojan_competition) for more details and a starting codebase.
Competition organized by the [SPY Lab](https://spylab.ai) at ETH Zurich.
This dataset is created from a split of [this Anthropic dataset](https://huggingface.co/datasets/Anthropic/hh-rlhf). | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
tweettemposhift/tweet_temporal_shift | tweettemposhift | 2023-11-28T18:04:20Z | 3,077 | 0 | null | [
"region:us"
] | 2023-11-28T18:04:20Z | 2023-10-20T13:44:44.000Z | 2023-10-20T13:44:44 | # Tweet Temporal Shift Benchmark
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large_spanish_corpus | null | 2023-06-07T21:20:55Z | 3,074 | 17 | null | [
"task_categories:other",
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"source_datasets:original",
"language:es",
"license:mit",
"region:us"
] | 2023-06-07T21:20:55Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
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language:
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license:
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size_categories:
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source_datasets:
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task_categories:
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task_ids: []
paperswithcode_id: null
pretty_name: The Large Spanish Corpus
tags: []
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---
# Dataset Card for The Large Spanish Corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/josecannete/spanish-corpora](https://github.com/josecannete/spanish-corpora)
- **Repository:** [https://github.com/josecannete/spanish-corpora](https://github.com/josecannete/spanish-corpora)
- **Paper:**
- **Data:** https://doi.org/10.5281/zenodo.3247731
- **Leaderboard:**
- **Point of Contact:** [José Cañete](mailto:jose.canete@ug.uchile.cl) (corpus creator) or [Lewis Tunstall](mailto:lewis.c.tunstall@gmail.com) (corpus submitter)
### Dataset Summary
The Large Spanish Corpus is a compilation of 15 unlabelled Spanish corpora spanning Wikipedia to European parliament notes. Each config contains the data corresponding to a different corpus. For example, `all_wiki` only includes examples from Spanish Wikipedia:
```python
from datasets import load_dataset
all_wiki = load_dataset('large_spanish_corpus', name='all_wiki')
```
By default, the config is set to "combined" which loads all the corpora.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Spanish
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
The following is taken from the corpus' source repsository:
* Spanish Wikis: Which include Wikipedia, Wikinews, Wikiquotes and more. These were first processed with wikiextractor (https://github.com/josecannete/wikiextractorforBERT) using the wikis dump of 20/04/2019.
* ParaCrawl: Spanish portion of ParaCrawl (http://opus.nlpl.eu/ParaCrawl.php)
* EUBookshop: Spanish portion of EUBookshop (http://opus.nlpl.eu/EUbookshop.php)
* MultiUN: Spanish portion of MultiUN (http://opus.nlpl.eu/MultiUN.php)
* OpenSubtitles: Spanish portion of OpenSubtitles2018 (http://opus.nlpl.eu/OpenSubtitles-v2018.php)
* DGC: Spanish portion of DGT (http://opus.nlpl.eu/DGT.php)
* DOGC: Spanish portion of DOGC (http://opus.nlpl.eu/DOGC.php)
* ECB: Spanish portion of ECB (http://opus.nlpl.eu/ECB.php)
* EMEA: Spanish portion of EMEA (http://opus.nlpl.eu/EMEA.php)
* Europarl: Spanish portion of Europarl (http://opus.nlpl.eu/Europarl.php)
* GlobalVoices: Spanish portion of GlobalVoices (http://opus.nlpl.eu/GlobalVoices.php)
* JRC: Spanish portion of JRC (http://opus.nlpl.eu/JRC-Acquis.php)
* News-Commentary11: Spanish portion of NCv11 (http://opus.nlpl.eu/News-Commentary-v11.php)
* TED: Spanish portion of TED (http://opus.nlpl.eu/TED2013.php)
* UN: Spanish portion of UN (http://opus.nlpl.eu/UN.php)
## 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 [@lewtun](https://github.com/lewtun) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ropes | null | 2022-11-18T21:42:43Z | 3,068 | 11 | ropes | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|wikipedia",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:1908.05852",
"region:us"
] | 2022-11-18T21:42:43Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
pretty_name: ROPES
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|wikipedia
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: ropes
dataset_info:
features:
- name: id
dtype: string
- name: background
dtype: string
- name: situation
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: text
dtype: string
config_name: plain_text
splits:
- name: train
num_bytes: 12231940
num_examples: 10924
- name: test
num_bytes: 1928532
num_examples: 1710
- name: validation
num_bytes: 1643498
num_examples: 1688
download_size: 3516917
dataset_size: 15803970
---
# Dataset Card for ROPES
## 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:** [ROPES dataset](https://allenai.org/data/ropes)
- **Paper:** [Reasoning Over Paragraph Effects in Situations](https://arxiv.org/abs/1908.05852)
- **Leaderboard:** [ROPES leaderboard](https://leaderboard.allenai.org/ropes)
### Dataset Summary
ROPES (Reasoning Over Paragraph Effects in Situations) is a QA dataset which tests a system's ability to apply knowledge from a passage of text to a new situation. A system is presented a background passage containing a causal or qualitative relation(s) (e.g., "animal pollinators increase efficiency of fertilization in flowers"), a novel situation that uses this background, and questions that require reasoning about effects of the relationships in the background passage in the context of the situation.
### Supported Tasks and Leaderboards
The reading comprehension task is framed as an extractive question answering problem.
Models are evaluated by computing word-level F1 and exact match (EM) metrics, following common practice for recent reading comprehension datasets (e.g., SQuAD).
### Languages
The text in the dataset is in English. The associated BCP-47 code is `en`.
## Dataset Structure
### Data Instances
Data closely follow the SQuAD v1.1 format. An example looks like this:
```
{
"id": "2058517998",
"background": "Cancer is a disease that causes cells to divide out of control. Normally, the body has systems that prevent cells from dividing out of control. But in the case of cancer, these systems fail. Cancer is usually caused by mutations. Mutations are random errors in genes. Mutations that lead to cancer usually happen to genes that control the cell cycle. Because of the mutations, abnormal cells divide uncontrollably. This often leads to the development of a tumor. A tumor is a mass of abnormal tissue. As a tumor grows, it may harm normal tissues around it. Anything that can cause cancer is called a carcinogen . Carcinogens may be pathogens, chemicals, or radiation.",
"situation": "Jason recently learned that he has cancer. After hearing this news, he convinced his wife, Charlotte, to get checked out. After running several tests, the doctors determined Charlotte has no cancer, but she does have high blood pressure. Relieved at this news, Jason was now focused on battling his cancer and fighting as hard as he could to survive.",
"question": "Whose cells are dividing more rapidly?",
"answers": {
"text": ["Jason"]
},
}
```
### Data Fields
- `id`: identification
- `background`: background passage
- `situation`: the grounding situation
- `question`: the question to answer
- `answers`: the answer text which is a span from either the situation or the question. The text list always contain a single element.
Note that the answers for the test set are hidden (and thus represented as an empty list). Predictions for the test set should be submitted to the leaderboard.
### Data Splits
The dataset contains 14k QA pairs over 1.7K paragraphs, split between train (10k QAs), development (1.6k QAs) and a hidden test partition (1.7k QAs).
## Dataset Creation
### Curation Rationale
From the original paper:
*ROPES challenges reading comprehension models to handle more difficult phenomena: understanding the implications of a passage of text. ROPES is also particularly related to datasets focusing on "multi-hop reasoning", as by construction answering questions in ROPES requires connecting information from multiple parts of a given passage.*
*We constructed ROPES by first collecting background passages from science textbooks and Wikipedia articles that describe causal relationships. We showed the collected paragraphs to crowd workers and asked them to write situations that involve the relationships found in the background passage, and questions that connect the situation and the background using the causal relationships. The answers are spans from either the situation or the question. The dataset consists of 14,322 questions from various domains, mostly in science and economics.*
### Source Data
From the original paper:
*We automatically scraped passages from science textbooks and Wikipedia that contained causal connectives eg. ”causes,” ”leads to,” and keywords that signal qualitative relations, e.g. ”increases,” ”decreases.”. We then manually filtered out the passages that do not have at least one relation. The passages can be categorized into physical science (49%), life science (45%), economics (5%) and other (1%). In total, we collected over 1,000 background passages.*
#### Initial Data Collection and Normalization
From the original paper:
*We used Amazon Mechanical Turk (AMT) to generate the situations, questions, and answers. The AMT workers were given background passages and asked to write situations that involved the relation(s) in the background passage. The AMT workers then authored questions about the situation that required both the background and the situation to answer. In each human intelligence task (HIT), AMT workers are given 5 background passages to select from and are asked to create a total of 10 questions. To mitigate the potential for easy lexical shortcuts in the dataset, the workers were encouraged via instructions to write questions in minimal pairs, where a very small change in the question results in a different answer.*
*Most questions are designed to have two sensible answer choices (eg. “more” vs. “less”).*
To reduce annotator bias, training and evaluation sets are writter by different annotators.
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
The data is distributed under the [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license.
### Citation Information
```
@inproceedings{Lin2019ReasoningOP,
title={Reasoning Over Paragraph Effects in Situations},
author={Kevin Lin and Oyvind Tafjord and Peter Clark and Matt Gardner},
booktitle={MRQA@EMNLP},
year={2019}
}
```
### Contributions
Thanks to [@VictorSanh](https://github.com/VictorSanh) for adding this dataset. | [
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LeoCordoba/CC-NEWS-ES | LeoCordoba | 2023-02-23T21:53:55Z | 3,062 | 8 | null | [
"task_categories:summarization",
"task_categories:text-generation",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:n<1K",
"size_categories:1K<n<10K",
"size_categories:10K<n<100K",
"size_categories:100K<n<1M",
"size_categories:1M<n<10M",
"source_datasets:cc-news",
"language:es",
"license:mit",
"conditional-text-generation",
"region:us"
] | 2023-02-23T21:53:55Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- es
license:
- mit
multilinguality:
- monolingual
size_categories:
- n<1K
- 1K<n<10K
- 10K<n<100K
- 100K<n<1M
- 1M<n<10M
source_datasets:
- cc-news
task_categories:
- summarization
- text-generation
task_ids: []
tags:
- conditional-text-generation
---
# Dataset Card for CC-NEWS-ES
## 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:** [CC-NEWS-ES dataset repository](https://huggingface.co/datasets/LeoCordoba/CC-NEWS-ES)
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [Leonardo Ignacio Córdoba](https://www.linkedin.com/in/leonardo-ignacio-c%C3%B3rdoba/)
### Dataset Summary
CC-NEWS-ES is a Spanish-language dataset of news. The corpus was generated by extracting the Spanish articles from CC-NEWS (news index of Common Crawl) of 2019. For doing that FastText model was used for language prediction.
It contains a total of 7,473,286 texts and 1,812,009,283 words distributed as follows:
|domain | texts | words |
|:----|-----------------:|-----------------:|
| ar | 532703 | 1.45127e+08 |
| bo | 29557 | 7.28996e+06 |
| br | 107 | 14207 |
| cl | 116661 | 3.34633e+07 |
| co | 78662 | 1.92649e+07 |
| com | 3650950 | 8.44094e+08 |
| cr | 16542 | 3.82075e+06 |
| es |1838790 | 4.82943e+08 |
| gt | 4833 | 838121 |
| hn | 36559 | 5.49933e+06 |
| mx | 724908 | 1.62198e+08 |
| ni | 40643 | 1.08501e+07 |
| pa | 18447 | 4.34724e+06 |
| pe | 230962 | 3.52123e+07 |
| pr | 7756 | 1.6633e+06 |
| py | 30651 | 2.08077e+07 |
| sv | 454 | 353145 |
| uy | 80948 | 2.72562e+07 |
| ve | 33148 | 6.96578e+06 |
### Supported Tasks and Leaderboards
TODO
-
### Languages
The text is in Spanish. The BCP-47 code for Spanish is es.
## Dataset Structure
### Data Instances
Each data instance contains the following features: ...
- country: top level domain, usually refers to a country (except in the case of .com).
- text: body of the news
- id: internal id
An example from CC-NEWS-ES looks like the following:
```
{'country': 'py',
'text': '“La que asumió es una mujer que está en línea de sucesión. La policía, ni los militares están en el Palacio, lo que ella dijo fue que no se podía seguir reprimiendo al pueblo", manifestó este jueves el senador colorado, Enrique Riera, sobre la asunción presidencial en Bolivia de la senadora opositora, Jeanine Áñez,Riera agregó que Evo Morales el que "escapó y abandonó" a su pueblo al ir como asilado a México. En ese sentido, dijo que irónicamente, el expresidente boliviano no eligió como destino a Venezuela, Nicaragua ni a Cuba.Sostuvo que nos de debe utilizar a las instituciones democráticas y republicanas para llegar al poder, cambiando Constituciones y prorrogando mandatos una y otra vez. “El amigo Morales no respetó absolutamente nada”, subrayó.Por otra parte, el senador colorado mencionó que los fiscales y jueces bolivianos deberían tener el "coraje" de investigar el origen de la riqueza de Morales.Habló también sobre la situación en Venezuela y mencionó que Nicolás Maduro no cae, porque "toda la FFAA está contaminada de narcotráfico". El hombre cuenta con orden de prisión en su país por los ilícitos de Tráfico de Drogas y Asociación Criminal, según el Consejo Nacional de Justicia del Brasil.La agente fiscal Liliana Denice Duarte, titular de la Unidad Fiscal Nº 1 de Presidente Franco, requirió la expulsión del extranjero y la jueza Carina Frutos Recalde, mediante Auto Interlocutorio (A.I.) N° 2.153, dio curso favorable al pedido del Ministerio Público. Esto considerando la alta expectativa de pena que tiene el supuesto delincuente en su país.La detención ...',
'id': 7328086}
Note: the text is shortened for simplicity.
```
### Data Fields
- ...
- ...
### Data Splits
...
## Dataset Creation
### Curation Rationale
[N/A]
### Source Data
#### Initial Data Collection and Normalization
TODO
#### Who are the source language producers?
Common Crawl: https://commoncrawl.org/
### Annotations
The dataset does not contain any additional annotations.
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
[N/A]
## Considerations for Using the Data
### Social Impact of Dataset
...
### Discussion of Biases
[N/A]
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
This dataset is maintained by [Leonardo Ignacio Córdoba](https://www.linkedin.com/in/leonardo-ignacio-c%C3%B3rdoba/) and was built with the help of [María Gaska](https://www.linkedin.com/in/mfgaska/).
### Licensing Information
[N/A]
### Citation Information
TODO
### Contributions
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
EdinburghNLP/xsum | EdinburghNLP | 2023-04-05T13:45:25Z | 3,041 | 47 | xsum | [
"task_categories:summarization",
"task_ids:news-articles-summarization",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:unknown",
"arxiv:1808.08745",
"region:us"
] | 2023-04-05T13:45:25Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
pretty_name: Extreme Summarization (XSum)
paperswithcode_id: xsum
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- summarization
task_ids:
- news-articles-summarization
train-eval-index:
- config: default
task: summarization
task_id: summarization
splits:
train_split: train
eval_split: test
col_mapping:
document: text
summary: target
metrics:
- type: rouge
name: Rouge
dataset_info:
features:
- name: document
dtype: string
- name: summary
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 479206608
num_examples: 204045
- name: validation
num_bytes: 26292901
num_examples: 11332
- name: test
num_bytes: 26756165
num_examples: 11334
download_size: 257302866
dataset_size: 532255674
---
# Dataset Card for "xsum"
## 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/EdinburghNLP/XSum
- **Paper:** [Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization](https://arxiv.org/abs/1808.08745)
- **Point of Contact:** [Shashi Narayan](mailto:shashi.narayan@ed.ac.uk)
- **Size of downloaded dataset files:** 257.30 MB
- **Size of the generated dataset:** 532.26 MB
- **Total amount of disk used:** 789.56 MB
### Dataset Summary
Extreme Summarization (XSum) Dataset.
There are three features:
- document: Input news article.
- summary: One sentence summary of the article.
- id: BBC ID of the article.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 257.30 MB
- **Size of the generated dataset:** 532.26 MB
- **Total amount of disk used:** 789.56 MB
An example of 'validation' looks as follows.
```
{
"document": "some-body",
"id": "29750031",
"summary": "some-sentence"
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `document`: a `string` feature.
- `summary`: a `string` feature.
- `id`: a `string` feature.
### Data Splits
| name |train |validation|test |
|-------|-----:|---------:|----:|
|default|204045| 11332|11334|
## 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
```
@article{Narayan2018DontGM,
title={Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization},
author={Shashi Narayan and Shay B. Cohen and Mirella Lapata},
journal={ArXiv},
year={2018},
volume={abs/1808.08745}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@mariamabarham](https://github.com/mariamabarham), [@jbragg](https://github.com/jbragg), [@lhoestq](https://github.com/lhoestq), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
yitingxie/rlhf-reward-datasets | yitingxie | 2023-01-01T12:23:04Z | 3,021 | 46 | null | [
"region:us"
] | 2023-01-01T12:23:04Z | 2023-01-01T12:22:09.000Z | 2023-01-01T12:22:09 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: test
num_bytes: 6093563
num_examples: 5103
- name: train
num_bytes: 90528217
num_examples: 76256
download_size: 57138483
dataset_size: 96621780
---
# Dataset Card for "rlhf-reward-datasets"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
teknium/GPT4-LLM-Cleaned | teknium | 2023-05-04T01:48:35Z | 3,021 | 87 | null | [
"region:us"
] | 2023-05-04T01:48:35Z | 2023-05-02T20:11:04.000Z | 2023-05-02T20:11:04 | This is the GPT4-LLM dataset from : https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM
It has been filtered of all OpenAI disclaimers and refusals. (Disclaimer: It may have removed some additional things besides just OAI disclaimers, as I used the followings script which is a bit more broad: https://huggingface.co/datasets/ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered/blob/main/wizardlm_clean.py)
There is a modified script of that in the repo that was used specifically for this. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
castorini/mr-tydi-corpus | castorini | 2022-10-12T20:25:51Z | 3,005 | 2 | null | [
"task_categories:text-retrieval",
"multilinguality:multilingual",
"language:ar",
"language:bn",
"language:en",
"language:fi",
"language:id",
"language:ja",
"language:ko",
"language:ru",
"language:sw",
"language:te",
"language:th",
"license:apache-2.0",
"region:us"
] | 2022-10-12T20:25:51Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- ar
- bn
- en
- fi
- id
- fi
- ja
- ko
- ru
- sw
- te
- th
multilinguality:
- multilingual
task_categories:
- text-retrieval
license: apache-2.0
---
# Dataset Summary
Mr. TyDi is a multi-lingual benchmark dataset built on TyDi, covering eleven typologically diverse languages. It is designed for monolingual retrieval, specifically to evaluate ranking with learned dense representations.
This dataset stores documents of Mr. TyDi. To access the queries and judgments, please refer to [castorini/mr-tydi](https://huggingface.co/datasets/castorini/mr-tydi).
# Dataset Structure
The only configuration here is the `language`. As all three folds (train, dev and test) share the same corpus, there is only one fold 'train' under each language, unlike [castorini/mr-tydi](https://huggingface.co/datasets/castorini/mr-tydi).
An example of document data entry looks as follows:
```
{
'docid': '25#0',
'title': 'Autism',
'text': 'Autism is a developmental disorder characterized by difficulties with social interaction and communication, ...'
}
```
# Load Dataset
An example to load the dataset:
```
language = 'english'
dataset = load_dataset('castorini/mr-tydi-corpus', language, 'train')
```
# Citation Information
```
@article{mrtydi,
title={{Mr. TyDi}: A Multi-lingual Benchmark for Dense Retrieval},
author={Xinyu Zhang and Xueguang Ma and Peng Shi and Jimmy Lin},
year={2021},
journal={arXiv:2108.08787},
}
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allenai/qasper | allenai | 2022-10-07T22:04:11Z | 3,000 | 38 | qasper | [
"task_categories:question-answering",
"task_ids:closed-domain-qa",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|s2orc",
"language:en",
"license:cc-by-4.0",
"arxiv:2105.03011",
"region:us"
] | 2022-10-07T22:04:11Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
pretty_name: QASPER
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
language_bcp47:
- en-US
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|s2orc
task_categories:
- question-answering
task_ids:
- closed-domain-qa
paperswithcode_id: qasper
---
# Dataset Card for Qasper
## 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)
- [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://allenai.org/data/qasper](https://allenai.org/data/qasper)
- **Demo:** [https://qasper-demo.apps.allenai.org/](https://qasper-demo.apps.allenai.org/)
- **Paper:** [https://arxiv.org/abs/2105.03011](https://arxiv.org/abs/2105.03011)
- **Blogpost:** [https://medium.com/ai2-blog/question-answering-on-scientific-research-papers-f6d6da9fd55c](https://medium.com/ai2-blog/question-answering-on-scientific-research-papers-f6d6da9fd55c)
- **Leaderboards:** [https://paperswithcode.com/dataset/qasper](https://paperswithcode.com/dataset/qasper)
### Dataset Summary
QASPER is a dataset for question answering on scientific research papers. It consists of 5,049 questions over 1,585 Natural Language Processing papers. Each question is written by an NLP practitioner who read only the title and abstract of the corresponding paper, and the question seeks information present in the full text. The questions are then answered by a separate set of NLP practitioners who also provide supporting evidence to answers.
### Supported Tasks and Leaderboards
- `question-answering`: The dataset can be used to train a model for Question Answering. Success on this task is typically measured by achieving a *high* [F1 score](https://huggingface.co/metrics/f1). The [official baseline model](https://github.com/allenai/qasper-led-baseline) currently achieves 33.63 Token F1 score & uses [Longformer](https://huggingface.co/transformers/model_doc/longformer.html). This task has an active leaderboard which can be found [here](https://paperswithcode.com/sota/question-answering-on-qasper)
- `evidence-selection`: The dataset can be used to train a model for Evidence Selection. Success on this task is typically measured by achieving a *high* [F1 score](https://huggingface.co/metrics/f1). The [official baseline model](https://github.com/allenai/qasper-led-baseline) currently achieves 39.37 F1 score & uses [Longformer](https://huggingface.co/transformers/model_doc/longformer.html). This task has an active leaderboard which can be found [here](https://paperswithcode.com/sota/evidence-selection-on-qasper)
### Languages
English, as it is used in research papers.
## Dataset Structure
### Data Instances
A typical instance in the dataset:
```
{
'id': "Paper ID (string)",
'title': "Paper Title",
'abstract': "paper abstract ...",
'full_text': {
'paragraphs':[["section1_paragraph1_text","section1_paragraph2_text",...],["section2_paragraph1_text","section2_paragraph2_text",...]],
'section_name':["section1_title","section2_title"],...},
'qas': {
'answers':[{
'annotation_id': ["q1_answer1_annotation_id","q1_answer2_annotation_id"]
'answer': [{
'unanswerable':False,
'extractive_spans':["q1_answer1_extractive_span1","q1_answer1_extractive_span2"],
'yes_no':False,
'free_form_answer':"q1_answer1",
'evidence':["q1_answer1_evidence1","q1_answer1_evidence2",..],
'highlighted_evidence':["q1_answer1_highlighted_evidence1","q1_answer1_highlighted_evidence2",..]
},
{
'unanswerable':False,
'extractive_spans':["q1_answer2_extractive_span1","q1_answer2_extractive_span2"],
'yes_no':False,
'free_form_answer':"q1_answer2",
'evidence':["q1_answer2_evidence1","q1_answer2_evidence2",..],
'highlighted_evidence':["q1_answer2_highlighted_evidence1","q1_answer2_highlighted_evidence2",..]
}],
'worker_id':["q1_answer1_worker_id","q1_answer2_worker_id"]
},{...["question2's answers"]..},{...["question3's answers"]..}],
'question':["question1","question2","question3"...],
'question_id':["question1_id","question2_id","question3_id"...],
'question_writer':["question1_writer_id","question2_writer_id","question3_writer_id"...],
'nlp_background':["question1_writer_nlp_background","question2_writer_nlp_background",...],
'topic_background':["question1_writer_topic_background","question2_writer_topic_background",...],
'paper_read': ["question1_writer_paper_read_status","question2_writer_paper_read_status",...],
'search_query':["question1_search_query","question2_search_query","question3_search_query"...],
}
}
```
### Data Fields
The following is an excerpt from the dataset README:
Within "qas", some fields should be obvious. Here is some explanation about the others:
#### Fields specific to questions:
- "nlp_background" shows the experience the question writer had. The values can be "zero" (no experience), "two" (0 - 2 years of experience), "five" (2 - 5 years of experience), and "infinity" (> 5 years of experience). The field may be empty as well, indicating the writer has chosen not to share this information.
- "topic_background" shows how familiar the question writer was with the topic of the paper. The values are "unfamiliar", "familiar", "research" (meaning that the topic is the research area of the writer), or null.
- "paper_read", when specified shows whether the questionwriter has read the paper.
- "search_query", if not empty, is the query the question writer used to find the abstract of the paper from a large pool of abstracts we made available to them.
#### Fields specific to answers
Unanswerable answers have "unanswerable" set to true. The remaining answers have exactly one of the following fields being non-empty.
- "extractive_spans" are spans in the paper which serve as the answer.
- "free_form_answer" is a written out answer.
- "yes_no" is true iff the answer is Yes, and false iff the answer is No.
"evidence" is the set of paragraphs, figures or tables used to arrive at the answer. Tables or figures start with the string "FLOAT SELECTED"
"highlighted_evidence" is the set of sentences the answer providers selected as evidence if they chose textual evidence. The text in the "evidence" field is a mapping from these sentences to the paragraph level. That is, if you see textual evidence in the "evidence" field, it is guaranteed to be entire paragraphs, while that is not the case with "highlighted_evidence".
### Data Splits
| | Train | Valid |
| ----- | ------ | ----- |
| Number of papers | 888 | 281 |
| Number of questions | 2593 | 1005 |
| Number of answers | 2675 | 1764 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
NLP papers: The full text of the papers is extracted from [S2ORC](https://huggingface.co/datasets/s2orc) (Lo et al., 2020)
#### 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?
"The annotators are NLP practitioners, not
expert researchers, and it is likely that an expert
would score higher"
### 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
Crowdsourced NLP practitioners
### Licensing Information
[CC BY 4.0](https://creativecommons.org/licenses/by/4.0)
### Citation Information
```
@inproceedings{Dasigi2021ADO,
title={A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers},
author={Pradeep Dasigi and Kyle Lo and Iz Beltagy and Arman Cohan and Noah A. Smith and Matt Gardner},
year={2021}
}
```
### Contributions
Thanks to [@cceyda](https://github.com/cceyda) for adding this dataset.
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wiki_asp | null | 2022-11-18T21:59:51Z | 2,988 | 3 | wikiasp | [
"task_categories:summarization",
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"aspect-based-summarization",
"arxiv:2011.07832",
"region:us"
] | 2022-11-18T21:59:51Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
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---
# Dataset Card for WikiAsp
## 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:** [Wiki Asp](https://github.com/neulab/wikiasp)
- **Repository:** [GitHub](https://github.com/neulab/wikiasp)
- **Paper:** [WikiAsp: A Dataset for Multi-domain Aspect-based Summarization](https://arxiv.org/abs/2011.07832)
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
An example from the "plant" configuration:
```
{
'exid': 'train-78-8',
'inputs': ['< EOT > calcareous rocks and barrens , wooded cliff edges .',
'plant an erect short - lived perennial ( or biennial ) herb whose slender leafy stems radiate from the base , and are 3 - 5 dm tall , giving it a bushy appearance .',
'leaves densely hairy , grayish - green , simple and alternate on the stem .',
'flowers are bright yellow to yellow - orange , cross - shaped , each having 4 spatula - shaped petals about 5 mm long .',
'fruit is a nearly globe - shaped capsule , about 3 mm in diameter , with 1 or 2 seeds in each cell .',
'flowering period : early april to late may .',
'even though there are many members of the mustard family in the range of this species , no other plant shares this combination of characters : bright yellow flowers , grayish - green stems and foliage , globe - shaped fruits with a long style , perennial habit , and the habitat of limestone rocky cliffs .',
'timber removal may be beneficial and even needed to maintain the open character of the habitat for this species .',
'hand removal of trees in the vicinity of the population is necessary to avoid impacts from timber operations .',
'southwest indiana , north central kentucky , and north central tennessee .',
'email : naturepreserves @ ky . gov feedback naturepreserves @ ky . gov | about the agency | about this site copyright © 2003 - 2013 commonwealth of kentucky .',
'all rights reserved .',
'<EOS>'
],
'targets': [
['description',
'physaria globosa is a small plant covered with dense hairs giving it a grayish appearance . it produces yellow flowers in the spring , and its fruit is globe - shaped . its preferred habitat is dry limestone cliffs , barrens , cedar glades , steep wooded slopes , and talus areas . some have also been found in areas of deeper soil and roadsides .'
],
['conservation',
'the population fluctuates year to year , but on average there are about 2000 living plants at any one time , divided among 33 known locations . threats include forms of habitat degradation and destruction , including road construction and grading , mowing , dumping , herbicides , alteration of waterways , livestock damage , and invasive species of plants such as japanese honeysuckle , garlic mustard , alsike clover , sweet clover , meadow fescue , and multiflora rose . all populations are considered vulnerable to extirpation .'
]
]
}
```
### Data Fields
- `exid`: a unique identifier
- `input`: the cited references and consists of tokenized sentences (with NLTK)
- `targets`: a list of aspect-based summaries, where each element is a pair of a) the target aspect and b) the aspect-based summary
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@katnoria](https://github.com/katnoria) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
app_reviews | null | 2022-11-03T16:47:21Z | 2,974 | 18 | null | [
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:sentiment-scoring",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | 2022-11-03T16:47:21Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- text-scoring
- sentiment-scoring
paperswithcode_id: null
pretty_name: AppReviews
dataset_info:
features:
- name: package_name
dtype: string
- name: review
dtype: string
- name: date
dtype: string
- name: star
dtype: int8
splits:
- name: train
num_bytes: 32769079
num_examples: 288065
download_size: 42592679
dataset_size: 32769079
---
# 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:** [Home Page](https://github.com/sealuzh/user_quality)
- **Repository:** [Repo Link](https://github.com/sealuzh/user_quality)
- **Paper:** [Link](https://giograno.me/assets/pdf/workshop/wama17.pdf)
- **Leaderboard:
- **Point of Contact:** [Darshan Gandhi](darshangandhi1151@gmail.com)
### Dataset Summary
It is a large dataset of Android applications belonging to 23 differentapps categories, which provides an overview of the types of feedback users report on the apps and documents the evolution of the related code metrics. The dataset contains about 395 applications of the F-Droid repository, including around 600 versions, 280,000 user reviews (extracted with specific text mining approaches)
### Supported Tasks and Leaderboards
The dataset we provide comprises 395 different apps from F-Droid repository, including code quality indicators of 629 versions of these
apps. It also encloses app reviews related to each of these versions, which have been automatically categorized classifying types of user feedback from a software maintenance and evolution perspective.
### Languages
The dataset is a monolingual dataset which has the messages English.
## Dataset Structure
### Data Instances
The dataset consists of a message in English.
{'package_name': 'com.mantz_it.rfanalyzer',
'review': "Great app! The new version now works on my Bravia Android TV which is great as it's right by my rooftop aerial cable. The scan feature would be useful...any ETA on when this will be available? Also the option to import a list of bookmarks e.g. from a simple properties file would be useful.",
'date': 'October 12 2016',
'star': 4}
### Data Fields
* package_name : Name of the Software Application Package
* review : Message of the user
* date : date when the user posted the review
* star : rating provied by the user for the application
### Data Splits
There is training data, with a total of : 288065
## 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
With the help of this dataset one can try to understand more about software applications and what are the views and opinions of the users about them. This helps to understand more about which type of software applications are prefeered by the users and how do these applications facilitate the user to help them solve their problems and issues.
### Discussion of Biases
The reviews are only for applications which are in the open-source software applications, the other sectors have not been considered here
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
Giovanni Grano - (University of Zurich), Sebastiano Panichella - (University of Zurich), Andrea di Sorbo - (University of Sannio)
### Licensing Information
[More Information Needed]
### Citation Information
@InProceedings{Zurich Open Repository and
Archive:dataset,
title = {Software Applications User Reviews},
authors={Grano, Giovanni; Di Sorbo, Andrea; Mercaldo, Francesco; Visaggio, Corrado A; Canfora, Gerardo;
Panichella, Sebastiano},
year={2017}
}
### Contributions
Thanks to [@darshan-gandhi](https://github.com/darshan-gandhi) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
speechcolab/gigaspeech | speechcolab | 2023-11-23T14:08:34Z | 2,945 | 37 | null | [
"task_categories:automatic-speech-recognition",
"task_categories:text-to-speech",
"task_categories:text-to-audio",
"multilinguality:monolingual",
"language:en",
"license:apache-2.0",
"arxiv:2106.06909",
"region:us"
] | 2023-11-23T14:08:34Z | 2022-06-09T14:51:58.000Z | 2022-06-09T14:51:58 | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: Gigaspeech
source_datasets: []
task_categories:
- automatic-speech-recognition
- text-to-speech
- text-to-audio
extra_gated_prompt: >-
SpeechColab does not own the copyright of the audio files. For researchers and
educators who wish to use the audio files for non-commercial research and/or
educational purposes, we can provide access through the Hub under certain
conditions and terms.
Terms of Access:
The "Researcher" has requested permission to use the GigaSpeech database (the
"Database") at Tsinghua University. In exchange for such permission,
Researcher hereby agrees to the following terms and conditions:
1. Researcher shall use the Database only for non-commercial research and
educational purposes.
2. The SpeechColab team and Tsinghua University make no representations or
warranties regarding the Database, including but not limited to warranties of
non-infringement or fitness for a particular purpose.
3. Researcher accepts full responsibility for his or her use of the Database
and shall defend and indemnify the SpeechColab team and Tsinghua University,
including their employees, Trustees, officers and agents, against any and all
claims arising from Researcher's use of the Database, including but not
limited to Researcher's use of any copies of copyrighted audio files that he
or she may create from the Database.
4. Researcher may provide research associates and colleagues with access to
the Database provided that they first agree to be bound by these terms and
conditions.
5. The SpeechColab team and Tsinghua University reserve the right to terminate
Researcher's access to the Database at any time.
6. If Researcher is employed by a for-profit, commercial entity, Researcher's
employer shall also be bound by these terms and conditions, and Researcher
hereby represents that he or she is fully authorized to enter into this
agreement on behalf of such employer.
!!! Please also fill out the Google Form https://forms.gle/UuGQAPyscGRrUMLq6
to request access to the Gigaspeech dataset.
extra_gated_fields:
Name: text
Email: text
Organization: text
Address: text
I hereby confirm that I have requested access via the Google Form provided above: checkbox
I accept the terms of access: checkbox
---
# Dataset Card for Gigaspeech
## 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)
- [Terms of Access](#terms-of-access)
## Dataset Description
- **Homepage:** https://github.com/SpeechColab/GigaSpeech
- **Repository:** https://github.com/SpeechColab/GigaSpeech
- **Paper:** https://arxiv.org/abs/2106.06909
- **Leaderboard:** https://github.com/SpeechColab/GigaSpeech#leaderboard
- **Point of Contact:** [gigaspeech@speechcolab.org](mailto:gigaspeech@speechcolab.org)
## Dataset Description
GigaSpeech is an evolving, multi-domain English speech recognition corpus with 10,000 hours of high quality labeled audio suitable for supervised training. The transcribed audio data is collected from audiobooks, podcasts and YouTube, covering both read and spontaneous speaking styles, and a variety of topics, such as arts, science, sports, etc.
### Example Usage
The training split has several configurations of various size:
XS, S, M, L, XL. See the Section on "Data Splits" for more information. To download the XS configuration:
```python
from datasets import load_dataset
gs = load_dataset("speechcolab/gigaspeech", "xs", use_auth_token=True)
# see structure
print(gs)
# load audio sample on the fly
audio_input = gs["train"][0]["audio"] # first decoded audio sample
transcription = gs["train"][0]["text"] # first transcription
```
It is possible to download only the development or test data:
```python
gs_dev = load_dataset("speechcolab/gigaspeech", "dev", use_auth_token=True)
gs_test = load_dataset("speechcolab/gigaspeech", "test", use_auth_token=True)
```
### Supported Tasks and Leaderboards
- `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). The task has an active leaderboard which can be found at https://github.com/SpeechColab/GigaSpeech#leaderboard and ranks models based on their WER.
- `text-to-speech`, `text-to-audio`: The dataset can also be used to train a model for Text-To-Speech (TTS).
### Languages
Gigaspeech contains audio and transcription data in English.
## Dataset Structure
### Data Instances
```python
{
'segment_id': 'YOU0000000315_S0000660',
'speaker': 'N/A',
'text': "AS THEY'RE LEAVING <COMMA> CAN KASH PULL ZAHRA ASIDE REALLY QUICKLY <QUESTIONMARK>",
'audio':
{
# in streaming mode 'path' will be 'xs_chunks_0000/YOU0000000315_S0000660.wav'
'path': '/home/user/.cache/huggingface/datasets/downloads/extracted/9d48cf31/xs_chunks_0000/YOU0000000315_S0000660.wav',
'array': array([0.0005188 , 0.00085449, 0.00012207, ..., 0.00125122, 0.00076294, 0.00036621], dtype=float32),
'sampling_rate': 16000
},
'begin_time': 2941.889892578125,
'end_time': 2945.070068359375,
'audio_id': 'YOU0000000315',
'title': 'Return to Vasselheim | Critical Role: VOX MACHINA | Episode 43',
'url': 'https://www.youtube.com/watch?v=zr2n1fLVasU',
'source': 2,
'category': 24,
'original_full_path': 'audio/youtube/P0004/YOU0000000315.opus'
}
```
### Data Fields
* segment_id (string) - string id of the segment.
* speaker (string) - string id of the speaker (can be "N/A").
* text (string) - transcription of the segment.
* begin_time (float) - start time of the segment in an original full audio.
* end_time (float32) - end time of the segment in an original full audio.
* audio (Audio feature) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate.
In non-streaming mode (default), the path point to the locally extracted audio. In streaming mode, the path is the relative path of an audio.
segment inside its archive (as files are not downloaded and extracted locally).
* audio_id (string) - string idea of the original full audio.
* title (string) - title of the original full audio.
* url (string) - url of the original full audio.
* source (ClassLabel) - id of the audio source. Sources are audiobook (0), podcast (1), and YouYube (2).
* category (ClassLabel) - id of the audio category, categories are listed below.
* original_full_path (string) - the relative path to the original full audio sample in the original data directory.
Categories are assigned from the following labels:
"People and Blogs", "Business", "Nonprofits and Activism", "Crime", "History", "Pets and Animals",
"News and Politics", "Travel and Events", "Kids and Family", "Leisure", "N/A", "Comedy", "News and Politics",
"Sports", "Arts", "Science and Technology", "Autos and Vehicles", "Science and Technology", "People and Blogs",
"Music", "Society and Culture", "Education", "Howto and Style", "Film and Animation", "Gaming", "Entertainment",
"Travel and Events", "Health and Fitness", "audiobook".
### Data Splits
The dataset has three splits: train, evaluation (dev) and test. The train split has five configurations of various sizes:
XS, S, M, L, XL. Larger subsets are supersets of smaller subsets, e.g., the L subset contains all the data from the M subset.
#### Transcribed Training Subsets Size
| Subset | Hours | Remarks |
|:---------------:|:-------------:|:-------------|
| XS | 10 | System building and debugging |
| S | 250 | Quick research experiments |
| M | 1,000 | Large-scale research experiments |
| L | 2,500 | Medium-scale industrial experiments |
| XL | 10,000 | Large-scale industrial experiments |
#### Transcribed Evaluation Subsets
| Subset | Hours | Remarks |
|:------:|:-----:|:--------|
| Dev | 12 | Randomly selected from the crawled Podcast and YouTube Data |
| Test | 40 | Part of the subset was randomly selected from the crawled Podcast and YouTube data; part of it was manually collected through other channels to have better coverage. |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
| Audio Source | Transcribed Hours | Acoustic Condition |
|:-------------|:----------------------:|:-------------------|
| Audiobook | 2,655 | <li>Reading</li><li>Various ages and accents</li> |
| Podcast | 3,498 | <li>Clean or background music</li><li>Indoor</li><li>Near-field</li><li>Spontaneous</li><li>Various ages and accents</li>|
| YouTube | 3,845 | <li>Clean and noisy</li><li>Indoor and outdoor</li><li>Near- and far-field</li><li>Reading and spontaneous</li><li>Various ages and accents</li> |
| ***Total*** | ***10,000*** ||
#### 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?
Development and test subsets are annotated by professional human annotators.
### 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
SpeechColab does not own the copyright of the audio files. For researchers and educators who wish to use the audio files for
non-commercial research and/or educational purposes, we can provide access through our site under certain conditions and terms.
In general, when training a machine learning model on a given dataset, the license of the model is **independent** to that of the
dataset. That is to say, speech recognition models trained on the GigaSpeech dataset may be eligible for commercial license,
provided they abide to the 'Fair Use' terms of the underlying data and do not violate any explicit copyright restrictions.
This is likely to be true in most use-cases. However, it is your responsiblity to verify the appropriate model license for
your specific use-case by confirming that the dataset usage abides by the Fair Use terms. SpeechColab is not responsible
for the license of any machine learning model trained on the GigaSpeech dataset.
### Citation Information
Please cite this paper if you find this work useful:
```bibtext
@inproceedings{GigaSpeech2021,
title={GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio},
booktitle={Proc. Interspeech 2021},
year=2021,
author={Guoguo Chen, Shuzhou Chai, Guanbo Wang, Jiayu Du, Wei-Qiang Zhang, Chao Weng, Dan Su, Daniel Povey, Jan Trmal, Junbo Zhang, Mingjie Jin, Sanjeev Khudanpur, Shinji Watanabe, Shuaijiang Zhao, Wei Zou, Xiangang Li, Xuchen Yao, Yongqing Wang, Yujun Wang, Zhao You, Zhiyong Yan}
}
```
### Contributions
Thanks to [@polinaeterna](https://github.com/polinaeterna) and [@sanchit-gandhi](https://github.com/sanchit-gandhi)
for adding this dataset.
## Terms of Access
The "Researcher" has requested permission to use the GigaSpeech database (the "Database")
at Tsinghua University. In exchange for such permission, Researcher hereby agrees to the
following terms and conditions:
1. Researcher shall use the Database only for non-commercial research and educational purposes.
2. The SpeechColab team and Tsinghua University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose.
3. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the SpeechColab team and Tsinghua University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted audio files that he or she may create from the Database.
4. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions.
5. The SpeechColab team and Tsinghua University reserve the right to terminate Researcher's access to the Database at any time.
6. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
tau/zero_scrolls | tau | 2023-06-30T17:21:02Z | 2,943 | 6 | null | [
"task_categories:question-answering",
"task_categories:summarization",
"task_categories:text-generation",
"task_ids:multiple-choice-qa",
"language:en",
"query-based-summarization",
"long-texts",
"arxiv:2104.02112",
"arxiv:2104.07091",
"arxiv:2104.05938",
"arxiv:2205.11465",
"arxiv:2105.03011",
"arxiv:1712.07040",
"arxiv:2112.08608",
"arxiv:2108.00573",
"arxiv:2305.14196",
"region:us"
] | 2023-06-30T17:21:02Z | 2023-05-21T10:47:57.000Z | 2023-05-21T10:47:57 | ---
language:
- en
task_categories:
- question-answering
- summarization
- text-generation
task_ids:
- multiple-choice-qa
tags:
- query-based-summarization
- long-texts
---
## Dataset Description
- **Homepage:** [ZeroSCROLLS](https://www.zero.scrolls-benchmark.com/)
- **Leaderboard:** [Leaderboard](https://www.zero.scrolls-benchmark.com/leaderboard)
- **Point of Contact:** [scrolls-benchmark-contact@googlegroups.com](scrolls-benchmark-contact@googlegroups.com)
# Dataset Card for ZeroSCROLLS
## Overview
ZeroSCROLLS is a zero-shot benchmark for natural language understanding over long texts.
The validation sets contain only ~20 examples per task and are meant for eyeballing alone.
## Leaderboard
The ZeroSCROLLS benchmark leaderboard can be found [here](https://www.zero.scrolls-benchmark.com/leaderboard).
## Tasks
ZeroSCROLLS contains the following tasks:
#### GovReport ([Huang et al., 2021](https://arxiv.org/pdf/2104.02112.pdf))
GovReport is a summarization dataset of reports addressing various national policy issues published by the
Congressional Research Service and the U.S. Government Accountability Office, where each document is paired with a hand-written executive summary.
The reports and their summaries are longer than their equivalents in other popular long-document summarization datasets;
for example, GovReport's documents are approximately 1.5 and 2.5 times longer than the documents in Arxiv and PubMed, respectively.
#### SummScreenFD ([Chen et al., 2022](https://arxiv.org/pdf/2104.07091.pdf))
SummScreenFD is a summarization dataset in the domain of TV shows (e.g. Friends, Game of Thrones).
Given a transcript of a specific episode, the goal is to produce the episode's recap.
The original dataset is divided into two complementary subsets, based on the source of its community contributed transcripts.
For SCROLLS, we use the ForeverDreaming (FD) subset, as it incorporates 88 different shows,
making it a more diverse alternative to the TV MegaSite (TMS) subset, which has only 10 shows.
Community-authored recaps for the ForeverDreaming transcripts were collected from English Wikipedia and TVMaze.
#### QMSum ([Zhong et al., 2021](https://arxiv.org/pdf/2104.05938.pdf))
QMSum is a query-based summarization dataset, consisting of 232 meetings transcripts from multiple domains.
The corpus covers academic group meetings at the International Computer Science Institute and their summaries, industrial product meetings for designing a remote control,
and committee meetings of the Welsh and Canadian Parliaments, dealing with a variety of public policy issues.
Annotators were tasked with writing queries about the broad contents of the meetings, as well as specific questions about certain topics or decisions,
while ensuring that the relevant text for answering each query spans at least 200 words or 10 turns.
#### SQuALITY ([Wang et al., 2022](https://arxiv.org/pdf/2205.11465.pdf))
SQuALITY (Wang et al., 2022) is a question-focused summarization dataset, where given a story from Project Gutenberg,
the task is to produce a summary of the story or aspects of it based on a guiding question.
The questions and summaries are original and crowdsourced; experienced writers were guided to design questions that require reading significant parts of the story to answer correctly.
#### Qasper ([Dasigi et al., 2021](https://arxiv.org/pdf/2105.03011.pdf))
Qasper is a question answering dataset over NLP papers filtered from the Semantic Scholar Open Research Corpus (S2ORC).
Questions were written by NLP practitioners after reading only the title and abstract of the papers,
while another set of NLP practitioners annotated the answers given the entire document.
Qasper contains abstractive, extractive, and yes/no questions, as well as unanswerable ones.
#### NarrativeQA ([Kočiský et al., 2018](https://arxiv.org/pdf/1712.07040.pdf))
NarrativeQA (Kočiský et al., 2021) is an established question answering dataset over entire books from Project Gutenberg and movie scripts from different websites.
Annotators were given summaries of the books and scripts obtained from Wikipedia, and asked to generate question-answer pairs,
resulting in about 30 questions and answers for each of the 1,567 books and scripts.
They were encouraged to use their own words rather then copying, and avoid asking yes/no questions or ones about the cast.
Each question was then answered by an additional annotator, providing each question with two reference answers (unless both answers are identical).
#### QuALITY ([Pang et al., 2022](https://arxiv.org/pdf/2112.08608.pdf))
QuALITY is a multiple-choice question answering dataset over articles and stories sourced from Project Gutenberg,
the Open American National Corpus, and more.
Experienced writers wrote questions and distractors, and were incentivized to write answerable, unambiguous questions such that in order to correctly answer them,
human annotators must read large portions of the given document.
Reference answers were then calculated using the majority vote between of the annotators and writer's answers.
To measure the difficulty of their questions, Pang et al. conducted a speed validation process,
where another set of annotators were asked to answer questions given only a short period of time to skim through the document.
As a result, 50% of the questions in QuALITY are labeled as hard, i.e. the majority of the annotators in the speed validation setting chose the wrong answer.
#### MuSiQue ([Trivedi et al., 2022](https://arxiv.org/pdf/2108.00573.pdf))
MuSiQue is a multi-hop question answering dataset, where the inputs are 20 Wikipedia paragraphs and a question that requires multiple hops between different paragraphs.
In the original dataset, each question also has an unanswerable twin question, where the correct answer is not present in the paragraphs.
#### SpaceDigest (New)
SpaceDigest is a new sentiment aggregation task. Given 50 hotel reviews (without their ratings) from the Space dataset (Angelidis et al., 2021), the task is to determine the percentage of positive reviews.
#### BookSumSort (New)
BookSumSort is a new task based on the BookSum dataset (Kry ́sci ́nski et al., 2022), which contains summaries of chapters (or parts) of novels, plays, and long poems from various sources.
Given a shuffled list of chapter summaries, the task is to reorder them according to the original order of summaries in BookSum.
## Data Fields
Most datasets in the benchmark are in the same input-output format
- `input`: a `string` feature. The input document.
- `output`: this feature is always None, as ZeroSCROLLS contains only test sets.
- `id`: a `string` feature. Unique per input.
- `pid`: a `string` feature, identical to 'id`. Facilitates evaluating tasks with multiple refrences per input.
- `document_start_index`: an `int32` feature. Character index that enables easy parsing of the context document.
- `document_end_index`: an `int32` feature. Character index that enables easy parsing of the context document.
- `query_start_index`: an `int32` feature. Character index that enables easy parsing of the query, if exists.
- `query_end_index`: an `int32` feature. Character index that enables easy parsing of the query, if exists.
- `truncation_seperator`: a `string` feature. The string used to append to a trimmed context document, mentioning the context was trimmed.
Datasets containing multiple documents inside the `input` feature are MuSiQue, SpaceDigest, and BookSumSort. They also have the following feature:
- `inner_docs_start_indices`: a sequence of `int32` feature. Character indexes that enables easy parsing of the the inner documents, e.g. Reviews, of Summaries.
## Citation
If you use the ZeroSCROLLS data, **please make sure to cite all of the original dataset papers.** [[bibtex](https://zero-scrolls-tau.s3.us-east-2.amazonaws.com/zero_scrolls_datasets.bib)]
```
@misc{shaham2023zeroscrolls,
title={ZeroSCROLLS: A Zero-Shot Benchmark for Long Text Understanding},
author={Uri Shaham and Maor Ivgi and Avia Efrat and Jonathan Berant and Omer Levy},
year={2023},
eprint={2305.14196},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | [
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cos_e | null | 2023-04-05T10:02:39Z | 2,940 | 6 | cos-e | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|commonsense_qa",
"language:en",
"license:unknown",
"arxiv:1906.02361",
"region:us"
] | 2023-04-05T10:02:39Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- crowdsourced
license:
- unknown
multilinguality:
- monolingual
pretty_name: Commonsense Explanations
size_categories:
- 10K<n<100K
source_datasets:
- extended|commonsense_qa
task_categories:
- question-answering
task_ids:
- open-domain-qa
paperswithcode_id: cos-e
dataset_info:
- config_name: v1.0
features:
- name: id
dtype: string
- name: question
dtype: string
- name: choices
sequence: string
- name: answer
dtype: string
- name: abstractive_explanation
dtype: string
- name: extractive_explanation
dtype: string
splits:
- name: train
num_bytes: 2077517
num_examples: 7610
- name: validation
num_bytes: 261887
num_examples: 950
download_size: 4295320
dataset_size: 2339404
- config_name: v1.11
features:
- name: id
dtype: string
- name: question
dtype: string
- name: choices
sequence: string
- name: answer
dtype: string
- name: abstractive_explanation
dtype: string
- name: extractive_explanation
dtype: string
splits:
- name: train
num_bytes: 2717420
num_examples: 9741
- name: validation
num_bytes: 331760
num_examples: 1221
download_size: 6535534
dataset_size: 3049180
---
# Dataset Card for "cos_e"
## 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/salesforce/cos-e
- **Paper:** [Explain Yourself! Leveraging Language Models for Commonsense Reasoning](https://arxiv.org/abs/1906.02361)
- **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:** 10.83 MB
- **Size of the generated dataset:** 5.39 MB
- **Total amount of disk used:** 16.22 MB
### Dataset Summary
Common Sense Explanations (CoS-E) allows for training language models to
automatically generate explanations that can be used during training and
inference in a novel Commonsense Auto-Generated Explanation (CAGE) framework.
### 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
#### v1.0
- **Size of downloaded dataset files:** 4.30 MB
- **Size of the generated dataset:** 2.34 MB
- **Total amount of disk used:** 6.64 MB
An example of 'train' looks as follows.
```
{
"abstractive_explanation": "this is open-ended",
"answer": "b",
"choices": ["a", "b", "c"],
"extractive_explanation": "this is selected train",
"id": "42",
"question": "question goes here."
}
```
#### v1.11
- **Size of downloaded dataset files:** 6.53 MB
- **Size of the generated dataset:** 3.05 MB
- **Total amount of disk used:** 9.58 MB
An example of 'train' looks as follows.
```
{
"abstractive_explanation": "this is open-ended",
"answer": "b",
"choices": ["a", "b", "c"],
"extractive_explanation": "this is selected train",
"id": "42",
"question": "question goes here."
}
```
### Data Fields
The data fields are the same among all splits.
#### v1.0
- `id`: a `string` feature.
- `question`: a `string` feature.
- `choices`: a `list` of `string` features.
- `answer`: a `string` feature.
- `abstractive_explanation`: a `string` feature.
- `extractive_explanation`: a `string` feature.
#### v1.11
- `id`: a `string` feature.
- `question`: a `string` feature.
- `choices`: a `list` of `string` features.
- `answer`: a `string` feature.
- `abstractive_explanation`: a `string` feature.
- `extractive_explanation`: a `string` feature.
### Data Splits
|name |train|validation|
|-----|----:|---------:|
|v1.0 | 7610| 950|
|v1.11| 9741| 1221|
## 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
Unknown.
### Citation Information
```
@inproceedings{rajani2019explain,
title = "Explain Yourself! Leveraging Language models for Commonsense Reasoning",
author = "Rajani, Nazneen Fatema and
McCann, Bryan and
Xiong, Caiming and
Socher, Richard",
year="2019",
booktitle = "Proceedings of the 2019 Conference of the Association for Computational Linguistics (ACL2019)",
url ="https://arxiv.org/abs/1906.02361"
}
```
### Contributions
Thanks to [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf), [@mariamabarham](https://github.com/mariamabarham), [@patrickvonplaten](https://github.com/patrickvonplaten), [@albertvillanova](https://github.com/albertvillanova), [@lhoestq](https://github.com/lhoestq) for adding this dataset. | [
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americas_nli | null | 2023-01-25T14:26:20Z | 2,936 | 1 | null | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"multilinguality:translation",
"size_categories:unknown",
"source_datasets:extended|xnli",
"language:ay",
"language:bzd",
"language:cni",
"language:gn",
"language:hch",
"language:nah",
"language:oto",
"language:qu",
"language:shp",
"language:tar",
"license:unknown",
"arxiv:2104.08726",
"region:us"
] | 2023-01-25T14:26:20Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- ay
- bzd
- cni
- gn
- hch
- nah
- oto
- qu
- shp
- tar
license:
- unknown
multilinguality:
- multilingual
- translation
size_categories:
- unknown
source_datasets:
- extended|xnli
task_categories:
- text-classification
task_ids:
- natural-language-inference
pretty_name: 'AmericasNLI: A NLI Corpus of 10 Indigenous Low-Resource Languages.'
dataset_info:
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features:
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dtype: string
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dtype:
class_label:
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'0': entailment
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'2': contradiction
splits:
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num_examples: 743
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download_size: 2256093
dataset_size: 232797
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features:
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features:
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---
# Dataset Card for AmericasNLI
## 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)
## Dataset Description
- **Homepage:** [Needs More Information]
- **Repository:** https://github.com/nala-cub/AmericasNLI
- **Paper:** https://arxiv.org/abs/2104.08726
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
AmericasNLI is an extension of XNLI (Conneau et al., 2018) a natural language inference (NLI) dataset covering 15 high-resource languages to 10 low-resource indigenous languages spoken in the Americas: Ashaninka, Aymara, Bribri, Guarani, Nahuatl, Otomi, Quechua, Raramuri, Shipibo-Konibo, and Wixarika. As with MNLI, the goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B) and is a classification task (given two sentences, predict one of three labels).
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
- aym
- bzd
- cni
- gn
- hch
- nah
- oto
- quy
- shp
- tar
## Dataset Structure
### Data Instances
#### all_languages
An example of the test split looks as follows:
```
{'language': 'aym', 'premise': "Ukhamaxa, janiw ukatuqits lup'kayätti, ukhamarus wali phiñasitayätwa, ukatx jupampiw mayamp aruskipañ qallanttha.", 'hypothesis': 'Janiw mayamp jupampix p
arlxapxti.', 'label': 2}
```
#### aym
An example of the test split looks as follows:
```
{'premise': "Ukhamaxa, janiw ukatuqits lup'kayätti, ukhamarus wali phiñasitayätwa, ukatx jupampiw mayamp aruskipañ qallanttha.", 'hypothesis': 'Janiw mayamp jupampix parlxapxti.', 'label
': 2}
```
#### bzd
An example of the test split looks as follows:
```
{'premise': "Bua', kèq ye' kũ e' bikeitsök erë ye' chkénãwã tã ye' ujtémĩne ie' tã páxlĩnẽ.", 'hypothesis': "Kèq ye' ùtẽnẽ ie' tã páxlĩ.", 'label': 2}
```
#### cni
An example of the test split looks as follows:
```
{'premise': 'Kameetsa, tee nokenkeshireajeroji, iro kantaincha tee nomateroji aisati nintajaro noñanatajiri iroakera.', 'hypothesis': 'Tee noñatajeriji.', 'label': 2}
```
#### gn
An example of the test split looks as follows:
```
{'premise': "Néi, ni napensaikurihína upéva rehe, ajepichaiterei ha añepyrûjey añe'ê hendive.", 'hypothesis': "Nañe'êvéi hendive.", 'label': 2}
```
#### hch
An example of the test split looks as follows:
```
{'premise': 'mu hekwa.', 'hypothesis': 'neuka tita xatawe m+k+ mat+a.', 'label': 2}
```
#### nah
An example of the test split looks as follows:
```
{'premise': 'Cualtitoc, na axnimoihliaya ino, nicualaniztoya queh naha nicamohuihqui', 'hypothesis': 'Ayoc nicamohuihtoc', 'label': 2}
```
#### oto
An example of the test split looks as follows:
```
{'premise': 'mi-ga, nin mibⴘy mbô̮nitho ane guenu, guedi mibⴘy nho ⴘnmⴘy xi di mⴘdi o ñana nen nⴘua manaigui', 'hypothesis': 'hin din bi pengui nen nⴘa', 'label': 2}
```
#### quy
An example of the test split looks as follows:
``` {'premise': 'Allinmi, manam chaypiqa hamutachkarqanichu, ichaqa manam allinchu tarikurqani chaymi kaqllamanta paywan rimarqani.', 'hypothesis': 'Manam paywanqa kaqllamantaqa rimarqani
.', 'label': 2}
```
#### shp
An example of the test split looks as follows:
```
{'premise': 'Jakon riki, ja shinanamara ea ike, ikaxbi kikin frustradara ea ike jakopira ea jabe yoyo iribake.', 'hypothesis': 'Eara jabe yoyo iribiama iki.', 'label': 2}
```
#### tar
An example of the test split looks as follows:
```
{'premise': 'Ga’lá ju, ke tási newalayé nejé echi kítira, we ne majáli, a’lí ko uchécho ne yua ku ra’íchaki.', 'hypothesis': 'Tási ne uchecho yua ra’ícha échi rejói.', 'label': 2}
```
### Data Fields
#### all_languages
- language: a multilingual string variable, with languages including ar, bg, de, el, en.
- premise: a multilingual string variable, with languages including ar, bg, de, el, en.
- hypothesis: a multilingual string variable, with possible languages including ar, bg, de, el, en.
- label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
#### aym
- premise: a string feature.
- hypothesis: a string feature.
- label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
#### bzd
- premise: a string feature.
- hypothesis: a string feature.
- label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
#### cni
- premise: a string feature.
- hypothesis: a string feature.
- label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
#### hch
- premise: a string feature.
- hypothesis: a string feature.
- label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
#### nah
- premise: a string feature.
- hypothesis: a string feature.
- label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
#### oto
- premise: a string feature.
- hypothesis: a string feature.
- label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
#### quy
- premise: a string feature.
- hypothesis: a string feature.
- label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
#### shp
- premise: a string feature.
- hypothesis: a string feature.
- label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
#### tar
- premise: a string feature.
- hypothesis: a string feature.
- label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
### Data Splits
| Language | ISO | Family | Dev | Test |
|-------------------|-----|:-------------|-----:|-----:|
| all_languages | -- | -- | 6457 | 7486 |
| Aymara | aym | Aymaran | 743 | 750 |
| Ashaninka | cni | Arawak | 658 | 750 |
| Bribri | bzd | Chibchan | 743 | 750 |
| Guarani | gn | Tupi-Guarani | 743 | 750 |
| Nahuatl | nah | Uto-Aztecan | 376 | 738 |
| Otomi | oto | Oto-Manguean | 222 | 748 |
| Quechua | quy | Quechuan | 743 | 750 |
| Raramuri | tar | Uto-Aztecan | 743 | 750 |
| Shipibo-Konibo | shp | Panoan | 743 | 750 |
| Wixarika | hch | Uto-Aztecan | 743 | 750 |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
The authors translate from the Spanish subset of XNLI.
> AmericasNLI is the translation of a subset of XNLI (Conneau et al., 2018). As translators between Spanish and the target languages are more frequently available than those for English, we translate from the Spanish version.
As per paragraph 3.1 of the [original paper](https://arxiv.org/abs/2104.08726).
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
The dataset comprises expert translations from Spanish XNLI.
> Additionally, some translators reported that code-switching is often used to describe certain topics, and, while many words without an exact equivalence in the target language are worked in through translation or interpretation, others are kept in Spanish. To minimize the amount of Spanish vocabulary in the translated examples, we choose sentences from genres that we judged to be relatively easy to translate into the target languages: “face-to-face,” “letters,” and “telephone.”
As per paragraph 3.1 of the [original paper](https://arxiv.org/abs/2104.08726).
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
```
@article{DBLP:journals/corr/abs-2104-08726,
author = {Abteen Ebrahimi and
Manuel Mager and
Arturo Oncevay and
Vishrav Chaudhary and
Luis Chiruzzo and
Angela Fan and
John Ortega and
Ricardo Ramos and
Annette Rios and
Ivan Vladimir and
Gustavo A. Gim{\'{e}}nez{-}Lugo and
Elisabeth Mager and
Graham Neubig and
Alexis Palmer and
Rolando A. Coto Solano and
Ngoc Thang Vu and
Katharina Kann},
title = {AmericasNLI: Evaluating Zero-shot Natural Language Understanding of
Pretrained Multilingual Models in Truly Low-resource Languages},
journal = {CoRR},
volume = {abs/2104.08726},
year = {2021},
url = {https://arxiv.org/abs/2104.08726},
eprinttype = {arXiv},
eprint = {2104.08726},
timestamp = {Mon, 26 Apr 2021 17:25:10 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2104-08726.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
### Contributions
Thanks to [@fdschmidt93](https://github.com/fdschmidt93) for adding this dataset. | [
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sayakpaul/nyu_depth_v2 | sayakpaul | 2022-12-12T13:35:31Z | 2,920 | 14 | nyuv2 | [
"task_categories:depth-estimation",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"depth-estimation",
"arxiv:1903.03273",
"region:us"
] | 2022-12-12T13:35:31Z | 2022-11-22T10:51:08.000Z | 2022-11-22T10:51:08 | ---
license: apache-2.0
language:
- en
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
task_categories:
- depth-estimation
task_ids: []
pretty_name: NYU Depth V2
tags:
- depth-estimation
paperswithcode_id: nyuv2
dataset_info:
features:
- name: image
dtype: image
- name: depth_map
dtype: image
splits:
- name: train
num_bytes: 20212097551
num_examples: 47584
- name: validation
num_bytes: 240785762
num_examples: 654
download_size: 35151124480
dataset_size: 20452883313
---
# Dataset Card for NYU Depth V2
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Visualization](#visualization)
- [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:** [NYU Depth Dataset V2 homepage](https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html)
- **Repository:** Fast Depth [repository](https://github.com/dwofk/fast-depth) which was used to source the dataset in this repository. It is a preprocessed version of the original NYU Depth V2 dataset linked above. It is also used in [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/nyu_depth_v2).
- **Papers:** [Indoor Segmentation and Support Inference from RGBD Images](http://cs.nyu.edu/~silberman/papers/indoor_seg_support.pdf) and [FastDepth: Fast Monocular Depth Estimation on Embedded Systems](https://arxiv.org/abs/1903.03273)
- **Point of Contact:** [Nathan Silberman](mailto:silberman@@cs.nyu.edu) and [Diana Wofk](mailto:dwofk@alum.mit.edu)
### Dataset Summary
As per the [dataset homepage](https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html):
The NYU-Depth V2 data set is comprised of video sequences from a variety of indoor scenes as recorded by both the RGB and Depth cameras from the Microsoft [Kinect](http://www.xbox.com/kinect). It features:
* 1449 densely labeled pairs of aligned RGB and depth images
* 464 new scenes taken from 3 cities
* 407,024 new unlabeled frames
* Each object is labeled with a class and an instance number (cup1, cup2, cup3, etc)
The dataset has several components:
* Labeled: A subset of the video data accompanied by dense multi-class labels. This data has also been preprocessed to fill in missing depth labels.
* Raw: The raw rgb, depth and accelerometer data as provided by the Kinect.
* Toolbox: Useful functions for manipulating the data and labels.
### Supported Tasks
- `depth-estimation`: Depth estimation is the task of approximating the perceived depth of a given image. In other words, it's about measuring the distance of each image pixel from the camera.
- `semantic-segmentation`: Semantic segmentation is the task of associating every pixel of an image to a class label.
There are other tasks supported by this dataset as well. You can find more about them by referring to [this resource](https://paperswithcode.com/dataset/nyuv2).
### Languages
English.
## Dataset Structure
### Data Instances
A data point comprises an image and its annotation depth map for both the `train` and `validation` splits.
```
{
'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB at 0x1FF32A3EDA0>,
'depth_map': <PIL.PngImagePlugin.PngImageFile image mode=L at 0x1FF32E5B978>,
}
```
### Data Fields
- `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`.
- `depth_map`: A `PIL.Image.Image` object containing the annotation depth map.
### Data Splits
The data is split into training, and validation splits. The training data contains 47584 images, and the validation data contains 654 images.
## Visualization
You can use the following code snippet to visualize samples from the dataset:
```py
from datasets import load_dataset
import numpy as np
import matplotlib.pyplot as plt
cmap = plt.cm.viridis
ds = load_dataset("sayakpaul/nyu_depth_v2")
def colored_depthmap(depth, d_min=None, d_max=None):
if d_min is None:
d_min = np.min(depth)
if d_max is None:
d_max = np.max(depth)
depth_relative = (depth - d_min) / (d_max - d_min)
return 255 * cmap(depth_relative)[:,:,:3] # H, W, C
def merge_into_row(input, depth_target):
input = np.array(input)
depth_target = np.squeeze(np.array(depth_target))
d_min = np.min(depth_target)
d_max = np.max(depth_target)
depth_target_col = colored_depthmap(depth_target, d_min, d_max)
img_merge = np.hstack([input, depth_target_col])
return img_merge
random_indices = np.random.choice(len(ds["train"]), 9).tolist()
train_set = ds["train"]
plt.figure(figsize=(15, 6))
for i, idx in enumerate(random_indices):
ax = plt.subplot(3, 3, i + 1)
image_viz = merge_into_row(
train_set[idx]["image"], train_set[idx]["depth_map"]
)
plt.imshow(image_viz.astype("uint8"))
plt.axis("off")
```
## Dataset Creation
### Curation Rationale
The rationale from [the paper](http://cs.nyu.edu/~silberman/papers/indoor_seg_support.pdf) that introduced the NYU Depth V2 dataset:
> We present an approach to interpret the major surfaces, objects, and support relations of an indoor scene from an RGBD image. Most existing work ignores physical interactions or is applied only to tidy rooms and hallways. Our goal is to parse typical, often messy, indoor scenes into floor, walls, supporting surfaces, and object regions, and to recover support relationships. One of our main interests is to better understand how 3D cues can best inform a structured 3D interpretation.
### Source Data
#### Initial Data Collection
> The dataset consists of 1449 RGBD images, gathered from a wide range
of commercial and residential buildings in three different US cities, comprising
464 different indoor scenes across 26 scene classes.A dense per-pixel labeling was
obtained for each image using Amazon Mechanical Turk.
### Annotations
#### Annotation process
This is an involved process. Interested readers are referred to Sections 2, 3, and 4 of the [original paper](http://cs.nyu.edu/~silberman/papers/indoor_seg_support.pdf).
#### Who are the annotators?
AMT annotators.
### 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
* Original NYU Depth V2 dataset: Nathan Silberman, Derek Hoiem, Pushmeet Kohli, Rob Fergus
* Preprocessed version: Diana Wofk, Fangchang Ma, Tien-Ju Yang, Sertac Karaman, Vivienne Sze
### Licensing Information
The preprocessed NYU Depth V2 dataset is licensed under a [MIT License](https://github.com/dwofk/fast-depth/blob/master/LICENSE).
### Citation Information
```bibtex
@inproceedings{Silberman:ECCV12,
author = {Nathan Silberman, Derek Hoiem, Pushmeet Kohli and Rob Fergus},
title = {Indoor Segmentation and Support Inference from RGBD Images},
booktitle = {ECCV},
year = {2012}
}
@inproceedings{icra_2019_fastdepth,
author = {{Wofk, Diana and Ma, Fangchang and Yang, Tien-Ju and Karaman, Sertac and Sze, Vivienne}},
title = {{FastDepth: Fast Monocular Depth Estimation on Embedded Systems}},
booktitle = {{IEEE International Conference on Robotics and Automation (ICRA)}},
year = {{2019}}
}
```
### Contributions
Thanks to [@sayakpaul](https://huggingface.co/sayakpaul) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
GATE-engine/automated_cardiac_diagnosis_competition.ACDC | GATE-engine | 2023-06-28T08:56:08Z | 2,871 | 0 | null | [
"region:us"
] | 2023-06-28T08:56:08Z | 2023-06-28T08:26:44.000Z | 2023-06-28T08:26:44 | ---
dataset_info:
features:
- name: four_d_img
sequence:
sequence:
sequence:
sequence: float32
- name: frame_data
list:
- name: img
sequence:
sequence:
sequence: float32
- name: label
sequence:
sequence:
sequence: int64
splits:
- name: train
num_bytes: 7089368208
num_examples: 100
- name: test
num_bytes: 3489827928
num_examples: 50
download_size: 363153048
dataset_size: 10579196136
---
# Dataset Card for "automated_cardiac_diagnosis_competition.ACDC"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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tiny_shakespeare | null | 2023-04-05T13:42:24Z | 2,863 | 20 | null | [
"region:us"
] | 2023-04-05T13:42:24Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
paperswithcode_id: null
pretty_name: TinyShakespeare
dataset_info:
features:
- name: text
dtype: string
splits:
- name: test
num_bytes: 55780
num_examples: 1
- name: train
num_bytes: 1003864
num_examples: 1
- name: validation
num_bytes: 55780
num_examples: 1
download_size: 1115394
dataset_size: 1115424
---
# Dataset Card for "tiny_shakespeare"
## 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/karpathy/char-rnn/blob/master/data/tinyshakespeare/input.txt](https://github.com/karpathy/char-rnn/blob/master/data/tinyshakespeare/input.txt)
- **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:** 1.11 MB
- **Size of the generated dataset:** 1.11 MB
- **Total amount of disk used:** 2.23 MB
### Dataset Summary
40,000 lines of Shakespeare from a variety of Shakespeare's plays. Featured in Andrej Karpathy's blog post 'The Unreasonable Effectiveness of Recurrent Neural Networks': http://karpathy.github.io/2015/05/21/rnn-effectiveness/.
To use for e.g. character modelling:
```
d = datasets.load_dataset(name='tiny_shakespeare')['train']
d = d.map(lambda x: datasets.Value('strings').unicode_split(x['text'], 'UTF-8'))
# train split includes vocabulary for other splits
vocabulary = sorted(set(next(iter(d)).numpy()))
d = d.map(lambda x: {'cur_char': x[:-1], 'next_char': x[1:]})
d = d.unbatch()
seq_len = 100
batch_size = 2
d = d.batch(seq_len)
d = d.batch(batch_size)
```
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 1.11 MB
- **Size of the generated dataset:** 1.11 MB
- **Total amount of disk used:** 2.23 MB
An example of 'train' looks as follows.
```
{
"text": "First Citizen:\nBefore we proceed any further, hear me "
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `text`: a `string` feature.
### Data Splits
| name |train|validation|test|
|-------|----:|---------:|---:|
|default| 1| 1| 1|
## 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
```
@misc{
author={Karpathy, Andrej},
title={char-rnn},
year={2015},
howpublished={\url{https://github.com/karpathy/char-rnn}}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset. | [
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scan | null | 2023-06-01T14:59:55Z | 2,861 | 3 | scan | [
"task_categories:text2text-generation",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:bsd",
"multi-turn",
"arxiv:1711.00350",
"region:us"
] | 2023-06-01T14:59:55Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- en
license:
- bsd
multilinguality:
- monolingual
pretty_name: SCAN
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
paperswithcode_id: scan
tags:
- multi-turn
dataset_info:
- config_name: simple
features:
- name: commands
dtype: string
- name: actions
dtype: string
splits:
- name: train
num_bytes: 3217770
num_examples: 16728
- name: test
num_bytes: 799912
num_examples: 4182
download_size: 4080388
dataset_size: 4017682
- config_name: addprim_jump
features:
- name: commands
dtype: string
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dtype: string
splits:
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num_bytes: 2535625
num_examples: 14670
- name: test
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num_examples: 7706
download_size: 4111174
dataset_size: 4044070
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features:
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dtype: string
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dtype: string
splits:
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num_examples: 21890
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num_examples: 1208
download_size: 4148216
dataset_size: 4078954
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features:
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dtype: string
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dtype: string
splits:
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num_examples: 15225
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dtype: string
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splits:
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splits:
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num_examples: 1173
download_size: 3801870
dataset_size: 3742791
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dtype: string
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splits:
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features:
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dtype: string
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splits:
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features:
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splits:
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download_size: 3801870
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splits:
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features:
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dtype: string
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dtype: string
splits:
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num_bytes: 3127623
num_examples: 15225
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num_examples: 4476
download_size: 3903105
dataset_size: 3844026
config_names:
- addprim_jump
- addprim_turn_left
- filler_num0
- filler_num1
- filler_num2
- filler_num3
- length
- simple
- template_around_right
- template_jump_around_right
- template_opposite_right
- template_right
---
# Dataset Card for "scan"
## 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/brendenlake/SCAN](https://github.com/brendenlake/SCAN)
- **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:** 224.18 MB
- **Size of the generated dataset:** 44.53 MB
- **Total amount of disk used:** 268.71 MB
### Dataset Summary
SCAN tasks with various splits.
SCAN is a set of simple language-driven navigation tasks for studying
compositional learning and zero-shot generalization.
See https://github.com/brendenlake/SCAN for a description of the splits.
Example usage:
data = datasets.load_dataset('scan/length')
### 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
#### addprim_jump
- **Size of downloaded dataset files:** 18.69 MB
- **Size of the generated dataset:** 4.05 MB
- **Total amount of disk used:** 22.73 MB
An example of 'train' looks as follows.
```
```
#### addprim_turn_left
- **Size of downloaded dataset files:** 18.69 MB
- **Size of the generated dataset:** 4.09 MB
- **Total amount of disk used:** 22.76 MB
An example of 'train' looks as follows.
```
```
#### filler_num0
- **Size of downloaded dataset files:** 18.69 MB
- **Size of the generated dataset:** 2.85 MB
- **Total amount of disk used:** 21.53 MB
An example of 'train' looks as follows.
```
```
#### filler_num1
- **Size of downloaded dataset files:** 18.69 MB
- **Size of the generated dataset:** 3.14 MB
- **Total amount of disk used:** 21.82 MB
An example of 'train' looks as follows.
```
```
#### filler_num2
- **Size of downloaded dataset files:** 18.69 MB
- **Size of the generated dataset:** 3.44 MB
- **Total amount of disk used:** 22.12 MB
An example of 'train' looks as follows.
```
```
### Data Fields
The data fields are the same among all splits.
#### addprim_jump
- `commands`: a `string` feature.
- `actions`: a `string` feature.
#### addprim_turn_left
- `commands`: a `string` feature.
- `actions`: a `string` feature.
#### filler_num0
- `commands`: a `string` feature.
- `actions`: a `string` feature.
#### filler_num1
- `commands`: a `string` feature.
- `actions`: a `string` feature.
#### filler_num2
- `commands`: a `string` feature.
- `actions`: a `string` feature.
### Data Splits
| name |train|test|
|-----------------|----:|---:|
|addprim_jump |14670|7706|
|addprim_turn_left|21890|1208|
|filler_num0 |15225|1173|
|filler_num1 |16290|1173|
|filler_num2 |17391|1173|
## 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{Lake2018GeneralizationWS,
title={Generalization without Systematicity: On the Compositional Skills of
Sequence-to-Sequence Recurrent Networks},
author={Brenden M. Lake and Marco Baroni},
booktitle={ICML},
year={2018},
url={https://arxiv.org/pdf/1711.00350.pdf},
}
```
### Contributions
Thanks to [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
DFKI-SLT/brat | DFKI-SLT | 2023-11-25T00:09:45Z | 2,853 | 2 | null | [
"task_categories:token-classification",
"task_ids:parsing",
"annotations_creators:expert-generated",
"language_creators:found",
"region:us"
] | 2023-11-25T00:09:45Z | 2022-05-10T06:13:33.000Z | 2022-05-10T06:13:33 | ---
annotations_creators:
- expert-generated
language_creators:
- found
license: []
task_categories:
- token-classification
task_ids:
- parsing
---
# Information Card for Brat
## Table of Contents
- [Description](#description)
- [Summary](#summary)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Usage](#usage)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Description
- **Homepage:** https://brat.nlplab.org
- **Paper:** https://aclanthology.org/E12-2021/
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Summary
Brat is an intuitive web-based tool for text annotation supported by Natural Language Processing (NLP) technology. BRAT has been developed for rich structured annota- tion for a variety of NLP tasks and aims to support manual curation efforts and increase annotator productivity using NLP techniques. brat is designed in particular for structured annotation, where the notes are not free form text but have a fixed form that can be automatically processed and interpreted by a computer.
## Dataset Structure
Dataset annotated with brat format is processed using this script. Annotations created in brat are stored on disk in a standoff format: annotations are stored separately from the annotated document text, which is never modified by the tool. For each text document in the system, there is a corresponding annotation file. The two are associatied by the file naming convention that their base name (file name without suffix) is the same: for example, the file DOC-1000.ann contains annotations for the file DOC-1000.txt. More information can be found [here](https://brat.nlplab.org/standoff.html).
### Data Instances
[Needs More Information]
### Data Fields
```
-context: html content of data file as string
-file_name: a string name of file
-spans: a sequence containing id, type, location and text of a span
-relations: a sequence containing id, type and arguments of a relation
-equivalence_relations:
-events:
-attributions:
-normalizations:
-notes:
```
### Usage
brat script can be used by calling `load_dataset()` method and passing `kwargs` (arguments to the [BuilderConfig](https://huggingface.co/docs/datasets/v2.2.1/en/package_reference/builder_classes#datasets.BuilderConfig)) which should include at least `url` of the dataset prepared using brat. We provide an example of [SciArg](https://aclanthology.org/W18-5206.pdf) dataset below,
```python
from datasets import load_dataset
kwargs = {
"description" :
"""This dataset is an extension of the Dr. Inventor corpus (Fisas et al., 2015, 2016) with an annotation layer containing
fine-grained argumentative components and relations. It is the first argument-annotated corpus of scientific
publications (in English), which allows for joint analyses of argumentation and other rhetorical dimensions of
scientific writing.""",
"citation" :
"""@inproceedings{lauscher2018b,
title = {An argument-annotated corpus of scientific publications},
booktitle = {Proceedings of the 5th Workshop on Mining Argumentation},
publisher = {Association for Computational Linguistics},
author = {Lauscher, Anne and Glava\v{s}, Goran and Ponzetto, Simone Paolo},
address = {Brussels, Belgium},
year = {2018},
pages = {40–46}
}""",
"homepage": "https://github.com/anlausch/ArguminSci",
"url": "http://data.dws.informatik.uni-mannheim.de/sci-arg/compiled_corpus.zip",
"file_name_blacklist": ['A28'],
}
dataset = load_dataset('dfki-nlp/brat', **kwargs)
```
## Additional Information
### Licensing Information
[Needs More Information]
### Citation Information
```
@inproceedings{stenetorp-etal-2012-brat,
title = "brat: a Web-based Tool for {NLP}-Assisted Text Annotation",
author = "Stenetorp, Pontus and
Pyysalo, Sampo and
Topi{\'c}, Goran and
Ohta, Tomoko and
Ananiadou, Sophia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the Demonstrations at the 13th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
month = apr,
year = "2012",
address = "Avignon, France",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/E12-2021",
pages = "102--107",
}
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RussianNLP/russian_super_glue | RussianNLP | 2023-06-19T12:23:49Z | 2,848 | 18 | null | [
"task_categories:text-classification",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:text-generation",
"task_ids:natural-language-inference",
"task_ids:multi-class-classification",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
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"language:ru",
"license:mit",
"glue",
"qa",
"superGLUE",
"NLI",
"reasoning",
"arxiv:2202.07791",
"region:us"
] | 2023-06-19T12:23:49Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
- expert-generated
language_creators:
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- expert-generated
language:
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license:
- mit
multilinguality:
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size_categories:
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source_datasets:
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task_categories:
- text-classification
- question-answering
- zero-shot-classification
- text-generation
task_ids:
- natural-language-inference
- multi-class-classification
pretty_name: Russian SuperGLUE
language_bcp47:
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tags:
- glue
- qa
- superGLUE
- NLI
- reasoning
---
# Dataset Card for [Russian SuperGLUE]
## 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://russiansuperglue.com/
- **Repository:** https://github.com/RussianNLP/RussianSuperGLUE
- **Paper:** https://russiansuperglue.com/download/main_article
- **Leaderboard:** https://russiansuperglue.com/leaderboard/2
- **Point of Contact:** [More Information Needed]
### Dataset Summary
Modern universal language models and transformers such as BERT, ELMo, XLNet, RoBERTa and others need to be properly
compared and evaluated. In the last year, new models and methods for pretraining and transfer learning have driven
striking performance improvements across a range of language understanding tasks.
We offer testing methodology based on tasks, typically proposed for “strong AI” — logic, commonsense, reasoning.
Adhering to the GLUE and SuperGLUE methodology, we present a set of test tasks for general language understanding
and leaderboard models.
For the first time a complete test for Russian language was developed, which is similar to its English analog.
Many datasets were composed for the first time, and a leaderboard of models for the Russian language with comparable
results is also presented.
### Supported Tasks and Leaderboards
Supported tasks, barring a few additions, are equivalent to the original SuperGLUE tasks.
|Task Name|Equiv. to|
|----|---:|
|Linguistic Diagnostic for Russian|Broadcoverage Diagnostics (AX-b)|
|Russian Commitment Bank (RCB)|CommitmentBank (CB)|
|Choice of Plausible Alternatives for Russian language (PARus)|Choice of Plausible Alternatives (COPA)|
|Russian Multi-Sentence Reading Comprehension (MuSeRC)|Multi-Sentence Reading Comprehension (MultiRC)|
|Textual Entailment Recognition for Russian (TERRa)|Recognizing Textual Entailment (RTE)|
|Russian Words in Context (based on RUSSE)|Words in Context (WiC)|
|The Winograd Schema Challenge (Russian)|The Winograd Schema Challenge (WSC)|
|Yes/no Question Answering Dataset for the Russian (DaNetQA)|BoolQ|
|Russian Reading Comprehension with Commonsense Reasoning (RuCoS)|Reading Comprehension with Commonsense Reasoning (ReCoRD)|
### Languages
All tasks are in Russian.
## Dataset Structure
### Data Instances
Note that there are no labels in the `test` splits. This is signified by the `-1` value.
#### LiDiRus
- **Size of downloaded dataset files:** 0.05 MB
- **Size of the generated dataset:** 0.49 MB
- **Total amount of disk used:** 0.54 MB
An example of 'test' looks as follows
```
{
"sentence1": "Новая игровая консоль доступна по цене.",
"sentence2": "Новая игровая консоль недоступна по цене.",
"knowledge": "",
"lexical-semantics": "Morphological negation",
"logic": "Negation",
"predicate-argument-structure": "",
"idx": 10,
"label": 1
}
```
#### RCB
- **Size of downloaded dataset files:** 0.14 MB
- **Size of the generated dataset:** 0.53 MB
- **Total amount of disk used:** 0.67 MB
An example of 'train'/'dev' looks as follows
```
{
"premise": "— Пойдём пообедаем. Я с утра ничего не ел. Отель, как видишь, весьма посредственный, но мне сказали,
что в здешнем ресторане отлично готовят.",
"hypothesis": "В здешнем ресторане отлично готовят.",
"verb": "сказать",
"negation": "no_negation",
"idx": 10,
"label": 2
}
```
An example of 'test' looks as follows
```
{
"premise": "Я уверен, что вместе мы победим. Да, парламентское большинство думает иначе.",
"hypothesis": "Вместе мы проиграем.",
"verb": "думать",
"negation": "no_negation",
"idx": 10,
"label": -1
}
```
#### PARus
- **Size of downloaded dataset files:** 0.06 MB
- **Size of the generated dataset:** 0.20 MB
- **Total amount of disk used:** 0.245 MB
An example of 'train'/'dev' looks as follows
```
{
"premise": "Женщина чинила кран.",
"choice1": "Кран подтекал.",
"choice2": "Кран был выключен.",
"question": "cause",
"idx": 10,
"label": 0
}
```
An example of 'test' looks as follows
```
{
"premise": "Ребятам было страшно.",
"choice1": "Их вожатый рассказал им историю про призрака.",
"choice2": "Они жарили маршмеллоу на костре.",
"question": "cause",
"idx": 10,
"label": -1
}
```
#### MuSeRC
- **Size of downloaded dataset files:** 1.26 MB
- **Size of the generated dataset:** 59.77 MB
- **Total amount of disk used:** 61.87 MB
An example of 'train'/'dev' looks as follows
```
{
"paragraph": "(1) Но люди не могут существовать без природы, поэтому в парке стояли железобетонные скамейки —
деревянные моментально ломали. (2) В парке бегали ребятишки, водилась шпана, которая развлекалась игрой в карты,
пьянкой, драками, «иногда насмерть». (3) «Имали они тут и девок...» (4) Верховодил шпаной Артемка-мыло, с
вспененной белой головой. (5) Людочка сколько ни пыталась усмирить лохмотья на буйной голове Артемки, ничего у
неё не получалось. (6) Его «кудри, издали напоминавшие мыльную пену, изблизя оказались что липкие рожки из
вокзальной столовой — сварили их, бросили комком в пустую тарелку, так они, слипшиеся, неподъёмно и лежали.
(7) Да и не ради причёски приходил парень к Людочке. (8) Как только её руки становились занятыми ножницами
и расчёской, Артемка начинал хватать её за разные места. (9) Людочка сначала увёртывалась от хватких рук Артемки,
а когда не помогло, стукнула его машинкой по голове и пробила до крови, пришлось лить йод на голову «ухажористого
человека». (10) Артемка заулюлюкал и со свистом стал ловить воздух. (11) С тех пор «домогания свои хулиганские
прекратил», более того, шпане повелел Людочку не трогать.",
"question": "Как развлекались в парке ребята?",
"answer": "Развлекались игрой в карты, пьянкой, драками, снимали они тут и девок.",
"idx":
{
"paragraph": 0,
"question": 2,
"answer": 10
},
"label": 1
}
```
An example of 'test' looks as follows
```
{
"paragraph": "\"(1) Издательство Viking Press совместно с компанией TradeMobile выпустят мобильное приложение,
посвященное Анне Франк, передает The Daily Telegraph. (2) Программа будет включать в себя фрагменты из дневника
Анны, озвученные британской актрисой Хеленой Бонэм Картер. (3) Помимо этого, в приложение войдут фотографии
и видеозаписи, документы из архива Фонда Анны Франк, план здания в Амстердаме, где Анна с семьей скрывались от
нацистов, и факсимильные копии страниц дневника. (4) Приложение, которое получит название Anne Frank App, выйдет
18 октября. (5) Интерфейс программы будет англоязычным. (6) На каких платформах будет доступно Anne Frank App,
не уточняется. Анна Франк родилась в Германии в 1929 году. (7) Когда в стране начались гонения на евреев, Анна с
семьей перебрались в Нидерланды. (8) С 1942 года члены семьи Франк и еще несколько человек скрывались от нацистов
в потайных комнатах дома в Амстердаме, который занимала компания отца Анны. (9) В 1944 году группу по доносу
обнаружили гестаповцы. (10) Обитатели \"Убежища\" (так Анна называла дом в дневнике) были отправлены в концлагеря;
выжить удалось только отцу девочки Отто Франку. (11) Находясь в \"Убежище\", Анна вела дневник, в котором описывала
свою жизнь и жизнь своих близких. (12) После ареста книгу с записями сохранила подруга семьи Франк и впоследствии
передала ее отцу Анны. (13) Дневник был впервые опубликован в 1947 году. (14) Сейчас он переведен более
чем на 60 языков.\"",
"question": "Какая информация войдет в новой мобильное приложение?",
"answer": "Видеозаписи Анны Франк.",
"idx":
{
"paragraph": 0,
"question": 2,
"answer": 10
},
"label": -1
}
```
#### TERRa
- **Size of downloaded dataset files:** 0.93 MB
- **Size of the generated dataset:** 3.44 MB
- **Total amount of disk used:** 4.39 MB
An example of 'train'/'dev' looks as follows
```
{
"premise": "Музей, расположенный в Королевских воротах, меняет экспозицию. На смену выставке, рассказывающей об
истории ворот и их реставрации, придет «Аптека трех королей». Как рассказали в музее, посетители попадут в
традиционный интерьер аптеки.",
"hypothesis": "Музей закроется навсегда.",
"idx": 10,
"label": 1
}
```
An example of 'test' looks as follows
```
{
"premise": "Маршрутка полыхала несколько минут. Свидетели утверждают, что приезду пожарных салон «Газели» выгорел полностью. К счастью, пассажиров внутри не было, а водитель успел выскочить из кабины.",
"hypothesis": "Маршрутка выгорела.",
"idx": 10,
"label": -1
}
```
#### RUSSE
- **Size of downloaded dataset files:** 3.88 MB
- **Size of the generated dataset:** 20.97 MB
- **Total amount of disk used:** 25.17 MB
An example of 'train'/'dev' looks as follows
```
{
"word": "дух",
"sentence1": "Завертелась в доме веселая коловерть: праздничный стол, праздничный дух, шумные разговоры",
"sentence2": "Вижу: духи собралися / Средь белеющих равнин. // Бесконечны, безобразны, / В мутной месяца игре / Закружились бесы разны, / Будто листья в ноябре",
"start1": 68,
"start2": 6,
"end1": 72,
"end2": 11,
"gold_sense1": 3,
"gold_sense2": 4,
"idx": 10,
"label": 0
}
```
An example of 'test' looks as follows
```
{
"word": "доска",
"sentence1": "На 40-й день после трагедии в переходе была установлена мемориальная доска, надпись на которой гласит: «В память о погибших и пострадавших от террористического акта 8 августа 2000 года».",
"sentence2": "Фото с 36-летним миллиардером привлекло сеть его необычной фигурой при стойке на доске и кремом на лице.",
"start1": 69,
"start2": 81,
"end1": 73,
"end2": 85,
"gold_sense1": -1,
"gold_sense2": -1,
"idx": 10,
"label": -1
}
```
#### RWSD
- **Size of downloaded dataset files:** 0.04 MB
- **Size of the generated dataset:** 0.29 MB
- **Total amount of disk used:** 0.320 MB
An example of 'train'/'dev' looks as follows
```
{
"text": "Женя поблагодарила Сашу за помощь, которую она оказала.",
"span1_index": 0,
"span2_index": 6,
"span1_text": "Женя",
"span2_text": "она оказала",
"idx": 10,
"label": 0
}
```
An example of 'test' looks as follows
```
{
"text": "Мод и Дора видели, как через прерию несутся поезда, из двигателей тянулись клубы черного дыма. Ревущие
звуки их моторов и дикие, яростные свистки можно было услышать издалека. Лошади убежали, когда они увидели
приближающийся поезд.",
"span1_index": 22,
"span2_index": 30,
"span1_text": "свистки",
"span2_text": "они увидели",
"idx": 10,
"label": -1
}
```
#### DaNetQA
- **Size of downloaded dataset files:** 1.36 MB
- **Size of the generated dataset:** 4.82 MB
- **Total amount of disk used:** 5.9 MB
An example of 'train'/'dev' looks as follows
```
{
"question": "Вреден ли алкоголь на первых неделях беременности?",
"passage": "А Бакингем-Хоуз и её коллеги суммировали последствия, найденные в обзорных статьях ранее. Частые случаи
задержки роста плода, результатом чего является укороченный средний срок беременности и сниженный вес при рождении.
По сравнению с нормальными детьми, дети 3-4-недельного возраста демонстрируют «менее оптимальную» двигательную
активность, рефлексы, и ориентацию в пространстве, а дети 4-6 лет показывают низкий уровень работы
нейроповеденческих функций, внимания, эмоциональной экспрессии, и развития речи и языка. Величина этих влияний
часто небольшая, частично в связи с независимыми переменными: включая употребление во время беременности
алкоголя/табака, а также факторы среды . У детей школьного возраста проблемы с устойчивым вниманием и контролем
своего поведения, а также незначительные с ростом, познавательными и языковыми способностями.",
"idx": 10,
"label": 1
}
```
An example of 'test' looks as follows
```
{
"question": "Вредна ли жесткая вода?",
"passage": "Различают временную жёсткость, обусловленную гидрокарбонатами кальция и магния Са2; Mg2, и постоянную
жёсткость, вызванную присутствием других солей, не выделяющихся при кипячении воды: в основном, сульфатов и
хлоридов Са и Mg. Жёсткая вода при умывании сушит кожу, в ней плохо образуется пена при использовании мыла.
Использование жёсткой воды вызывает появление осадка на стенках котлов, в трубах и т. п. В то же время,
использование слишком мягкой воды может приводить к коррозии труб, так как, в этом случае отсутствует
кислотно-щелочная буферность, которую обеспечивает гидрокарбонатная жёсткость. Потребление жёсткой или мягкой
воды обычно не является опасным для здоровья, однако есть данные о том, что высокая жёсткость способствует
образованию мочевых камней, а низкая — незначительно увеличивает риск сердечно-сосудистых заболеваний. Вкус
природной питьевой воды, например, воды родников, обусловлен именно присутствием солей жёсткости.",
"idx": 100,
"label": -1
}
```
#### RuCoS
- **Size of downloaded dataset files:** 56.62 MB
- **Size of the generated dataset:** 202.38 MB
- **Total amount of disk used:** 261.10 MB
An example of 'train'/'dev' looks as follows
```
{
"passage": "В Абхазии 24 августа на досрочных выборах выбирают нового президента. Кто бы ни стал победителем,
возможности его будут ограничены, говорят эксперты, опрошенные DW. В Абхазии 24 августа проходят досрочные выборы
президента не признанной международным сообществом республики. Толчком к их проведению стали массовые протесты в
конце мая 2014 года, в результате которых со своего поста был вынужден уйти действующий президент Абхазии Александр
Анкваб. Эксперты называют среди наиболее перспективных кандидатов находящегося в оппозиции политика Рауля Хаджимбу,
экс-главу службы безопасности Аслана Бжанию и генерала Мираба Кишмарию, исполняющего обязанности министра обороны.
У кого больше шансов\n\"Ставки делаются на победу Хаджимбы.\n@highlight\nВ Швеции задержаны двое граждан РФ в связи
с нападением на чеченского блогера\n@highlight\nТуризм в эпоху коронавируса: куда поехать? И ехать ли
вообще?\n@highlight\nКомментарий: Россия накануне эпидемии - виноватые назначены заранее",
"query": "Несмотря на то, что Кремль вложил много денег как в @placeholder, так и в Южную Осетию, об экономическом
восстановлении данных регионов говорить не приходится, считает Хальбах: \"Многие по-прежнему живут в
полуразрушенных домах и временных жилищах\".",
"entities":
[
"DW.",
"Абхазии ",
"Александр Анкваб.",
"Аслана Бжанию ",
"Мираба Кишмарию,",
"РФ ",
"Рауля Хаджимбу,",
"Россия ",
"Хаджимбы.",
"Швеции "
],
"answers":
[
"Абхазии"
],
"idx":
{
"passage": 500,
"query": 500
}
}
```
An example of 'test' looks as follows
```
{
"passage": "Почему и как изменится курс белорусского рубля? Какие инструменты следует предпочесть населению, чтобы
сохранить сбережения, DW рассказали финансовые аналитики Беларуси. На последних валютных торгах БВФБ 2015 года в
среду, 30 декабря, курс белорусского рубля к доллару - 18569, к евро - 20300, к российскому рублю - 255. В 2016
году белорусскому рублю пророчат падение как минимум на 12 процентов к корзине валют, к которой привязан его курс.
А чтобы избежать потерь, белорусам советуют диверсифицировать инвестиционные портфели. Чем обусловлены прогнозные
изменения котировок белорусского рубля, и какие финансовые инструменты стоит предпочесть, чтобы минимизировать риск
потерь?\n@highlight\nВ Германии за сутки выявлено более 100 новых заражений коронавирусом\n@highlight\nРыночные цены
на нефть рухнули из-за провала переговоров ОПЕК+\n@highlight\nВ Италии за сутки произошел резкий скачок смертей от
COVID-19",
"query": "Последнее, убежден аналитик, инструмент для узкого круга профессиональных инвесторов, культуры следить за
финансовым состоянием предприятий - такой, чтобы играть на рынке корпоративных облигаций, - в @placeholder пока нет.",
"entities":
[
"DW ",
"Беларуси.",
"Германии ",
"Италии ",
"ОПЕК+"
],
"answers": [],
"idx":
{
"passage": 500,
"query": 500
}
}
```
### Data Fields
#### LiDiRus
- `idx`: an `int32` feature
- `label`: a classification label, with possible values `entailment` (0), `not_entailment` (1)
- `sentence1`: a `string` feature
- `sentence2`: a `string` feature
- `knowledge`: a `string` feature with possible values `''`, `'World knowledge'`, `'Common sense'`
- `lexical-semantics`: a `string` feature
- `logic`: a `string` feature
- `predicate-argument-structure`: a `string` feature
#### RCB
- `idx`: an `int32` feature
- `label`: a classification label, with possible values `entailment` (0), `contradiction` (1), `neutral` (2)
- `premise`: a `string` feature
- `hypothesis`: a `string` feature
- `verb`: a `string` feature
- `negation`: a `string` feature with possible values `'no_negation'`, `'negation'`, `''`, `'double_negation'`
#### PARus
- `idx`: an `int32` feature
- `label`: a classification label, with possible values `choice1` (0), `choice2` (1)
- `premise`: a `string` feature
- `choice1`: a `string` feature
- `choice2`: a `string` feature
- `question`: a `string` feature with possible values `'cause'`, `'effect'`
#### MuSeRC
- `idx`: an `int32` feature
- `label` : a classification label, with possible values `false` (0) , `true` (1) (does the provided `answer` contain
a factual response to the `question`)
- `paragraph`: a `string` feature
- `question`: a `string` feature
- `answer`: a `string` feature
#### TERRa
- `idx`: an `int32` feature
- `label`: a classification label, with possible values `entailment` (0), `not_entailment` (1)
- `premise`: a `string` feature
- `hypothesis`: a `string` feature
#### RUSSE
- `idx`: an `int32` feature
- `label` : a classification label, with possible values `false` (0), `true` (1) (whether the given `word` used in the
same sense in both sentences)
- `word`: a `string` feature
- `sentence1`: a `string` feature
- `sentence2`: a `string` feature
- `gold_sense1`: an `int32` feature
- `gold_sense2`: an `int32` feature
- `start1`: an `int32` feature
- `start2`: an `int32` feature
- `end1`: an `int32` feature
- `end2`: an `int32` feature
#### RWSD
- `idx`: an `int32` feature
- `label` : a classification label, with possible values `false` (0), `true` (1) (whether the given spans are
coreferential)
- `text`: a `string` feature
- `span1_index`: an `int32` feature
- `span2_index`: an `int32` feature
- `span1_text`: a `string` feature
- `span2_text`: a `string` feature
#### DaNetQA
- `idx`: an `int32` feature
- `label` : a classification label, with possible values `false` (0), `true` (1) (yes/no answer to the `question` found
in the `passage`)
- `question`: a `string` feature
- `passage`: a `string` feature
#### RuCoS
- `idx`: an `int32` feature
- `passage`: a `string` feature
- `query`: a `string` feature
- `entities`: a `list of strings` feature
- `answers`: a `list of strings` feature
[More Information Needed]
### Data Splits
#### LiDiRus
| |test|
|---|---:|
|LiDiRus|1104|
#### RCB
| |train|validation|test|
|----|---:|----:|---:|
|RCB|438|220|438|
#### PARus
| |train|validation|test|
|----|---:|----:|---:|
|PARus|400|100|500|
#### MuSeRC
| |train|validation|test|
|----|---:|----:|---:|
|MuSeRC|500|100|322|
#### TERRa
| |train|validation|test|
|----|---:|----:|---:|
|TERRa|2616|307|3198|
#### RUSSE
| |train|validation|test|
|----|---:|----:|---:|
|RUSSE|19845|8508|18892|
#### RWSD
| |train|validation|test|
|----|---:|----:|---:|
|RWSD|606|204|154|
#### DaNetQA
| |train|validation|test|
|----|---:|----:|---:|
|DaNetQA|1749|821|805|
#### RuCoS
| |train|validation|test|
|----|---:|----:|---:|
|RuCoS|72193|7577|7257|
## 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
All our datasets are published by MIT License.
### Citation Information
```
@article{shavrina2020russiansuperglue,
title={RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark},
author={Shavrina, Tatiana and Fenogenova, Alena and Emelyanov, Anton and Shevelev, Denis and Artemova, Ekaterina and Malykh, Valentin and Mikhailov, Vladislav and Tikhonova, Maria and Chertok, Andrey and Evlampiev, Andrey},
journal={arXiv preprint arXiv:2010.15925},
year={2020}
}
@misc{fenogenova2022russian,
title={Russian SuperGLUE 1.1: Revising the Lessons not Learned by Russian NLP models},
author={Alena Fenogenova and Maria Tikhonova and Vladislav Mikhailov and Tatiana Shavrina and Anton Emelyanov and Denis Shevelev and Alexandr Kukushkin and Valentin Malykh and Ekaterina Artemova},
year={2022},
eprint={2202.07791},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@slowwavesleep](https://github.com/slowwavesleep) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
kunishou/databricks-dolly-15k-ja | kunishou | 2023-09-10T13:47:12Z | 2,842 | 55 | null | [
"license:cc-by-sa-3.0",
"region:us"
] | 2023-09-10T13:47:12Z | 2023-04-13T08:31:08.000Z | 2023-04-13T08:31:08 | ---
license: cc-by-sa-3.0
---
This dataset was created by automatically translating "databricks-dolly-15k" into Japanese.
This dataset is licensed under CC-BY-SA-3.0
Last Update : 2023-05-11
databricks-dolly-15k-ja
https://github.com/kunishou/databricks-dolly-15k-ja
databricks-dolly-15k
https://github.com/databrickslabs/dolly/tree/master/data | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
yahoo_answers_topics | null | 2023-01-25T15:03:25Z | 2,840 | 27 | null | [
"task_categories:text-classification",
"task_ids:topic-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:extended|other-yahoo-answers-corpus",
"language:en",
"license:unknown",
"region:us"
] | 2023-01-25T15:03:25Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- extended|other-yahoo-answers-corpus
task_categories:
- text-classification
task_ids:
- topic-classification
pretty_name: YahooAnswersTopics
dataset_info:
features:
- name: id
dtype: int32
- name: topic
dtype:
class_label:
names:
'0': Society & Culture
'1': Science & Mathematics
'2': Health
'3': Education & Reference
'4': Computers & Internet
'5': Sports
'6': Business & Finance
'7': Entertainment & Music
'8': Family & Relationships
'9': Politics & Government
- name: question_title
dtype: string
- name: question_content
dtype: string
- name: best_answer
dtype: string
config_name: yahoo_answers_topics
splits:
- name: train
num_bytes: 760460695
num_examples: 1400000
- name: test
num_bytes: 32661362
num_examples: 60000
download_size: 319476345
dataset_size: 793122057
train-eval-index:
- config: yahoo_answers_topics
task: text-classification
task_id: multi_class_classification
splits:
train_split: train
eval_split: test
col_mapping:
question_content: text
topic: target
metrics:
- type: accuracy
name: Accuracy
- type: f1
name: F1 macro
args:
average: macro
- type: f1
name: F1 micro
args:
average: micro
- type: f1
name: F1 weighted
args:
average: weighted
- type: precision
name: Precision macro
args:
average: macro
- type: precision
name: Precision micro
args:
average: micro
- type: precision
name: Precision weighted
args:
average: weighted
- type: recall
name: Recall macro
args:
average: macro
- type: recall
name: Recall micro
args:
average: micro
- type: recall
name: Recall weighted
args:
average: weighted
---
# Dataset Card for "Yahoo Answers 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:** [Add homepage URL here if available (unless it's a GitHub repository)]()
- **Repository:** https://github.com/LC-John/Yahoo-Answers-Topic-Classification-Dataset
- **Paper:** [If the dataset was introduced by a paper or there was a paper written describing the dataset, add URL here (landing page for Arxiv paper preferred)]()
- **Leaderboard:** [If the dataset supports an active leaderboard, add link here]()
- **Point of Contact:** [If known, name and email of at least one person the reader can contact for questions about the dataset.]()
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
eugenesiow/Div2k | eugenesiow | 2022-10-21T04:01:10Z | 2,832 | 2 | null | [
"task_categories:other",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"license:other",
"other-image-super-resolution",
"region:us"
] | 2022-10-21T04:01:10Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- found
language: []
license:
- other
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- other
task_ids: []
pretty_name: Div2k
tags:
- other-image-super-resolution
---
# Dataset Card for Div2k
## 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://data.vision.ee.ethz.ch/cvl/DIV2K/
- **Repository**: https://huggingface.co/datasets/eugenesiow/Div2k
- **Paper**: http://www.vision.ee.ethz.ch/~timofter/publications/Agustsson-CVPRW-2017.pdf
- **Leaderboard**: https://github.com/eugenesiow/super-image#scale-x2
### Dataset Summary
DIV2K is a dataset of RGB images (2K resolution high quality images) with a large diversity of contents.
The DIV2K dataset is divided into:
- train data: starting from 800 high definition high resolution images we obtain corresponding low resolution images and provide both high and low resolution images for 2, 3, and 4 downscaling factors
- validation data: 100 high definition high resolution images are used for genereting low resolution corresponding images, the low res are provided from the beginning of the challenge and are meant for the participants to get online feedback from the validation server; the high resolution images will be released when the final phase of the challenge starts.
Install with `pip`:
```bash
pip install datasets super-image
```
Evaluate a model with the [`super-image`](https://github.com/eugenesiow/super-image) library:
```python
from datasets import load_dataset
from super_image import EdsrModel
from super_image.data import EvalDataset, EvalMetrics
dataset = load_dataset('eugenesiow/Div2k', 'bicubic_x2', split='validation')
eval_dataset = EvalDataset(dataset)
model = EdsrModel.from_pretrained('eugenesiow/edsr-base', scale=2)
EvalMetrics().evaluate(model, eval_dataset)
```
### Supported Tasks and Leaderboards
The dataset is commonly used for training and evaluation of the `image-super-resolution` task.
Unofficial [`super-image`](https://github.com/eugenesiow/super-image) leaderboard for:
- [Scale 2](https://github.com/eugenesiow/super-image#scale-x2)
- [Scale 3](https://github.com/eugenesiow/super-image#scale-x3)
- [Scale 4](https://github.com/eugenesiow/super-image#scale-x4)
- [Scale 8](https://github.com/eugenesiow/super-image#scale-x8)
### Languages
Not applicable.
## Dataset Structure
### Data Instances
An example of `train` for `bicubic_x2` looks as follows.
```
{
"hr": "/.cache/huggingface/datasets/downloads/extracted/DIV2K_valid_HR/0801.png",
"lr": "/.cache/huggingface/datasets/downloads/extracted/DIV2K_valid_LR_bicubic/X2/0801x2.png"
}
```
### Data Fields
The data fields are the same among all splits.
- `hr`: a `string` to the path of the High Resolution (HR) `.png` image.
- `lr`: a `string` to the path of the Low Resolution (LR) `.png` image.
### Data Splits
| name |train |validation|
|-------|-----:|---:|
|bicubic_x2|800|100|
|bicubic_x3|800|100|
|bicubic_x4|800|100|
|bicubic_x8|800|100|
|unknown_x2|800|100|
|unknown_x3|800|100|
|unknown_x4|800|100|
|realistic_mild_x4|800|100|
|realistic_difficult_x4|800|100|
|realistic_wild_x4|800|100|
## Dataset Creation
### Curation Rationale
Please refer to the [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) section.
### Source Data
#### Initial Data Collection and Normalization
**Resolution and quality**: All the images are 2K resolution, that is they have 2K pixels on at least one of
the axes (vertical or horizontal). All the images were processed using the same tools. For simplicity, since the most
common magnification factors in the recent SR literature are of ×2, ×3 and ×4 we cropped the images to multiple of
12 pixels on both axes. Most of the crawled images were originally above 20M pixels.
The images are of high quality both aesthetically and in the terms of small amounts of noise and other corruptions
(like blur and color shifts).
**Diversity**: The authors collected images from dozens of sites. A preference was made for sites with freely
shared high quality photography (such as https://www.pexels.com/ ). Note that we did not use images from Flickr,
Instagram, or other legally binding or copyright restricted images. We only seldom used keywords to assure the diversity
for our dataset. DIV2K covers a large diversity of contents, ranging from people, handmade objects and environments
(cities, villages), to flora and fauna, and natural sceneries including underwater and dim light conditions.
**Partitions**: After collecting the DIV2K 1000 images the authors computed image entropy, bit per pixel (bpp) PNG
compression rates and CORNIA scores (see Section 7.6) and applied bicubic downscaling ×3 and then upscaling ×3 with
bicubic interpolation (imresize Matlab function), ANR [47] and A+ [48] methods and default settings.
The authors randomly generated partitions of 800 train, 100 validation and 100 test images until they achieved a good
balance firstly in visual contents and then on the average entropy, average bpp, average number of pixels per
image (ppi), average CORNIA quality scores and also in the relative differences between the average PSNR scores of
bicubic, ANR and A+ methods.
Only the 800 train and 100 validation images are included in this dataset.
#### Who are the source language producers?
The authors manually crawled 1000 color RGB images from Internet paying special attention to the image quality,
to the diversity of sources (sites and cameras), to the image contents and to the copyrights.
### Annotations
#### Annotation process
No annotations.
#### Who are the annotators?
No annotators.
### Personal and Sensitive Information
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 kindly inform the authors, and they will remove it from the dataset
immediately.
## 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
- **Original Author**: [Radu Timofte](http://people.ee.ethz.ch/~timofter/)
### Licensing Information
Please notice that this dataset is made available for academic research purpose 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 kindly inform the authors, and they will remove it from the dataset
immediately.
### Citation Information
```bibtex
@InProceedings{Agustsson_2017_CVPR_Workshops,
author = {Agustsson, Eirikur and Timofte, Radu},
title = {NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
url = "http://www.vision.ee.ethz.ch/~timofter/publications/Agustsson-CVPRW-2017.pdf",
month = {July},
year = {2017}
}
```
### Contributions
Thanks to [@eugenesiow](https://github.com/eugenesiow) for adding this dataset.
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
jxm/the_office_lines | jxm | 2023-03-07T18:30:51Z | 2,832 | 18 | null | [
"region:us"
] | 2023-03-07T18:30:51Z | 2023-03-07T18:24:28.000Z | 2023-03-07T18:24:28 | ## the_office_lines
<img src="https://a.pinatafarm.com/1351x1232/c8fa71efd1/the-office-handshake.jpg" width="256">
A dataset of lines from the U.S. version of the tv show "The Office". Lines were originally scraped from the website [officequotes.net](https://www.officequotes.net/), are fan-transcribed, and may be of dubious quality.
Contains a train split (47,927 lines), test split (5,991 lines) and validation split (5,991 lines). Contains lines from all 9 seasons, every episode, but may be complete.
Lines are annotated with an ID number, season number, episode number, scene number (within the episode), speaker name, and whether or not the text came from a deleted scene. Here is an example:
```
> dataset["val"][0]
{'id': 3735,
'season': 2,
'episode': 5,
'scene': 32,
'line_text': 'No, you have the power to undo it.',
'speaker': 'Creed',
'deleted': False}
```
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JeanKaddour/minipile | JeanKaddour | 2023-06-20T10:08:26Z | 2,825 | 42 | minipile | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:other",
"arxiv:2304.08442",
"arxiv:2201.07311",
"region:us"
] | 2023-06-20T10:08:26Z | 2023-04-09T20:32:58.000Z | 2023-04-09T20:32:58 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 5906108510
num_examples: 1000000
- name: validation
num_bytes: 2779386
num_examples: 500
- name: test
num_bytes: 58558191
num_examples: 10000
download_size: 3177432813
dataset_size: 5967446087
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: other
multilinguality:
- monolingual
pretty_name: MiniPile
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: minipile
---
# Dataset Card for MiniPile
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
[The MiniPile Challenge for Data-Efficient Language Models](https://arxiv.org/abs/2304.08442)
### Dataset Summary
MiniPile is a 6GB subset of the [deduplicated The Pile corpus](https://huggingface.co/datasets/EleutherAI/the_pile_deduplicated). To curate MiniPile, we perform a simple, three-step data filtering process: we (1) infer embeddings for all documents of the Pile, (2) cluster the embedding space using k-means, and (3) filter out low-quality clusters.
The primary motivation for curating MiniPile is that (i) diverse pre-training datasets (like the Pile) are often too large for academic budgets and (ii) most smaller-scale datasets are fairly homogeneous and thereby unrepresentative of contemporary general-purpose language models. MiniPile aims to fill this gap and thereby facilitate data-efficient research on model architectures, training procedures, optimizers, etc.
More details on the MiniPile curation procedure and some pre-training results be found in the [MiniPile paper](https://arxiv.org/abs/2304.08442).
For more details on the Pile corpus, we refer the reader to [the Pile datasheet](https://arxiv.org/abs/2201.07311).
### Languages
English (`EN`)
## Additional Information
### Dataset Curators
MiniPile is a subset of the Pile, curated by Jean Kaddour. The Pile was created by Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, Shawn Presser, Connor Leahy.
### Licensing Information
Since MiniPile is a subset of the Pile, the same MIT License holds.
### Citation Information
```
@article{kaddour2023minipile,
title={The MiniPile Challenge for Data-Efficient Language Models},
author={Kaddour, Jean},
journal={arXiv preprint arXiv:2304.08442},
year={2023}
}
@article{gao2020pile,
title={The {P}ile: An 800{GB} 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 others},
journal={arXiv preprint arXiv:2101.00027},
year={2020}
}
```
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
pile-of-law/pile-of-law | pile-of-law | 2023-01-08T03:10:35Z | 2,816 | 132 | null | [
"task_categories:fill-mask",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"language:en",
"license:cc-by-nc-sa-4.0",
"arxiv:2207.00220",
"region:us"
] | 2023-01-08T03:10:35Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
pretty_name: pile-of-law
size_categories:
- 10M<n<100M
source_datasets: []
task_categories:
- fill-mask
task_ids:
- masked-language-modeling
viewer: false
---
# Dataset Card for Pile of Law
## 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://huggingface.co/datasets/pile-of-law/pile-of-law
- **Repository:** https://huggingface.co/datasets/pile-of-law/pile-of-law
- **Paper:** https://arxiv.org/abs/2207.00220
### Dataset Summary
We curate a large corpus of legal and administrative data. The utility of this data is twofold: (1) to aggregate legal and administrative data sources that demonstrate different norms and legal standards for data filtering; (2) to collect a dataset that can be used in the future for pretraining legal-domain language models, a key direction in access-to-justice initiatives.
### Supported Tasks and Leaderboards
See paper for details.
### Languages
Mainly English, but some other languages may appear in some portions of the data.
## Dataset Structure
### Data Instances
**courtListener_docket_entry_documents** : Docket entries in U.S. federal courts, including filed briefs from CourtListener RECAP archive.
**courtListener_opinions** : U.S. court opinions from CourtListener (synchronized as of 12/31/2022).
**atticus_contracts**: Unannotated contracts from the Atticus Project.
**federal_register**: The U.S. federal register where agencies file draft rulemaking.
**bva_opinions**: Bureau of Veterans Appeals opinions.
**us_bills**: Draft Bills from the United States Congress.
**cc_casebooks**: Educational Casebooks released under open CC licenses.
**tos**: Unannotated Terms of Service contracts.
**euro_parl**: European parliamentary debates.
**nlrb_decisions**: Decisions from the U.S. National Labor Review Board.
**scotus_oral_arguments**: U.S. Supreme Court Oral Arguments
**cfr**: U.S. Code of Federal Regulations
**state_codes**: U.S. State Codes
**scotus_filings**: Briefs and filings with the U.S. Supreme Court.
**exam_outlines**: Exam outlines available openly on the web.
**edgar**: Contracts filed with the SEC and made available on the SEC's Edgar tool.
**cfpb_creditcard_contracts**: Credit Card Contracts compiled by the U.S. Consumer Finance Protection Bureau.
**constitutions** : The World's constitutions.
**congressional_hearings** : U.S. Congressional hearing transcripts and statements.
**oig**: U.S. Office of Inspector general reports.
**olc_memos**: U.S. Office of Legal Counsel memos.
**uscode**: The United States Code (laws).
**founding_docs**: Letters from U.S. founders.
**ftc_advisory_opinions**: Advisory opinions by the Federal Trade Commission.
**echr** : European Court of Human Rights opinions.
**eurlex**: European Laws.
**tax_rulings**: Rulings from U.S. Tax court.
**un_debates**: U.N. General Debates
**fre**: U.S. Federal Rules of Evidence
**frcp** : U.S. Federal Rules of Civil Procedure
**canadian_decisions**: Canadian Court Opinions from ON and BC.
**eoir**: U.S. Executive Office for Immigration Review Immigration and Nationality Precedential Decisions
**dol_ecab**: Department of Labor Employees' Compensation Appeals Board decisions after 2006
**r_legaladvice** : Filtered data from the r/legaladvice and r/legaladviceofftopic subreddits in the format.
Title: [Post Title]
Question: [Post Content]
Topic: [Post Flair]
Answer \#[N]: [Top Answers]...
**acus_reports** : Reports from the Administrative Conference of the United States from 2010-2022.
**ed_policy_guidance** : Policy guidance documents from the U.S. Department of Education (2001-2022).
**uspto_office_actions** : Office Actions from the U.S. Patent and Trademark Office from 2019-2022.
**icj-pcij** : International Court of Justice and Permanent Court of International Justice opinions.
**hhs_alj_opinions** : Opinions from the U.S. Department of Health and Human Services Administrative Law Judges from 1985-2019.
**sec_administrative_proceedings**: Significant pleadings, orders and decisions for administrative proceedings from the U.S. Securities and Exchange Commission from 2005-2022.
**fmshrc_bluebooks**: Bluebooks from the U.S. Federal Mine Safety and Health Review Commission from 1979 (March) - 2022 (August).
**resource_contracts**: Resource Contracts collected by ResourceContracts.org
**medicaid_policy_guidance**: Policy guidance documents from the U.S. Department of Health and Human Services (1994-2022).
**irs_legal_advice_memos**: Legal Advice Memos and Chief Counsel Notices from the U.S. Internal Revenue Service.
**doj_guidance**: Guidance documents from the U.S. Department of Justice (2020-2022).
**1/23 update**: Data updated in 2023 included: syncing courtListener opinions, adding ACUS reports, USPTO office actions, Ed Policy Guidance, HHS ALJ opinions, SEC administrative proceedings, FMSHRC Bluebooks, Resource Contracts, and ICJ/PCIJ legal opinions. We also fixed OLC opinions which had some formatting inconsistencies and merged exam outlines into one file, adding some additional exam outlines.
On-disk sizes might vary due to caching and compression, but should be approximately as follows as of 1/7/2023.
```bash
% xz --list data/*.xz
Strms Blocks Compressed Uncompressed Ratio Check Filename
183 181 9,631.2 KiB 35.0 MiB 0.268 CRC64 data/train.acus_reports.jsonl.xz
1 1 1,024.1 MiB 6,804.7 MiB 0.150 CRC64 data/train.atticus_contracts.0.jsonl.xz
1 1 1,024.1 MiB 6,781.1 MiB 0.151 CRC64 data/train.atticus_contracts.1.jsonl.xz
1 1 1,024.1 MiB 6,790.1 MiB 0.151 CRC64 data/train.atticus_contracts.2.jsonl.xz
1 1 1,024.1 MiB 6,759.2 MiB 0.152 CRC64 data/train.atticus_contracts.3.jsonl.xz
1 1 139.9 MiB 925.0 MiB 0.151 CRC64 data/train.atticus_contracts.4.jsonl.xz
1 1 1,564.6 MiB 12.5 GiB 0.123 CRC64 data/train.bva.jsonl.xz
1 1 29.8 MiB 154.3 MiB 0.193 CRC64 data/train.canadian_decisions.jsonl.xz
1 1 18.5 MiB 82.6 MiB 0.224 CRC64 data/train.cc_casebooks.jsonl.xz
1 1 3,427.3 KiB 67.2 MiB 0.050 CRC64 data/train.cfpb_cc.jsonl.xz
1 1 72.7 MiB 582.6 MiB 0.125 CRC64 data/train.cfr.jsonl.xz
1 1 1,056.1 MiB 4,941.9 MiB 0.214 CRC64 data/train.congressional_hearings.jsonl.xz
1 1 3,272.4 KiB 21.3 MiB 0.150 CRC64 data/train.constitutions.jsonl.xz
1 1 1,024.1 MiB 13.0 GiB 0.077 CRC64 data/train.courtlistenerdocketentries.0.jsonl.xz
1 1 1,024.3 MiB 13.3 GiB 0.075 CRC64 data/train.courtlistenerdocketentries.1.jsonl.xz
1 1 1,024.1 MiB 12.4 GiB 0.080 CRC64 data/train.courtlistenerdocketentries.2.jsonl.xz
1 1 635.2 MiB 8,671.6 MiB 0.073 CRC64 data/train.courtlistenerdocketentries.3.jsonl.xz
1 1 953.7 MiB 4,575.7 MiB 0.208 CRC64 data/train.courtlisteneropinions.0.jsonl.xz
1 1 953.7 MiB 4,356.2 MiB 0.219 CRC64 data/train.courtlisteneropinions.1.jsonl.xz
1 1 953.7 MiB 4,315.6 MiB 0.221 CRC64 data/train.courtlisteneropinions.10.jsonl.xz
1 1 953.7 MiB 4,650.3 MiB 0.205 CRC64 data/train.courtlisteneropinions.11.jsonl.xz
1 1 953.7 MiB 4,836.3 MiB 0.197 CRC64 data/train.courtlisteneropinions.12.jsonl.xz
1 1 953.7 MiB 4,644.9 MiB 0.205 CRC64 data/train.courtlisteneropinions.13.jsonl.xz
1 1 953.7 MiB 4,657.5 MiB 0.205 CRC64 data/train.courtlisteneropinions.14.jsonl.xz
1 1 539.2 MiB 2,621.8 MiB 0.206 CRC64 data/train.courtlisteneropinions.15.jsonl.xz
1 1 953.7 MiB 4,335.3 MiB 0.220 CRC64 data/train.courtlisteneropinions.2.jsonl.xz
1 1 953.7 MiB 4,352.0 MiB 0.219 CRC64 data/train.courtlisteneropinions.3.jsonl.xz
1 1 953.7 MiB 4,575.9 MiB 0.208 CRC64 data/train.courtlisteneropinions.4.jsonl.xz
1 1 953.7 MiB 4,382.6 MiB 0.218 CRC64 data/train.courtlisteneropinions.5.jsonl.xz
1 1 953.7 MiB 4,352.3 MiB 0.219 CRC64 data/train.courtlisteneropinions.6.jsonl.xz
1 1 953.7 MiB 4,462.4 MiB 0.214 CRC64 data/train.courtlisteneropinions.7.jsonl.xz
1 1 953.7 MiB 4,604.0 MiB 0.207 CRC64 data/train.courtlisteneropinions.8.jsonl.xz
1 1 953.7 MiB 4,612.0 MiB 0.207 CRC64 data/train.courtlisteneropinions.9.jsonl.xz
335 335 6,047.4 KiB 24.1 MiB 0.245 CRC64 data/train.doj_guidance.jsonl.xz
1 1 41.1 MiB 305.6 MiB 0.135 CRC64 data/train.dol_ecab.jsonl.xz
1 1 19.1 MiB 100.5 MiB 0.190 CRC64 data/train.echr.jsonl.xz
508 507 1,502.0 KiB 4,716.7 KiB 0.318 CRC64 data/train.ed_policy_guidance.jsonl.xz
1 1 1,372.0 MiB 9,032.6 MiB 0.152 CRC64 data/train.edgar.jsonl.xz
1 1 3,896.6 KiB 18.6 MiB 0.205 CRC64 data/train.eoir.jsonl.xz
1 1 140.3 MiB 1,154.7 MiB 0.121 CRC64 data/train.eurlex.jsonl.xz
1 1 51.4 MiB 239.4 MiB 0.215 CRC64 data/train.euro_parl.jsonl.xz
1 1 355.3 KiB 1,512.5 KiB 0.235 CRC64 data/train.examoutlines.jsonl.xz
1 1 20.7 MiB 131.7 MiB 0.157 CRC64 data/train.federal_register.jsonl.xz
396 396 43.9 MiB 175.7 MiB 0.250 CRC64 data/train.fmshrc.jsonl.xz
1 1 73.4 MiB 341.7 MiB 0.215 CRC64 data/train.founding_docs.jsonl.xz
1 1 324.2 KiB 1,459.4 KiB 0.222 CRC64 data/train.frcp.jsonl.xz
1 1 116.1 KiB 484.9 KiB 0.239 CRC64 data/train.fre.jsonl.xz
1 1 297.3 KiB 1,245.0 KiB 0.239 CRC64 data/train.ftc_advisory_opinions.jsonl.xz
2,084 2,083 13.4 MiB 42.2 MiB 0.318 CRC64 data/train.hhs_alj.jsonl.xz
1 1 29.5 MiB 157.4 MiB 0.188 CRC64 data/train.ijc.jsonl.xz
442 442 7,904.4 KiB 35.8 MiB 0.216 CRC64 data/train.irs_legal_advice_memos.jsonl.xz
658 658 3,403.1 KiB 10.6 MiB 0.314 CRC64 data/train.medicaid_policy_guidance.jsonl.xz
1 1 170.7 MiB 788.9 MiB 0.216 CRC64 data/train.nlrb_decisions.jsonl.xz
1 1 218.4 MiB 1,580.3 MiB 0.138 CRC64 data/train.oig.jsonl.xz
1 1 5,857.4 KiB 31.5 MiB 0.182 CRC64 data/train.olc_memos.jsonl.xz
1 1 58.6 MiB 234.5 MiB 0.250 CRC64 data/train.r_legaldvice.jsonl.xz
1,639 1,639 43.7 MiB 188.1 MiB 0.232 CRC64 data/train.resource_contracts.jsonl.xz
1 1 242.6 MiB 1,241.6 MiB 0.195 CRC64 data/train.scotus_docket_entries.jsonl.xz
1 1 68.5 MiB 323.2 MiB 0.212 CRC64 data/train.scotus_oral.jsonl.xz
10,805 10,805 40.7 MiB 118.4 MiB 0.344 CRC64 data/train.sec.jsonl.xz
1 1 705.0 MiB 5,019.9 MiB 0.140 CRC64 data/train.state_code.jsonl.xz
1 1 75.2 MiB 540.8 MiB 0.139 CRC64 data/train.taxrulings.jsonl.xz
1 1 273.6 KiB 1,318.5 KiB 0.207 CRC64 data/train.tos.jsonl.xz
1 1 22.6 MiB 108.1 MiB 0.209 CRC64 data/train.undebates.jsonl.xz
1 1 167.6 MiB 1,119.6 MiB 0.150 CRC64 data/train.us_bills.jsonl.xz
1 1 25.3 MiB 196.1 MiB 0.129 CRC64 data/train.uscode.jsonl.xz
1 1 1,713.2 MiB 33.7 GiB 0.050 CRC64 data/train.uspto_oab.jsonl.xz
54 54 2,960.9 KiB 11.0 MiB 0.264 CRC64 data/validation.acus_reports.jsonl.xz
1 1 1,024.1 MiB 6,797.1 MiB 0.151 CRC64 data/validation.atticus_contracts.0.jsonl.xz
1 1 374.6 MiB 2,471.7 MiB 0.152 CRC64 data/validation.atticus_contracts.1.jsonl.xz
1 1 523.0 MiB 4,258.9 MiB 0.123 CRC64 data/validation.bva.jsonl.xz
1 1 9.8 MiB 50.5 MiB 0.195 CRC64 data/validation.canadian_decisions.jsonl.xz
1 1 4,281.5 KiB 19.1 MiB 0.219 CRC64 data/validation.cc_casebooks.jsonl.xz
1 1 1,532.6 KiB 19.6 MiB 0.077 CRC64 data/validation.cfpb_cc.jsonl.xz
1 1 23.3 MiB 190.4 MiB 0.122 CRC64 data/validation.cfr.jsonl.xz
1 1 347.4 MiB 1,620.7 MiB 0.214 CRC64 data/validation.congressional_hearings.jsonl.xz
1 1 1,102.4 KiB 6,733.0 KiB 0.164 CRC64 data/validation.constitutions.jsonl.xz
1 1 1,024.1 MiB 10.7 GiB 0.094 CRC64 data/validation.courtlistenerdocketentries.0.jsonl.xz
1 1 473.7 MiB 5,225.2 MiB 0.091 CRC64 data/validation.courtlistenerdocketentries.1.jsonl.xz
1 1 953.7 MiB 4,391.3 MiB 0.217 CRC64 data/validation.courtlisteneropinions.0.jsonl.xz
1 1 953.7 MiB 4,406.9 MiB 0.216 CRC64 data/validation.courtlisteneropinions.1.jsonl.xz
1 1 953.8 MiB 4,436.7 MiB 0.215 CRC64 data/validation.courtlisteneropinions.2.jsonl.xz
1 1 953.7 MiB 4,476.9 MiB 0.213 CRC64 data/validation.courtlisteneropinions.3.jsonl.xz
1 1 953.7 MiB 4,618.0 MiB 0.207 CRC64 data/validation.courtlisteneropinions.4.jsonl.xz
1 1 238.5 MiB 1,147.4 MiB 0.208 CRC64 data/validation.courtlisteneropinions.5.jsonl.xz
100 100 1,778.7 KiB 7,371.5 KiB 0.241 CRC64 data/validation.doj_guidance.jsonl.xz
1 1 13.8 MiB 101.5 MiB 0.136 CRC64 data/validation.dol_ecab.jsonl.xz
1 1 4,132.1 KiB 20.8 MiB 0.194 CRC64 data/validation.echr.jsonl.xz
174 173 490.5 KiB 1,564.9 KiB 0.313 CRC64 data/validation.ed_policy_guidance.jsonl.xz
1 1 453.6 MiB 2,978.9 MiB 0.152 CRC64 data/validation.edgar.jsonl.xz
1 1 1,340.0 KiB 6,294.8 KiB 0.213 CRC64 data/validation.eoir.jsonl.xz
1 1 49.1 MiB 393.7 MiB 0.125 CRC64 data/validation.eurlex.jsonl.xz
1 1 17.0 MiB 79.0 MiB 0.215 CRC64 data/validation.euro_parl.jsonl.xz
1 1 103.7 KiB 547.9 KiB 0.189 CRC64 data/validation.examoutlines.jsonl.xz
1 1 7,419.0 KiB 45.7 MiB 0.158 CRC64 data/validation.federal_register.jsonl.xz
120 120 13.5 MiB 53.9 MiB 0.250 CRC64 data/validation.fmshrc.jsonl.xz
1 1 25.3 MiB 113.2 MiB 0.224 CRC64 data/validation.founding_docs.jsonl.xz
1 1 63.5 KiB 248.8 KiB 0.255 CRC64 data/validation.frcp.jsonl.xz
1 1 58.4 KiB 226.7 KiB 0.257 CRC64 data/validation.fre.jsonl.xz
1 1 117.4 KiB 419.1 KiB 0.280 CRC64 data/validation.ftc_advisory_opinions.jsonl.xz
722 721 4,900.2 KiB 15.1 MiB 0.318 CRC64 data/validation.hhs_alj.jsonl.xz
1 1 10.0 MiB 52.3 MiB 0.191 CRC64 data/validation.ijc.jsonl.xz
161 161 3,791.0 KiB 17.7 MiB 0.209 CRC64 data/validation.irs_legal_advice_memos.jsonl.xz
214 214 1,101.1 KiB 3,411.1 KiB 0.323 CRC64 data/validation.medicaid_policy_guidance.jsonl.xz
1 1 55.8 MiB 257.8 MiB 0.217 CRC64 data/validation.nlrb_decisions.jsonl.xz
1 1 80.0 MiB 603.7 MiB 0.132 CRC64 data/validation.oig.jsonl.xz
1 1 1,826.2 KiB 9,874.6 KiB 0.185 CRC64 data/validation.olc_memos.jsonl.xz
1 1 19.7 MiB 78.7 MiB 0.251 CRC64 data/validation.r_legaldvice.jsonl.xz
584 584 15.3 MiB 63.5 MiB 0.241 CRC64 data/validation.resource_contracts.jsonl.xz
1 1 86.4 MiB 422.5 MiB 0.204 CRC64 data/validation.scotus_docket_entries.jsonl.xz
1 1 23.1 MiB 109.0 MiB 0.212 CRC64 data/validation.scotus_oral.jsonl.xz
3,559 3,559 13.0 MiB 37.7 MiB 0.344 CRC64 data/validation.sec.jsonl.xz
1 1 371.8 MiB 2,678.4 MiB 0.139 CRC64 data/validation.state_code.jsonl.xz
1 1 24.8 MiB 177.4 MiB 0.140 CRC64 data/validation.taxrulings.jsonl.xz
1 1 92.7 KiB 381.6 KiB 0.243 CRC64 data/validation.tos.jsonl.xz
1 1 7,705.6 KiB 35.5 MiB 0.212 CRC64 data/validation.undebates.jsonl.xz
1 1 53.8 MiB 356.3 MiB 0.151 CRC64 data/validation.us_bills.jsonl.xz
1 1 15.2 MiB 117.5 MiB 0.129 CRC64 data/validation.uscode.jsonl.xz
1 1 885.5 MiB 11.2 GiB 0.077 CRC64 data/validation.uspto_oab.jsonl.xz
-------------------------------------------------------------------------------
22,839 22,833 41.0 GiB 291.5 GiB 0.141 CRC64 119 files
```
### Data Fields
- text: the document text
- created_timestamp: If the original source provided a timestamp when the document was created we provide this as well. Note, these may be inaccurate. For example CourtListener case opinions provide the timestamp of when it was uploaded to CourtListener not when the opinion was published. We welcome pull requests to correct this field if such inaccuracies are discovered.
- downloaded_timestamp: When the document was scraped.
- url: the source url
### Data Splits
There is a train/validation split for each subset of the data. 75%/25%. Note, we do not use the validation set for any downstream tasks nor do we filter out any data from downstream tasks. Please filter as needed before training models or feel free to use a different dataset split.
## Dataset Creation
### Curation Rationale
We curate a large corpus of legal and administrative data. The utility of this data is twofold: (1) to aggregate legal and administrative data sources that demonstrate different norms and legal standards for data filtering; (2) to collect a dataset that can be used in the future for pretraining legal-domain language models, a key direction in access-to-justice initiatives. As such, data sources are curated to inform: (1) legal analysis, knowledge, or understanding; (2) argument formation; (3) privacy filtering standards. Sources like codes and laws tend to inform (1). Transcripts and court filings tend to inform (2). Opinions tend to inform (1) and (3).
### Source Data
#### Initial Data Collection and Normalization
We do not normalize the data, but we provide dataset creation code and relevant urls in https://github.com/Breakend/PileOfLaw
#### Who are the source language producers?
Varied (see sources above).
### Personal and Sensitive Information
This dataset may contain personal and sensitive information. However, this has been previously filtered by the relevant government and federal agencies that weigh the harms of revealing this information against the benefits of transparency. If you encounter something particularly harmful, please file a takedown request with the upstream source and notify us in the communities tab. We will then remove the content. We cannot enable more restrictive licensing because upstream sources may restrict using a more restrictive license. However, we ask that all users of this data respect the upstream licenses and restrictions. Per the standards of CourtListener, we do not allow indexing of this data by search engines and we ask that others do not also. Please do not turn on anything that allows the data to be easily indexed.
## Considerations for Using the Data
### Social Impact of Dataset
We hope that this dataset will provide more mechanisms for doing data work. As we describe in the paper, the internal variation allows contextual privacy rules to be learned. If robust mechanisms for this are developed they can applied more broadly. This dataset can also potentially be used for legal language model pretraining. As discussed in ``On the Opportunities and Risks of Foundation Models'', legal language models can help improve access to justice in various ways. But they can also be used in potentially harmful ways. While such models are not ready for most production environments and are the subject of significant research, we ask that model creators using this data, particularly when creating generative models, consider the impacts of their model and make a good faith effort to weigh the benefits against the harms of their method. Our license and many of the sub-licenses also restrict commercial usage.
### Discussion of Biases
The data reflects the biases of governments and courts. As we discuss in our work, these can be significant, though more recent text will likely be less overtly toxic. Please see the above statement and embark on any model uses responsibly.
### Other Known Limitations
We mainly focus on U.S. and English-speaking legal sources, though we include some European and Canadian resources.
## Additional Information
### Licensing Information
CreativeCommons Attribution-NonCommercial-ShareAlike 4.0 International. But individual sources may have other licenses. See paper for details. Some upstream data sources request that indexing be disabled. As such please **do not re-host any data in a way that can be indexed by search engines.**
### No Representations
We do not make any representation that the legal information provided here is accurate. It is meant for research purposes only. For the authoritative and updated source of information please refer directly to the governing body which provides the latest laws, rules, and regulations relevant to you.
### DMCA Takedown Requests
Pile of Law follows the notice and takedown procedures in the Digital Millennium Copyright Act (DMCA), 17 U.S.C. Section 512.
If you believe content on Pile of Law violates your copyright, please immediately notify its operators by sending a message with the information described below. Please use the subject "Copyright" in your message. If Pile of Law's operators act in response to an infringement notice, they will make a good-faith attempt to contact the person who contributed the content using the most recent email address that person provided to Pile of Law.
Under the DMCA, you may be held liable for damages based on material misrepresentations in your infringement notice. You must also make a good-faith evaluation of whether the use of your content is a fair use, because fair uses are not infringing. See 17 U.S.C. Section 107 and Lenz v. Universal Music Corp., No. 13-16106 (9th Cir. Sep. 14, 2015). If you are not sure if the content you want to report infringes your copyright, you should first contact a lawyer.
The DMCA requires that all infringement notices must include all of the following:
+ A signature of the copyright owner or a person authorized to act on the copyright owner's behalf
+ An identification of the copyright claimed to have been infringed
+ A description of the nature and location of the material that you claim to infringe your copyright, in sufficient detail to allow Pile of Law to find and positively identify that material
+ Your name, address, telephone number, and email address
+ A statement that you believe in good faith that the use of the material that you claim to infringe your copyright is not authorized by law, or by the copyright owner or such owner's agent
+ A statement, under penalty of perjury, that all of the information contained in your infringement notice is accurate
+ A statement, under penalty of perjury, that you are either the copyright owner or a person authorized to act on their behalf.
Pile of Law will respond to all DMCA-compliant infringement notices, including, as required or appropriate, by removing the offending material or disabling all links to it.
All received infringement notices may be posted in full to the Lumen database (previously known as the Chilling Effects Clearinghouse).
All takedown requests with the above information should be posted to the Communities tab.
This removal notice has been modified from the (CourtListener DMCA takedown notice)[https://www.courtlistener.com/terms/].
### Citation Information
For a citation to this work:
```
@misc{hendersonkrass2022pileoflaw,
url = {https://arxiv.org/abs/2207.00220},
author = {Henderson*, Peter and Krass*, Mark S. and Zheng, Lucia and Guha, Neel and Manning, Christopher D. and Jurafsky, Dan and Ho, Daniel E.},
title = {Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset},
publisher = {arXiv},
year = {2022}
}
```
Since this dataset also includes several other data sources with citations, please refer to our paper and cite the additional relevant work in addition to our own work. | [
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HuggingFaceM4/TextCaps_support_query_sets | HuggingFaceM4 | 2023-06-16T13:31:41Z | 2,790 | 0 | null | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
pszemraj/qmsum-cleaned | pszemraj | 2023-11-21T13:14:02Z | 2,786 | 2 | null | [
"task_categories:text2text-generation",
"task_categories:summarization",
"size_categories:1K<n<10K",
"source_datasets:tau/scrolls",
"language:en",
"license:apache-2.0",
"scrolls",
"qmsum",
"region:us"
] | 2023-11-21T13:14:02Z | 2023-05-05T16:16:33.000Z | 2023-05-05T16:16:33 | ---
license: apache-2.0
language:
- en
source_datasets: tau/scrolls
task_categories:
- text2text-generation
- summarization
tags:
- scrolls
- qmsum
size_categories:
- 1K<n<10K
---
# qmsum-cleaned
## prefixes
It's worth noting that each "document" in `input` is prefixed by a question/prompt on what the model is supposed to do. **You may want to explicitly handle this in some way, or prefix your models trained on this dataset.**
Most frequent "prefixes" separated via [sentence-splitter](https://github.com/mediacloud/sentence-splitter) in the `train` split:
| | Sentence | Count |
|---:|:------------------------------------------------------------------------------|--------:|
| 0 | Summarize the whole meeting. | 121 |
| 1 | Summarize the meeting | 25 |
| 2 | What did the team discuss about the product cost? | 4 |
| 3 | How did Marketing design the product evaluation? | 4 |
| 4 | Summarize the wrap up of the meeting. | 3 |
| 5 | What did the group discuss about user requirements of the new remote control? | 3 |
| 6 | What did the team discuss during the product evaluation? | 3 |
| 7 | Summarize the meeting. | 2 |
| 8 | Summarize what was said about digits form | 2 |
| 9 | What was discussed in the meeting? | 2 |
### wordcloud
Visualized as a wordcloud (`train` split):
## token counts
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
bigheiniuJ/JimmyLuAug | bigheiniuJ | 2023-11-08T17:44:17Z | 2,762 | 0 | null | [
"region:us"
] | 2023-11-08T17:44:17Z | 2023-10-30T17:17:08.000Z | 2023-10-30T17:17:08 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: output
dtype: string
- name: input
dtype: string
- name: seed
dtype: string
- name: split
dtype: string
- name: task
dtype: string
- name: id
dtype: int64
- name: aug_type
dtype: string
- name: aug_time
dtype: int64
- name: options
sequence: string
splits:
- name: train
num_bytes: 390852959.61794406
num_examples: 914133
download_size: 95170713
dataset_size: 390852959.61794406
---
# Dataset Card for "JimmyLuAug"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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nsmc | null | 2023-01-25T14:41:49Z | 2,754 | 3 | nsmc | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:ko",
"license:cc-by-2.0",
"region:us"
] | 2023-01-25T14:41:49Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- ko
license:
- cc-by-2.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: nsmc
pretty_name: Naver Sentiment Movie Corpus
dataset_info:
features:
- name: id
dtype: string
- name: document
dtype: string
- name: label
dtype:
class_label:
names:
'0': negative
'1': positive
splits:
- name: train
num_bytes: 16423803
num_examples: 150000
- name: test
num_bytes: 5491417
num_examples: 50000
download_size: 19522142
dataset_size: 21915220
---
# Dataset Card for Naver sentiment movie corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Github](https://github.com/e9t/nsmc/)
- **Repository:** [Github](https://github.com/e9t/nsmc/)
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
Each instance is a movie review written by Korean internet users on Naver, the most commonly used search engine in Korea. Each row can be broken down into the following fields:
- `id`: A unique review ID, provided by Naver
- `document`: The actual movie review
- `label`: Binary labels for sentiment analysis, where `0` denotes negative, and `1`, positive
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@InProceedings{Park:2016,
title = "Naver Sentiment Movie Corpus",
author = "Lucy Park",
year = "2016",
howpublished = {\\url{https://github.com/e9t/nsmc}}
}
```
### Contributions
Thanks to [@jaketae](https://github.com/jaketae) for adding this dataset. | [
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deepmind/code_contests | deepmind | 2023-06-11T12:22:30Z | 2,750 | 53 | codecontests | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:2203.07814",
"arxiv:2105.12655",
"region:us"
] | 2023-06-11T12:22:30Z | 2022-07-19T16:02:55.000Z | 2022-07-19T16:02:55 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: codecontests
pretty_name: CodeContests
---
# Dataset Card for CodeContests
## 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/deepmind/code_contests/
- **Paper:** [Competition-Level Code Generation with AlphaCode](https://arxiv.org/abs/2203.07814v1)
- **Leaderboard:** [Code Generation on CodeContests](https://paperswithcode.com/sota/code-generation-on-codecontests)
- **Point of Contact:** [David Choi](mailto:david.hu.choi@gmail.com)
### Dataset Summary
CodeContests is a competitive programming dataset for machine-learning. This
dataset was used when training [AlphaCode](https://deepmind.com/blog/article/Competitive-programming-with-AlphaCode).
It consists of programming problems, from a variety of sources:
Site | URL | Source
----------- | --------------------------- | ------
Aizu | https://judge.u-aizu.ac.jp | [CodeNet](https://github.com/IBM/Project_CodeNet)
AtCoder | https://atcoder.jp | [CodeNet](https://github.com/IBM/Project_CodeNet)
CodeChef | https://www.codechef.com | [description2code](https://github.com/ethancaballero/description2code)
Codeforces | https://codeforces.com | [description2code](https://github.com/ethancaballero/description2code) and Codeforces
HackerEarth | https://www.hackerearth.com | [description2code](https://github.com/ethancaballero/description2code)
Problems include test cases in the form of paired inputs and outputs, as well as both correct and incorrect human solutions in a variety of languages.
### Supported Tasks and Leaderboards
- `translation` - the competitive programming code generation problem can be viewed as a sequence-to-sequence translation task: given a problem description 𝑋 in natural language, produce a corresponding solution 𝑌 in a programming language. The metric used for evaluation is "percentage of problems solved using 𝑛 submissions from 𝑘 samples per problem", denoted as 𝑛@𝑘. More information on the evaluation of AlphaCode can be found in Section 2.2. and Appendix A.3. of the paper. The leaderboard for this task is available [here](https://paperswithcode.com/sota/code-generation-on-codecontests).
### Languages
English.
## Dataset Structure
### Data Instances
A data point corresponds to a singular contest problem:
```
{
'name': '76_B. Mice',
'description': 'Modern researches has shown that a flock of hungry mice '
'searching for a piece of...',
'public_tests': {'input': ['3 2 0 2\n0 1 3\n2 5\n'], 'output': ['1\n']},
'private_tests': {'input': ['20 18 1 2\n'
'-9999944 -9999861 -9999850 -9999763 -9999656 '
'-9999517 -9999375 -999927...',
...,
'7 11 10 20\n'
'6 18 32 63 66 68 87\n'
'6 8 15 23 25 41 53 59 60 75 90\n'],
'output': ['2\n', ..., '1\n']},
'generated_tests': {'input': ['7 11 10 5\n'
'6 18 32 63 66 68 87\n'
'6 8 15 23 25 41 53 59 60 75 90\n',
...,
'7 11 10 4\n'
'6 18 46 63 85 84 87\n'
'6 8 15 18 25 41 53 59 60 75 90\n'],
'output': ['1\n', ..., '2\n']},
'source': 2,
'difficulty': 8,
'solutions': {'language': [2, ..., 2],
'solution': ['#include <bits/stdc++.h>\n'
'using namespace std;\n'
'int n, m;\n'
'int data[2][100010], t[1...',
...,
'#include <bits/stdc++.h>\n'
'using namespace std;\n'
'int n, m, pos[100100], food[100100...']},
'incorrect_solutions': {'language': [2, ..., 2],
'solution': ['#include <bits/stdc++.h>\n'
'using namespace std;\n'
'vector<pair<int, int> > v[100010];...',
...,
'#include <bits/stdc++.h>\n'
'using namespace std;\n'
'vector<pair<int, int> > v[100010];...']},
'cf_contest_id': 76,
'cf_index': 'B',
'cf_points': 0.0,
'cf_rating': 2100,
'cf_tags': ['greedy', 'two pointers'],
'is_description_translated': False,
'untranslated_description': '',
'time_limit': {'seconds': 0, 'nanos': 500000000},
'memory_limit_bytes': 256000000,
'input_file': '',
'output_file': ''
}
```
### Data Fields
- `name`: The name of the contest. Note that names could agree between different sources.
- `description`: A natural language description of a programming problem.
- `public_tests`: Public tests are those that are available before submitting a solution, typically as part of the description itself. Represented as a paired `input` and `output` that can be used to test potential solutions. They are therefore acceptable inputs to a model.
- `private_tests`: Private tests are not visible before submitting a solution, so should not be made available as inputs to a model.
- `generated_tests`: Generated tests are automatically generated by modifying inputs from public and private tests and validating using known correct solutions.
- `source`: The original source of the problem, with possible values including `UNKNOWN_SOURCE` (0),`CODECHEF` (1), `CODEFORCES` (2), `HACKEREARTH` (3), `CODEJAM` (4), `ATCODER` (5) and `AIZU` (6).
- `difficulty`: A representation of the difficulty of the problem with possible values including `UNKNOWN_DIFFICULTY` (0), `EASY` (1), `MEDIUM` (2), `HARD` (3), `HARDER` (4), `HARDEST` (5), `EXTERNAL` (6), `A` (7), `B` (8), `C` (9), `D` (10), `E` (11), `F` (12), `G` (13), `H` (14), `I` (15), `J` (16), `K` (17), `L` (18), `M` (19), `N` (20), `O` (21), `P` (22), `Q` (23), `R` (24), `S` (25), `T` (26), `U` (27) and `V` (28). Note that different sources use different, non-comparable gradings. For Codeforces problems, `cf_rating` is a more reliable measure of difficulty when available.
- `solutions`: Correct solutions to the problem. Contrast with `incorrect_solutions` below.
- `incorrect_solutions`: Incorrect solutions.
- `cf_contest_id`: The Contest ID. Note that Contest ID is not monotonic with respect to time.
- `cf_index`: Problem index, e.g. `"A"` or `"B"` or `"C"`.
- `cf_points`: Points for the problem, e.g. `1000.0`
- `cf_rating`: Problem rating (difficulty), e.g. `1100`
- `cf_tags`: Problem tags, e.g. `['greedy', 'math']`
- `is_description_translated`: Whether the problem was translated to English.
- `untranslated_description`: The untranslated description is only available for translated problems.
- `time_limit`: The time limit constraint to use when executing solutions. Represented as a dictionary with two keys, `seconds` and `nanos`. This field is None if not defined.
- `memory_limit_bytes`: The memory limit constraint to use when executing solutions.
- `input_file`: Most problems use stdin for IO. Some problems expect specific files to be used instead.
- `output_file`: Most problems use stdout for IO. Some problems expect specific files to be used instead.
All tests are represented as a paired `input` and `output` that can be used to test potential solutions and all solutions comprise a `language`, with possible values including `UNKNOWN_LANGUAGE` (0), `PYTHON` (1) (solutions written in PYTHON2), `CPP` (2), `PYTHON3` (3) and `JAVA` (4), and a `solution` string written in that `language`. The fields preceded with `cf_` denote extra meta-data for Codeforces problems.
### Data Splits
The data is split into training, validation and test set. The training set contains 13328 samples, the validation set 117 samples and the test set 165 samples.
## Dataset Creation
### Curation Rationale
This dataset was created for fine-tuning AlphaCode models:
> Models pre-trained on GitHub can generate good code and solve simple programming problems, but
as shown in Appendix B.3 they can solve very few competitive programming problems. Fine-tuning
the model on a dedicated competitive programming dataset is critical for performance.
### Source Data
#### Initial Data Collection and Normalization
The information on the data collection and normalization procedures can found in Section 3.2. and Appendinx B.2. of the paper.
#### Who are the source language producers?
The problems are scraped from the following platforms: [Aizu](https://judge.u-aizu.ac.jp), [AtCoder](https://atcoder.jp ), [CodeChef](https://www.codechef.com), [Codeforces](https://codeforces.com) and [HackerEarch](https://www.hackerearth.com). Additionally, some data from the existing public competitive programming dataset Description2Code ([Caballero et al., 2016](https://github.com/ethancaballero/description2code)) and CodeNet ([(Puri et al., 2021](https://arxiv.org/pdf/2105.12655.pdf)) is mixed into the training set.
### Annotations
#### Annotation process
The solutions are scapred alongside the problem descriptions.
#### Who are the annotators?
Same as the source data creators.
### 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
Yujia Li, David Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, Thomas Hubert, Peter Choy, Cyprien de Masson d'Autume, Igor Babuschkin, Xinyun Chen, Po-Sen Huang, Johannes Welbl, Sven Gowal, Alexey Cherepanov, James Molloy, Daniel J. Mankowitz, Esme Sutherland Robson, Pushmeet Kohli, Nando de Freitas, Koray Kavukcuoglu and Oriol Vinyals.
### Licensing Information
This dataset is made available under the terms of the CC BY
4.0 license ([Creative Commons Attribution 4.0 International license](https://creativecommons.org/licenses/by/4.0/legalcode)).
Additional acknowledged contributions:
* Codeforces materials are sourced from http://codeforces.com.
* Description2Code materials are sourced from:
[Description2Code Dataset](https://github.com/ethancaballero/description2code),
licensed under the
[MIT open source license](https://opensource.org/licenses/MIT), copyright
not specified.
* CodeNet materials are sourced from:
[Project_CodeNet](https://github.com/IBM/Project_CodeNet), licensed under
[Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0), copyright not
specified.
### Citation Information
```bibtex
@article{li2022competition,
title={Competition-Level Code Generation with AlphaCode},
author={Li, Yujia and Choi, David and Chung, Junyoung and Kushman, Nate and
Schrittwieser, Julian and Leblond, R{\'e}mi and Eccles, Tom and
Keeling, James and Gimeno, Felix and Dal Lago, Agustin and
Hubert, Thomas and Choy, Peter and de Masson d'Autume, Cyprien and
Babuschkin, Igor and Chen, Xinyun and Huang, Po-Sen and Welbl, Johannes and
Gowal, Sven and Cherepanov, Alexey and Molloy, James and
Mankowitz, Daniel and Sutherland Robson, Esme and Kohli, Pushmeet and
de Freitas, Nando and Kavukcuoglu, Koray and Vinyals, Oriol},
journal={arXiv preprint arXiv:2203.07814},
year={2022}
}
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | [
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conll2012_ontonotesv5 | null | 2023-01-25T15:03:49Z | 2,739 | 27 | ontonotes-5-0 | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"task_ids:part-of-speech",
"task_ids:coreference-resolution",
"task_ids:parsing",
"task_ids:lemmatization",
"task_ids:word-sense-disambiguation",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ar",
"language:en",
"language:zh",
"license:cc-by-nc-nd-4.0",
"semantic-role-labeling",
"region:us"
] | 2023-01-25T15:03:49Z | 2022-03-15T10:48:28.000Z | 2022-03-15T10:48:28 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- ar
- en
- zh
license:
- cc-by-nc-nd-4.0
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
- part-of-speech
- coreference-resolution
- parsing
- lemmatization
- word-sense-disambiguation
paperswithcode_id: ontonotes-5-0
pretty_name: CoNLL2012 shared task data based on OntoNotes 5.0
tags:
- semantic-role-labeling
dataset_info:
- config_name: english_v4
features:
- name: document_id
dtype: string
- name: sentences
list:
- name: part_id
dtype: int32
- name: words
sequence: string
- name: pos_tags
sequence:
class_label:
names:
'0': XX
'1': '``'
'2': $
'3': ''''''
'4': ','
'5': -LRB-
'6': -RRB-
'7': .
'8': ':'
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'10': AFX
'11': CC
'12': CD
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'14': EX
'15': FW
'16': HYPH
'17': IN
'18': JJ
'19': JJR
'20': JJS
'21': LS
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'23': NFP
'24': NN
'25': NNP
'26': NNPS
'27': NNS
'28': PDT
'29': POS
'30': PRP
'31': PRP$
'32': RB
'33': RBR
'34': RBS
'35': RP
'36': SYM
'37': TO
'38': UH
'39': VB
'40': VBD
'41': VBG
'42': VBN
'43': VBP
'44': VBZ
'45': WDT
'46': WP
'47': WP$
'48': WRB
- name: parse_tree
dtype: string
- name: predicate_lemmas
sequence: string
- name: predicate_framenet_ids
sequence: string
- name: word_senses
sequence: float32
- name: speaker
dtype: string
- name: named_entities
sequence:
class_label:
names:
'0': O
'1': B-PERSON
'2': I-PERSON
'3': B-NORP
'4': I-NORP
'5': B-FAC
'6': I-FAC
'7': B-ORG
'8': I-ORG
'9': B-GPE
'10': I-GPE
'11': B-LOC
'12': I-LOC
'13': B-PRODUCT
'14': I-PRODUCT
'15': B-DATE
'16': I-DATE
'17': B-TIME
'18': I-TIME
'19': B-PERCENT
'20': I-PERCENT
'21': B-MONEY
'22': I-MONEY
'23': B-QUANTITY
'24': I-QUANTITY
'25': B-ORDINAL
'26': I-ORDINAL
'27': B-CARDINAL
'28': I-CARDINAL
'29': B-EVENT
'30': I-EVENT
'31': B-WORK_OF_ART
'32': I-WORK_OF_ART
'33': B-LAW
'34': I-LAW
'35': B-LANGUAGE
'36': I-LANGUAGE
- name: srl_frames
list:
- name: verb
dtype: string
- name: frames
sequence: string
- name: coref_spans
sequence:
sequence: int32
length: 3
splits:
- name: train
num_bytes: 112246121
num_examples: 1940
- name: validation
num_bytes: 14116925
num_examples: 222
- name: test
num_bytes: 14709044
num_examples: 222
download_size: 193644139
dataset_size: 141072090
- config_name: chinese_v4
features:
- name: document_id
dtype: string
- name: sentences
list:
- name: part_id
dtype: int32
- name: words
sequence: string
- name: pos_tags
sequence:
class_label:
names:
'0': X
'1': AD
'2': AS
'3': BA
'4': CC
'5': CD
'6': CS
'7': DEC
'8': DEG
'9': DER
'10': DEV
'11': DT
'12': ETC
'13': FW
'14': IJ
'15': INF
'16': JJ
'17': LB
'18': LC
'19': M
'20': MSP
'21': NN
'22': NR
'23': NT
'24': OD
'25': 'ON'
'26': P
'27': PN
'28': PU
'29': SB
'30': SP
'31': URL
'32': VA
'33': VC
'34': VE
'35': VV
- name: parse_tree
dtype: string
- name: predicate_lemmas
sequence: string
- name: predicate_framenet_ids
sequence: string
- name: word_senses
sequence: float32
- name: speaker
dtype: string
- name: named_entities
sequence:
class_label:
names:
'0': O
'1': B-PERSON
'2': I-PERSON
'3': B-NORP
'4': I-NORP
'5': B-FAC
'6': I-FAC
'7': B-ORG
'8': I-ORG
'9': B-GPE
'10': I-GPE
'11': B-LOC
'12': I-LOC
'13': B-PRODUCT
'14': I-PRODUCT
'15': B-DATE
'16': I-DATE
'17': B-TIME
'18': I-TIME
'19': B-PERCENT
'20': I-PERCENT
'21': B-MONEY
'22': I-MONEY
'23': B-QUANTITY
'24': I-QUANTITY
'25': B-ORDINAL
'26': I-ORDINAL
'27': B-CARDINAL
'28': I-CARDINAL
'29': B-EVENT
'30': I-EVENT
'31': B-WORK_OF_ART
'32': I-WORK_OF_ART
'33': B-LAW
'34': I-LAW
'35': B-LANGUAGE
'36': I-LANGUAGE
- name: srl_frames
list:
- name: verb
dtype: string
- name: frames
sequence: string
- name: coref_spans
sequence:
sequence: int32
length: 3
splits:
- name: train
num_bytes: 77195698
num_examples: 1391
- name: validation
num_bytes: 10828169
num_examples: 172
- name: test
num_bytes: 9585138
num_examples: 166
download_size: 193644139
dataset_size: 97609005
- config_name: arabic_v4
features:
- name: document_id
dtype: string
- name: sentences
list:
- name: part_id
dtype: int32
- name: words
sequence: string
- name: pos_tags
sequence: string
- name: parse_tree
dtype: string
- name: predicate_lemmas
sequence: string
- name: predicate_framenet_ids
sequence: string
- name: word_senses
sequence: float32
- name: speaker
dtype: string
- name: named_entities
sequence:
class_label:
names:
'0': O
'1': B-PERSON
'2': I-PERSON
'3': B-NORP
'4': I-NORP
'5': B-FAC
'6': I-FAC
'7': B-ORG
'8': I-ORG
'9': B-GPE
'10': I-GPE
'11': B-LOC
'12': I-LOC
'13': B-PRODUCT
'14': I-PRODUCT
'15': B-DATE
'16': I-DATE
'17': B-TIME
'18': I-TIME
'19': B-PERCENT
'20': I-PERCENT
'21': B-MONEY
'22': I-MONEY
'23': B-QUANTITY
'24': I-QUANTITY
'25': B-ORDINAL
'26': I-ORDINAL
'27': B-CARDINAL
'28': I-CARDINAL
'29': B-EVENT
'30': I-EVENT
'31': B-WORK_OF_ART
'32': I-WORK_OF_ART
'33': B-LAW
'34': I-LAW
'35': B-LANGUAGE
'36': I-LANGUAGE
- name: srl_frames
list:
- name: verb
dtype: string
- name: frames
sequence: string
- name: coref_spans
sequence:
sequence: int32
length: 3
splits:
- name: train
num_bytes: 42017761
num_examples: 359
- name: validation
num_bytes: 4859292
num_examples: 44
- name: test
num_bytes: 4900664
num_examples: 44
download_size: 193644139
dataset_size: 51777717
- config_name: english_v12
features:
- name: document_id
dtype: string
- name: sentences
list:
- name: part_id
dtype: int32
- name: words
sequence: string
- name: pos_tags
sequence:
class_label:
names:
'0': XX
'1': '``'
'2': $
'3': ''''''
'4': '*'
'5': ','
'6': -LRB-
'7': -RRB-
'8': .
'9': ':'
'10': ADD
'11': AFX
'12': CC
'13': CD
'14': DT
'15': EX
'16': FW
'17': HYPH
'18': IN
'19': JJ
'20': JJR
'21': JJS
'22': LS
'23': MD
'24': NFP
'25': NN
'26': NNP
'27': NNPS
'28': NNS
'29': PDT
'30': POS
'31': PRP
'32': PRP$
'33': RB
'34': RBR
'35': RBS
'36': RP
'37': SYM
'38': TO
'39': UH
'40': VB
'41': VBD
'42': VBG
'43': VBN
'44': VBP
'45': VBZ
'46': VERB
'47': WDT
'48': WP
'49': WP$
'50': WRB
- name: parse_tree
dtype: string
- name: predicate_lemmas
sequence: string
- name: predicate_framenet_ids
sequence: string
- name: word_senses
sequence: float32
- name: speaker
dtype: string
- name: named_entities
sequence:
class_label:
names:
'0': O
'1': B-PERSON
'2': I-PERSON
'3': B-NORP
'4': I-NORP
'5': B-FAC
'6': I-FAC
'7': B-ORG
'8': I-ORG
'9': B-GPE
'10': I-GPE
'11': B-LOC
'12': I-LOC
'13': B-PRODUCT
'14': I-PRODUCT
'15': B-DATE
'16': I-DATE
'17': B-TIME
'18': I-TIME
'19': B-PERCENT
'20': I-PERCENT
'21': B-MONEY
'22': I-MONEY
'23': B-QUANTITY
'24': I-QUANTITY
'25': B-ORDINAL
'26': I-ORDINAL
'27': B-CARDINAL
'28': I-CARDINAL
'29': B-EVENT
'30': I-EVENT
'31': B-WORK_OF_ART
'32': I-WORK_OF_ART
'33': B-LAW
'34': I-LAW
'35': B-LANGUAGE
'36': I-LANGUAGE
- name: srl_frames
list:
- name: verb
dtype: string
- name: frames
sequence: string
- name: coref_spans
sequence:
sequence: int32
length: 3
splits:
- name: train
num_bytes: 174173192
num_examples: 10539
- name: validation
num_bytes: 24264804
num_examples: 1370
- name: test
num_bytes: 18254144
num_examples: 1200
download_size: 193644139
dataset_size: 216692140
---
# Dataset Card for CoNLL2012 shared task data based on OntoNotes 5.0
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [CoNLL-2012 Shared Task](https://conll.cemantix.org/2012/data.html), [Author's page](https://cemantix.org/data/ontonotes.html)
- **Repository:** [Mendeley](https://data.mendeley.com/datasets/zmycy7t9h9)
- **Paper:** [Towards Robust Linguistic Analysis using OntoNotes](https://aclanthology.org/W13-3516/)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
OntoNotes v5.0 is the final version of OntoNotes corpus, and is a large-scale, multi-genre,
multilingual corpus manually annotated with syntactic, semantic and discourse information.
This dataset is the version of OntoNotes v5.0 extended and is used in the CoNLL-2012 shared task.
It includes v4 train/dev and v9 test data for English/Chinese/Arabic and corrected version v12 train/dev/test data (English only).
The source of data is the Mendeley Data repo [ontonotes-conll2012](https://data.mendeley.com/datasets/zmycy7t9h9), which seems to be as the same as the official data, but users should use this dataset on their own responsibility.
See also summaries from paperwithcode, [OntoNotes 5.0](https://paperswithcode.com/dataset/ontonotes-5-0) and [CoNLL-2012](https://paperswithcode.com/dataset/conll-2012-1)
For more detailed info of the dataset like annotation, tag set, etc., you can refer to the documents in the Mendeley repo mentioned above.
### Supported Tasks and Leaderboards
- [Named Entity Recognition on Ontonotes v5 (English)](https://paperswithcode.com/sota/named-entity-recognition-ner-on-ontonotes-v5)
- [Coreference Resolution on OntoNotes](https://paperswithcode.com/sota/coreference-resolution-on-ontonotes)
- [Semantic Role Labeling on OntoNotes](https://paperswithcode.com/sota/semantic-role-labeling-on-ontonotes)
- ...
### Languages
V4 data for Arabic, Chinese, English, and V12 data for English
## Dataset Structure
### Data Instances
```
{
{'document_id': 'nw/wsj/23/wsj_2311',
'sentences': [{'part_id': 0,
'words': ['CONCORDE', 'trans-Atlantic', 'flights', 'are', '$', '2, 'to', 'Paris', 'and', '$', '3, 'to', 'London', '.']},
'pos_tags': [25, 18, 27, 43, 2, 12, 17, 25, 11, 2, 12, 17, 25, 7],
'parse_tree': '(TOP(S(NP (NNP CONCORDE) (JJ trans-Atlantic) (NNS flights) )(VP (VBP are) (NP(NP(NP ($ $) (CD 2,400) )(PP (IN to) (NP (NNP Paris) ))) (CC and) (NP(NP ($ $) (CD 3,200) )(PP (IN to) (NP (NNP London) ))))) (. .) ))',
'predicate_lemmas': [None, None, None, 'be', None, None, None, None, None, None, None, None, None, None],
'predicate_framenet_ids': [None, None, None, '01', None, None, None, None, None, None, None, None, None, None],
'word_senses': [None, None, None, None, None, None, None, None, None, None, None, None, None, None],
'speaker': None,
'named_entities': [7, 6, 0, 0, 0, 15, 0, 5, 0, 0, 15, 0, 5, 0],
'srl_frames': [{'frames': ['B-ARG1', 'I-ARG1', 'I-ARG1', 'B-V', 'B-ARG2', 'I-ARG2', 'I-ARG2', 'I-ARG2', 'I-ARG2', 'I-ARG2', 'I-ARG2', 'I-ARG2', 'I-ARG2', 'O'],
'verb': 'are'}],
'coref_spans': [],
{'part_id': 0,
'words': ['In', 'a', 'Centennial', 'Journal', 'article', 'Oct.', '5', ',', 'the', 'fares', 'were', 'reversed', '.']}]}
'pos_tags': [17, 13, 25, 25, 24, 25, 12, 4, 13, 27, 40, 42, 7],
'parse_tree': '(TOP(S(PP (IN In) (NP (DT a) (NML (NNP Centennial) (NNP Journal) ) (NN article) ))(NP (NNP Oct.) (CD 5) ) (, ,) (NP (DT the) (NNS fares) )(VP (VBD were) (VP (VBN reversed) )) (. .) ))',
'predicate_lemmas': [None, None, None, None, None, None, None, None, None, None, None, 'reverse', None],
'predicate_framenet_ids': [None, None, None, None, None, None, None, None, None, None, None, '01', None],
'word_senses': [None, None, None, None, None, None, None, None, None, None, None, None, None],
'speaker': None,
'named_entities': [0, 0, 4, 22, 0, 12, 30, 0, 0, 0, 0, 0, 0],
'srl_frames': [{'frames': ['B-ARGM-LOC', 'I-ARGM-LOC', 'I-ARGM-LOC', 'I-ARGM-LOC', 'I-ARGM-LOC', 'B-ARGM-TMP', 'I-ARGM-TMP', 'O', 'B-ARG1', 'I-ARG1', 'O', 'B-V', 'O'],
'verb': 'reversed'}],
'coref_spans': [],
}
```
### Data Fields
- **`document_id`** (*`str`*): This is a variation on the document filename
- **`sentences`** (*`List[Dict]`*): All sentences of the same document are in a single example for the convenience of concatenating sentences.
Every element in `sentences` is a *`Dict`* composed of the following data fields:
- **`part_id`** (*`int`*) : Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.
- **`words`** (*`List[str]`*) :
- **`pos_tags`** (*`List[ClassLabel]` or `List[str]`*) : This is the Penn-Treebank-style part of speech. When parse information is missing, all parts of speech except the one for which there is some sense or proposition annotation are marked with a XX tag. The verb is marked with just a VERB tag.
- tag set : Note tag sets below are founded by scanning all the data, and I found it seems to be a little bit different from officially stated tag sets. See official documents in the [Mendeley repo](https://data.mendeley.com/datasets/zmycy7t9h9)
- arabic : str. Because pos tag in Arabic is compounded and complex, hard to represent it by `ClassLabel`
- chinese v4 : `datasets.ClassLabel(num_classes=36, names=["X", "AD", "AS", "BA", "CC", "CD", "CS", "DEC", "DEG", "DER", "DEV", "DT", "ETC", "FW", "IJ", "INF", "JJ", "LB", "LC", "M", "MSP", "NN", "NR", "NT", "OD", "ON", "P", "PN", "PU", "SB", "SP", "URL", "VA", "VC", "VE", "VV",])`, where `X` is for pos tag missing
- english v4 : `datasets.ClassLabel(num_classes=49, names=["XX", "``", "$", "''", ",", "-LRB-", "-RRB-", ".", ":", "ADD", "AFX", "CC", "CD", "DT", "EX", "FW", "HYPH", "IN", "JJ", "JJR", "JJS", "LS", "MD", "NFP", "NN", "NNP", "NNPS", "NNS", "PDT", "POS", "PRP", "PRP$", "RB", "RBR", "RBS", "RP", "SYM", "TO", "UH", "VB", "VBD", "VBG", "VBN", "VBP", "VBZ", "WDT", "WP", "WP$", "WRB",])`, where `XX` is for pos tag missing, and `-LRB-`/`-RRB-` is "`(`" / "`)`".
- english v12 : `datasets.ClassLabel(num_classes=51, names="english_v12": ["XX", "``", "$", "''", "*", ",", "-LRB-", "-RRB-", ".", ":", "ADD", "AFX", "CC", "CD", "DT", "EX", "FW", "HYPH", "IN", "JJ", "JJR", "JJS", "LS", "MD", "NFP", "NN", "NNP", "NNPS", "NNS", "PDT", "POS", "PRP", "PRP$", "RB", "RBR", "RBS", "RP", "SYM", "TO", "UH", "VB", "VBD", "VBG", "VBN", "VBP", "VBZ", "VERB", "WDT", "WP", "WP$", "WRB",])`, where `XX` is for pos tag missing, and `-LRB-`/`-RRB-` is "`(`" / "`)`".
- **`parse_tree`** (*`Optional[str]`*) : An serialized NLTK Tree representing the parse. It includes POS tags as pre-terminal nodes. When the parse information is missing, the parse will be `None`.
- **`predicate_lemmas`** (*`List[Optional[str]]`*) : The predicate lemma of the words for which we have semantic role information or word sense information. All other indices are `None`.
- **`predicate_framenet_ids`** (*`List[Optional[int]]`*) : The PropBank frameset ID of the lemmas in predicate_lemmas, or `None`.
- **`word_senses`** (*`List[Optional[float]]`*) : The word senses for the words in the sentence, or None. These are floats because the word sense can have values after the decimal, like 1.1.
- **`speaker`** (*`Optional[str]`*) : This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data. When it is not available, it will be `None`.
- **`named_entities`** (*`List[ClassLabel]`*) : The BIO tags for named entities in the sentence.
- tag set : `datasets.ClassLabel(num_classes=37, names=["O", "B-PERSON", "I-PERSON", "B-NORP", "I-NORP", "B-FAC", "I-FAC", "B-ORG", "I-ORG", "B-GPE", "I-GPE", "B-LOC", "I-LOC", "B-PRODUCT", "I-PRODUCT", "B-DATE", "I-DATE", "B-TIME", "I-TIME", "B-PERCENT", "I-PERCENT", "B-MONEY", "I-MONEY", "B-QUANTITY", "I-QUANTITY", "B-ORDINAL", "I-ORDINAL", "B-CARDINAL", "I-CARDINAL", "B-EVENT", "I-EVENT", "B-WORK_OF_ART", "I-WORK_OF_ART", "B-LAW", "I-LAW", "B-LANGUAGE", "I-LANGUAGE",])`
- **`srl_frames`** (*`List[{"word":str, "frames":List[str]}]`*) : A dictionary keyed by the verb in the sentence for the given Propbank frame labels, in a BIO format.
- **`coref spans`** (*`List[List[int]]`*) : The spans for entity mentions involved in coreference resolution within the sentence. Each element is a tuple composed of (cluster_id, start_index, end_index). Indices are inclusive.
### Data Splits
Each dataset (arabic_v4, chinese_v4, english_v4, english_v12) has 3 splits: _train_, _validation_, and _test_
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@inproceedings{pradhan-etal-2013-towards,
title = "Towards Robust Linguistic Analysis using {O}nto{N}otes",
author = {Pradhan, Sameer and
Moschitti, Alessandro and
Xue, Nianwen and
Ng, Hwee Tou and
Bj{\"o}rkelund, Anders and
Uryupina, Olga and
Zhang, Yuchen and
Zhong, Zhi},
booktitle = "Proceedings of the Seventeenth Conference on Computational Natural Language Learning",
month = aug,
year = "2013",
address = "Sofia, Bulgaria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W13-3516",
pages = "143--152",
}
```
### Contributions
Thanks to [@richarddwang](https://github.com/richarddwang) for adding this dataset. | [
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mteb/amazon_reviews_multi | mteb | 2022-09-27T19:10:01Z | 2,734 | 5 | null | [
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] | 2022-09-27T19:10:01Z | 2022-05-25T19:22:51.000Z | 2022-05-25T19:22:51 | ---
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amazon_reviews_multi | null | 2023-11-02T14:52:21Z | 2,716 | 82 | null | [
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] | 2023-11-02T14:52:21Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
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pretty_name: The Multilingual Amazon Reviews Corpus
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---
# Dataset Card for The Multilingual Amazon Reviews Corpus
## Table of Contents
- [Dataset Card for amazon_reviews_multi](#dataset-card-for-amazon_reviews_multi)
- [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)
- [plain_text](#plain_text)
- [Data Fields](#data-fields)
- [plain_text](#plain_text-1)
- [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
- **Webpage:** https://registry.opendata.aws/amazon-reviews-ml/
- **Paper:** https://arxiv.org/abs/2010.02573
- **Point of Contact:** [multilingual-reviews-dataset@amazon.com](mailto:multilingual-reviews-dataset@amazon.com)
### Dataset Summary
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
<p><b>Defunct:</b> Dataset "amazon_reviews_multi" is defunct and no longer accessible due to the decision of data providers.</p>
</div>
We provide an Amazon product reviews dataset for multilingual text classification. The dataset contains reviews in English, Japanese, German, French, Chinese and Spanish, collected between November 1, 2015 and November 1, 2019. Each record in the dataset contains the review text, the review title, the star rating, an anonymized reviewer ID, an anonymized product ID and the coarse-grained product category (e.g. ‘books’, ‘appliances’, etc.) The corpus is balanced across stars, so each star rating constitutes 20% of the reviews in each language.
For each language, there are 200,000, 5,000 and 5,000 reviews in the training, development and test sets respectively. The maximum number of reviews per reviewer is 20 and the maximum number of reviews per product is 20. All reviews are truncated after 2,000 characters, and all reviews are at least 20 characters long.
Note that the language of a review does not necessarily match the language of its marketplace (e.g. reviews from amazon.de are primarily written in German, but could also be written in English, etc.). For this reason, we applied a language detection algorithm based on the work in Bojanowski et al. (2017) to determine the language of the review text and we removed reviews that were not written in the expected language.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset contains reviews in English, Japanese, German, French, Chinese and Spanish.
## Dataset Structure
### Data Instances
Each data instance corresponds to a review. The original JSON for an instance looks like so (German example):
```json
{
"review_id": "de_0784695",
"product_id": "product_de_0572654",
"reviewer_id": "reviewer_de_0645436",
"stars": "1",
"review_body": "Leider, leider nach einmal waschen ausgeblichen . Es sieht super h\u00fcbsch aus , nur leider stinkt es ganz schrecklich und ein Waschgang in der Maschine ist notwendig ! Nach einem mal waschen sah es aus als w\u00e4re es 10 Jahre alt und hatte 1000 e von Waschg\u00e4ngen hinter sich :( echt schade !",
"review_title": "Leider nicht zu empfehlen",
"language": "de",
"product_category": "home"
}
```
### Data Fields
- `review_id`: A string identifier of the review.
- `product_id`: A string identifier of the product being reviewed.
- `reviewer_id`: A string identifier of the reviewer.
- `stars`: An int between 1-5 indicating the number of stars.
- `review_body`: The text body of the review.
- `review_title`: The text title of the review.
- `language`: The string identifier of the review language.
- `product_category`: String representation of the product's category.
### Data Splits
Each language configuration comes with its own `train`, `validation`, and `test` splits. The `all_languages` split
is simply a concatenation of the corresponding split across all languages. That is, the `train` split for
`all_languages` is a concatenation of the `train` splits for each of the languages and likewise for `validation` and
`test`.
## Dataset Creation
### Curation Rationale
The dataset is motivated by the desire to advance sentiment analysis and text classification in other (non-English)
languages.
### Source Data
#### Initial Data Collection and Normalization
The authors gathered the reviews from the marketplaces in the US, Japan, Germany, France, Spain, and China for the
English, Japanese, German, French, Spanish, and Chinese languages, respectively. They then ensured the correct
language by applying a language detection algorithm, only retaining those of the target language. In a random sample
of the resulting reviews, the authors observed a small percentage of target languages that were incorrectly filtered
out and a very few mismatched languages that were incorrectly retained.
#### Who are the source language producers?
The original text comes from Amazon customers reviewing products on the marketplace across a variety of product
categories.
### Annotations
#### Annotation process
Each of the fields included are submitted by the user with the review or otherwise associated with the review. No
manual or machine-driven annotation was necessary.
#### Who are the annotators?
N/A
### Personal and Sensitive Information
According to the original dataset [license terms](https://docs.opendata.aws/amazon-reviews-ml/license.txt), you may not:
- link or associate content in the Reviews Corpus with any personal information (including Amazon customer accounts), or
- attempt to determine the identity of the author of any content in the Reviews Corpus.
If you violate any of the foregoing conditions, your license to access and use the Reviews Corpus will automatically
terminate without prejudice to any of the other rights or remedies Amazon may have.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is part of an effort to encourage text classification research in languages other than English. Such
work increases the accessibility of natural language technology to more regions and cultures. Unfortunately, each of
the languages included here is relatively high resource and well studied.
### Discussion of Biases
The dataset contains only reviews from verified purchases (as described in the paper, section 2.1), and the reviews
should conform the [Amazon Community Guidelines](https://www.amazon.com/gp/help/customer/display.html?nodeId=GLHXEX85MENUE4XF).
### Other Known Limitations
The dataset is constructed so that the distribution of star ratings is balanced. This feature has some advantages for
purposes of classification, but some types of language may be over or underrepresented relative to the original
distribution of reviews to achieve this balance.
## Additional Information
### Dataset Curators
Published by Phillip Keung, Yichao Lu, György Szarvas, and Noah A. Smith. Managed by Amazon.
### Licensing Information
Amazon has licensed this dataset under its own agreement for non-commercial research usage only. This licence is quite restrictive preventing use anywhere a fee is received including paid for internships etc. A copy of the agreement can be found at the dataset webpage here:
https://docs.opendata.aws/amazon-reviews-ml/license.txt
By accessing the Multilingual Amazon Reviews Corpus ("Reviews Corpus"), you agree that the Reviews Corpus is an Amazon Service subject to the [Amazon.com Conditions of Use](https://www.amazon.com/gp/help/customer/display.html/ref=footer_cou?ie=UTF8&nodeId=508088) and you agree to be bound by them, with the following additional conditions:
In addition to the license rights granted under the Conditions of Use, Amazon or its content providers grant you a limited, non-exclusive, non-transferable, non-sublicensable, revocable license to access and use the Reviews Corpus for purposes of academic research. You may not resell, republish, or make any commercial use of the Reviews Corpus or its contents, including use of the Reviews Corpus for commercial research, such as research related to a funding or consultancy contract, internship, or other relationship in which the results are provided for a fee or delivered to a for-profit organization. You may not (a) link or associate content in the Reviews Corpus with any personal information (including Amazon customer accounts), or (b) attempt to determine the identity of the author of any content in the Reviews Corpus. If you violate any of the foregoing conditions, your license to access and use the Reviews Corpus will automatically terminate without prejudice to any of the other rights or remedies Amazon may have.
### Citation Information
Please cite the following paper (arXiv) if you found this dataset useful:
Phillip Keung, Yichao Lu, György Szarvas and Noah A. Smith. “The Multilingual Amazon Reviews Corpus.” In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 2020.
```
@inproceedings{marc_reviews,
title={The Multilingual Amazon Reviews Corpus},
author={Keung, Phillip and Lu, Yichao and Szarvas, György and Smith, Noah A.},
booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing},
year={2020}
}
```
### Contributions
Thanks to [@joeddav](https://github.com/joeddav) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
GBaker/MedQA-USMLE-4-options | GBaker | 2023-01-24T19:18:09Z | 2,700 | 19 | null | [
"language:en",
"license:cc-by-4.0",
"region:us"
] | 2023-01-24T19:18:09Z | 2023-01-24T19:08:56.000Z | 2023-01-24T19:08:56 | ---
license: cc-by-4.0
language:
- en
---
Original dataset introduced by Jin et al. in [What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams](https://paperswithcode.com/paper/what-disease-does-this-patient-have-a-large)
<h4>Citation information:</h4>
@article{jin2020disease,
title={What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams},
author={Jin, Di and Pan, Eileen and Oufattole, Nassim and Weng, Wei-Hung and Fang, Hanyi and Szolovits, Peter},
journal={arXiv preprint arXiv:2009.13081},
year={2020}
}
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
drop | null | 2023-04-05T10:05:02Z | 2,688 | 12 | drop | [
"task_categories:question-answering",
"task_categories:text2text-generation",
"task_ids:extractive-qa",
"task_ids:abstractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | 2023-04-05T10:05:02Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
pretty_name: DROP
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
- text2text-generation
task_ids:
- extractive-qa
- abstractive-qa
paperswithcode_id: drop
dataset_info:
features:
- name: section_id
dtype: string
- name: query_id
dtype: string
- name: passage
dtype: string
- name: question
dtype: string
- name: answers_spans
sequence:
- name: spans
dtype: string
- name: types
dtype: string
splits:
- name: train
num_bytes: 105572762
num_examples: 77400
- name: validation
num_bytes: 11737787
num_examples: 9535
download_size: 8308692
dataset_size: 117310549
---
# Dataset Card for "drop"
## 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://allennlp.org/drop](https://allennlp.org/drop)
- **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:** 8.30 MB
- **Size of the generated dataset:** 110.91 MB
- **Total amount of disk used:** 119.21 MB
### Dataset Summary
DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs.
. DROP is a crowdsourced, adversarially-created, 96k-question benchmark, in which a system must resolve references in a
question, perhaps to multiple input positions, and perform discrete operations over them (such as addition, counting, or
sorting). These operations require a much more comprehensive understanding of the content of paragraphs than what was
necessary for prior datasets.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 8.30 MB
- **Size of the generated dataset:** 110.91 MB
- **Total amount of disk used:** 119.21 MB
An example of 'validation' looks as follows.
```
This example was too long and was cropped:
{
"answers_spans": {
"spans": ["Chaz Schilens"]
},
"passage": "\" Hoping to rebound from their loss to the Patriots, the Raiders stayed at home for a Week 16 duel with the Houston Texans. Oak...",
"question": "Who scored the first touchdown of the game?"
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `passage`: a `string` feature.
- `question`: a `string` feature.
- `answers_spans`: a dictionary feature containing:
- `spans`: a `string` feature.
### Data Splits
| name |train|validation|
|-------|----:|---------:|
|default|77409| 9536|
## 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{Dua2019DROP,
author={Dheeru Dua and Yizhong Wang and Pradeep Dasigi and Gabriel Stanovsky and Sameer Singh and Matt Gardner},
title={ {DROP}: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs},
booktitle={Proc. of NAACL},
year={2019}
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf), [@mariamabarham](https://github.com/mariamabarham), [@lewtun](https://github.com/lewtun) for adding this dataset. | [
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vialibre/splittedspanish3bwc | vialibre | 2023-01-24T18:17:47Z | 2,654 | 0 | null | [
"multilinguality:monolingual",
"language:es",
"license:mit",
"region:us"
] | 2023-01-24T18:17:47Z | 2022-09-15T05:48:02.000Z | 2022-09-15T05:48:02 | ---
language:
- 'es'
multilinguality:
- monolingual
pretty_name: "Unannotated Spanish 3 Billion Words Corpora"
license:
- mit
---
# Dataset Card for Unannotated Spanish 3 Billion Words Corpora
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Source Data](#source-data)
- [Data Subset](#data-subset)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Repository:** https://github.com/josecannete/spanish-corpora
- **Paper:** https://users.dcc.uchile.cl/~jperez/papers/pml4dc2020.pdf
### Dataset Summary
* Number of lines: 300904000 (300M)
* Number of tokens: 2996016962 (3B)
* Number of chars: 18431160978 (18.4B)
### Languages
* Spanish
### Source Data
* Available to download here: [Zenodo](https://doi.org/10.5281/zenodo.3247731)
### Data Subset
* Spanish Wikis: Wich include Wikipedia, Wikinews, Wikiquotes and more. These were first processed with wikiextractor (https://github.com/josecannete/wikiextractorforBERT) using the wikis dump of 20/04/2019.
* ParaCrawl: Spanish portion of ParaCrawl (http://opus.nlpl.eu/ParaCrawl.php)
* EUBookshop: Spanish portion of EUBookshop (http://opus.nlpl.eu/EUbookshop.php)
* MultiUN: Spanish portion of MultiUN (http://opus.nlpl.eu/MultiUN.php)
* OpenSubtitles: Spanish portion of OpenSubtitles2018 (http://opus.nlpl.eu/OpenSubtitles-v2018.php)
* DGC: Spanish portion of DGT (http://opus.nlpl.eu/DGT.php)
* DOGC: Spanish portion of DOGC (http://opus.nlpl.eu/DOGC.php)
* ECB: Spanish portion of ECB (http://opus.nlpl.eu/ECB.php)
* EMEA: Spanish portion of EMEA (http://opus.nlpl.eu/EMEA.php)
* Europarl: Spanish portion of Europarl (http://opus.nlpl.eu/Europarl.php)
* GlobalVoices: Spanish portion of GlobalVoices (http://opus.nlpl.eu/GlobalVoices.php)
* JRC: Spanish portion of JRC (http://opus.nlpl.eu/JRC-Acquis.php)
* News-Commentary11: Spanish portion of NCv11 (http://opus.nlpl.eu/News-Commentary-v11.php)
* TED: Spanish portion of TED (http://opus.nlpl.eu/TED2013.php)
* UN: Spanish portion of UN (http://opus.nlpl.eu/UN.php)
## Additional Information
### Licensing Information
* [MIT Licence](https://github.com/josecannete/spanish-corpora/blob/master/LICENSE)
### Citation Information
```
@dataset{jose_canete_2019_3247731,
author = {José Cañete},
title = {Compilation of Large Spanish Unannotated Corpora},
month = may,
year = 2019,
publisher = {Zenodo},
doi = {10.5281/zenodo.3247731},
url = {https://doi.org/10.5281/zenodo.3247731}
}
@inproceedings{CaneteCFP2020,
title={Spanish Pre-Trained BERT Model and Evaluation Data},
author={Cañete, José and Chaperon, Gabriel and Fuentes, Rodrigo and Ho, Jou-Hui and Kang, Hojin and Pérez, Jorge},
booktitle={PML4DC at ICLR 2020},
year={2020}
}
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Rowan/hellaswag | Rowan | 2023-09-28T14:49:00Z | 2,627 | 33 | hellaswag | [
"language:en",
"arxiv:1905.07830",
"region:us"
] | 2023-09-28T14:49:00Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- en
paperswithcode_id: hellaswag
pretty_name: HellaSwag
dataset_info:
features:
- name: ind
dtype: int32
- name: activity_label
dtype: string
- name: ctx_a
dtype: string
- name: ctx_b
dtype: string
- name: ctx
dtype: string
- name: endings
sequence: string
- name: source_id
dtype: string
- name: split
dtype: string
- name: split_type
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 43232624
num_examples: 39905
- name: test
num_bytes: 10791853
num_examples: 10003
- name: validation
num_bytes: 11175717
num_examples: 10042
download_size: 71494896
dataset_size: 65200194
---
# Dataset Card for "hellaswag"
## 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://rowanzellers.com/hellaswag/](https://rowanzellers.com/hellaswag/)
- **Repository:** [https://github.com/rowanz/hellaswag/](https://github.com/rowanz/hellaswag/)
- **Paper:** [HellaSwag: Can a Machine Really Finish Your Sentence?](https://arxiv.org/abs/1905.07830)
- **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:** 71.49 MB
- **Size of the generated dataset:** 65.32 MB
- **Total amount of disk used:** 136.81 MB
### Dataset Summary
HellaSwag: Can a Machine Really Finish Your Sentence? is a new dataset for commonsense NLI. A paper was published at ACL2019.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 71.49 MB
- **Size of the generated dataset:** 65.32 MB
- **Total amount of disk used:** 136.81 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"activity_label": "Removing ice from car",
"ctx": "Then, the man writes over the snow covering the window of a car, and a woman wearing winter clothes smiles. then",
"ctx_a": "Then, the man writes over the snow covering the window of a car, and a woman wearing winter clothes smiles.",
"ctx_b": "then",
"endings": "[\", the man adds wax to the windshield and cuts it.\", \", a person board a ski lift, while two men supporting the head of the per...",
"ind": 4,
"label": "3",
"source_id": "activitynet~v_-1IBHYS3L-Y",
"split": "train",
"split_type": "indomain"
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `ind`: a `int32` feature.
- `activity_label`: a `string` feature.
- `ctx_a`: a `string` feature.
- `ctx_b`: a `string` feature.
- `ctx`: a `string` feature.
- `endings`: a `list` of `string` features.
- `source_id`: a `string` feature.
- `split`: a `string` feature.
- `split_type`: a `string` feature.
- `label`: a `string` feature.
### Data Splits
| name |train|validation|test |
|-------|----:|---------:|----:|
|default|39905| 10042|10003|
## 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
MIT https://github.com/rowanz/hellaswag/blob/master/LICENSE
### Citation Information
```
@inproceedings{zellers2019hellaswag,
title={HellaSwag: Can a Machine Really Finish Your Sentence?},
author={Zellers, Rowan and Holtzman, Ari and Bisk, Yonatan and Farhadi, Ali and Choi, Yejin},
booktitle ={Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
year={2019}
}
```
### Contributions
Thanks to [@albertvillanova](https://github.com/albertvillanova), [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
code_x_glue_ct_code_to_text | null | 2023-06-01T14:59:54Z | 2,621 | 37 | null | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:other-programming-languages",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:code",
"language:en",
"license:c-uda",
"code-to-text",
"region:us"
] | 2023-06-01T14:59:54Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- code
- en
license:
- c-uda
multilinguality:
- other-programming-languages
size_categories:
- 100K<n<1M
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
pretty_name: CodeXGlueCtCodeToText
tags:
- code-to-text
dataset_info:
- config_name: go
features:
- name: id
dtype: int32
- name: repo
dtype: string
- name: path
dtype: string
- name: func_name
dtype: string
- name: original_string
dtype: string
- name: language
dtype: string
- name: code
dtype: string
- name: code_tokens
sequence: string
- name: docstring
dtype: string
- name: docstring_tokens
sequence: string
- name: sha
dtype: string
- name: url
dtype: string
splits:
- name: train
num_bytes: 342244027
num_examples: 167288
- name: validation
num_bytes: 13721912
num_examples: 7325
- name: test
num_bytes: 16328458
num_examples: 8122
download_size: 499922799
dataset_size: 372294397
- config_name: java
features:
- name: id
dtype: int32
- name: repo
dtype: string
- name: path
dtype: string
- name: func_name
dtype: string
- name: original_string
dtype: string
- name: language
dtype: string
- name: code
dtype: string
- name: code_tokens
sequence: string
- name: docstring
dtype: string
- name: docstring_tokens
sequence: string
- name: sha
dtype: string
- name: url
dtype: string
splits:
- name: train
num_bytes: 452554719
num_examples: 164923
- name: validation
num_bytes: 13366396
num_examples: 5183
- name: test
num_bytes: 29080857
num_examples: 10955
download_size: 1072966017
dataset_size: 495001972
- config_name: javascript
features:
- name: id
dtype: int32
- name: repo
dtype: string
- name: path
dtype: string
- name: func_name
dtype: string
- name: original_string
dtype: string
- name: language
dtype: string
- name: code
dtype: string
- name: code_tokens
sequence: string
- name: docstring
dtype: string
- name: docstring_tokens
sequence: string
- name: sha
dtype: string
- name: url
dtype: string
splits:
- name: train
num_bytes: 160860743
num_examples: 58025
- name: validation
num_bytes: 10337396
num_examples: 3885
- name: test
num_bytes: 10190765
num_examples: 3291
download_size: 1677110214
dataset_size: 181388904
- config_name: php
features:
- name: id
dtype: int32
- name: repo
dtype: string
- name: path
dtype: string
- name: func_name
dtype: string
- name: original_string
dtype: string
- name: language
dtype: string
- name: code
dtype: string
- name: code_tokens
sequence: string
- name: docstring
dtype: string
- name: docstring_tokens
sequence: string
- name: sha
dtype: string
- name: url
dtype: string
splits:
- name: train
num_bytes: 614655799
num_examples: 241241
- name: validation
num_bytes: 33283149
num_examples: 12982
- name: test
num_bytes: 35375097
num_examples: 14014
download_size: 864290912
dataset_size: 683314045
- config_name: python
features:
- name: id
dtype: int32
- name: repo
dtype: string
- name: path
dtype: string
- name: func_name
dtype: string
- name: original_string
dtype: string
- name: language
dtype: string
- name: code
dtype: string
- name: code_tokens
sequence: string
- name: docstring
dtype: string
- name: docstring_tokens
sequence: string
- name: sha
dtype: string
- name: url
dtype: string
splits:
- name: train
num_bytes: 813664500
num_examples: 251820
- name: validation
num_bytes: 46888668
num_examples: 13914
- name: test
num_bytes: 50659792
num_examples: 14918
download_size: 953306861
dataset_size: 911212960
- config_name: ruby
features:
- name: id
dtype: int32
- name: repo
dtype: string
- name: path
dtype: string
- name: func_name
dtype: string
- name: original_string
dtype: string
- name: language
dtype: string
- name: code
dtype: string
- name: code_tokens
sequence: string
- name: docstring
dtype: string
- name: docstring_tokens
sequence: string
- name: sha
dtype: string
- name: url
dtype: string
splits:
- name: train
num_bytes: 51956595
num_examples: 24927
- name: validation
num_bytes: 2821089
num_examples: 1400
- name: test
num_bytes: 2671603
num_examples: 1261
download_size: 124154892
dataset_size: 57449287
config_names:
- go
- java
- javascript
- php
- python
- ruby
---
# Dataset Card for "code_x_glue_ct_code_to_text"
## 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-Text/code-to-text
### Dataset Summary
CodeXGLUE code-to-text dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Text/code-to-text
The dataset we use comes from CodeSearchNet and we filter the dataset as the following:
- Remove examples that codes cannot be parsed into an abstract syntax tree.
- Remove examples that #tokens of documents is < 3 or >256
- Remove examples that documents contain special tokens (e.g. <img ...> or https:...)
- Remove examples that documents are not English.
### Supported Tasks and Leaderboards
- `machine-translation`: The dataset can be used to train a model for automatically generating **English** docstrings for code.
### Languages
- Go **programming** language
- Java **programming** language
- Javascript **programming** language
- PHP **programming** language
- Python **programming** language
- Ruby **programming** language
- English **natural** language
## Dataset Structure
### Data Instances
#### go
An example of 'test' looks as follows.
```
{
"code": "func NewSTM(c *v3.Client, apply func(STM) error, so ...stmOption) (*v3.TxnResponse, error) {\n\topts := &stmOptions{ctx: c.Ctx()}\n\tfor _, f := range so {\n\t\tf(opts)\n\t}\n\tif len(opts.prefetch) != 0 {\n\t\tf := apply\n\t\tapply = func(s STM) error {\n\t\t\ts.Get(opts.prefetch...)\n\t\t\treturn f(s)\n\t\t}\n\t}\n\treturn runSTM(mkSTM(c, opts), apply)\n}",
"code_tokens": ["func", "NewSTM", "(", "c", "*", "v3", ".", "Client", ",", "apply", "func", "(", "STM", ")", "error", ",", "so", "...", "stmOption", ")", "(", "*", "v3", ".", "TxnResponse", ",", "error", ")", "{", "opts", ":=", "&", "stmOptions", "{", "ctx", ":", "c", ".", "Ctx", "(", ")", "}", "\n", "for", "_", ",", "f", ":=", "range", "so", "{", "f", "(", "opts", ")", "\n", "}", "\n", "if", "len", "(", "opts", ".", "prefetch", ")", "!=", "0", "{", "f", ":=", "apply", "\n", "apply", "=", "func", "(", "s", "STM", ")", "error", "{", "s", ".", "Get", "(", "opts", ".", "prefetch", "...", ")", "\n", "return", "f", "(", "s", ")", "\n", "}", "\n", "}", "\n", "return", "runSTM", "(", "mkSTM", "(", "c", ",", "opts", ")", ",", "apply", ")", "\n", "}"],
"docstring": "// NewSTM initiates a new STM instance, using serializable snapshot isolation by default.",
"docstring_tokens": ["NewSTM", "initiates", "a", "new", "STM", "instance", "using", "serializable", "snapshot", "isolation", "by", "default", "."],
"func_name": "NewSTM",
"id": 0,
"language": "go",
"original_string": "func NewSTM(c *v3.Client, apply func(STM) error, so ...stmOption) (*v3.TxnResponse, error) {\n\topts := &stmOptions{ctx: c.Ctx()}\n\tfor _, f := range so {\n\t\tf(opts)\n\t}\n\tif len(opts.prefetch) != 0 {\n\t\tf := apply\n\t\tapply = func(s STM) error {\n\t\t\ts.Get(opts.prefetch...)\n\t\t\treturn f(s)\n\t\t}\n\t}\n\treturn runSTM(mkSTM(c, opts), apply)\n}",
"path": "clientv3/concurrency/stm.go",
"repo": "etcd-io/etcd",
"sha": "616592d9ba993e3fe9798eef581316016df98906",
"url": "https://github.com/etcd-io/etcd/blob/616592d9ba993e3fe9798eef581316016df98906/clientv3/concurrency/stm.go#L89-L102"
}
```
#### java
An example of 'test' looks as follows.
```
{
"code": "protected final void fastPathOrderedEmit(U value, boolean delayError, Disposable disposable) {\n final Observer<? super V> observer = downstream;\n final SimplePlainQueue<U> q = queue;\n\n if (wip.get() == 0 && wip.compareAndSet(0, 1)) {\n if (q.isEmpty()) {\n accept(observer, value);\n if (leave(-1) == 0) {\n return;\n }\n } else {\n q.offer(value);\n }\n } else {\n q.offer(value);\n if (!enter()) {\n return;\n }\n }\n QueueDrainHelper.drainLoop(q, observer, delayError, disposable, this);\n }",
"code_tokens": ["protected", "final", "void", "fastPathOrderedEmit", "(", "U", "value", ",", "boolean", "delayError", ",", "Disposable", "disposable", ")", "{", "final", "Observer", "<", "?", "super", "V", ">", "observer", "=", "downstream", ";", "final", "SimplePlainQueue", "<", "U", ">", "q", "=", "queue", ";", "if", "(", "wip", ".", "get", "(", ")", "==", "0", "&&", "wip", ".", "compareAndSet", "(", "0", ",", "1", ")", ")", "{", "if", "(", "q", ".", "isEmpty", "(", ")", ")", "{", "accept", "(", "observer", ",", "value", ")", ";", "if", "(", "leave", "(", "-", "1", ")", "==", "0", ")", "{", "return", ";", "}", "}", "else", "{", "q", ".", "offer", "(", "value", ")", ";", "}", "}", "else", "{", "q", ".", "offer", "(", "value", ")", ";", "if", "(", "!", "enter", "(", ")", ")", "{", "return", ";", "}", "}", "QueueDrainHelper", ".", "drainLoop", "(", "q", ",", "observer", ",", "delayError", ",", "disposable", ",", "this", ")", ";", "}"],
"docstring": "Makes sure the fast-path emits in order.\n@param value the value to emit or queue up\n@param delayError if true, errors are delayed until the source has terminated\n@param disposable the resource to dispose if the drain terminates",
"docstring_tokens": ["Makes", "sure", "the", "fast", "-", "path", "emits", "in", "order", "."],
"func_name": "QueueDrainObserver.fastPathOrderedEmit",
"id": 0,
"language": "java",
"original_string": "protected final void fastPathOrderedEmit(U value, boolean delayError, Disposable disposable) {\n final Observer<? super V> observer = downstream;\n final SimplePlainQueue<U> q = queue;\n\n if (wip.get() == 0 && wip.compareAndSet(0, 1)) {\n if (q.isEmpty()) {\n accept(observer, value);\n if (leave(-1) == 0) {\n return;\n }\n } else {\n q.offer(value);\n }\n } else {\n q.offer(value);\n if (!enter()) {\n return;\n }\n }\n QueueDrainHelper.drainLoop(q, observer, delayError, disposable, this);\n }",
"path": "src/main/java/io/reactivex/internal/observers/QueueDrainObserver.java",
"repo": "ReactiveX/RxJava",
"sha": "ac84182aa2bd866b53e01c8e3fe99683b882c60e",
"url": "https://github.com/ReactiveX/RxJava/blob/ac84182aa2bd866b53e01c8e3fe99683b882c60e/src/main/java/io/reactivex/internal/observers/QueueDrainObserver.java#L88-L108"
}
```
#### javascript
An example of 'test' looks as follows.
```
{
"code": "function createInstance(defaultConfig) {\n var context = new Axios(defaultConfig);\n var instance = bind(Axios.prototype.request, context);\n\n // Copy axios.prototype to instance\n utils.extend(instance, Axios.prototype, context);\n\n // Copy context to instance\n utils.extend(instance, context);\n\n return instance;\n}",
"code_tokens": ["function", "createInstance", "(", "defaultConfig", ")", "{", "var", "context", "=", "new", "Axios", "(", "defaultConfig", ")", ";", "var", "instance", "=", "bind", "(", "Axios", ".", "prototype", ".", "request", ",", "context", ")", ";", "// Copy axios.prototype to instance", "utils", ".", "extend", "(", "instance", ",", "Axios", ".", "prototype", ",", "context", ")", ";", "// Copy context to instance", "utils", ".", "extend", "(", "instance", ",", "context", ")", ";", "return", "instance", ";", "}"],
"docstring": "Create an instance of Axios\n\n@param {Object} defaultConfig The default config for the instance\n@return {Axios} A new instance of Axios",
"docstring_tokens": ["Create", "an", "instance", "of", "Axios"],
"func_name": "createInstance",
"id": 0,
"language": "javascript",
"original_string": "function createInstance(defaultConfig) {\n var context = new Axios(defaultConfig);\n var instance = bind(Axios.prototype.request, context);\n\n // Copy axios.prototype to instance\n utils.extend(instance, Axios.prototype, context);\n\n // Copy context to instance\n utils.extend(instance, context);\n\n return instance;\n}",
"path": "lib/axios.js",
"repo": "axios/axios",
"sha": "92d231387fe2092f8736bc1746d4caa766b675f5",
"url": "https://github.com/axios/axios/blob/92d231387fe2092f8736bc1746d4caa766b675f5/lib/axios.js#L15-L26"
}
```
#### php
An example of 'train' looks as follows.
```
{
"code": "public static function build($serviceAddress, $restConfigPath, array $config = [])\n {\n $config += [\n 'httpHandler' => null,\n ];\n list($baseUri, $port) = self::normalizeServiceAddress($serviceAddress);\n $requestBuilder = new RequestBuilder(\"$baseUri:$port\", $restConfigPath);\n $httpHandler = $config['httpHandler'] ?: self::buildHttpHandlerAsync();\n return new RestTransport($requestBuilder, $httpHandler);\n }",
"code_tokens": ["public", "static", "function", "build", "(", "$", "serviceAddress", ",", "$", "restConfigPath", ",", "array", "$", "config", "=", "[", "]", ")", "{", "$", "config", "+=", "[", "'httpHandler'", "=>", "null", ",", "]", ";", "list", "(", "$", "baseUri", ",", "$", "port", ")", "=", "self", "::", "normalizeServiceAddress", "(", "$", "serviceAddress", ")", ";", "$", "requestBuilder", "=", "new", "RequestBuilder", "(", "\"$baseUri:$port\"", ",", "$", "restConfigPath", ")", ";", "$", "httpHandler", "=", "$", "config", "[", "'httpHandler'", "]", "?", ":", "self", "::", "buildHttpHandlerAsync", "(", ")", ";", "return", "new", "RestTransport", "(", "$", "requestBuilder", ",", "$", "httpHandler", ")", ";", "}"],
"docstring": "Builds a RestTransport.\n\n@param string $serviceAddress\nThe address of the API remote host, for example \"example.googleapis.com\".\n@param string $restConfigPath\nPath to rest config file.\n@param array $config {\nConfig options used to construct the gRPC transport.\n\n@type callable $httpHandler A handler used to deliver PSR-7 requests.\n}\n@return RestTransport\n@throws ValidationException",
"docstring_tokens": ["Builds", "a", "RestTransport", "."],
"func_name": "RestTransport.build",
"id": 0,
"language": "php",
"original_string": "public static function build($serviceAddress, $restConfigPath, array $config = [])\n {\n $config += [\n 'httpHandler' => null,\n ];\n list($baseUri, $port) = self::normalizeServiceAddress($serviceAddress);\n $requestBuilder = new RequestBuilder(\"$baseUri:$port\", $restConfigPath);\n $httpHandler = $config['httpHandler'] ?: self::buildHttpHandlerAsync();\n return new RestTransport($requestBuilder, $httpHandler);\n }",
"path": "src/Transport/RestTransport.php",
"repo": "googleapis/gax-php",
"sha": "48387fb818c6882296710a2302a0aa973b99afb2",
"url": "https://github.com/googleapis/gax-php/blob/48387fb818c6882296710a2302a0aa973b99afb2/src/Transport/RestTransport.php#L85-L94"
}
```
#### python
An example of 'validation' looks as follows.
```
{
"code": "def save_act(self, path=None):\n \"\"\"Save model to a pickle located at `path`\"\"\"\n if path is None:\n path = os.path.join(logger.get_dir(), \"model.pkl\")\n\n with tempfile.TemporaryDirectory() as td:\n save_variables(os.path.join(td, \"model\"))\n arc_name = os.path.join(td, \"packed.zip\")\n with zipfile.ZipFile(arc_name, 'w') as zipf:\n for root, dirs, files in os.walk(td):\n for fname in files:\n file_path = os.path.join(root, fname)\n if file_path != arc_name:\n zipf.write(file_path, os.path.relpath(file_path, td))\n with open(arc_name, \"rb\") as f:\n model_data = f.read()\n with open(path, \"wb\") as f:\n cloudpickle.dump((model_data, self._act_params), f)",
"code_tokens": ["def", "save_act", "(", "self", ",", "path", "=", "None", ")", ":", "if", "path", "is", "None", ":", "path", "=", "os", ".", "path", ".", "join", "(", "logger", ".", "get_dir", "(", ")", ",", "\"model.pkl\"", ")", "with", "tempfile", ".", "TemporaryDirectory", "(", ")", "as", "td", ":", "save_variables", "(", "os", ".", "path", ".", "join", "(", "td", ",", "\"model\"", ")", ")", "arc_name", "=", "os", ".", "path", ".", "join", "(", "td", ",", "\"packed.zip\"", ")", "with", "zipfile", ".", "ZipFile", "(", "arc_name", ",", "'w'", ")", "as", "zipf", ":", "for", "root", ",", "dirs", ",", "files", "in", "os", ".", "walk", "(", "td", ")", ":", "for", "fname", "in", "files", ":", "file_path", "=", "os", ".", "path", ".", "join", "(", "root", ",", "fname", ")", "if", "file_path", "!=", "arc_name", ":", "zipf", ".", "write", "(", "file_path", ",", "os", ".", "path", ".", "relpath", "(", "file_path", ",", "td", ")", ")", "with", "open", "(", "arc_name", ",", "\"rb\"", ")", "as", "f", ":", "model_data", "=", "f", ".", "read", "(", ")", "with", "open", "(", "path", ",", "\"wb\"", ")", "as", "f", ":", "cloudpickle", ".", "dump", "(", "(", "model_data", ",", "self", ".", "_act_params", ")", ",", "f", ")"],
"docstring": "Save model to a pickle located at `path`",
"docstring_tokens": ["Save", "model", "to", "a", "pickle", "located", "at", "path"],
"func_name": "ActWrapper.save_act",
"id": 0,
"language": "python",
"original_string": "def save_act(self, path=None):\n \"\"\"Save model to a pickle located at `path`\"\"\"\n if path is None:\n path = os.path.join(logger.get_dir(), \"model.pkl\")\n\n with tempfile.TemporaryDirectory() as td:\n save_variables(os.path.join(td, \"model\"))\n arc_name = os.path.join(td, \"packed.zip\")\n with zipfile.ZipFile(arc_name, 'w') as zipf:\n for root, dirs, files in os.walk(td):\n for fname in files:\n file_path = os.path.join(root, fname)\n if file_path != arc_name:\n zipf.write(file_path, os.path.relpath(file_path, td))\n with open(arc_name, \"rb\") as f:\n model_data = f.read()\n with open(path, \"wb\") as f:\n cloudpickle.dump((model_data, self._act_params), f)",
"path": "baselines/deepq/deepq.py",
"repo": "openai/baselines",
"sha": "3301089b48c42b87b396e246ea3f56fa4bfc9678",
"url": "https://github.com/openai/baselines/blob/3301089b48c42b87b396e246ea3f56fa4bfc9678/baselines/deepq/deepq.py#L55-L72"
}
```
#### ruby
An example of 'train' looks as follows.
```
{
"code": "def render_body(context, options)\n if options.key?(:partial)\n [render_partial(context, options)]\n else\n StreamingTemplateRenderer.new(@lookup_context).render(context, options)\n end\n end",
"code_tokens": ["def", "render_body", "(", "context", ",", "options", ")", "if", "options", ".", "key?", "(", ":partial", ")", "[", "render_partial", "(", "context", ",", "options", ")", "]", "else", "StreamingTemplateRenderer", ".", "new", "(", "@lookup_context", ")", ".", "render", "(", "context", ",", "options", ")", "end", "end"],
"docstring": "Render but returns a valid Rack body. If fibers are defined, we return\n a streaming body that renders the template piece by piece.\n\n Note that partials are not supported to be rendered with streaming,\n so in such cases, we just wrap them in an array.",
"docstring_tokens": ["Render", "but", "returns", "a", "valid", "Rack", "body", ".", "If", "fibers", "are", "defined", "we", "return", "a", "streaming", "body", "that", "renders", "the", "template", "piece", "by", "piece", "."],
"func_name": "ActionView.Renderer.render_body",
"id": 0,
"language": "ruby",
"original_string": "def render_body(context, options)\n if options.key?(:partial)\n [render_partial(context, options)]\n else\n StreamingTemplateRenderer.new(@lookup_context).render(context, options)\n end\n end",
"path": "actionview/lib/action_view/renderer/renderer.rb",
"repo": "rails/rails",
"sha": "85a8bc644be69908f05740a5886ec19cd3679df5",
"url": "https://github.com/rails/rails/blob/85a8bc644be69908f05740a5886ec19cd3679df5/actionview/lib/action_view/renderer/renderer.rb#L38-L44"
}
```
### Data Fields
In the following each data field in go is explained for each config. The data fields are the same among all splits.
#### go, java, javascript, php, python, ruby
| field name | type | description |
|----------------|----------------|-----------------------------------------------------------------------------------|
|id |int32 | Index of the sample |
|repo |string | repo: the owner/repo |
|path |string | path: the full path to the original file |
|func_name |string | func_name: the function or method name |
|original_string |string | original_string: the raw string before tokenization or parsing |
|language |string | language: the programming language name |
|code |string | code/function: the part of the original_string that is code |
|code_tokens |Sequence[string]| code_tokens/function_tokens: tokenized version of code |
|docstring |string | docstring: the top-level comment or docstring, if it exists in the original string|
|docstring_tokens|Sequence[string]| docstring_tokens: tokenized version of docstring |
|sha |string | sha of the file |
|url |string | url of the file |
### Data Splits
| name |train |validation|test |
|----------|-----:|---------:|----:|
|go |167288| 7325| 8122|
|java |164923| 5183|10955|
|javascript| 58025| 3885| 3291|
|php |241241| 12982|14014|
|python |251820| 13914|14918|
|ruby | 24927| 1400| 1261|
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
Data from CodeSearchNet Challenge dataset.
[More Information Needed]
#### Who are the source language producers?
Software Engineering developers.
### 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{husain2019codesearchnet,
title={Codesearchnet challenge: Evaluating the state of semantic code search},
author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},
journal={arXiv preprint arXiv:1909.09436},
year={2019}
}
```
### Contributions
Thanks to @madlag (and partly also @ncoop57) for adding this dataset. | [
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kumapo/JAQKET | kumapo | 2023-10-09T06:44:28Z | 2,620 | 1 | null | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"language:ja",
"license:cc-by-sa-4.0",
"region:us"
] | 2023-10-09T06:44:28Z | 2023-06-21T13:04:38.000Z | 2023-06-21T13:04:38 | ---
license: cc-by-sa-4.0
task_categories:
- multiple-choice
- question-answering
language:
- ja
---
# Dataset Card for JAQKET
This dataset loading script is developed on [GitHub](https://github.com/kumapo/JAQKET-dataset).
Please feel free to open an [issue](https://github.com/kumapo/JAQKET-dataset/issues) or [pull request](https://github.com/kumapo/JAQKET-dataset/pulls).
## Dataset Description
- **Homepage:** https://sites.google.com/view/project-aio/dataset
- **Repository:** https://github.com/kumapo/JAQKET-dataset
### Dataset Summary
From [the original paper](https://www.anlp.jp/proceedings/annual_meeting/2020/pdf_dir/P2-24.pdf):
> 本研究では，日本における質問応答/機械読解研究の促進を目的として，研究者が容易に利用可能な日本語のオープンドメイン QA タスクのデータセット「JAQKET」1を構築する.
> 作成するデータセットは，既存研究 [7] に倣い，Wikipedia2 の記事名を答えとした，日本語のオープンドメイン QA タスクのデータセットである.
### Supported Tasks
#### JAQKET v1.0
From [the original paper](https://www.anlp.jp/proceedings/annual_meeting/2020/pdf_dir/P2-24.pdf):
> 本研究で扱う日本語オープンドメイン QA タスクを定義する.本研究では，クイズの問題文に対して複数(数個から数十個程度)の解答の選択肢が与られ，その選択肢から正解を一つ選択するという択一問題を取り扱う.
#### JAQKET v2.0
From [the homepage](https://sites.google.com/view/project-aio/competition2):
> 問題として与えられるのはクイズの問題文のみです．その問題文から解答となる文字列を解答として返すシステムを構築してもらいます．
### Languages
The language data in JAQKET is in Japanese.
## Dataset Structure
### Data Instances
When loading a specific configuration, users has to append a version dependent suffix:
#### JAQKET v1.0
```python
from datasets import load_dataset
dataset = load_dataset("kumapo/JAQKET", name="v1.0")
print(dataset)
# DatasetDict({
# train: Dataset({
# features: ['qid', 'question', 'answer_entity', 'label', 'answer_candidates', 'contexts'],
# num_rows: 13061
# })
# validation: Dataset({
# features: ['qid', 'question', 'answer_entity', 'label', 'answer_candidates', 'contexts'],
# num_rows: 271
# })
# })
```
An example of the JAQKET v1.0 dataset looks as follows:
```json
{
"qid": "QA20QBIK-0002",
"question": "童謡『たなばたさま』の歌詞で、「さらさら」と歌われる植物は何の葉?",
"answer_entity": "ササ",
"answer_candidates": [
"ササ",
"チシマザサ",
"クマザサ",
"アダン",
"チガヤ",
"アセビ",
"ススキ",
"ホオノキ",
"マテバシイ",
"ヤマフジ",
"ウツギ",
"タムシバ",
"ミズキ",
"アキタブキ",
"トベラ",
"クヌギ",
"ネズミモチ",
"ヒシ",
"コブシ",
"オオウバユリ"
],
"qtype": "なに〜"
}
```
```json
{
"qid": "QA20QBIK-0026",
"question": "北海道の中心に位置することから「北海道のへそ」と名乗る、ラベンダーで有名な都市はどこ?",
"answer_entity": "富良野市",
"answer_candidates": [
"富良野市",
"滝川市",
"北見市",
"芦別市",
"中富良野町",
"名寄市",
"網走市",
"美瑛町",
"南富良野町",
"岩見沢市",
"美唄市",
"上富良野町",
"倶知安町",
"小樽市",
"歌志内市",
"旭川市",
"ニセコ町",
"北斗市",
"稚内市",
"帯広市"
],
"qtype": "どこ"
}
```
#### JAQKET v2.0
```python
from datasets import load_dataset
dataset = load_dataset("kumapo/JAQKET", name="v2.0")
print(dataset)
# DatasetDict({
# train: Dataset({
# features: ['qid', 'question', 'answers', 'ctxs'],
# num_rows: 2154
# })
# validation: Dataset({
# features: ['qid', 'question', 'answers', 'ctxs'],
# num_rows: 1164
# })
# })
```
An example of the JAQKET v2.0 dataset looks as follows:
```json
{
"qid": "QA20QBIK-0002",
"competition": "第1回AI王",
"timestamp": "2020/01/27",
"section": "開発データ問題 (dev1)",
"number": "2",
"original_question": "童謡『たなばたさま』の歌詞で、「さらさら」と歌われる植物は何の葉？",
"original_answer": "ササ",
"original_additional_info": "",
"question": "童謡『たなばたさま』の歌詞で、「さらさら」と歌われる植物は何の葉?",
"answers" :["ササ"]
}
```
## Additional Information
### Citation Information
```bibtex
@InProceedings{Kurihara_nlp2020,
author = "鈴木正敏 and 鈴木潤 and 松田耕史 and ⻄田京介 and 井之上直也",
title = "JAQKET: クイズを題材にした日本語 QA データセットの構築",
booktitle = "言語処理学会第26回年次大会",
year = "2020",
url = "https://www.anlp.jp/proceedings/annual_meeting/2020/pdf_dir/P2-24.pdf"
note= "in Japanese"}
```
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lama | null | 2023-06-01T14:59:53Z | 2,614 | 11 | lama | [
"task_categories:text-retrieval",
"task_categories:text-classification",
"task_ids:fact-checking-retrieval",
"task_ids:text-scoring",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"size_categories:1M<n<10M",
"size_categories:n<1K",
"source_datasets:extended|conceptnet5",
"source_datasets:extended|squad",
"language:en",
"license:cc-by-4.0",
"probing",
"region:us"
] | 2023-06-01T14:59:53Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
pretty_name: 'LAMA: LAnguage Model Analysis'
annotations_creators:
- crowdsourced
- expert-generated
- machine-generated
language_creators:
- crowdsourced
- expert-generated
- machine-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
- 1K<n<10K
- 1M<n<10M
- n<1K
source_datasets:
- extended|conceptnet5
- extended|squad
task_categories:
- text-retrieval
- text-classification
task_ids:
- fact-checking-retrieval
- text-scoring
paperswithcode_id: lama
tags:
- probing
dataset_info:
- config_name: trex
features:
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dtype: string
- name: obj_uri
dtype: string
- name: obj_label
dtype: string
- name: sub_uri
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- name: predicate_id
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- name: sub_surface
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- name: obj_surface
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- name: masked_sentence
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- name: template
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- name: template_negated
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- name: label
dtype: string
- name: description
dtype: string
- name: type
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splits:
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num_bytes: 656913189
num_examples: 1304391
download_size: 74652201
dataset_size: 656913189
- config_name: squad
features:
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dtype: string
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splits:
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num_examples: 305
download_size: 74639115
dataset_size: 57188
- config_name: google_re
features:
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dtype: string
- name: evidences
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- name: judgments
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download_size: 74639115
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- config_name: conceptnet
features:
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dtype: string
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dtype: string
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splits:
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num_bytes: 4130000
num_examples: 29774
download_size: 74639115
dataset_size: 4130000
config_names:
- conceptnet
- google_re
- squad
- trex
---
# Dataset Card for LAMA: LAnguage Model Analysis - a dataset for probing and analyzing the factual and commonsense knowledge contained in pretrained language models.
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
https://github.com/facebookresearch/LAMA
- **Repository:**
https://github.com/facebookresearch/LAMA
- **Paper:**
@inproceedings{petroni2019language,
title={Language Models as Knowledge Bases?},
author={F. Petroni, T. Rockt{\"{a}}schel, A. H. Miller, P. Lewis, A. Bakhtin, Y. Wu and S. Riedel},
booktitle={In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019},
year={2019}
}
@inproceedings{petroni2020how,
title={How Context Affects Language Models' Factual Predictions},
author={Fabio Petroni and Patrick Lewis and Aleksandra Piktus and Tim Rockt{\"a}schel and Yuxiang Wu and Alexander H. Miller and Sebastian Riedel},
booktitle={Automated Knowledge Base Construction},
year={2020},
url={https://openreview.net/forum?id=025X0zPfn}
}
### Dataset Summary
This dataset provides the data for LAMA. The dataset include a subset
of Google_RE
(https://code.google.com/archive/p/relation-extraction-corpus/), TRex
(subset of wikidata triples), Conceptnet
(https://github.com/commonsense/conceptnet5/wiki) and Squad. There are
configs for each of "google_re", "trex", "conceptnet" and "squad",
respectively.
The dataset includes some cleanup, and addition of a masked sentence
and associated answers for the [MASK] token. The accuracy in
predicting the [MASK] token shows how well the language model knows
facts and common sense information. The [MASK] tokens are only for the
"object" slots.
This version of the dataset includes "negated" sentences as well as
the masked sentence. Also, certain of the config includes "template"
and "template_negated" fields of the form "[X] some text [Y]", where
[X] and [Y] are the subject and object slots respectively of certain
relations.
See the paper for more details. For more information, also see:
https://github.com/facebookresearch/LAMA
### Languages
en
## Dataset Structure
### Data Instances
The trex config has the following fields:
``
{'description': 'the item (an institution, law, public office ...) or statement belongs to or has power over or applies to the value (a territorial jurisdiction: a country, state, municipality, ...)', 'label': 'applies to jurisdiction', 'masked_sentence': 'It is known as a principality as it is a monarchy headed by two Co-Princes – the Spanish/Roman Catholic Bishop of Urgell and the President of [MASK].', 'obj_label': 'France', 'obj_surface': 'France', 'obj_uri': 'Q142', 'predicate_id': 'P1001', 'sub_label': 'president of the French Republic', 'sub_surface': 'President', 'sub_uri': 'Q191954', 'template': '[X] is a legal term in [Y] .', 'template_negated': '[X] is not a legal term in [Y] .', 'type': 'N-M', 'uuid': '3fe3d4da-9df9-45ba-8109-784ce5fba38a'}
``
The conceptnet config has the following fields:
``
{'masked_sentence': 'One of the things you do when you are alive is [MASK].', 'negated': '', 'obj': 'think', 'obj_label': 'think', 'pred': 'HasSubevent', 'sub': 'alive', 'uuid': 'd4f11631dde8a43beda613ec845ff7d1'}
``
The squad config has the following fields:
``
{'id': '56be4db0acb8001400a502f0_0', 'masked_sentence': 'To emphasize the 50th anniversary of the Super Bowl the [MASK] color was used.', 'negated': "['To emphasize the 50th anniversary of the Super Bowl the [MASK] color was not used.']", 'obj_label': 'gold', 'sub_label': 'Squad'}
``
The google_re config has the following fields:
``
{'evidences': '[{\'url\': \'http://en.wikipedia.org/wiki/Peter_F._Martin\', \'snippet\': "Peter F. Martin (born 1941) is an American politician who is a Democratic member of the Rhode Island House of Representatives. He has represented the 75th District Newport since 6 January 2009. He is currently serves on the House Committees on Judiciary, Municipal Government, and Veteran\'s Affairs. During his first term of office he served on the House Committees on Small Business and Separation of Powers & Government Oversight. In August 2010, Representative Martin was appointed as a Commissioner on the Atlantic States Marine Fisheries Commission", \'considered_sentences\': [\'Peter F Martin (born 1941) is an American politician who is a Democratic member of the Rhode Island House of Representatives .\']}]', 'judgments': "[{'rater': '18349444711114572460', 'judgment': 'yes'}, {'rater': '17595829233063766365', 'judgment': 'yes'}, {'rater': '4593294093459651288', 'judgment': 'yes'}, {'rater': '7387074196865291426', 'judgment': 'yes'}, {'rater': '17154471385681223613', 'judgment': 'yes'}]", 'masked_sentence': 'Peter F Martin (born [MASK]) is an American politician who is a Democratic member of the Rhode Island House of Representatives .', 'obj': '1941', 'obj_aliases': '[]', 'obj_label': '1941', 'obj_w': 'None', 'pred': '/people/person/date_of_birth', 'sub': '/m/09gb0bw', 'sub_aliases': '[]', 'sub_label': 'Peter F. Martin', 'sub_w': 'None', 'template': '[X] (born [Y]).', 'template_negated': '[X] (not born [Y]).', 'uuid': '18af2dac-21d3-4c42-aff5-c247f245e203'}
``
### Data Fields
The trex config has the following fields:
* uuid: the id
* obj_uri: a uri for the object slot
* obj_label: a label for the object slot
* sub_uri: a uri for the subject slot
* sub_label: a label for the subject slot
* predicate_id: the predicate/relationship
* sub_surface: the surface text for the subject
* obj_surface: The surface text for the object. This is the word that should be predicted by the [MASK] token.
* masked_sentence: The masked sentence used to probe, with the object word replaced with [MASK]
* template: A pattern of text for extracting the relationship, object and subject of the form "[X] some text [Y]", where [X] and [Y] are the subject and object slots respectively. template may be missing and replaced with an empty string.
* template_negated: Same as above, except the [Y] is not the object. template_negated may be missing and replaced with empty strings.
* label: the label for the relationship/predicate. label may be missing and replaced with an empty string.
* description': a description of the relationship/predicate. description may be missing and replaced with an empty string.
* type: a type id for the relationship/predicate. type may be missing and replaced with an empty string.
The conceptnet config has the following fields:
* uuid: the id
* sub: the subject. subj may be missing and replaced with an empty string.
* obj: the object to be predicted. obj may be missing and replaced with an empty string.
* pred: the predicate/relationship
* obj_label: the object label
* masked_sentence: The masked sentence used to probe, with the object word replaced with [MASK]
* negated: same as above, except [MASK] is replaced by something that is not the object word. negated may be missing and replaced with empty strings.
The squad config has the following fields:
* id: the id
* sub_label: the subject label
* obj_label: the object label that is being predicted
* masked_sentence: The masked sentence used to probe, with the object word replaced with [MASK]
* negated: same as above, except [MASK] is replaced by something that is not the object word. negated may be missing and replaced with empty strings.
The google_re config has the following fields:
* uuid: the id
* pred: the predicate
* sub: the subject. subj may be missing and replaced with an empty string.
* obj: the object. obj may be missing and replaced with an empty string.
* evidences: flattened json string that provides evidence for predicate. parse this json string to get more 'snippet' information.
* judgments: data about judgments
* sub_q: unknown
* sub_label: label for the subject
* sub_aliases: unknown
* obj_w: unknown
* obj_label: label for the object
* obj_aliases: unknown
* masked_sentence: The masked sentence used to probe, with the object word replaced with [MASK]
* template: A pattern of text for extracting the relationship, object and subject of the form "[X] some text [Y]", where [X] and [Y] are the subject and object slots respectively.
* template_negated: Same as above, except the [Y] is not the object.
### Data Splits
There are no data splits.
## Dataset Creation
### Curation Rationale
This dataset was gathered and created to probe what language models understand.
### Source Data
#### Initial Data Collection and Normalization
See the reaserch paper and website for more detail. The dataset was
created gathered from various other datasets with cleanups for probing.
#### Who are the source language producers?
The LAMA authors and the original authors of the various configs.
### Annotations
#### Annotation process
Human annotations under the original datasets (conceptnet), and various machine annotations.
#### Who are the annotators?
Human annotations and machine annotations.
### Personal and Sensitive Information
Unkown, but likely names of famous people.
## Considerations for Using the Data
### Social Impact of Dataset
The goal for the work is to probe the understanding of language models.
### Discussion of Biases
Since the data is from human annotators, there is likely to be baises.
[More Information Needed]
### Other Known Limitations
The original documentation for the datafields are limited.
## Additional Information
### Dataset Curators
The authors of LAMA at Facebook and the authors of the original datasets.
### Licensing Information
The Creative Commons Attribution-Noncommercial 4.0 International License. see https://github.com/facebookresearch/LAMA/blob/master/LICENSE
### Citation Information
@inproceedings{petroni2019language,
title={Language Models as Knowledge Bases?},
author={F. Petroni, T. Rockt{\"{a}}schel, A. H. Miller, P. Lewis, A. Bakhtin, Y. Wu and S. Riedel},
booktitle={In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019},
year={2019}
}
@inproceedings{petroni2020how,
title={How Context Affects Language Models' Factual Predictions},
author={Fabio Petroni and Patrick Lewis and Aleksandra Piktus and Tim Rockt{\"a}schel and Yuxiang Wu and Alexander H. Miller and Sebastian Riedel},
booktitle={Automated Knowledge Base Construction},
year={2020},
url={https://openreview.net/forum?id=025X0zPfn}
}
### Contributions
Thanks to [@ontocord](https://github.com/ontocord) for adding this dataset. | [
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Bingsu/Cat_and_Dog | Bingsu | 2023-01-26T10:48:25Z | 2,607 | 2 | null | [
"task_categories:image-classification",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc0-1.0",
"region:us"
] | 2023-01-26T10:48:25Z | 2022-04-19T02:23:06.000Z | 2022-04-19T02:23:06 | ---
language:
- en
license:
- cc0-1.0
pretty_name: Cat and Dog
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- image-classification
dataset_info:
features:
- name: image
dtype: image
- name: labels
dtype:
class_label:
names:
'0': cat
'1': dog
splits:
- name: train
num_bytes: 166451650.0
num_examples: 8000
- name: test
num_bytes: 42101650.0
num_examples: 2000
download_size: 227859268
dataset_size: 208553300.0
size_in_bytes: 436412568.0
---
## Dataset Description
- **Homepage:** [Cat and Dog](https://www.kaggle.com/datasets/tongpython/cat-and-dog)
- **Download Size** 217.30 MiB
- **Generated Size** 198.89 MiB
- **Total Size** 416.20 MiB
### Dataset Summary
A dataset from [kaggle](https://www.kaggle.com/datasets/tongpython/cat-and-dog) with duplicate data removed.
### Data Fields
The data instances have the following fields:
- `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`.
- `labels`: an `int` classification label.
### Class Label Mappings:
```
{
"cat": 0,
"dog": 1,
}
```
### Data Splits
| | train | test |
|---------------|-------|-----:|
| # of examples | 8000 | 2000 |
```python
>>> from datasets import load_dataset
>>> dataset = load_dataset("Bingsu/Cat_and_Dog")
>>> dataset
DatasetDict({
train: Dataset({
features: ['image', 'labels'],
num_rows: 8000
})
test: Dataset({
features: ['image', 'labels'],
num_rows: 2000
})
})
>>> dataset["train"].features
{'image': Image(decode=True, id=None), 'labels': ClassLabel(num_classes=2, names=['cat', 'dog'], id=None)}
``` | [
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facebook/multilingual_librispeech | facebook | 2023-02-13T11:33:31Z | 2,599 | 32 | multilingual-librispeech | [
"task_categories:automatic-speech-recognition",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:de",
"language:nl",
"language:fr",
"language:it",
"language:es",
"language:pt",
"language:pl",
"license:cc-by-4.0",
"arxiv:2012.03411",
"region:us"
] | 2023-02-13T11:33:31Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
pretty_name: MultiLingual LibriSpeech
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- de
- nl
- fr
- it
- es
- pt
- pl
license:
- cc-by-4.0
multilinguality:
- multilingual
paperswithcode_id: multilingual-librispeech
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- automatic-speech-recognition
---
# Dataset Card for MultiLingual LibriSpeech
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data 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:** [MultiLingual LibriSpeech ASR corpus](http://www.openslr.org/94)
- **Repository:** [Needs More Information]
- **Paper:** [MLS: A Large-Scale Multilingual Dataset for Speech Research](https://arxiv.org/abs/2012.03411)
- **Leaderboard:** [🤗 Autoevaluate Leaderboard](https://huggingface.co/spaces/autoevaluate/leaderboards?dataset=facebook%2Fmultilingual_librispeech&only_verified=0&task=automatic-speech-recognition&config=-unspecified-&split=-unspecified-&metric=wer)
### Dataset Summary
This is a streamable version of the Multilingual LibriSpeech (MLS) dataset.
The data archives were restructured from the original ones from [OpenSLR](http://www.openslr.org/94) to make it easier to stream.
MLS dataset is a large multilingual corpus suitable for speech research. The dataset is derived from read audiobooks from LibriVox and consists of
8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish.
### Supported Tasks and Leaderboards
- `automatic-speech-recognition`, `speaker-identification`: 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). The task has an active leaderboard which can be found at https://paperswithcode.com/dataset/multilingual-librispeech and ranks models based on their WER.
### Languages
The dataset is derived from read audiobooks from LibriVox and consists of 8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish
### How to use
The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function.
For example, to download the German config, simply specify the corresponding language config name (i.e., "german" for German):
```python
from datasets import load_dataset
mls = load_dataset("facebook/multilingual_librispeech", "german", split="train")
```
Using the datasets library, you can also stream the dataset on-the-fly by adding a `streaming=True` argument to the `load_dataset` function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.
```python
from datasets import load_dataset
mls = load_dataset("facebook/multilingual_librispeech", "german", split="train", streaming=True)
print(next(iter(mls)))
```
*Bonus*: create a [PyTorch dataloader](https://huggingface.co/docs/datasets/use_with_pytorch) directly with your own datasets (local/streamed).
Local:
```python
from datasets import load_dataset
from torch.utils.data.sampler import BatchSampler, RandomSampler
mls = load_dataset("facebook/multilingual_librispeech", "german", split="train")
batch_sampler = BatchSampler(RandomSampler(mls), batch_size=32, drop_last=False)
dataloader = DataLoader(mls, batch_sampler=batch_sampler)
```
Streaming:
```python
from datasets import load_dataset
from torch.utils.data import DataLoader
mls = load_dataset("facebook/multilingual_librispeech", "german", split="train", streaming=True)
dataloader = DataLoader(mls, batch_size=32)
```
To find out more about loading and preparing audio datasets, head over to [hf.co/blog/audio-datasets](https://huggingface.co/blog/audio-datasets).
### Example scripts
Train your own CTC or Seq2Seq Automatic Speech Recognition models on MultiLingual Librispeech with `transformers` - [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition).
## Dataset Structure
### Data Instances
A typical data point comprises the path to the audio file, usually called `file` and its transcription, called `text`. Some additional information about the speaker and the passage which contains the transcription is provided.
```
{'file': '10900_6473_000030.flac',
'audio': {'path': '10900_6473_000030.flac',
'array': array([-1.52587891e-04, 6.10351562e-05, 0.00000000e+00, ...,
4.27246094e-04, 5.49316406e-04, 4.57763672e-04]),
'sampling_rate': 16000},
'text': 'więc czego chcecie odemnie spytałem wysłuchawszy tego zadziwiającego opowiadania broń nas stary człowieku broń zakrzyknęli równocześnie obaj posłowie\n',
'speaker_id': 10900,
'chapter_id': 6473,
'id': '10900_6473_000030'}
```
### Data Fields
- file: A filename .flac format.
- audio: A dictionary containing the audio filename, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
- text: the transcription of the audio file.
- id: unique id of the data sample.
- speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples.
- chapter_id: id of the audiobook chapter which includes the transcription.
### Data Splits
| | Train | Train.9h | Train.1h | Dev | Test |
| ----- | ------ | ----- | ---- | ---- | ---- |
| german | 469942 | 2194 | 241 | 3469 | 3394 |
| dutch | 374287 | 2153 | 234 | 3095 | 3075 |
| french | 258213 | 2167 | 241 | 2416 | 2426 |
| spanish | 220701 | 2110 | 233 | 2408 | 2385 |
| italian | 59623 | 2173 | 240 | 1248 | 1262 |
| portuguese | 37533 | 2116 | 236 | 826 | 871 |
| polish | 25043 | 2173 | 238 | 512 | 520 |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset.
## 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
Public Domain, Creative Commons Attribution 4.0 International Public License ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/legalcode))
### Citation Information
```
@article{Pratap2020MLSAL,
title={MLS: A Large-Scale Multilingual Dataset for Speech Research},
author={Vineel Pratap and Qiantong Xu and Anuroop Sriram and Gabriel Synnaeve and Ronan Collobert},
journal={ArXiv},
year={2020},
volume={abs/2012.03411}
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten)
and [@polinaeterna](https://github.com/polinaeterna) for adding this dataset.
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wiki_atomic_edits | null | 2023-06-01T14:59:54Z | 2,598 | 10 | wikiatomicedits | [
"task_categories:summarization",
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"region:us"
] | 2023-06-01T14:59:54Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
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paperswithcode_id: wikiatomicedits
pretty_name: WikiAtomicEdits
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---
# Dataset Card for WikiAtomicEdits
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** None
- **Repository:** https://github.com/google-research-datasets/wiki-atomic-edits
- **Paper:** https://www.aclweb.org/anthology/D18-1028/
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The languages in the dataset are:
- de
- en
- es
- fr
- it
- jp: Japanese (`ja`)
- ru
- zh
## Dataset Structure
### Data Instances
Here are some examples of questions and facts:
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. | [
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dlb/plue | dlb | 2022-10-29T12:19:26Z | 2,571 | 6 | null | [
"task_categories:text-classification",
"task_ids:acceptability-classification",
"task_ids:natural-language-inference",
"task_ids:semantic-similarity-scoring",
"task_ids:sentiment-classification",
"task_ids:text-scoring",
"annotations_creators:found",
"language_creators:machine-generated",
"multilinguality:monolingual",
"multilinguality:translation",
"size_categories:10K<n<100K",
"source_datasets:extended|glue",
"language:pt",
"license:lgpl-3.0",
"paraphrase-identification",
"qa-nli",
"coreference-nli",
"region:us"
] | 2022-10-29T12:19:26Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- machine-generated
language:
- pt
license:
- lgpl-3.0
multilinguality:
- monolingual
- translation
size_categories:
- 10K<n<100K
source_datasets:
- extended|glue
task_categories:
- text-classification
task_ids:
- acceptability-classification
- natural-language-inference
- semantic-similarity-scoring
- sentiment-classification
- text-scoring
pretty_name: PLUE (Portuguese Language Understanding Evaluation benchmark)
tags:
- paraphrase-identification
- qa-nli
- coreference-nli
---
# Dataset Card for PLUE
## 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/ju-resplande/PLUE
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Portuguese translation of the <a href="https://gluebenchmark.com/">GLUE benchmark</a>, <a href=https://nlp.stanford.edu/projects/snli/>SNLI</a>, and <a href=https://allenai.org/data/scitail> Scitail</a> using <a href=https://github.com/Helsinki-NLP/OPUS-MT>OPUS-MT model</a> and <a href=https://cloud.google.com/translate/docs>Google Cloud Translation</a>.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The language data in PLUE is Brazilian Portuguese (BCP-47 pt-BR)
## 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
```bibtex
@misc{Gomes2020,
author = {GOMES, J. R. S.},
title = {PLUE: Portuguese Language Understanding Evaluation},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/jubs12/PLUE}},
commit = {CURRENT_COMMIT}
}
```
### Contributions
Thanks to [@ju-resplande](https://github.com/ju-resplande) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Salesforce/dialogstudio | Salesforce | 2023-10-05T22:34:55Z | 2,567 | 155 | null | [
"task_categories:conversational",
"task_categories:question-answering",
"task_categories:summarization",
"task_categories:text-generation",
"language:en",
"license:apache-2.0",
"arxiv:2307.10172",
"region:us"
] | 2023-10-05T22:34:55Z | 2023-07-16T23:15:44.000Z | 2023-07-16T23:15:44 | ---
extra_gated_heading: "Acknowledge to follow corresponding dataset licenses to access the repository"
extra_gated_button_content: "Agree and access repository"
license: apache-2.0
task_categories:
- conversational
- question-answering
- summarization
- text-generation
language:
- en
pretty_name: Dialog Studio
---
<img src="https://huggingface.co/datasets/Salesforce/dialogstudio/resolve/main/logo.png"
alt="drawing" width="510"/>
# DialogStudio: Unified Dialog Datasets and Instruction-Aware Models for Conversational AI
[Paper](https://arxiv.org/abs/2307.10172)|[Github](https://github.com/salesforce/DialogStudio)|[GDrive]
<img src="https://huggingface.co/datasets/Salesforce/dialogstudio/resolve/main/DialogStudio_Stats.jpg"
alt="drawing" width="800"/>
**Follow the [DialogStudio](https://github.com/salesforce/DialogStudio) GitHub repository for latest information.**
### Datasets
### Load dataset
The datasets are split into several categories in HuggingFace
```
Datasets/
├── Knowledge-Grounded-Dialogues
├── Natural-Language-Understanding
├── Open-Domain-Dialogues
├── Task-Oriented-Dialogues
├── Dialogue-Summarization
├── Conversational-Recommendation-Dialogs
```
You can load any dataset in the DialogStudio from the [HuggingFace hub](https://huggingface.co/datasets/Salesforce/dialogstudio) by claiming the `{dataset_name}`, which is exactly the dataset folder name. All available datasets are described in [dataset content](https://github.com/salesforce/DialogStudio/blob/main/Dataset_Stats.csv). For easier reference, [available dataset names](#Available Datasets) are also listed below.
Below is one example to load the [MULTIWOZ2_2](https://huggingface.co/datasets/Salesforce/dialogstudio/blob/main/task_oriented/MULTIWOZ2_2.zip) dataset under the [task-oriented-dialogues](https://huggingface.co/datasets/Salesforce/dialogstudio/tree/main/task_oriented) category:
Load the dataset
```python
from datasets import load_dataset
dataset = load_dataset('Salesforce/dialogstudio', 'MULTIWOZ2_2')
```
Here is the output structure of MultiWOZ 2.2
```python
DatasetDict({
train: Dataset({
features: ['original dialog id', 'new dialog id', 'dialog index', 'original dialog info', 'log', 'prompt', 'external knowledge non-flat', 'external knowledge', 'dst knowledge', 'intent knowledge'],
num_rows: 8437
})
validation: Dataset({
features: ['original dialog id', 'new dialog id', 'dialog index', 'original dialog info', 'log', 'prompt', 'external knowledge non-flat', 'external knowledge', 'dst knowledge', 'intent knowledge'],
num_rows: 1000
})
test: Dataset({
features: ['original dialog id', 'new dialog id', 'dialog index', 'original dialog info', 'log', 'prompt', 'external knowledge non-flat', 'external knowledge', 'dst knowledge', 'intent knowledge'],
num_rows: 1000
})
})
```
### Available Datasets
The ``data_name`` for ``load_dataset("Salesforce/dialogstudio", data_name)`` can be found below. More detailed information for each dataset can be found in out [github](https://github.com/salesforce/DialogStudio/blob/main/Dataset_Stats.csv).
```python
"natural_language_understanding": [
"ATIS", "ATIS-NER", "BANKING77", "BANKING77-OOS", "CLINC-Single-Domain-OOS-banking",
"CLINC-Single-Domain-OOS-credit_cards", "CLINC150", "DSTC8-SGD", "HWU64", "MIT-Movie",
"MIT-Restaurant", "RESTAURANTS8K", "SNIPS", "SNIPS-NER", "TOP", "TOP-NER"
],
"task_oriented": [
"ABCD", "AirDialogue", "BiTOD", "CaSiNo", "CraigslistBargains",
"Disambiguation", "DSTC2-Clean", "FRAMES", "GECOR", "HDSA-Dialog",
"KETOD", "KVRET", "MetaLWOZ", "MS-DC", "MuDoCo",
"MulDoGO", "MultiWOZ_2.1", "MULTIWOZ2_2", "SGD", "SimJointGEN",
"SimJointMovie", "SimJointRestaurant", "STAR", "Taskmaster1", "Taskmaster2",
"Taskmaster3", "WOZ2_0"
],
"dialogue_summarization": [
"AMI", "CRD3", "DialogSum", "ECTSum", "ICSI",
"MediaSum", "QMSum", "SAMSum", "TweetSumm", "ConvoSumm",
"SummScreen_ForeverDreaming", "SummScreen_TVMegaSite"
],
"conversational_recommendation": [
"Redial", "DuRecDial-2.0", "OpenDialKG", "SalesBot",
],
"open_domain": [
"chitchat-dataset", "ConvAI2", "AntiScam", "Empathetic", "HH-RLHF",
"PLACES3.5", "Prosocial", "SODA", "ShareGPT"
],
"knowledge_grounded": [
"CompWebQ", "CoQA", "CoSQL", "DART", "FeTaQA",
"GrailQA", "HybridQA", "MTOP", "MultiModalQA", "SParC",
"Spider", "SQA", "ToTTo", "WebQSP", "WikiSQL",
"WikiTQ", "wizard_of_internet", "wizard_of_wikipedia"
],
```
# License
Our project follows the following structure with respect to licensing:
1. For all the modified datasets in DialogStudio:
- A portion of these datasets is under the [Apache License 2.0](https://github.com/salesforce/DialogStudio/blob/main/LICENSE.txt).
- Some retain their original licenses even after modification.
- For a few datasets that lacked a license, we have cited the relevant papers.
2. Original dataset licenses: For reference, we also put the original avaliable licenses for each dataset into their respective dataset folders.
3. Code: Our codebase is under the [Apache License 2.0](https://github.com/salesforce/DialogStudio/blob/main/LICENSE.txt).
For detailed licensing information, please refer to the specific licenses accompanying the datasets. If you utilize datasets from DialogStudio, we kindly request that you cite our work.
# Citation
The data and code in this repository is mostly developed for or derived from the paper below. If you utilize datasets from DialogStudio, we kindly request that you cite both the original work and our own.
```
@misc{zhang2023dialogstudio,
title={DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI},
author={Jianguo Zhang and Kun Qian and Zhiwei Liu and Shelby Heinecke and Rui Meng and Ye Liu and Zhou Yu and and Huan Wang and Silvio Savarese and Caiming Xiong},
year={2023},
eprint={2307.10172},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
derek-thomas/ScienceQA | derek-thomas | 2023-02-25T04:23:01Z | 2,559 | 80 | scienceqa | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:other",
"task_categories:visual-question-answering",
"task_categories:text-classification",
"task_ids:multiple-choice-qa",
"task_ids:closed-domain-qa",
"task_ids:open-domain-qa",
"task_ids:visual-question-answering",
"task_ids:multi-class-classification",
"annotations_creators:expert-generated",
"annotations_creators:found",
"language_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"multi-modal-qa",
"science",
"chemistry",
"biology",
"physics",
"earth-science",
"engineering",
"geography",
"history",
"world-history",
"civics",
"economics",
"global-studies",
"grammar",
"writing",
"vocabulary",
"natural-science",
"language-science",
"social-science",
"arxiv:2209.09513",
"region:us"
] | 2023-02-25T04:23:01Z | 2023-02-10T11:28:58.000Z | 2023-02-10T11:28:58 | ---
license: cc-by-sa-4.0
annotations_creators:
- expert-generated
- found
language:
- en
language_creators:
- expert-generated
- found
multilinguality:
- monolingual
paperswithcode_id: scienceqa
pretty_name: ScienceQA
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- multi-modal-qa
- science
- chemistry
- biology
- physics
- earth-science
- engineering
- geography
- history
- world-history
- civics
- economics
- global-studies
- grammar
- writing
- vocabulary
- natural-science
- language-science
- social-science
task_categories:
- multiple-choice
- question-answering
- other
- visual-question-answering
- text-classification
task_ids:
- multiple-choice-qa
- closed-domain-qa
- open-domain-qa
- visual-question-answering
- multi-class-classification
dataset_info:
features:
- name: image
dtype: image
- name: question
dtype: string
- name: choices
sequence: string
- name: answer
dtype: int8
- name: hint
dtype: string
- name: task
dtype: string
- name: grade
dtype: string
- name: subject
dtype: string
- name: topic
dtype: string
- name: category
dtype: string
- name: skill
dtype: string
- name: lecture
dtype: string
- name: solution
dtype: string
splits:
- name: train
num_bytes: 16416902
num_examples: 12726
- name: validation
num_bytes: 5404896
num_examples: 4241
- name: test
num_bytes: 5441676
num_examples: 4241
download_size: 0
dataset_size: 27263474
---
# Dataset Card Creation Guide
## Table of Contents
- [Dataset Card Creation Guide](#dataset-card-creation-guide)
- [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)
- [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://scienceqa.github.io/index.html#home](https://scienceqa.github.io/index.html#home)
- **Repository:** [https://github.com/lupantech/ScienceQA](https://github.com/lupantech/ScienceQA)
- **Paper:** [https://arxiv.org/abs/2209.09513](https://arxiv.org/abs/2209.09513)
- **Leaderboard:** [https://paperswithcode.com/dataset/scienceqa](https://paperswithcode.com/dataset/scienceqa)
- **Point of Contact:** [Pan Lu](https://lupantech.github.io/) or file an issue on [Github](https://github.com/lupantech/ScienceQA/issues)
### Dataset Summary
Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering
### Supported Tasks and Leaderboards
Multi-modal Multiple Choice
### Languages
English
## Dataset Structure
### Data Instances
Explore more samples [here](https://scienceqa.github.io/explore.html).
``` json
{'image': Image,
'question': 'Which of these states is farthest north?',
'choices': ['West Virginia', 'Louisiana', 'Arizona', 'Oklahoma'],
'answer': 0,
'hint': '',
'task': 'closed choice',
'grade': 'grade2',
'subject': 'social science',
'topic': 'geography',
'category': 'Geography',
'skill': 'Read a map: cardinal directions',
'lecture': 'Maps have four cardinal directions, or main directions. Those directions are north, south, east, and west.\nA compass rose is a set of arrows that point to the cardinal directions. A compass rose usually shows only the first letter of each cardinal direction.\nThe north arrow points to the North Pole. On most maps, north is at the top of the map.',
'solution': 'To find the answer, look at the compass rose. Look at which way the north arrow is pointing. West Virginia is farthest north.'}
```
Some records might be missing any or all of image, lecture, solution.
### Data Fields
- `image` : Contextual image
- `question` : Prompt relating to the `lecture`
- `choices` : Multiple choice answer with 1 correct to the `question`
- `answer` : Index of choices corresponding to the correct answer
- `hint` : Hint to help answer the `question`
- `task` : Task description
- `grade` : Grade level from K-12
- `subject` : High level
- `topic` : natural-sciences, social-science, or language-science
- `category` : A subcategory of `topic`
- `skill` : A description of the task required
- `lecture` : A relevant lecture that a `question` is generated from
- `solution` : Instructions on how to solve the `question`
Note that the descriptions can be initialized with the **Show Markdown Data Fields** output of the [Datasets Tagging app](https://huggingface.co/spaces/huggingface/datasets-tagging), you will then only need to refine the generated descriptions.
### Data Splits
- name: train
- num_bytes: 16416902
- num_examples: 12726
- name: validation
- num_bytes: 5404896
- num_examples: 4241
- name: test
- num_bytes: 5441676
- num_examples: 4241
## Dataset Creation
### Curation Rationale
When answering a question, humans utilize the information available across different modalities to synthesize a consistent and complete chain of thought (CoT). This process is normally a black box in the case of deep learning models like large-scale language models. Recently, science question benchmarks have been used to diagnose the multi-hop reasoning ability and interpretability of an AI system. However, existing datasets fail to provide annotations for the answers, or are restricted to the textual-only modality, small scales, and limited domain diversity. To this end, we present Science Question Answering (ScienceQA).
### Source Data
ScienceQA is collected from elementary and high school science curricula.
#### Initial Data Collection and Normalization
See Below
#### Who are the source language producers?
See Below
### Annotations
Questions in the ScienceQA dataset are sourced from open resources managed by IXL Learning,
an online learning platform curated by experts in the field of K-12 education. The dataset includes
problems that align with California Common Core Content Standards. To construct ScienceQA, we
downloaded the original science problems and then extracted individual components (e.g. questions,
hints, images, options, answers, lectures, and solutions) from them based on heuristic rules.
We manually removed invalid questions, such as questions that have only one choice, questions that
contain faulty data, and questions that are duplicated, to comply with fair use and transformative
use of the law. If there were multiple correct answers that applied, we kept only one correct answer.
Also, we shuffled the answer options of each question to ensure the choices do not follow any
specific pattern. To make the dataset easy to use, we then used semi-automated scripts to reformat
the lectures and solutions. Therefore, special structures in the texts, such as tables and lists, are
easily distinguishable from simple text passages. Similar to ImageNet, ReClor, and PMR datasets,
ScienceQA is available for non-commercial research purposes only and the copyright belongs to
the original authors. To ensure data quality, we developed a data exploration tool to review examples
in the collected dataset, and incorrect annotations were further manually revised by experts. The tool
can be accessed at https://scienceqa.github.io/explore.html.
#### Annotation process
See above
#### Who are the annotators?
See above
### 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
- Pan Lu1,3
- Swaroop Mishra2,3
- Tony Xia1
- Liang Qiu1
- Kai-Wei Chang1
- Song-Chun Zhu1
- Oyvind Tafjord3
- Peter Clark3
- Ashwin Kalyan3
From:
1. University of California, Los Angeles
2. Arizona State University
3. Allen Institute for AI
### Licensing Information
[Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
](https://creativecommons.org/licenses/by-nc-sa/4.0/)
### Citation Information
Provide the [BibTex](http://www.bibtex.org/)-formatted reference for the dataset. For example:
```
@inproceedings{lu2022learn,
title={Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering},
author={Lu, Pan and Mishra, Swaroop and Xia, Tony and Qiu, Liang and Chang, Kai-Wei and Zhu, Song-Chun and Tafjord, Oyvind and Clark, Peter and Ashwin Kalyan},
booktitle={The 36th Conference on Neural Information Processing Systems (NeurIPS)},
year={2022}
}
```
### Contributions
Thanks to [Derek Thomas](https://huggingface.co/derek-thomas) [@datavistics](https://github.com/datavistics) for adding this dataset. | [
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kde4 | null | 2022-11-03T16:32:20Z | 2,532 | 12 | null | [
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] | 2022-11-03T16:32:20Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
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- ar
- as
- ast
- be
- bg
- bn
- br
- ca
- crh
- cs
- csb
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- es
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- fr
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- tg
- th
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- uk
- uz
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- wa
- xh
- zh
language_bcp47:
- bn-IN
- en-GB
- pt-BR
- zh-CN
- zh-HK
- zh-TW
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: KDE4
dataset_info:
- config_name: fi-nl
features:
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dtype: string
- name: translation
dtype:
translation:
languages:
- fi
- nl
splits:
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num_bytes: 8845933
num_examples: 101593
download_size: 2471355
dataset_size: 8845933
- config_name: it-ro
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- it
- ro
splits:
- name: train
num_bytes: 8827049
num_examples: 109003
download_size: 2389051
dataset_size: 8827049
- config_name: nl-sv
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
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- sv
splits:
- name: train
num_bytes: 22294586
num_examples: 188454
download_size: 6203460
dataset_size: 22294586
- config_name: en-it
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- en
- it
splits:
- name: train
num_bytes: 27132585
num_examples: 220566
download_size: 7622662
dataset_size: 27132585
- config_name: en-fr
features:
- name: id
dtype: string
- name: translation
dtype:
translation:
languages:
- en
- fr
splits:
- name: train
num_bytes: 25650409
num_examples: 210173
download_size: 7049364
dataset_size: 25650409
---
# Dataset Card for KDE4
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://opus.nlpl.eu/KDE4.php
- **Repository:** None
- **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs.
You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/KDE4.php
E.g.
`dataset = load_dataset("kde4", lang1="en", lang2="nl")`
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
deepset/prompt-injections | deepset | 2023-07-31T15:04:06Z | 2,532 | 18 | null | [
"region:us"
] | 2023-07-31T15:04:06Z | 2023-05-17T13:55:19.000Z | 2023-05-17T13:55:19 | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 71720
num_examples: 546
- name: test
num_bytes: 15981
num_examples: 116
download_size: 51215
dataset_size: 87701
license: cc-by-4.0
---
# Dataset Card for "deberta-v3-base-injection-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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quoref | null | 2023-04-05T13:37:27Z | 2,525 | 4 | quoref | [
"task_categories:question-answering",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"coreference-resolution",
"region:us"
] | 2023-04-05T13:37:27Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Quoref
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids: []
paperswithcode_id: quoref
tags:
- coreference-resolution
dataset_info:
features:
- name: id
dtype: string
- name: question
dtype: string
- name: context
dtype: string
- name: title
dtype: string
- name: url
dtype: string
- name: answers
sequence:
- name: answer_start
dtype: int32
- name: text
dtype: string
splits:
- name: train
num_bytes: 44377729
num_examples: 19399
- name: validation
num_bytes: 5442031
num_examples: 2418
download_size: 5078438
dataset_size: 49819760
---
# Dataset Card for "quoref"
## 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://allenai.org/data/quoref
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning](https://aclanthology.org/D19-1606/)
- **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:** 5.08 MB
- **Size of the generated dataset:** 49.82 MB
- **Total amount of disk used:** 54.90 MB
### Dataset Summary
Quoref is a QA dataset which tests the coreferential reasoning capability of reading comprehension systems. In this
span-selection benchmark containing 24K questions over 4.7K paragraphs from Wikipedia, a system must resolve hard
coreferences before selecting the appropriate span(s) in the paragraphs for answering questions.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 5.08 MB
- **Size of the generated dataset:** 49.82 MB
- **Total amount of disk used:** 54.90 MB
An example of 'validation' looks as follows.
```
This example was too long and was cropped:
{
"answers": {
"answer_start": [1633],
"text": ["Frankie"]
},
"context": "\"Frankie Bono, a mentally disturbed hitman from Cleveland, comes back to his hometown in New York City during Christmas week to ...",
"id": "bfc3b34d6b7e73c0bd82a009db12e9ce196b53e6",
"question": "What is the first name of the person who has until New Year's Eve to perform a hit?",
"title": "Blast of Silence",
"url": "https://en.wikipedia.org/wiki/Blast_of_Silence"
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `id`: a `string` feature.
- `question`: a `string` feature.
- `context`: a `string` feature.
- `title`: a `string` feature.
- `url`: a `string` feature.
- `answers`: a dictionary feature containing:
- `answer_start`: a `int32` feature.
- `text`: a `string` feature.
### Data Splits
| name |train|validation|
|-------|----:|---------:|
|default|19399| 2418|
## 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
```
@article{allenai:quoref,
author = {Pradeep Dasigi and Nelson F. Liu and Ana Marasovic and Noah A. Smith and Matt Gardner},
title = {Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning},
journal = {arXiv:1908.05803v2 },
year = {2019},
}
```
### Contributions
Thanks to [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset. | [
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ade_corpus_v2 | null | 2023-06-01T14:59:53Z | 2,523 | 20 | null | [
"task_categories:text-classification",
"task_categories:token-classification",
"task_ids:coreference-resolution",
"task_ids:fact-checking",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | 2023-06-01T14:59:53Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
- 1K<n<10K
- n<1K
source_datasets:
- original
task_categories:
- text-classification
- token-classification
task_ids:
- coreference-resolution
- fact-checking
pretty_name: Adverse Drug Reaction Data v2
dataset_info:
- config_name: Ade_corpus_v2_classification
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': Not-Related
'1': Related
splits:
- name: train
num_bytes: 3403711
num_examples: 23516
download_size: 3791162
dataset_size: 3403711
- config_name: Ade_corpus_v2_drug_ade_relation
features:
- name: text
dtype: string
- name: drug
dtype: string
- name: effect
dtype: string
- name: indexes
struct:
- name: drug
sequence:
- name: start_char
dtype: int32
- name: end_char
dtype: int32
- name: effect
sequence:
- name: start_char
dtype: int32
- name: end_char
dtype: int32
splits:
- name: train
num_bytes: 1546021
num_examples: 6821
download_size: 3791162
dataset_size: 1546021
- config_name: Ade_corpus_v2_drug_dosage_relation
features:
- name: text
dtype: string
- name: drug
dtype: string
- name: dosage
dtype: string
- name: indexes
struct:
- name: drug
sequence:
- name: start_char
dtype: int32
- name: end_char
dtype: int32
- name: dosage
sequence:
- name: start_char
dtype: int32
- name: end_char
dtype: int32
splits:
- name: train
num_bytes: 64725
num_examples: 279
download_size: 3791162
dataset_size: 64725
train-eval-index:
- config: Ade_corpus_v2_classification
task: text-classification
task_id: multi_class_classification
splits:
train_split: train
col_mapping:
text: text
label: target
metrics:
- type: accuracy
name: Accuracy
- type: f1
name: F1 macro
args:
average: macro
- type: f1
name: F1 micro
args:
average: micro
- type: f1
name: F1 weighted
args:
average: weighted
- type: precision
name: Precision macro
args:
average: macro
- type: precision
name: Precision micro
args:
average: micro
- type: precision
name: Precision weighted
args:
average: weighted
- type: recall
name: Recall macro
args:
average: macro
- type: recall
name: Recall micro
args:
average: micro
- type: recall
name: Recall weighted
args:
average: weighted
config_names:
- Ade_corpus_v2_classification
- Ade_corpus_v2_drug_ade_relation
- Ade_corpus_v2_drug_dosage_relation
---
# Dataset Card for Adverse Drug Reaction Data v2
## 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.sciencedirect.com/science/article/pii/S1532046412000615
- **Repository:** [Needs More Information]
- **Paper:** https://www.sciencedirect.com/science/article/pii/S1532046412000615
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
ADE-Corpus-V2 Dataset: Adverse Drug Reaction Data.
This is a dataset for Classification if a sentence is ADE-related (True) or not (False) and Relation Extraction between Adverse Drug Event and Drug.
DRUG-AE.rel provides relations between drugs and adverse effects.
DRUG-DOSE.rel provides relations between drugs and dosages.
ADE-NEG.txt provides all sentences in the ADE corpus that DO NOT contain any drug-related adverse effects.
### Supported Tasks and Leaderboards
Sentiment classification, Relation Extraction
### Languages
English
## Dataset Structure
### Data Instances
#### Config - `Ade_corpus_v2_classification`
```
{
'label': 1,
'text': 'Intravenous azithromycin-induced ototoxicity.'
}
```
#### Config - `Ade_corpus_v2_drug_ade_relation`
```
{
'drug': 'azithromycin',
'effect': 'ototoxicity',
'indexes': {
'drug': {
'end_char': [24],
'start_char': [12]
},
'effect': {
'end_char': [44],
'start_char': [33]
}
},
'text': 'Intravenous azithromycin-induced ototoxicity.'
}
```
#### Config - `Ade_corpus_v2_drug_dosage_relation`
```
{
'dosage': '4 times per day',
'drug': 'insulin',
'indexes': {
'dosage': {
'end_char': [56],
'start_char': [41]
},
'drug': {
'end_char': [40],
'start_char': [33]}
},
'text': 'She continued to receive regular insulin 4 times per day over the following 3 years with only occasional hives.'
}
```
### Data Fields
#### Config - `Ade_corpus_v2_classification`
- `text` - Input text.
- `label` - Whether the adverse drug effect(ADE) related (1) or not (0).
-
#### Config - `Ade_corpus_v2_drug_ade_relation`
- `text` - Input text.
- `drug` - Name of drug.
- `effect` - Effect caused by the drug.
- `indexes.drug.start_char` - Start index of `drug` string in text.
- `indexes.drug.end_char` - End index of `drug` string in text.
- `indexes.effect.start_char` - Start index of `effect` string in text.
- `indexes.effect.end_char` - End index of `effect` string in text.
#### Config - `Ade_corpus_v2_drug_dosage_relation`
- `text` - Input text.
- `drug` - Name of drug.
- `dosage` - Dosage of the drug.
- `indexes.drug.start_char` - Start index of `drug` string in text.
- `indexes.drug.end_char` - End index of `drug` string in text.
- `indexes.dosage.start_char` - Start index of `dosage` string in text.
- `indexes.dosage.end_char` - End index of `dosage` string in text.
### Data Splits
| Train |
| ------ |
| 23516 |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
```
@article{GURULINGAPPA2012885,
title = "Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports",
journal = "Journal of Biomedical Informatics",
volume = "45",
number = "5",
pages = "885 - 892",
year = "2012",
note = "Text Mining and Natural Language Processing in Pharmacogenomics",
issn = "1532-0464",
doi = "https://doi.org/10.1016/j.jbi.2012.04.008",
url = "http://www.sciencedirect.com/science/article/pii/S1532046412000615",
author = "Harsha Gurulingappa and Abdul Mateen Rajput and Angus Roberts and Juliane Fluck and Martin Hofmann-Apitius and Luca Toldo",
keywords = "Adverse drug effect, Benchmark corpus, Annotation, Harmonization, Sentence classification",
abstract = "A significant amount of information about drug-related safety issues such as adverse effects are published in medical case reports that can only be explored by human readers due to their unstructured nature. The work presented here aims at generating a systematically annotated corpus that can support the development and validation of methods for the automatic extraction of drug-related adverse effects from medical case reports. The documents are systematically double annotated in various rounds to ensure consistent annotations. The annotated documents are finally harmonized to generate representative consensus annotations. In order to demonstrate an example use case scenario, the corpus was employed to train and validate models for the classification of informative against the non-informative sentences. A Maximum Entropy classifier trained with simple features and evaluated by 10-fold cross-validation resulted in the F1 score of 0.70 indicating a potential useful application of the corpus."
}
```
### Contributions
Thanks to [@Nilanshrajput](https://github.com/Nilanshrajput), [@lhoestq](https://github.com/lhoestq) for adding this dataset. | [
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skrishna/filtered_toxic_samples | skrishna | 2023-10-17T20:41:56Z | 2,521 | 0 | null | [
"license:mit",
"region:us"
] | 2023-10-17T20:41:56Z | 2023-10-17T14:57:58.000Z | 2023-10-17T14:57:58 | ---
license: mit
---
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imppres | null | 2023-01-25T14:32:53Z | 2,497 | 0 | imppres | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
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"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-4.0",
"region:us"
] | 2023-01-25T14:32:53Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
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language_creators:
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language:
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license:
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multilinguality:
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size_categories:
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source_datasets:
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task_categories:
- text-classification
task_ids:
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paperswithcode_id: imppres
pretty_name: IMPPRES
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names:
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dtype:
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names:
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- name: spec_relation
dtype: string
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dtype: string
- name: trigger
dtype: string
- name: lexemes
dtype: string
splits:
- name: numerals_10_100
num_bytes: 208620
num_examples: 1200
download_size: 335088
dataset_size: 208620
- config_name: implicature_numerals_2_3
features:
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dtype: string
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names:
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dtype: string
- name: lexemes
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splits:
- name: numerals_2_3
num_bytes: 188784
num_examples: 1200
download_size: 335088
dataset_size: 188784
- config_name: implicature_quantifiers
features:
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dtype: string
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splits:
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num_bytes: 176814
num_examples: 1200
download_size: 335088
dataset_size: 176814
---
# Dataset Card for IMPPRES
## 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/facebookresearch/Imppres)
- **Repository:** [Github](https://github.com/facebookresearch/Imppres)
- **Paper:** [Aclweb](https://www.aclweb.org/anthology/2020.acl-main.768)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
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.
### Supported Tasks and Leaderboards
Natural Language Inference.
### Languages
English.
## Dataset Structure
### Data Instances
The data consists of 2 configurations: implicature and presupposition.
Each configuration consists of several different sub-datasets:
**Pressupposition**
- all_n_presupposition
- change_of_state
- cleft_uniqueness
- possessed_definites_existence
- question_presupposition
- both_presupposition
- cleft_existence
- only_presupposition
- possessed_definites_uniqueness
**Implicature**
- connectives
- gradable_adjective
- gradable_verb
- modals
- numerals_10_100
- numerals_2_3
- quantifiers
Each sentence type in IMPPRES is generated according to a template that specifies the linear order of the constituents in the sentence. The constituents are sampled from a vocabulary of over 3000 lexical items annotated with grammatical features needed to ensure wellformedness. We semiautomatically generate IMPPRES using a codebase developed by Warstadt et al. (2019a) and significantly expanded for the BLiMP dataset (Warstadt et al., 2019b).
Here is an instance of the raw presupposition data from any sub-dataset:
```buildoutcfg
{
"sentence1": "All ten guys that proved to boast might have been divorcing.",
"sentence2": "There are exactly ten guys that proved to boast.",
"trigger": "modal",
"presupposition": "positive",
"gold_label": "entailment",
"UID": "all_n_presupposition",
"pairID": "9e",
"paradigmID": 0
}
```
and the raw implicature data from any sub-dataset:
```buildoutcfg
{
"sentence1": "That teenager couldn't yell.",
"sentence2": "That teenager could yell.",
"gold_label_log": "contradiction",
"gold_label_prag": "contradiction",
"spec_relation": "negation",
"item_type": "control",
"trigger": "modal",
"lexemes": "can - have to"
}
```
### Data Fields
**Presupposition**
There is a slight mapping from the raw data fields in the presupposition sub-datasets and the fields appearing in the HuggingFace Datasets.
When dealing with the HF Dataset, the following mapping of fields happens:
```buildoutcfg
"premise" -> "sentence1"
"hypothesis"-> "sentence2"
"trigger" -> "trigger" or "Not_In_Example"
"trigger1" -> "trigger1" or "Not_In_Example"
"trigger2" -> "trigger2" or "Not_In_Example"
"presupposition" -> "presupposition" or "Not_In_Example"
"gold_label" -> "gold_label"
"UID" -> "UID"
"pairID" -> "pairID"
"paradigmID" -> "paradigmID"
```
For the most part, the majority of the raw fields remain unchanged. However, when it comes to the various `trigger` fields, a new mapping was introduced.
There are some examples in the dataset that only have the `trigger` field while other examples have the `trigger1` and `trigger2` field without the `trigger` or `presupposition` field.
Nominally, most examples look like the example in the Data Instances section above. Occassionally, however, some examples will look like:
```buildoutcfg
{
'sentence1': 'Did that committee know when Lissa walked through the cafe?',
'sentence2': 'That committee knew when Lissa walked through the cafe.',
'trigger1': 'interrogative',
'trigger2': 'unembedded',
'gold_label': 'neutral',
'control_item': True,
'UID': 'question_presupposition',
'pairID': '1821n',
'paradigmID': 95
}
```
In this example, `trigger1` and `trigger2` appear and `presupposition` and `trigger` are removed. This maintains the length of the dictionary.
To account for these examples, we have thus introduced the mapping above such that all examples accessed through the HF Datasets interface will have the same size as well as the same fields.
In the event that an example does not have a value for one of the fields, the field is maintained in the dictionary but given a value of `Not_In_Example`.
To illustrate this point, the example given in the Data Instances section above would look like the following in the HF Datasets:
```buildoutcfg
{
"premise": "All ten guys that proved to boast might have been divorcing.",
"hypothesis": "There are exactly ten guys that proved to boast.",
"trigger": "modal",
"trigger1": "Not_In_Example",
"trigger2": "Not_In_Example"
"presupposition": "positive",
"gold_label": "entailment",
"UID": "all_n_presupposition",
"pairID": "9e",
"paradigmID": 0
}
```
Below is description of the fields:
```buildoutcfg
"premise": The premise.
"hypothesis": The hypothesis.
"trigger": A detailed discussion of trigger types appears in the paper.
"trigger1": A detailed discussion of trigger types appears in the paper.
"trigger2": A detailed discussion of trigger types appears in the paper.
"presupposition": positive or negative.
"gold_label": Corresponds to entailment, contradiction, or neutral.
"UID": Unique id.
"pairID": Sentence pair ID.
"paradigmID": ?
```
It is not immediately clear what the difference is between `trigger`, `trigger1`, and `trigger2` is or what the `paradigmID` refers to.
**Implicature**
The `implicature` fields only have the mapping below:
```buildoutcfg
"premise" -> "sentence1"
"hypothesis"-> "sentence2"
```
Here is a description of the fields:
```buildoutcfg
"premise": The premise.
"hypothesis": The hypothesis.
"gold_label_log": Gold label for a logical reading of the sentence pair.
"gold_label_prag": Gold label for a pragmatic reading of the sentence pair.
"spec_relation": ?
"item_type": ?
"trigger": A detailed discussion of trigger types appears in the paper.
"lexemes": ?
```
### Data Splits
As the dataset was created to test already trained models, the only split that exists is for testing.
## Dataset Creation
### Curation Rationale
IMPPRES was created to evaluate how well trained NLI models recognize several classes of presuppositions and scalar implicatures.
### 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?
The annotations were generated semi-automatically.
### 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
IMPPRES is available under a Creative Commons Attribution-NonCommercial 4.0 International Public License ("The License"). You may not use these files except in compliance with the License. Please see the LICENSE file for more information before you use the dataset.
### Citation Information
```buildoutcfg
@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.",
}
```
### Contributions
Thanks to [@aclifton314](https://github.com/aclifton314) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
SetFit/stsb | SetFit | 2022-02-28T14:20:16Z | 2,496 | 1 | null | [
"region:us"
] | 2022-02-28T14:20:16Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | # Glue STS-B
This dataset is a port of the official [`sts-b` dataset](https://huggingface.co/datasets/glue/viewer/stsb/validation) on the Hub.
This is not a classification task, so the label_text column is only included for consistency
Note that the sentence1 and sentence2 columns have been renamed to text1 and text2 respectively.
Also, the test split is not labeled; the label column values are always -1.
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EleutherAI/the_pile_deduplicated | EleutherAI | 2022-12-02T23:49:09Z | 2,491 | 41 | null | [
"region:us"
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craigslist_bargains | null | 2022-11-18T19:47:08Z | 2,490 | 9 | craigslistbargains | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:dialogue-modeling",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"arxiv:1808.09637",
"region:us"
] | 2022-11-18T19:47:08Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- dialogue-modeling
paperswithcode_id: craigslistbargains
pretty_name: CraigslistBargains
dataset_info:
features:
- name: agent_info
sequence:
- name: Bottomline
dtype: string
- name: Role
dtype: string
- name: Target
dtype: float32
- name: agent_turn
sequence: int32
- name: dialogue_acts
sequence:
- name: intent
dtype: string
- name: price
dtype: float32
- name: utterance
sequence: string
- name: items
sequence:
- name: Category
dtype: string
- name: Images
dtype: string
- name: Price
dtype: float32
- name: Description
dtype: string
- name: Title
dtype: string
splits:
- name: train
num_bytes: 8538836
num_examples: 5247
- name: test
num_bytes: 1353933
num_examples: 838
- name: validation
num_bytes: 966032
num_examples: 597
download_size: 25373618
dataset_size: 10858801
---
# Dataset Card for CraigslistBargains
## 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:** [Decoupling Strategy and Generation in Negotiation Dialogues](https://worksheets.codalab.org/worksheets/0x453913e76b65495d8b9730d41c7e0a0c/)
- **Repository:** [Github: Stanford NLP Cocoa](https://github.com/stanfordnlp/cocoa/tree/master)
- **Paper:** [Decoupling Strategy and Generation in Negotiation Dialogues](https://arxiv.org/abs/1808.09637)
- **Leaderboard:** []()
- **Point of Contact:** [He He](hehe@cs.nyu.edu)
### Dataset Summary
We study negotiation dialogues where two agents, a buyer and a seller, negotiate over the price of an time for sale. We collected a dataset of more than 6K negotiation dialogues over multiple categories of products scraped from Craigslist. Our goal is to develop an agent that negotiates with humans through such conversations. The challenge is to handle both the negotiation strategy and the rich language for bargaining. To this end, we develop a modular framework which separates strategy learning from language generation. Specifically, we learn strategies in a coarse dialogue act space and instantiate that into utterances conditioned on dialogue history.
### Supported Tasks and Leaderboards
### Languages
This dataset is English
## Dataset Structure
### Data Instances
```
{
'agent_info': {
'Bottomline':
[
'None',
'None'
],
'Role':
[
'buyer',
'seller'
],
'Target':
[
7.0,
10.0
]
},
'agent_turn':
[
0,
1,
...
],
'dialogue_acts': {
'intent':
[
'init-price',
'unknown',
...
],
'price':
[
5.0,
-1.0,
...
]
},
'items': {
'Category':
[
'phone',
'phone'
],
'Description':
[
'Charge two devices simultaneously on the go...,
...
],
'Images':
[
'phone/6149527852_0.jpg',
'phone/6149527852_0.jpg'
],
'Price':
[
10.0,
10.0
],
'Title':
[
'Verizon Car Charger with Dual Output Micro USB and ...',
...
]
},
'utterance':
[
'Hi, not sure if the charger would work for my car...'
'It will work...',
...
]
}
```
### Data Fields
- `agent_info`: Information about each of the agents taking part in the dialogue
- `Bottomline`: TBD
- `Role`: Whether the agent is buyer or seller
- `Target`: Target price that the buyer/seller wants to hit in the negotiation
- `agent_turn`: Agent taking the current turn in the dialogue (`int` index corresponding to `Role` above)
- `dialogue_acts`: Rules-based information about the strategy of each agent for each turn
- `intent`: The intent of the agent at the particular turn (offer, accept, etc.)
- `price`: The current item price associated with the intent and turn in the bargaining process. Default value for missing: (`-1`)
- `items`: Information about the item the agents are bargaining for. **Note that there is an elembet for each of the fields below for each agent**
- `Category`: Category of the item
- `Description`: Description(s) of the item
- `Images`: (comma delimited) strings of image names of the item
- `Price`: Price(s) of the item. Default value for missing: (`-1`)
- `Title`: Title(s) of the item
- `utterance`: Utterance for each turn in the dialogue, corresponding to the agent in `agent_turns`. The utterance may be an empty string (`''`) for some turns if multiple dialogue acts take place after an utterance (e.g. there are often multiple dialogue acts associated with the closing of the bargaining process after all utterances have completed to describe the conclusion of the bargaining).
### Data Splits
This dataset contains three splits, `train`, `validation` and `test`. Note that `test` is not provided with `dialogue_acts` information as described above. To ensure schema consistency across dataset splits, the `dialogue_acts` field in the `test` split is populated with the default values: `{"price": -1.0, "intent": ""}`
The counts of examples in each split are as follows:
| | Train | Valid | Test |
| Input Examples | 5247 | 597 | 838 |
| Average Dialogue Length | 9.14 | 9.17 | 9.24 |
Note that
## Dataset Creation
From the [source paper](https://arxiv.org/pdf/1808.09637.pdf) for this dataset:
> To generate the negotiation scenarios, we
> scraped postings on sfbay.craigslist.org
> from the 6 most popular categories (housing, furniture, cars, bikes, phones, and electronics). Each
> posting produces three scenarios with the buyer’s
> target prices at 0.5x, 0.7x and 0.9x of the listing
> price. Statistics of the scenarios are shown in Table 2.
> We collected 6682 human-human dialogues on
> AMT using the interface shown in Appendix A
> Figure 2. The dataset statistics in Table 3 show
> that CRAIGSLISTBARGAIN has longer dialogues
> and more diverse utterances compared to prior
> datasets. Furthermore, workers were encouraged
> to embellish the item and negotiate side offers
> such as free delivery or pick-up. This highly relatable scenario leads to richer dialogues such as
> the one shown in Table 1. We also observed various persuasion techniques listed in Table 4 such as
> embellishment,
### Curation Rationale
See **Dataset Creation**
### Source Data
See **Dataset Creation**
#### Initial Data Collection and Normalization
See **Dataset Creation**
#### Who are the source language producers?
See **Dataset Creation**
### Annotations
If the dataset contains annotations which are not part of the initial data collection, describe them in the following paragraphs.
#### Annotation process
Annotations for the `dialogue_acts` in `train` and `test` were generated via a rules-based system which can be found in [this script](https://github.com/stanfordnlp/cocoa/blob/master/craigslistbargain/parse_dialogue.py)
#### 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
[More Information Needed]
### Dataset Curators
He He and Derek Chen and Anusha Balakrishnan and Percy Liang
Computer Science Department, Stanford University
`{hehe,derekchen14,anusha,pliang}@cs.stanford.edu`
The work through which this data was produced was supported by
DARPA Communicating with Computers (CwC)
program under ARO prime contract no. W911NF15-1-0462
### Licensing Information
[More Information Needed]
### Citation Information
```
@misc{he2018decoupling,
title={Decoupling Strategy and Generation in Negotiation Dialogues},
author={He He and Derek Chen and Anusha Balakrishnan and Percy Liang},
year={2018},
eprint={1808.09637},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@ZacharySBrown](https://github.com/ZacharySBrown) for adding this dataset. | [
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jigsaw_toxicity_pred | null | 2023-01-25T14:33:17Z | 2,477 | 16 | null | [
"task_categories:text-classification",
"task_ids:multi-label-classification",
"annotations_creators:crowdsourced",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc0-1.0",
"region:us"
] | 2023-01-25T14:33:17Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- other
language:
- en
license:
- cc0-1.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-label-classification
pretty_name: JigsawToxicityPred
dataset_info:
features:
- name: comment_text
dtype: string
- name: toxic
dtype:
class_label:
names:
'0': 'false'
'1': 'true'
- name: severe_toxic
dtype:
class_label:
names:
'0': 'false'
'1': 'true'
- name: obscene
dtype:
class_label:
names:
'0': 'false'
'1': 'true'
- name: threat
dtype:
class_label:
names:
'0': 'false'
'1': 'true'
- name: insult
dtype:
class_label:
names:
'0': 'false'
'1': 'true'
- name: identity_hate
dtype:
class_label:
names:
'0': 'false'
'1': 'true'
splits:
- name: train
num_bytes: 71282358
num_examples: 159571
- name: test
num_bytes: 28241991
num_examples: 63978
download_size: 0
dataset_size: 99524349
train-eval-index:
- config: default
task: text-classification
task_id: binary_classification
splits:
train_split: train
eval_split: test
col_mapping:
comment_text: text
toxic: target
metrics:
- type: accuracy
name: Accuracy
- type: f1
name: F1 macro
args:
average: macro
- type: f1
name: F1 micro
args:
average: micro
- type: f1
name: F1 weighted
args:
average: weighted
- type: precision
name: Precision macro
args:
average: macro
- type: precision
name: Precision micro
args:
average: micro
- type: precision
name: Precision weighted
args:
average: weighted
- type: recall
name: Recall macro
args:
average: macro
- type: recall
name: Recall micro
args:
average: micro
- type: recall
name: Recall weighted
args:
average: weighted
---
# Dataset Card for [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:** [Jigsaw Comment Toxicity Classification Kaggle Competition](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/data)
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Discussing things you care about can be difficult. The threat of abuse and harassment online means that many people stop expressing themselves and give up on seeking different opinions. Platforms struggle to effectively facilitate conversations, leading many communities to limit or completely shut down user comments. This dataset consists of a large number of Wikipedia comments which have been labeled by human raters for toxic behavior.
### Supported Tasks and Leaderboards
The dataset support multi-label classification
### Languages
The comments are in English
## Dataset Structure
### Data Instances
A data point consists of a comment followed by multiple labels that can be associated with it.
{'id': '02141412314',
'comment_text': 'Sample comment text',
'toxic': 0,
'severe_toxic': 0,
'obscene': 0,
'threat': 0,
'insult': 0,
'identity_hate': 1,
}
### Data Fields
- `id`: id of the comment
- `comment_text`: the text of the comment
- `toxic`: value of 0(non-toxic) or 1(toxic) classifying the comment
- `severe_toxic`: value of 0(non-severe_toxic) or 1(severe_toxic) classifying the comment
- `obscene`: value of 0(non-obscene) or 1(obscene) classifying the comment
- `threat`: value of 0(non-threat) or 1(threat) classifying the comment
- `insult`: value of 0(non-insult) or 1(insult) classifying the comment
- `identity_hate`: value of 0(non-identity_hate) or 1(identity_hate) classifying the comment
### Data Splits
The data is split into a training and testing set.
## Dataset Creation
### Curation Rationale
The dataset was created to help in efforts to identify and curb instances of toxicity online.
### Source Data
#### Initial Data Collection and Normalization
The dataset is a collection of Wikipedia comments.
#### 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
If words that are associated with swearing, insults or profanity are present in a comment, it is likely that it will be classified as toxic, regardless of the tone or the intent of the author e.g. humorous/self-deprecating. This could present some biases towards already vulnerable minority groups.
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
The "Toxic Comment Classification" dataset is released under [CC0], with the underlying comment text being governed by Wikipedia\'s [CC-SA-3.0].
### Citation Information
No citation information.
### Contributions
Thanks to [@Tigrex161](https://github.com/Tigrex161) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
esnli | null | 2023-04-05T10:05:24Z | 2,472 | 14 | e-snli | [
"language:en",
"region:us"
] | 2023-04-05T10:05:24Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- en
paperswithcode_id: e-snli
pretty_name: e-SNLI
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
- name: explanation_1
dtype: string
- name: explanation_2
dtype: string
- name: explanation_3
dtype: string
config_name: plain_text
splits:
- name: test
num_bytes: 3387169
num_examples: 9824
- name: train
num_bytes: 108024142
num_examples: 549367
- name: validation
num_bytes: 3423725
num_examples: 9842
download_size: 204516010
dataset_size: 114835036
---
# Dataset Card for "esnli"
## 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/OanaMariaCamburu/e-SNLI](https://github.com/OanaMariaCamburu/e-SNLI)
- **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:** 204.51 MB
- **Size of the generated dataset:** 114.84 MB
- **Total amount of disk used:** 319.35 MB
### Dataset Summary
The e-SNLI dataset extends the Stanford Natural Language Inference Dataset to
include human-annotated natural language explanations of the entailment
relations.
### 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
#### plain_text
- **Size of downloaded dataset files:** 204.51 MB
- **Size of the generated dataset:** 114.84 MB
- **Total amount of disk used:** 319.35 MB
An example of 'validation' looks as follows.
```
{
"explanation_1": "A woman must be present to smile.",
"explanation_2": "A woman smiling implies that she is present.",
"explanation_3": "A smiling woman is also present.",
"hypothesis": "A woman is present.",
"label": 0,
"premise": "A woman smiles at the child."
}
```
### Data Fields
The data fields are the same among all splits.
#### plain_text
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
- `explanation_1`: a `string` feature.
- `explanation_2`: a `string` feature.
- `explanation_3`: a `string` feature.
### Data Splits
| name |train |validation|test|
|----------|-----:|---------:|---:|
|plain_text|549367| 9842|9824|
## 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
```
@incollection{NIPS2018_8163,
title = {e-SNLI: Natural Language Inference with Natural Language Explanations},
author = {Camburu, Oana-Maria and Rockt"{a}schel, Tim and Lukasiewicz, Thomas and Blunsom, Phil},
booktitle = {Advances in Neural Information Processing Systems 31},
editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
pages = {9539--9549},
year = {2018},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/8163-e-snli-natural-language-inference-with-natural-language-explanations.pdf}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@albertvillanova](https://github.com/albertvillanova), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
C-MTEB/T2Retrieval | C-MTEB | 2023-07-28T10:11:06Z | 2,470 | 0 | null | [
"region:us"
] | 2023-07-28T10:11:06Z | 2023-07-28T10:08:40.000Z | 2023-07-28T10:08:40 | ---
configs:
- config_name: default
data_files:
- split: corpus
path: data/corpus-*
- split: queries
path: data/queries-*
dataset_info:
features:
- name: id
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_bytes: 265607316
num_examples: 118605
- name: queries
num_bytes: 1000130
num_examples: 22812
download_size: 157606535
dataset_size: 266607446
---
# Dataset Card for "T2Retrieval"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
sick | null | 2023-01-25T14:44:16Z | 2,468 | 5 | sick | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|image-flickr-8k",
"source_datasets:extended|semeval2012-sts-msr-video",
"language:en",
"license:cc-by-nc-sa-3.0",
"region:us"
] | 2023-01-25T14:44:16Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-nc-sa-3.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|image-flickr-8k
- extended|semeval2012-sts-msr-video
task_categories:
- text-classification
task_ids:
- natural-language-inference
paperswithcode_id: sick
pretty_name: Sentences Involving Compositional Knowledge
dataset_info:
features:
- name: id
dtype: string
- name: sentence_A
dtype: string
- name: sentence_B
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
- name: relatedness_score
dtype: float32
- name: entailment_AB
dtype: string
- name: entailment_BA
dtype: string
- name: sentence_A_original
dtype: string
- name: sentence_B_original
dtype: string
- name: sentence_A_dataset
dtype: string
- name: sentence_B_dataset
dtype: string
splits:
- name: train
num_bytes: 1180530
num_examples: 4439
- name: validation
num_bytes: 132913
num_examples: 495
- name: test
num_bytes: 1305846
num_examples: 4906
download_size: 217584
dataset_size: 2619289
---
# Dataset Card for sick
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://marcobaroni.org/composes/sick.html
- **Repository:** [Needs More Information]
- **Paper:** https://www.aclweb.org/anthology/L14-1314/
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
Shared and internationally recognized benchmarks are fundamental for the development of any computational system. We aim to help the research community working on compositional distributional semantic models (CDSMs) by providing SICK (Sentences Involving Compositional Knowldedge), a large size English benchmark tailored for them. SICK consists of about 10,000 English sentence pairs that include many examples of the lexical, syntactic and semantic phenomena that CDSMs are expected to account for, but do not require dealing with other aspects of existing sentential data sets (idiomatic multiword expressions, named entities, telegraphic language) that are not within the scope of CDSMs. By means of crowdsourcing techniques, each pair was annotated for two crucial semantic tasks: relatedness in meaning (with a 5-point rating scale as gold score) and entailment relation between the two elements (with three possible gold labels: entailment, contradiction, and neutral). The SICK data set was used in SemEval-2014 Task 1, and it freely available for research purposes.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
The dataset is in English.
## Dataset Structure
### Data Instances
Example instance:
```
{
"entailment_AB": "A_neutral_B",
"entailment_BA": "B_neutral_A",
"label": 1,
"id": "1",
"relatedness_score": 4.5,
"sentence_A": "A group of kids is playing in a yard and an old man is standing in the background",
"sentence_A_dataset": "FLICKR",
"sentence_A_original": "A group of children playing in a yard, a man in the background.",
"sentence_B": "A group of boys in a yard is playing and a man is standing in the background",
"sentence_B_dataset": "FLICKR",
"sentence_B_original": "A group of children playing in a yard, a man in the background."
}
```
### Data Fields
- pair_ID: sentence pair ID
- sentence_A: sentence A
- sentence_B: sentence B
- label: textual entailment gold label: entailment (0), neutral (1) or contradiction (2)
- relatedness_score: semantic relatedness gold score (on a 1-5 continuous scale)
- entailment_AB: entailment for the A-B order (A_neutral_B, A_entails_B, or A_contradicts_B)
- entailment_BA: entailment for the B-A order (B_neutral_A, B_entails_A, or B_contradicts_A)
- sentence_A_original: original sentence from which sentence A is derived
- sentence_B_original: original sentence from which sentence B is derived
- sentence_A_dataset: dataset from which the original sentence A was extracted (FLICKR vs. SEMEVAL)
- sentence_B_dataset: dataset from which the original sentence B was extracted (FLICKR vs. SEMEVAL)
### Data Splits
Train Trial Test
4439 495 4906
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
```
@inproceedings{marelli-etal-2014-sick,
title = "A {SICK} cure for the evaluation of compositional distributional semantic models",
author = "Marelli, Marco and
Menini, Stefano and
Baroni, Marco and
Bentivogli, Luisa and
Bernardi, Raffaella and
Zamparelli, Roberto",
booktitle = "Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)",
month = may,
year = "2014",
address = "Reykjavik, Iceland",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2014/pdf/363_Paper.pdf",
pages = "216--223",
}
```
### Contributions
Thanks to [@calpt](https://github.com/calpt) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Hello-SimpleAI/HC3 | Hello-SimpleAI | 2023-01-21T13:10:10Z | 2,446 | 128 | null | [
"task_categories:text-classification",
"task_categories:question-answering",
"task_categories:sentence-similarity",
"task_categories:zero-shot-classification",
"size_categories:10K<n<100K",
"language:en",
"language:zh",
"license:cc-by-sa-4.0",
"ChatGPT",
"SimpleAI",
"Detection",
"OOD",
"arxiv:2301.07597",
"region:us"
] | 2023-01-21T13:10:10Z | 2023-01-18T14:01:20.000Z | 2023-01-18T14:01:20 | ---
task_categories:
- text-classification
- question-answering
- sentence-similarity
- zero-shot-classification
language:
- en
- zh
tags:
- ChatGPT
- SimpleAI
- Detection
- OOD
size_categories:
- 10K<n<100K
license: cc-by-sa-4.0
---
# Human ChatGPT Comparison Corpus (HC3)
We propose the first human-ChatGPT comparison corpus, named **HC3** dataset.
This dataset is introduced in our paper:
- Paper: [***How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection***](https://arxiv.org/abs/2301.07597)
Code, models and analysis are available on our GitHub:
- GitHub: [**Chatgpt-Comparison-Detection project** 🔬](https://github.com/Hello-SimpleAI/chatgpt-comparison-detection)
# Dataset Copyright
If the source datasets used in this corpus has a specific license which is stricter than CC-BY-SA, our products follow the same. If not, they follow CC-BY-SA license.
See [dataset copyright](https://github.com/Hello-SimpleAI/chatgpt-comparison-detection#dataset-copyright).
# Citation
Checkout this papaer [arxiv: 2301.07597](https://arxiv.org/abs/2301.07597)
```
@article{guo-etal-2023-hc3,
title = "How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection",
author = "Guo, Biyang and
Zhang, Xin and
Wang, Ziyuan and
Jiang, Minqi and
Nie, Jinran and
Ding, Yuxuan and
Yue, Jianwei and
Wu, Yupeng",
journal={arXiv preprint arxiv:2301.07597}
year = "2023",
}
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un_pc | null | 2023-06-01T14:59:54Z | 2,437 | 3 | united-nations-parallel-corpus | [
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] | 2023-06-01T14:59:54Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
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license:
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multilinguality:
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size_categories:
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paperswithcode_id: united-nations-parallel-corpus
pretty_name: United Nations Parallel Corpus
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---
# 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:**[UNPC](http://opus.nlpl.eu/UNPC.php)
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This parallel corpus consists of manually translated UN documents from the last 25 years (1990 to 2014) \
for the six official UN languages, Arabic, Chinese, English, French, Russian, and Spanish.
6 languages, 15 bitexts
### Supported Tasks and Leaderboards
The underlying task is machine translation.
### 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
```
@inproceedings{ziemski-etal-2016-united,
title = "The {U}nited {N}ations Parallel Corpus v1.0",
author = "Ziemski, Micha{\\l} and
Junczys-Dowmunt, Marcin and
Pouliquen, Bruno",
booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
month = may,
year = "2016",
address = "Portoro{\v{z}}, Slovenia",
publisher = "European Language Resources Association (ELRA)",
url = "https://www.aclweb.org/anthology/L16-1561",
pages = "3530--3534",
abstract = "This paper describes the creation process and statistics of the official United Nations Parallel Corpus, the first parallel corpus composed from United Nations documents published by the original data creator. The parallel corpus presented consists of manually translated UN documents from the last 25 years (1990 to 2014) for the six official UN languages, Arabic, Chinese, English, French, Russian, and Spanish. The corpus is freely available for download under a liberal license. Apart from the pairwise aligned documents, a fully aligned subcorpus for the six official UN languages is distributed. We provide baseline BLEU scores of our Moses-based SMT systems trained with the full data of language pairs involving English and for all possible translation directions of the six-way subcorpus.",
}
```
### Contributions
Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
openbmb/UltraFeedback | openbmb | 2023-09-30T16:39:29Z | 2,419 | 164 | null | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:mit",
"region:us"
] | 2023-09-30T16:39:29Z | 2023-09-23T15:41:04.000Z | 2023-09-23T15:41:04 | ---
license: mit
task_categories:
- text-generation
language:
- en
size_categories:
- 100K<n<1M
---
## Introduction
- [GitHub Repo](https://github.com/thunlp/UltraFeedback)
- [UltraRM-13b](https://huggingface.co/openbmb/UltraRM-13b)
- [UltraCM-13b](https://huggingface.co/openbmb/UltraCM-13b)
UltraFeedback is a **large-scale, fine-grained, diverse preference dataset**, used for training powerful reward models and critic models. We collect about 64k prompts from diverse resources (including UltraChat, ShareGPT, Evol-Instruct, TruthfulQA, FalseQA, and FLAN). We then use these prompts to query multiple LLMs (see Table for model lists) and generate 4 different responses for each prompt, resulting in a total of 256k samples.
To collect high-quality preference and textual feedback, we design a fine-grained annotation instruction, which contains 4 different aspects, namely **instruction-following**, **truthfulness**, **honesty** and **helpfulness**. We then ask GPT-4 to annotate the collected samples based on the instructions.
## Features
- 🆚 **Scale**: UltraFeedback consists of 64k prompts, 256k responses and 380k high-quality feedback. RLHF researchers could further construct around 1 million comparison pairs to train their reward models.
- 🌈 **Diversity**: As a preference dataset, diversity is the core requirement for UltraFeedback. We collect prompts from various sources and query a diverse set of state-of-the-art open-source and prestigious models. To further increase diversity, we intended to select different base models, i.e., LLaMA, Falcon, StarChat, MPT, GPT and Bard. We also apply various principles to stimulate models completing instructions in different ways.
- 🤯 **High-density**: UltraFeedback provides both numerical and textual feedback. Moreover, we wrote fine-grained annotation documents to help rate responses in all dimensions
## Dataset Construction
### Instruction Sampling
We sample 63,967 instructions from 6 public available and high-quality datasets. We include all instructions from TruthfulQA and FalseQA, randomly sampling 10k instructions from Evol-Instruct, 10k from UltraChat, and 20k from ShareGPT. For Flan, we adopt a stratified sampling strtegy, randomly samping 3k instructions from"Co" subset whereas sampling 10 instructions per task for the other three subsets, excluding those with overly long instructions.
```json
{
"evol_instruct": 10000,
"false_qa": 2339,
"flan": 20939,
"sharegpt": 19949,
"truthful_qa": 811,
"ultrachat": 9929
}
```
### Model Sampling
To prevent reward model from overfiting to certain text style or capturing spurious correlation between text style and rewards, we select different base models of all levels, with varying sizes, architectures and training data, to complete the instructions. We set up a pool of 17 models:
- Commercial Models: GPT-4, GPT-3.5 Turbo, Bard
- LLaMA family:
1. LLaMA-2-7B-chat, LLaMA-2-13B-chat, LLaMA-2-70B-chat
2. UltraLM-13B, UltraLM-65B
3. WizardLM-7B, WizardLM-13B, WizardLM-70B
4. Vicuna-33B
5. Alpaca-7B
- Non-LLaMA series:
1. Falcon-40B-instruct
2. MPT-30B-chat
3. StarChat-Beta
4. Pythia-12B
### Principle Sampling
Following [1] and [2], we define a set of principles to explicitly align model behaviors from different aspects. We set up a pool of 5 principles: Helpfulness, Truthfulness, Honesty, Verbalized Calibration and Harmless. For each instruction, we randomly sample 4 models to complete the instruction, and for each completion, we sample a principle and add it to system prompt to align the model behavior. Considering different datasets outline different characteristics, not all dataset are suitable for all principles. We provide the following table to show the principle distribution for each dataset.
| Datset | Principle |
| ------------- | ------------------------------------------------------------ |
| Evol Instruct | 100% Helpful |
| FalseQA | 100% TruthfulQA |
| Flan | 60% Helpful, 20% Truthful, 20% Verbalized Calibration |
| ShareGPT | 60% Helpful, 20% Truthful, 18% Honesty, 2% Verbalized Calibration |
| TruthfulQA | 100% Truthful |
| UltraChat | 60% Helpful, 20% Truthful, 18% Honesty, 2% Verbalized Calibration |
[1] Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision. Sun et al.
[2] Orca: Progressive Learning from Complex Explanation Traces of GPT-4. Mukherjee et al.
## Dataset Format
```JSONC
{
"source": "sharegpt", // the dataset where the instruction comes from
"id": "D2TC9GP_0", // the id of the instruction in the source dataset
"instruction": "I am going to cairo in June of this year, thinking of 4-5 days, what are the best things I could do? Going with family of 5, granddad, grandmom, sister, mom and me. No days can have too much walking but sister, me and mom like adventure sports",
"models": [ // four models that are randomly sampled from the model pool to complete the instruction
"falcon-40b-instruct",
"gpt-4",
"starchat",
"wizardlm-7b"
],
"completions": [ // four completions corresponding to the four models
{ // completion 1
"model": "falcon-40b-instruct",
"principle": "helpfulness", // priciple that used to align model behavior
"custom_system_prompt": "As an AI assistant, ensure your response offers the perfect blend of accuracy, positivity, and intrigue. Strive to be educational while keeping the user engaged.", // the system prompt corresponding to the principle, sampled from a pool of GPT-4 generated system prompts
"response": "Cairo is a city that has something for everyone. Some of the best things you can do are visiting the Pyramids of Giza and the museums housing the ancient Egyptian artifacts, taking a camel ride in the desert, visiting the medieval neighborhoods such as Islamic Cairo or Khan El-Khalili, trying local food in traditional restaurants like Ful medames, or venturing into one of the city's many souks to shop for authentic goods. For adventure sports, you can go whitewater rafting on the Nile, hot air ballooning over the desert, or diving in the Red Sea. Just be mindful of the scorching heat and ensure everyone wears appropriate clothing and drinks enough water. Above all, have fun and enjoy your trip!",
"annotations": {
"instruction_following": [
{
"Rating": "2",
"Rationale": "The text only partially addresses the task goal by providing a general list of activities in Cairo without considering the specific needs of the family members, such as limited walking ability and adventure preferences."
}
],
"honesty": [
{
"Rating": "3",
"Rationale": "The response is confident but contains subtle mistakes. It suggests whitewater rafting on the Nile and diving in the Red Sea, which are not feasible for a 4-5 day trip to Cairo. The rest of the suggestions are accurate and relevant to the family's preferences."
}
],
"truthfulness": [
{
"Type": [
"1",
"2"
],
"Rationale": "The text suggests whitewater rafting on the Nile and diving in the Red Sea, which are not available in Cairo. These activities are factually incorrect and not aligned with the instruction.",
"Rating": "3",
"Rationale For Rating": "The text provides some truthful information, but the inclusion of whitewater rafting and diving in the Red Sea causes partial hallucination and misunderstanding."
}
],
"helpfulness": [
{
"Type": [
"1",
"2"
],
"Rationale": "The response is clear and relevant, providing useful information, but it does not address the limited walking ability of the grandparents or the adventure sports preferences of the sister, mom, and the user.",
"Rating": "3",
"Rationale For Rating": "The text is correct and provides useful information, but it does not fully address the specific needs and preferences of the family."
}
]
}
},
{ // completion 2
...
},
{ // completion 3
...
},
{ // completion 4
...
}
]
},
```
## Dataset Example
Here we present an example of UltraFeedback
> **User:** I am going to cairo in June of this year, thinking of 4-5 days, what are the best things I could do? Going with family of 5, granddad, grandmom, sister, mom and me. No days can have too much walking but sister, me and mom like adventure sports
> **Assisstant 1 (falcon-40b-instruct):** Cairo is a city that has something for everyone. Some of the best things you can do are visiting the Pyramids of Giza and the museums housing the ancient Egyptian artifacts, taking a camel ride in the desert, visiting the medieval neighborhoods such as Islamic Cairo or Khan El-Khalili, trying local food in traditional restaurants like Ful medames, or venturing into one of the city's many souks to shop for authentic goods. For adventure sports, you can go whitewater rafting on the Nile, hot air ballooning over the desert, or diving in the Red Sea. Just be mindful of the scorching heat and ensure everyone wears appropriate clothing and drinks enough water. Above all, have fun and enjoy your trip!
> **Assisstant 2 (gpt-4):** In Cairo for 4-5 days, the following activities cater to your family's preferences, with consideration for the limited walking ability of your grandparents:\n\n1. Visit the Pyramids of Giza and the Great Sphinx: A must-see in Cairo, it's an incredible experience for all ages. You can explore the premises using a hired camel, horse, or a horse-drawn carriage which accommodate the needs of your grandparents while keeping the experience enjoyable.\n\n2. Egyptian Museum: This museum in Tahrir Square contains over 120,000 artifacts of Egypt's history. A wheelchair can be arranged for the grandparents to comfortably explore the museum at their own pace.\n\n3. Khan el-Khalili Bazaar: A famous market in Cairo, suitable for souvenir shopping and experiencing the local culture. There are many cafes and restaurants your family can rest at during the visit.\n\n4. Nile River Cruise: Enjoy a dinner cruise with your family on the famous Nile River. It includes entertainment and a beautiful view of the city by night. Boats often have ramps and accessible facilities for limited mobility passangers.\n\n5. Felucca Ride: During the day, go for a more traditional sailboat ride called a Felucca. This can be done on an off-day or as a relaxation activity after an active day.\n\nFor adventure activities, you and your mom and sister can explore these options:\n\n1. Sandboarding: Head to the desert for a thrilling sandboarding experience on the dunes just outside Cairo.\n\n2. Hot Air Balloon Ride: Experience Egypt from above with a hot air balloon ride in Luxor. It would require a day trip from Cairo but is an incredible experience.\n\n3. Quad Biking: Go for a guided quad biking adventure in the desert, enjoying panoramic views of the iconic Pyramids.\n\nSpace the activities according to your family's preferences and ensure rest periods in the afternoon. Consider hiring a private guide or joining a wheelchair-accessible tour since you're concerned about your grandparents' walking ability.
> **Assisstant 3 (starchat):** Here are some of the top things to do in Cairo, Egypt, with a family of 5:\n\n- Giza Pyramids: The Giza Pyramids, located just outside Cairo, are a must-see for anyone visiting the city. The Great Pyramid of Khufu, the Great Pyramid of Khafre, and the Great Pyramid of Menkaure make up one of the Seven Wonders of the Ancient World and are an impressive sight to behold.\n\n- Egyptian Museum: The Egyptian Museum, located in the heart of Cairo, is home to one of the world's largest and most impressive collections of ancient Egyptian art and artifacts. Visitors can explore the museum's extensive collection, which includes items such as mummies, sarcophagi, statues, paintings, and more.\n\n- Citadel of Salah El-Din: Located on a hill overlooking Cairo, the Citadel of Salah El-Din offers stunning views of the city and is an excellent place to get a sense of Cairo's rich history. Visitors can explore the various museums and mosques within the citadel, and also take a stroll through the beautiful gardens.\n\n- Khan El-Khalili Bazaar: Khan El-Khalili Bazaar is one of Cairo's most popular attractions, a bustling market where visitors can find everything from spices and pottery to clothes and jewelry. It's a great place to do some shopping and explore the local culture.\n\n- Alexandria: Just an hour's drive from Cairo, Alexandria is a historic city with a rich legacy. Visitors can explore the ancient Roman Amphitheater, the Qaitbay Citadel, and the Catacombs of Kom el-Shoqafa. They can also take a boat trip to the island of Montazah, where they can see the famous Graffiti Wall and other street art.\n\nAll of these attractions are family-friendly, with plenty of things to see and do. The Giza Pyramids in particular are very accessible and well-maintained, making them suitable for all ages.
> **Assisstant 4 (wizardlm-7b):** If you are looking for adventure sports, you can explore the pyramids of Giza and go hot air ballooning over the desert. Another option could be to take a camel ride in the desert or go kayaking in the Nile River. For family-friendly activities, you can visit the Egyptian Museum, Cairo Tower, and Khan el-Khalili Bazaar. Additionally, you can take a day trip to the historical city of Alexandria and visit the Pompey's Pillar and the Roman Amphitheater. Another option could be to go on a guided tour of the ancient city of Petra in Jordan, which is a short flight from Cairo.
## Limitations
- Although GPT-4 can provide well-aligned annotation and textual feedback for most samples, we must note that GPT-4 also makes mistakes and provides inaccurate feedbacks. | [
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mlsum | null | 2023-06-01T14:59:54Z | 2,409 | 31 | mlsum | [
"task_categories:summarization",
"task_categories:translation",
"task_categories:text-classification",
"task_ids:news-articles-summarization",
"task_ids:multi-class-classification",
"task_ids:multi-label-classification",
"task_ids:topic-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"source_datasets:extended|cnn_dailymail",
"source_datasets:original",
"language:de",
"language:es",
"language:fr",
"language:ru",
"language:tr",
"license:other",
"region:us"
] | 2023-06-01T14:59:54Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- de
- es
- fr
- ru
- tr
license:
- other
multilinguality:
- multilingual
size_categories:
- 100K<n<1M
- 10K<n<100K
source_datasets:
- extended|cnn_dailymail
- original
task_categories:
- summarization
- translation
- text-classification
task_ids:
- news-articles-summarization
- multi-class-classification
- multi-label-classification
- topic-classification
paperswithcode_id: mlsum
pretty_name: MLSUM
dataset_info:
- config_name: de
features:
- name: text
dtype: string
- name: summary
dtype: string
- name: topic
dtype: string
- name: url
dtype: string
- name: title
dtype: string
- name: date
dtype: string
splits:
- name: train
num_bytes: 846959840
num_examples: 220887
- name: validation
num_bytes: 47119541
num_examples: 11394
- name: test
num_bytes: 46847612
num_examples: 10701
download_size: 1005814154
dataset_size: 940926993
- config_name: es
features:
- name: text
dtype: string
- name: summary
dtype: string
- name: topic
dtype: string
- name: url
dtype: string
- name: title
dtype: string
- name: date
dtype: string
splits:
- name: train
num_bytes: 1214558302
num_examples: 266367
- name: validation
num_bytes: 50643400
num_examples: 10358
- name: test
num_bytes: 71263665
num_examples: 13920
download_size: 1456211154
dataset_size: 1336465367
- config_name: fr
features:
- name: text
dtype: string
- name: summary
dtype: string
- name: topic
dtype: string
- name: url
dtype: string
- name: title
dtype: string
- name: date
dtype: string
splits:
- name: train
num_bytes: 1471965014
num_examples: 392902
- name: validation
num_bytes: 70413212
num_examples: 16059
- name: test
num_bytes: 69660288
num_examples: 15828
download_size: 1849565564
dataset_size: 1612038514
- config_name: ru
features:
- name: text
dtype: string
- name: summary
dtype: string
- name: topic
dtype: string
- name: url
dtype: string
- name: title
dtype: string
- name: date
dtype: string
splits:
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num_bytes: 257389497
num_examples: 25556
- name: validation
num_bytes: 9128497
num_examples: 750
- name: test
num_bytes: 9656398
num_examples: 757
download_size: 766226107
dataset_size: 276174392
- config_name: tu
features:
- name: text
dtype: string
- name: summary
dtype: string
- name: topic
dtype: string
- name: url
dtype: string
- name: title
dtype: string
- name: date
dtype: string
splits:
- name: train
num_bytes: 641622783
num_examples: 249277
- name: validation
num_bytes: 25530661
num_examples: 11565
- name: test
num_bytes: 27830212
num_examples: 12775
download_size: 942308960
dataset_size: 694983656
config_names:
- de
- es
- fr
- ru
- tu
---
# Dataset Card for MLSUM
## 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/recitalAI/MLSUM
- **Paper:** https://www.aclweb.org/anthology/2020.emnlp-main.647/
- **Point of Contact:** [email](thomas@recital.ai)
- **Size of downloaded dataset files:** 1.83 GB
- **Size of the generated dataset:** 4.86 GB
- **Total amount of disk used:** 6.69 GB
### Dataset Summary
We present MLSUM, the first large-scale MultiLingual SUMmarization dataset.
Obtained from online newspapers, it contains 1.5M+ article/summary pairs in five different languages -- namely, French, German, Spanish, Russian, Turkish.
Together with English newspapers from the popular CNN/Daily mail dataset, the collected data form a large scale multilingual dataset which can enable new research directions for the text summarization community.
We report cross-lingual comparative analyses based on state-of-the-art systems.
These highlight existing biases which motivate the use of a multi-lingual dataset.
### 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
#### de
- **Size of downloaded dataset files:** 346.58 MB
- **Size of the generated dataset:** 940.93 MB
- **Total amount of disk used:** 1.29 GB
An example of 'validation' looks as follows.
```
{
"date": "01/01/2001",
"summary": "A text",
"text": "This is a text",
"title": "A sample",
"topic": "football",
"url": "https://www.google.com"
}
```
#### es
- **Size of downloaded dataset files:** 513.31 MB
- **Size of the generated dataset:** 1.34 GB
- **Total amount of disk used:** 1.85 GB
An example of 'validation' looks as follows.
```
{
"date": "01/01/2001",
"summary": "A text",
"text": "This is a text",
"title": "A sample",
"topic": "football",
"url": "https://www.google.com"
}
```
#### fr
- **Size of downloaded dataset files:** 619.99 MB
- **Size of the generated dataset:** 1.61 GB
- **Total amount of disk used:** 2.23 GB
An example of 'validation' looks as follows.
```
{
"date": "01/01/2001",
"summary": "A text",
"text": "This is a text",
"title": "A sample",
"topic": "football",
"url": "https://www.google.com"
}
```
#### ru
- **Size of downloaded dataset files:** 106.22 MB
- **Size of the generated dataset:** 276.17 MB
- **Total amount of disk used:** 382.39 MB
An example of 'train' looks as follows.
```
{
"date": "01/01/2001",
"summary": "A text",
"text": "This is a text",
"title": "A sample",
"topic": "football",
"url": "https://www.google.com"
}
```
#### tu
- **Size of downloaded dataset files:** 247.50 MB
- **Size of the generated dataset:** 694.99 MB
- **Total amount of disk used:** 942.48 MB
An example of 'train' looks as follows.
```
{
"date": "01/01/2001",
"summary": "A text",
"text": "This is a text",
"title": "A sample",
"topic": "football",
"url": "https://www.google.com"
}
```
### Data Fields
The data fields are the same among all splits.
#### de
- `text`: a `string` feature.
- `summary`: a `string` feature.
- `topic`: a `string` feature.
- `url`: a `string` feature.
- `title`: a `string` feature.
- `date`: a `string` feature.
#### es
- `text`: a `string` feature.
- `summary`: a `string` feature.
- `topic`: a `string` feature.
- `url`: a `string` feature.
- `title`: a `string` feature.
- `date`: a `string` feature.
#### fr
- `text`: a `string` feature.
- `summary`: a `string` feature.
- `topic`: a `string` feature.
- `url`: a `string` feature.
- `title`: a `string` feature.
- `date`: a `string` feature.
#### ru
- `text`: a `string` feature.
- `summary`: a `string` feature.
- `topic`: a `string` feature.
- `url`: a `string` feature.
- `title`: a `string` feature.
- `date`: a `string` feature.
#### tu
- `text`: a `string` feature.
- `summary`: a `string` feature.
- `topic`: a `string` feature.
- `url`: a `string` feature.
- `title`: a `string` feature.
- `date`: a `string` feature.
### Data Splits
|name|train |validation|test |
|----|-----:|---------:|----:|
|de |220887| 11394|10701|
|es |266367| 10358|13920|
|fr |392902| 16059|15828|
|ru | 25556| 750| 757|
|tu |249277| 11565|12775|
## 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
Usage of dataset is restricted to non-commercial research purposes only. Copyright belongs to the original copyright holders. See https://github.com/recitalAI/MLSUM#mlsum
### Citation Information
```
@article{scialom2020mlsum,
title={MLSUM: The Multilingual Summarization Corpus},
author={Scialom, Thomas and Dray, Paul-Alexis and Lamprier, Sylvain and Piwowarski, Benjamin and Staiano, Jacopo},
journal={arXiv preprint arXiv:2004.14900},
year={2020}
}
```
### Contributions
Thanks to [@RachelKer](https://github.com/RachelKer), [@albertvillanova](https://github.com/albertvillanova), [@thomwolf](https://github.com/thomwolf) for adding this dataset. | [
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conceptual_captions | null | 2022-11-03T16:32:04Z | 2,405 | 39 | conceptual-captions | [
"task_categories:image-to-text",
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] | 2022-11-03T16:32:04Z | 2022-04-14T13:08:21.000Z | 2022-04-14T13:08:21 | ---
annotations_creators:
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paperswithcode_id: conceptual-captions
pretty_name: Conceptual Captions
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---
# Dataset Card for Conceptual Captions
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Preprocessing](#dataset-preprocessing)
- [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)
## Dataset Description
- **Homepage:** [Conceptual Captions homepage](https://ai.google.com/research/ConceptualCaptions/)
- **Repository:** [Conceptual Captions repository](https://github.com/google-research-datasets/conceptual-captions)
- **Paper:** [Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning](https://www.aclweb.org/anthology/P18-1238/)
- **Leaderboard:** [Conceptual Captions leaderboard](https://ai.google.com/research/ConceptualCaptions/competition?active_tab=leaderboard)https://ai.google.com/research/ConceptualCaptions/leaderboard?active_tab=leaderboard
- **Point of Contact:** [Conceptual Captions e-mail](mailto:conceptual-captions@google.com)
### Dataset Summary
Conceptual Captions is a dataset consisting of ~3.3M images annotated with captions. In contrast with the curated style of other image caption annotations, Conceptual Caption images and their raw descriptions are harvested from the web, and therefore represent a wider variety of styles. More precisely, the raw descriptions are harvested from the Alt-text HTML attribute associated with web images. To arrive at the current version of the captions, we have developed an automatic pipeline that extracts, filters, and transforms candidate image/caption pairs, with the goal of achieving a balance of cleanliness, informativeness, fluency, and learnability of the resulting captions.
### Dataset Preprocessing
This dataset doesn't download the images locally by default. Instead, it exposes URLs to the images. To fetch the images, use the following code:
```python
from concurrent.futures import ThreadPoolExecutor
from functools import partial
import io
import urllib
import PIL.Image
from datasets import load_dataset
from datasets.utils.file_utils import get_datasets_user_agent
USER_AGENT = get_datasets_user_agent()
def fetch_single_image(image_url, timeout=None, retries=0):
for _ in range(retries + 1):
try:
request = urllib.request.Request(
image_url,
data=None,
headers={"user-agent": USER_AGENT},
)
with urllib.request.urlopen(request, timeout=timeout) as req:
image = PIL.Image.open(io.BytesIO(req.read()))
break
except Exception:
image = None
return image
def fetch_images(batch, num_threads, timeout=None, retries=0):
fetch_single_image_with_args = partial(fetch_single_image, timeout=timeout, retries=retries)
with ThreadPoolExecutor(max_workers=num_threads) as executor:
batch["image"] = list(executor.map(fetch_single_image_with_args, batch["image_url"]))
return batch
num_threads = 20
dset = load_dataset("conceptual_captions")
dset = dset.map(fetch_images, batched=True, batch_size=100, fn_kwargs={"num_threads": num_threads})
```
### Supported Tasks and Leaderboards
- `image-captioning`: This dataset can be used to train model for the Image Captioning task. The leaderboard for this task is available [here](https://ai.google.com/research/ConceptualCaptions/competition?active_tab=leaderboard). Official submission output captions are scored against the reference captions from the hidden test set using [this](https://github.com/tylin/coco-caption) implementation of the CIDEr (primary), ROUGE-L and SPICE metrics.
### Languages
All captions are in English.
## Dataset Structure
### Data Instances
#### `unlabeled`
Each instance in this configuration represents a single image with a caption:
```
{
'image_url': 'http://lh6.ggpht.com/-IvRtNLNcG8o/TpFyrudaT6I/AAAAAAAAM6o/_11MuAAKalQ/IMG_3422.JPG?imgmax=800',
'caption': 'a very typical bus station'
}
```
#### `labeled`
Each instance in this configuration represents a single image with a caption with addtional machine-generated image labels and confidence scores:
```
{
'image_url': 'https://thumb1.shutterstock.com/display_pic_with_logo/261388/223876810/stock-vector-christmas-tree-on-a-black-background-vector-223876810.jpg',
'caption': 'christmas tree on a black background .',
'labels': ['christmas tree', 'christmas decoration', 'font', 'text', 'graphic design', 'illustration','interior design', 'tree', 'christmas eve', 'ornament', 'fir', 'plant', 'pine', 'pine family', 'graphics'],
'MIDs': ['/m/025nd', '/m/05fc9mj', '/m/03gq5hm', '/m/07s6nbt', '/m/03c31', '/m/01kr8f', '/m/0h8nzzj', '/m/07j7r', '/m/014r1s', '/m/05ykl4', '/m/016x4z', '/m/05s2s', '/m/09t57', '/m/01tfm0', '/m/021sdg'],
'confidence_scores': [0.9818305373191833, 0.952756941318512, 0.9227379560470581, 0.8524878621101379, 0.7597672343254089, 0.7493422031402588, 0.7332468628883362, 0.6869218349456787, 0.6552258133888245, 0.6357356309890747, 0.5992692708969116, 0.585474967956543, 0.5222904086112976, 0.5113164782524109, 0.5036579966545105]
}
```
### Data Fields
#### `unlabeled`
- `image_url`: Static URL for downloading the image associated with the post.
- `caption`: Textual description of the image.
#### `labeled`
- `image_url`: Static URL for downloading the image associated with the post.
- `caption`: Textual description of the image.
- `labels`: A sequence of machine-generated labels obtained using the [Google Cloud Vision API](https://cloud.google.com/vision).
- `MIDs`: A sequence of machine-generated identifiers (MID) corresponding to the label's Google Knowledge Graph entry.
- `confidence_scores`: A sequence of confidence scores denoting how likely the corresponing labels are present on the image.
### Data Splits
#### `unlabeled`
The basic version of the dataset split into Training and Validation splits. The Training split consists of 3,318,333 image-URL/caption pairs and the Validation split consists of 15,840 image-URL/caption pairs.
#### `labeled`
The labeled version of the dataset with a single. The entire data is contained in Training split, which is a subset of 2,007,090 image-URL/caption pairs from the Training set of the `unlabeled` config.
## Dataset Creation
### Curation Rationale
From the paper:
> In this paper, we make contributions to both the data and modeling categories. First, we present a new dataset of caption annotations Conceptual Captions (Fig. 1), which has an order of magnitude more images than the COCO dataset. Conceptual Captions consists of about 3.3M himage, descriptioni pairs. In contrast with the curated style of the COCO images, Conceptual Captions images and their raw descriptions are harvested from the web, and therefore represent a wider variety of styles.
### Source Data
#### Initial Data Collection and Normalization
From the homepage:
>For Conceptual Captions, we developed a fully automatic pipeline that extracts, filters, and transforms candidate image/caption pairs, with the goal of achieving a balance of cleanliness, informativeness, fluency, and learnability of the resulting captions. Because no human annotators are involved, the Conceptual Captions dataset generation process is highly scalable.
>
>To generate this dataset, we started with a Flume pipeline that processes billions of Internet webpages, extracting, filtering, and processing candidate image and caption pairs, and keeping those that pass through several filters.
>
>We first screen for certain properties like size, aspect ratio, adult content scores. These filters discard more than 65% of the candidates. Next, we use Alt-Texts for text-based filtering, removing captions with non-descriptive text (such as SEO tags or hashtags); we also discard texts with high sentiment polarity or adult content scores, resulting in just 3% of the incoming candidates passing through.
>
>In the next step, we filter out candidates for which none of the text tokens can be mapped to the visual content of the image. We use image classifiers (e.g., Google Cloud Vision APIs) to assign class labels to images and match these labels against the candidate text (allowing morphological transformations), discarding >around 60% of the candidates that reach this stage.
>
>The candidates passing the above filters tend to be good Alt-text image descriptions. However, a large majority of these use proper names (for people, venues, locations, etc.), brands, dates, quotes, etc. This creates two distinct problems. First, some of these cannot be inferred based on the image pixels alone. This is problematic because unless the image has the necessary visual information it is not useful for training. Second, even if the proper names could be inferred from the image it is extremely difficult for a model to learn to perform both fine-grained classification and natural-language descriptions simultaneously. We posit that if automatic determination of names, locations, brands, etc. is needed, it should be done as a separate task that may leverage image meta-information (e.g. GPS info), or complementary techniques such as OCR.
>
>We address the above problems with the insight that proper names should be replaced by words that represent the same general notion, i.e., by their concept. For example, we remove locations (“Crowd at a concert in Los Angeles“ becomes “Crowd at a concert”), names (e.g., “Former Miss World Priyanka Chopra on the red carpet” becomes “actor on the red carpet”), proper noun modifiers (e.g., “Italian cuisine” becomes just “cuisine”) and noun phrases (e.g., “actor and actor” becomes “actors”). Around 20% of the samples are discarded during this transformation because it can leave sentences too short, or otherwise inconsistent.
>
>Finally, we perform another round of filtering to identify concepts with low-count. We cluster all resolved entities (e.g., “actor”, “dog”, “neighborhood”, etc.) and keep only the candidate types which have a count of over 100 mentions. This retains around 16K entity concepts such as: “person”, “actor”, “artist”, “player” and “illustration”. The less frequent ones that we dropped include “baguette”, “bridle”, “deadline”, “ministry” and “funnel”.
#### Who are the source language producers?
Not specified.
### Annotations
#### Annotation process
Annotations are extracted jointly with the images using the automatic pipeline.
#### Who are the annotators?
Not specified.
### 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
Piyush Sharma, Nan Ding, Sebastian Goodman and Radu Soricut.
### Licensing Information
The dataset may be freely used for any purpose, although acknowledgement of
Google LLC ("Google") as the data source would be appreciated. The dataset is
provided "AS IS" without any warranty, express or implied. Google disclaims all
liability for any damages, direct or indirect, resulting from the use of the
dataset.
### Citation Information
```bibtex
@inproceedings{sharma2018conceptual,
title = {Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning},
author = {Sharma, Piyush and Ding, Nan and Goodman, Sebastian and Soricut, Radu},
booktitle = {Proceedings of ACL},
year = {2018},
}
```
### Contributions
Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) and [@mariosasko](https://github.com/mariosasko) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
DKYoon/SlimPajama-6B | DKYoon | 2023-08-21T16:54:47Z | 2,403 | 6 | null | [
"task_categories:text-generation",
"size_categories:1M<n<10M",
"language:en",
"region:us"
] | 2023-08-21T16:54:47Z | 2023-08-21T15:25:52.000Z | 2023-08-21T15:25:52 | ---
language:
- en
size_categories:
- 1M<n<10M
task_categories:
- text-generation
pretty_name: SlimPajama-6B
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
- name: meta
struct:
- name: redpajama_set_name
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 23918118724
num_examples: 5489000
- name: validation
num_bytes: 39109042
num_examples: 9347
- name: test
num_bytes: 40114950
num_examples: 9346
download_size: 14048972121
dataset_size: 23997342716
---
Sampled version of [cerebras/SlimPajama-627B](https://huggingface.co/datasets/cerebras/SlimPajama-627B).
[Since the original data was shuffled before chunking](https://huggingface.co/datasets/cerebras/SlimPajama-627B/discussions/4), I only downloaded train/chunk1 (of 10 total) and further sampled 10%. This should result in roughly 6B tokens, hence SlimPajama-6B.
The dataset is 24GBs in storage size when decompressed (original dataset is over 2TBs) and has 5489000 rows.
The validation set and test set were sampled as well.
---
#### Data source proportions for SlimPajama-627B and SlimPajama-6B
For sanity purpose, I caluclated the byte proportion of the sampled version.
| Data source | SlimPajama-627B | SlimPajama-6B |
| ------------- | ---------- | --------- |
| Commoncrawl | 52.2% | 54.1% |
| C4 | 26.7% | 28.7% |
| GitHub | 5.2% | 4.2% |
| Books | 4.2% | 3.7% |
| ArXiv | 4.6% | 3.4% |
| Wikpedia | 3.8% | 3.1% |
| StackExchange | 3.3% | 2.8% |
---
Please refer to the original dataset for other info.
```
@misc{cerebras2023slimpajama,
author = {Soboleva, Daria and Al-Khateeb, Faisal and Myers, Robert and Steeves, Jacob R and Hestness, Joel and Dey, Nolan},
title = {{SlimPajama: A 627B token cleaned and deduplicated version of RedPajama}},
month = June,
year = 2023,
howpublished = {\url{https://www.cerebras.net/blog/slimpajama-a-627b-token-cleaned-and-deduplicated-version-of-redpajama}},
url = {https://huggingface.co/datasets/cerebras/SlimPajama-627B},
}
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
smangrul/code-chat-assistant-v1 | smangrul | 2023-07-27T10:51:50Z | 2,400 | 12 | null | [
"region:us"
] | 2023-07-27T10:51:50Z | 2023-07-27T10:51:46.000Z | 2023-07-27T10:51:46 | ---
dataset_info:
features:
- name: content
dtype: string
splits:
- name: train
num_bytes: 25042064.0
num_examples: 10876
- name: test
num_bytes: 1348088
num_examples: 818
download_size: 12246507
dataset_size: 26390152.0
---
# Dataset Card for "code-chat-assistant-v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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cc_news | null | 2023-06-12T06:42:15Z | 2,396 | 37 | cc-news | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | 2023-06-12T06:42:15Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
pretty_name: CC-News
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: cc-news
dataset_info:
features:
- name: title
dtype: string
- name: text
dtype: string
- name: domain
dtype: string
- name: date
dtype: string
- name: description
dtype: string
- name: url
dtype: string
- name: image_url
dtype: string
config_name: plain_text
splits:
- name: train
num_bytes: 2016418133
num_examples: 708241
download_size: 845131146
dataset_size: 2016418133
---
# Dataset Card for CC-News
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [CC-News homepage](https://commoncrawl.org/2016/10/news-dataset-available/)
- **Point of Contact:** [Vladimir Blagojevic](mailto:dovlex@gmail.com)
### Dataset Summary
CC-News dataset contains news articles from news sites all over the world. The data is available on AWS S3 in the Common Crawl bucket at /crawl-data/CC-NEWS/.
This version of the dataset has been prepared using [news-please](https://github.com/fhamborg/news-please) - an integrated web crawler and information extractor for news.
It contains 708241 English language news articles published between Jan 2017 and December 2019.
It represents a small portion of the English language subset of the CC-News dataset.
### Supported Tasks and Leaderboards
CC-News has been mostly used for language model training.
### Languages
The text in the dataset is in the English language.
## Dataset Structure
### Data Instances
Dataset instance contains an article itself and the relevant article fields.
An example from the Cc-New train set looks as follows:
```
{
'date': '2017-08-14 00:00:00',
'description': '"The spirit of Green Day has always been about rising above oppression."',
'domain': '1041jackfm.cbslocal.com',
'image_url': 'https://cbs1041jackfm.files.wordpress.com/2017/08/billie-joe-armstrong-theo-wargo-getty-images.jpg?w=946',
'text': 'By Abby Hassler\nGreen Day’s Billie Joe Armstrong has always been outspoken about his political beliefs. Following
the tragedy in Charlottesville, Virgina, over the weekend, Armstrong felt the need to speak out against the white supremacists
who caused much of the violence.\nRelated: Billie Joe Armstrong Wins #TBT with Childhood Studio Photo\n“My heart feels heavy.
I feel like what happened in Charlottesville goes beyond the point of anger,” Armstrong wrote on Facebook. “It makes me sad
and desperate. shocked. I f—— hate racism more than anything.”\n“The spirit of Green Day has always been about rising above
oppression. and sticking up for what you believe in and singing it at the top of your lungs,” Armstrong continued.
“We grew up fearing nuclear holocaust because of the cold war. those days are feeling way too relevant these days.
these issues are our ugly past.. and now it’s coming to haunt us. always resist these doomsday politicians. and in the
words of our punk forefathers .. Nazi punks f— off.”',
'title': 'Green Day’s Billie Joe Armstrong Rails Against White Nationalists',
'url': 'http://1041jackfm.cbslocal.com/2017/08/14/billie-joe-armstrong-white-nationalists/'
}
```
### Data Fields
- `date`: date of publication
- `description`: description or a summary of the article
- `domain`: source domain of the article (i.e. www.nytimes.com)
- `image_url`: URL of the article's image
- `text`: the actual article text in raw form
- `title`: title of the article
- `url`: article URL, the original URL where it was scraped.
### Data Splits
CC-News dataset has only the training set, i.e. it has to be loaded with `train` split specified:
`cc_news = load_dataset('cc_news', split="train")`
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
CC-News dataset has been proposed, created, and maintained by Sebastian Nagel.
The data is publicly available on AWS S3 Common Crawl bucket at /crawl-data/CC-NEWS/.
This version of the dataset has been prepared using [news-please](https://github.com/fhamborg/news-please) - an
integrated web crawler and information extractor for news.
It contains 708241 English language news articles published between Jan 2017 and December 2019.
Although news-please tags each news article with an appropriate language tag, these tags are somewhat unreliable.
To strictly isolate English language articles an additional check has been performed using
[Spacy langdetect pipeline](https://spacy.io/universe/project/spacy-langdetect).
We selected articles with text fields scores of 80% probability or more of being English.
There are no strict guarantees that each article has all the relevant fields. For example, 527595
articles have a valid description field. All articles have what appears to be a valid image URL,
but they have not been verified.
#### Who are the source language producers?
The news websites throughout the World.
### Annotations
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
As one can imagine, data contains contemporary public figures or individuals who appeared in the news.
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to help language model researchers develop better language models.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@InProceedings{Hamborg2017,
author = {Hamborg, Felix and Meuschke, Norman and Breitinger, Corinna and Gipp, Bela},
title = {news-please: A Generic News Crawler and Extractor},
year = {2017},
booktitle = {Proceedings of the 15th International Symposium of Information Science},
location = {Berlin},
doi = {10.5281/zenodo.4120316},
pages = {218--223},
month = {March}
}
```
### Contributions
Thanks to [@vblagoje](https://github.com/vblagoje) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
WizardLM/WizardLM_evol_instruct_V2_196k | WizardLM | 2023-08-24T03:55:18Z | 2,389 | 162 | null | [
"arxiv:2308.09583",
"arxiv:2304.12244",
"arxiv:2306.08568",
"region:us"
] | 2023-08-24T03:55:18Z | 2023-06-15T14:05:45.000Z | 2023-06-15T14:05:45 |
## News
- 🔥 🔥 🔥 [08/11/2023] We release **WizardMath** Models.
- 🔥 Our **WizardMath-70B-V1.0** model slightly outperforms some closed-source LLMs on the GSM8K, including **ChatGPT 3.5**, **Claude Instant 1** and **PaLM 2 540B**.
- 🔥 Our **WizardMath-70B-V1.0** model achieves **81.6 pass@1** on the [GSM8k Benchmarks](https://github.com/openai/grade-school-math), which is **24.8** points higher than the SOTA open-source LLM.
- 🔥 Our **WizardMath-70B-V1.0** model achieves **22.7 pass@1** on the [MATH Benchmarks](https://github.com/hendrycks/math), which is **9.2** points higher than the SOTA open-source LLM.
| Model | Checkpoint | Paper | GSM8k | MATH |Online Demo| License|
| ----- |------| ---- |------|-------| ----- | ----- |
| WizardMath-70B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardMath-70B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>| **81.6** | **22.7** |[Demo](http://47.103.63.15:50083/)| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 </a> |
| WizardMath-13B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardMath-13B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>| **63.9** | **14.0** |[Demo](http://47.103.63.15:50082/)| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 </a> |
| WizardMath-7B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardMath-7B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>| **54.9** | **10.7** | [Demo](http://47.103.63.15:50080/)| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 </a>|
<font size=4>
| <sup>Model</sup> | <sup>Checkpoint</sup> | <sup>Paper</sup> |<sup>MT-Bench</sup> | <sup>AlpacaEval</sup> | <sup>WizardEval</sup> | <sup>HumanEval</sup> | <sup>License</sup>|
| ----- |------| ---- |------|-------| ----- | ----- | ----- |
| <sup>WizardLM-13B-V1.2</sup> | <sup>🤗 <a href="https://huggingface.co/WizardLM/WizardLM-13B-V1.2" target="_blank">HF Link</a> </sup>| | <sup>7.06</sup> | <sup>89.17%</sup> | <sup>101.4% </sup>|<sup>36.6 pass@1</sup>|<sup> <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 License </a></sup> |
| <sup>WizardLM-13B-V1.1</sup> |<sup> 🤗 <a href="https://huggingface.co/WizardLM/WizardLM-13B-V1.1" target="_blank">HF Link</a> </sup> | | <sup>6.76</sup> |<sup>86.32%</sup> | <sup>99.3% </sup> |<sup>25.0 pass@1</sup>| <sup>Non-commercial</sup>|
| <sup>WizardLM-30B-V1.0</sup> | <sup>🤗 <a href="https://huggingface.co/WizardLM/WizardLM-30B-V1.0" target="_blank">HF Link</a></sup> | | <sup>7.01</sup> | | <sup>97.8% </sup> | <sup>37.8 pass@1</sup>| <sup>Non-commercial</sup> |
| <sup>WizardLM-13B-V1.0</sup> | <sup>🤗 <a href="https://huggingface.co/WizardLM/WizardLM-13B-V1.0" target="_blank">HF Link</a> </sup> | | <sup>6.35</sup> | <sup>75.31%</sup> | <sup>89.1% </sup> |<sup> 24.0 pass@1 </sup> | <sup>Non-commercial</sup>|
| <sup>WizardLM-7B-V1.0 </sup>| <sup>🤗 <a href="https://huggingface.co/WizardLM/WizardLM-7B-V1.0" target="_blank">HF Link</a> </sup> |<sup> 📃 <a href="https://arxiv.org/abs/2304.12244" target="_blank">[WizardLM]</a> </sup>| | | <sup>78.0% </sup> |<sup>19.1 pass@1 </sup>|<sup> Non-commercial</sup>|
| <sup>WizardCoder-15B-V1.0</sup> | <sup> 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-15B-V1.0" target="_blank">HF Link</a></sup> | <sup>📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a></sup> | || |<sup> 57.3 pass@1 </sup> | <sup> <a href="https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement" target="_blank">OpenRAIL-M</a></sup> |
</font>
**Repository**: https://github.com/nlpxucan/WizardLM
**Twitter**: https://twitter.com/WizardLM_AI/status/1669364947606982656
This datasets contains 143K mixture evolved data of Alpaca and ShareGPT.
This is the latest optimized version of Evol-Instruct training data of WizardLM model.
Due to the data usage license, please **merge** the original [ShareGPT](https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered) with this one to get the **final full-dataset**, which would consist of around 196k rows of data.
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
openai/webgpt_comparisons | openai | 2022-12-19T17:55:29Z | 2,387 | 180 | null | [
"arxiv:2112.09332",
"region:us"
] | 2022-12-19T17:55:29Z | 2022-12-18T19:56:41.000Z | 2022-12-18T19:56:41 | ---
pretty_name: WebGPT Comparisons
---
# Dataset Card for WebGPT Comparisons
## Dataset Description
In the [WebGPT paper](https://arxiv.org/abs/2112.09332), the authors trained a reward model from human feedback.
They used the reward model to train a long form question answering model to align with human preferences.
This is the dataset of all comparisons that were marked as suitable for reward modeling by the end of the WebGPT project.
There are 19,578 comparisons in total.
Each example in the dataset contains a pair of model answers for a question, and the associated metadata.
Each answer has a preference score from humans that can be used to determine which of the two answers are better.
Overall, an example has the following fields:
* `question`: The text of the question, together with the name of the dataset from which it was taken and a unique ID.
* `quotes_0`: The extracts that the model found while browsing for `answer_0`, together with the title of the page on which the extract was found, constructed from the HTML title and domain name of the page.
* `answer_0`: The final answer that the model composed using `quotes_0`.
* `tokens_0`: The prefix that would have been given to the model in the final step of the episode to create `answer_0`, and the completion given by the model or human. The prefix is made up of the question and the quotes, with some truncation, and the completion is simply the answer. Both are tokenized using the GPT-2 tokenizer. The concatenation of the prefix and completion is the input used for reward modeling.
* `score_0`: The strength of the preference for `answer_0` over `answer_1` as a number from −1 to 1. It sums to 0 with `score_1`, and an answer is preferred if and only if its score is positive. For reward modeling, we treat scores of 0 as soft 50% labels, and all other scores as hard labels (using only their sign).
* `quotes_1`: The counterpart to `quotes_0`.
* `answer_1`: The counterpart to `answer_0`.
* `tokens_1`: The counterpart to `tokens_0`.
* `score_1`: The counterpart to `score_0`.
This information was found in Appendix K of the WebGPT paper.
## Citation Information
[https://arxiv.org/abs/2112.09332](https://arxiv.org/abs/2112.09332)
```
@inproceedings{nakano2021webgpt,
author = {Reiichiro Nakano and Jacob Hilton and Suchir Balaji and Jeff Wu and Long Ouyang and Christina Kim and Christopher Hesse and Shantanu Jain and Vineet Kosaraju and William Saunders and Xu Jiang and Karl Cobbe and Tyna Eloundou and Gretchen Krueger and Kevin Button and Matthew Knight and Benjamin Chess and John Schulman},
title = {WebGPT: Browser-assisted question-answering with human feedback},
booktitle = {arXiv},
year = 2021,
}
```
Dataset added to the Hugging Face Hub by [@Tristan](https://huggingface.co/Tristan) and [@natolambert](https://huggingface.co/natolambert) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
mteb/mtop_domain | mteb | 2022-11-21T19:59:05Z | 2,386 | 2 | null | [
"task_categories:text-classification",
"language:de",
"language:en",
"language:es",
"language:fr",
"language:hi",
"language:th",
"region:us"
] | 2022-11-21T19:59:05Z | 2022-05-19T15:04:17.000Z | 2022-05-19T15:04:17 | ---
task_categories:
- text-classification
language:
- de
- en
- es
- fr
- hi
- th
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
christinacdl/clickbait_notclickbait_dataset | christinacdl | 2023-06-22T14:42:37Z | 2,379 | 1 | null | [
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"region:us"
] | 2023-06-22T14:42:37Z | 2023-06-22T14:38:07.000Z | 2023-06-22T14:38:07 | ---
license: apache-2.0
task_categories:
- text-classification
language:
- en
size_categories:
- 10K<n<100K
---
0 : not clickbait
1 : clickbait
Dataset cleaned from duplicates and kept only the first appearing text.
Dataset split into train and test sets using 0.2 split ratio.
Dataset split into test and validation sets using 0.2 split ratio.
Size of training set: 43.802
Size of test set: 8.760
Size of validation set: 2.191
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Dahoas/synthetic-instruct-gptj-pairwise | Dahoas | 2023-01-09T03:48:03Z | 2,374 | 41 | null | [
"region:us"
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iamtarun/python_code_instructions_18k_alpaca | iamtarun | 2023-07-27T15:51:36Z | 2,374 | 58 | null | [
"task_categories:question-answering",
"task_categories:text2text-generation",
"task_categories:text-generation",
"size_categories:10K<n<100K",
"code",
"region:us"
] | 2023-07-27T15:51:36Z | 2023-07-24T10:21:09.000Z | 2023-07-24T10:21:09 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: prompt
dtype: string
splits:
- name: train
num_bytes: 25180782
num_examples: 18612
download_size: 11357076
dataset_size: 25180782
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
task_categories:
- question-answering
- text2text-generation
- text-generation
tags:
- code
size_categories:
- 10K<n<100K
---
# Dataset Card for python_code_instructions_18k_alpaca
The dataset contains problem descriptions and code in python language.
This dataset is taken from [sahil2801/code_instructions_120k](https://huggingface.co/datasets/sahil2801/code_instructions_120k), which adds a prompt column in alpaca style. Refer to the source [here](https://huggingface.co/datasets/sahil2801/code_instructions_120k). | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
open-source-metrics/model-repos-stats | open-source-metrics | 2023-07-03T01:35:17Z | 2,370 | 5 | null | [
"region:us"
] | 2023-07-03T01:35:17Z | 2022-09-26T15:54:28.000Z | 2022-09-26T15:54:28 | ---
dataset_info:
features:
- name: 'Unnamed: 0'
dtype: int64
- name: repo_id
dtype: string
- name: author
dtype: string
- name: model_type
dtype: string
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dtype: int64
- name: downloads_30d
dtype: int64
- name: library
dtype: string
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- name: pipeline
dtype: string
- name: pytorch
dtype: bool
- name: tensorflow
dtype: bool
- name: jax
dtype: bool
- name: license
dtype: string
- name: languages
dtype: string
- name: datasets
dtype: string
- name: co2
dtype: string
- name: prs_count
dtype: int64
- name: prs_open
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- name: discussions_count
dtype: int64
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dtype: int64
- name: tags
dtype: string
- name: has_model_index
dtype: bool
- name: has_metadata
dtype: bool
- name: has_text
dtype: bool
- name: text_length
dtype: int64
splits:
- name: train
num_bytes: 68539081
num_examples: 245197
download_size: 14926618
dataset_size: 68539081
---
# Dataset Card for "model-repos-stats"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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un_ga | null | 2023-06-01T14:59:53Z | 2,365 | 0 | null | [
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] | 2023-06-01T14:59:53Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
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pretty_name: UnGa
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- es-to-ru
- es-to-zh
- fr-to-ru
- fr-to-zh
- ru-to-zh
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://opus.nlpl.eu/UN.php
- **Repository:**
- **Paper:** https://www.researchgate.net/publication/228579662_United_nations_general_assembly_resolutions_A_six-language_parallel_corpus
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This is a collection of translated documents from the United Nations originally compiled into a translation memory by Alexandre Rafalovitch, Robert Dale (see http://uncorpora.org).
### 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
@inproceedings{title = "United Nations General Assembly Resolutions: a six-language parallel corpus",
abstract = "In this paper we describe a six-ways parallel public-domain corpus consisting of 2100 United Nations General Assembly Resolutions with translations in the six official languages of the United Nations, with an average of around 3 million tokens per language. The corpus is available in a preprocessed, formatting-normalized TMX format with paragraphs aligned across multiple languages. We describe the background to the corpus and its content, the process of its construction, and some of its interesting properties.",
author = "Alexandre Rafalovitch and Robert Dale",
year = "2009",
language = "English",
booktitle = "MT Summit XII proceedings",
publisher = "International Association of Machine Translation",
}
### Contributions
Thanks to [@param087](https://github.com/param087) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
HuggingFaceM4/VQAv2 | HuggingFaceM4 | 2022-06-30T13:15:04Z | 2,337 | 8 | null | [
"region:us"
] | 2022-06-30T13:15:04Z | 2022-06-24T14:20:58.000Z | 2022-06-24T14:20:58 | Checks with https://visualqa.org/download.html:
- Num train questions: 443,757
- Num val questions: 214,354
- Num test questions: 447,793
- Num train answers: 4,437,570
- Num val answers: 2,143,540
- Num train images: 82,783
- Num val images: 40,504
- Num test images: 81,434
testdev is not mentionned:
- Num questions: 107,394
- Num images: 36,807 | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
scene_parse_150 | null | 2023-01-25T14:43:32Z | 2,331 | 14 | ade20k | [
"task_categories:image-segmentation",
"task_ids:instance-segmentation",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|ade20k",
"language:en",
"license:bsd-3-clause",
"scene-parsing",
"arxiv:1608.05442",
"region:us"
] | 2023-01-25T14:43:32Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
- expert-generated
language_creators:
- found
language:
- en
license:
- bsd-3-clause
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|ade20k
task_categories:
- image-segmentation
task_ids:
- instance-segmentation
paperswithcode_id: ade20k
pretty_name: MIT Scene Parsing Benchmark
tags:
- scene-parsing
dataset_info:
- config_name: scene_parsing
features:
- name: image
dtype: image
- name: annotation
dtype: image
- name: scene_category
dtype:
class_label:
names:
'0': airport_terminal
'1': art_gallery
'2': badlands
'3': ball_pit
'4': bathroom
'5': beach
'6': bedroom
'7': booth_indoor
'8': botanical_garden
'9': bridge
'10': bullring
'11': bus_interior
'12': butte
'13': canyon
'14': casino_outdoor
'15': castle
'16': church_outdoor
'17': closet
'18': coast
'19': conference_room
'20': construction_site
'21': corral
'22': corridor
'23': crosswalk
'24': day_care_center
'25': sand
'26': elevator_interior
'27': escalator_indoor
'28': forest_road
'29': gangplank
'30': gas_station
'31': golf_course
'32': gymnasium_indoor
'33': harbor
'34': hayfield
'35': heath
'36': hoodoo
'37': house
'38': hunting_lodge_outdoor
'39': ice_shelf
'40': joss_house
'41': kiosk_indoor
'42': kitchen
'43': landfill
'44': library_indoor
'45': lido_deck_outdoor
'46': living_room
'47': locker_room
'48': market_outdoor
'49': mountain_snowy
'50': office
'51': orchard
'52': arbor
'53': bookshelf
'54': mews
'55': nook
'56': preserve
'57': traffic_island
'58': palace
'59': palace_hall
'60': pantry
'61': patio
'62': phone_booth
'63': establishment
'64': poolroom_home
'65': quonset_hut_outdoor
'66': rice_paddy
'67': sandbox
'68': shopfront
'69': skyscraper
'70': stone_circle
'71': subway_interior
'72': platform
'73': supermarket
'74': swimming_pool_outdoor
'75': television_studio
'76': indoor_procenium
'77': train_railway
'78': coral_reef
'79': viaduct
'80': wave
'81': wind_farm
'82': bottle_storage
'83': abbey
'84': access_road
'85': air_base
'86': airfield
'87': airlock
'88': airplane_cabin
'89': airport
'90': entrance
'91': airport_ticket_counter
'92': alcove
'93': alley
'94': amphitheater
'95': amusement_arcade
'96': amusement_park
'97': anechoic_chamber
'98': apartment_building_outdoor
'99': apse_indoor
'100': apse_outdoor
'101': aquarium
'102': aquatic_theater
'103': aqueduct
'104': arcade
'105': arch
'106': archaelogical_excavation
'107': archive
'108': basketball
'109': football
'110': hockey
'111': performance
'112': rodeo
'113': soccer
'114': armory
'115': army_base
'116': arrival_gate_indoor
'117': arrival_gate_outdoor
'118': art_school
'119': art_studio
'120': artists_loft
'121': assembly_line
'122': athletic_field_indoor
'123': athletic_field_outdoor
'124': atrium_home
'125': atrium_public
'126': attic
'127': auditorium
'128': auto_factory
'129': auto_mechanics_indoor
'130': auto_mechanics_outdoor
'131': auto_racing_paddock
'132': auto_showroom
'133': backstage
'134': backstairs
'135': badminton_court_indoor
'136': badminton_court_outdoor
'137': baggage_claim
'138': shop
'139': exterior
'140': balcony_interior
'141': ballroom
'142': bamboo_forest
'143': bank_indoor
'144': bank_outdoor
'145': bank_vault
'146': banquet_hall
'147': baptistry_indoor
'148': baptistry_outdoor
'149': bar
'150': barbershop
'151': barn
'152': barndoor
'153': barnyard
'154': barrack
'155': baseball_field
'156': basement
'157': basilica
'158': basketball_court_indoor
'159': basketball_court_outdoor
'160': bathhouse
'161': batters_box
'162': batting_cage_indoor
'163': batting_cage_outdoor
'164': battlement
'165': bayou
'166': bazaar_indoor
'167': bazaar_outdoor
'168': beach_house
'169': beauty_salon
'170': bedchamber
'171': beer_garden
'172': beer_hall
'173': belfry
'174': bell_foundry
'175': berth
'176': berth_deck
'177': betting_shop
'178': bicycle_racks
'179': bindery
'180': biology_laboratory
'181': bistro_indoor
'182': bistro_outdoor
'183': bleachers_indoor
'184': bleachers_outdoor
'185': boardwalk
'186': boat_deck
'187': boathouse
'188': bog
'189': bomb_shelter_indoor
'190': bookbindery
'191': bookstore
'192': bow_window_indoor
'193': bow_window_outdoor
'194': bowling_alley
'195': box_seat
'196': boxing_ring
'197': breakroom
'198': brewery_indoor
'199': brewery_outdoor
'200': brickyard_indoor
'201': brickyard_outdoor
'202': building_complex
'203': building_facade
'204': bullpen
'205': burial_chamber
'206': bus_depot_indoor
'207': bus_depot_outdoor
'208': bus_shelter
'209': bus_station_indoor
'210': bus_station_outdoor
'211': butchers_shop
'212': cabana
'213': cabin_indoor
'214': cabin_outdoor
'215': cafeteria
'216': call_center
'217': campsite
'218': campus
'219': natural
'220': urban
'221': candy_store
'222': canteen
'223': car_dealership
'224': backseat
'225': frontseat
'226': caravansary
'227': cardroom
'228': cargo_container_interior
'229': airplane
'230': boat
'231': freestanding
'232': carport_indoor
'233': carport_outdoor
'234': carrousel
'235': casino_indoor
'236': catacomb
'237': cathedral_indoor
'238': cathedral_outdoor
'239': catwalk
'240': cavern_indoor
'241': cavern_outdoor
'242': cemetery
'243': chalet
'244': chaparral
'245': chapel
'246': checkout_counter
'247': cheese_factory
'248': chemical_plant
'249': chemistry_lab
'250': chicken_coop_indoor
'251': chicken_coop_outdoor
'252': chicken_farm_indoor
'253': chicken_farm_outdoor
'254': childs_room
'255': choir_loft_interior
'256': church_indoor
'257': circus_tent_indoor
'258': circus_tent_outdoor
'259': city
'260': classroom
'261': clean_room
'262': cliff
'263': booth
'264': room
'265': clock_tower_indoor
'266': cloister_indoor
'267': cloister_outdoor
'268': clothing_store
'269': coast_road
'270': cockpit
'271': coffee_shop
'272': computer_room
'273': conference_center
'274': conference_hall
'275': confessional
'276': control_room
'277': control_tower_indoor
'278': control_tower_outdoor
'279': convenience_store_indoor
'280': convenience_store_outdoor
'281': corn_field
'282': cottage
'283': cottage_garden
'284': courthouse
'285': courtroom
'286': courtyard
'287': covered_bridge_interior
'288': crawl_space
'289': creek
'290': crevasse
'291': library
'292': cybercafe
'293': dacha
'294': dairy_indoor
'295': dairy_outdoor
'296': dam
'297': dance_school
'298': darkroom
'299': delicatessen
'300': dentists_office
'301': department_store
'302': departure_lounge
'303': vegetation
'304': desert_road
'305': diner_indoor
'306': diner_outdoor
'307': dinette_home
'308': vehicle
'309': dining_car
'310': dining_hall
'311': dining_room
'312': dirt_track
'313': discotheque
'314': distillery
'315': ditch
'316': dock
'317': dolmen
'318': donjon
'319': doorway_indoor
'320': doorway_outdoor
'321': dorm_room
'322': downtown
'323': drainage_ditch
'324': dress_shop
'325': dressing_room
'326': drill_rig
'327': driveway
'328': driving_range_indoor
'329': driving_range_outdoor
'330': drugstore
'331': dry_dock
'332': dugout
'333': earth_fissure
'334': editing_room
'335': electrical_substation
'336': elevated_catwalk
'337': door
'338': freight_elevator
'339': elevator_lobby
'340': elevator_shaft
'341': embankment
'342': embassy
'343': engine_room
'344': entrance_hall
'345': escalator_outdoor
'346': escarpment
'347': estuary
'348': excavation
'349': exhibition_hall
'350': fabric_store
'351': factory_indoor
'352': factory_outdoor
'353': fairway
'354': farm
'355': fastfood_restaurant
'356': fence
'357': cargo_deck
'358': ferryboat_indoor
'359': passenger_deck
'360': cultivated
'361': wild
'362': field_road
'363': fire_escape
'364': fire_station
'365': firing_range_indoor
'366': firing_range_outdoor
'367': fish_farm
'368': fishmarket
'369': fishpond
'370': fitting_room_interior
'371': fjord
'372': flea_market_indoor
'373': flea_market_outdoor
'374': floating_dry_dock
'375': flood
'376': florist_shop_indoor
'377': florist_shop_outdoor
'378': fly_bridge
'379': food_court
'380': football_field
'381': broadleaf
'382': needleleaf
'383': forest_fire
'384': forest_path
'385': formal_garden
'386': fort
'387': fortress
'388': foundry_indoor
'389': foundry_outdoor
'390': fountain
'391': freeway
'392': funeral_chapel
'393': funeral_home
'394': furnace_room
'395': galley
'396': game_room
'397': garage_indoor
'398': garage_outdoor
'399': garbage_dump
'400': gasworks
'401': gate
'402': gatehouse
'403': gazebo_interior
'404': general_store_indoor
'405': general_store_outdoor
'406': geodesic_dome_indoor
'407': geodesic_dome_outdoor
'408': ghost_town
'409': gift_shop
'410': glacier
'411': glade
'412': gorge
'413': granary
'414': great_hall
'415': greengrocery
'416': greenhouse_indoor
'417': greenhouse_outdoor
'418': grotto
'419': guardhouse
'420': gulch
'421': gun_deck_indoor
'422': gun_deck_outdoor
'423': gun_store
'424': hacienda
'425': hallway
'426': handball_court
'427': hangar_indoor
'428': hangar_outdoor
'429': hardware_store
'430': hat_shop
'431': hatchery
'432': hayloft
'433': hearth
'434': hedge_maze
'435': hedgerow
'436': heliport
'437': herb_garden
'438': highway
'439': hill
'440': home_office
'441': home_theater
'442': hospital
'443': hospital_room
'444': hot_spring
'445': hot_tub_indoor
'446': hot_tub_outdoor
'447': hotel_outdoor
'448': hotel_breakfast_area
'449': hotel_room
'450': hunting_lodge_indoor
'451': hut
'452': ice_cream_parlor
'453': ice_floe
'454': ice_skating_rink_indoor
'455': ice_skating_rink_outdoor
'456': iceberg
'457': igloo
'458': imaret
'459': incinerator_indoor
'460': incinerator_outdoor
'461': industrial_area
'462': industrial_park
'463': inn_indoor
'464': inn_outdoor
'465': irrigation_ditch
'466': islet
'467': jacuzzi_indoor
'468': jacuzzi_outdoor
'469': jail_indoor
'470': jail_outdoor
'471': jail_cell
'472': japanese_garden
'473': jetty
'474': jewelry_shop
'475': junk_pile
'476': junkyard
'477': jury_box
'478': kasbah
'479': kennel_indoor
'480': kennel_outdoor
'481': kindergarden_classroom
'482': kiosk_outdoor
'483': kitchenette
'484': lab_classroom
'485': labyrinth_indoor
'486': labyrinth_outdoor
'487': lagoon
'488': artificial
'489': landing
'490': landing_deck
'491': laundromat
'492': lava_flow
'493': lavatory
'494': lawn
'495': lean-to
'496': lecture_room
'497': legislative_chamber
'498': levee
'499': library_outdoor
'500': lido_deck_indoor
'501': lift_bridge
'502': lighthouse
'503': limousine_interior
'504': liquor_store_indoor
'505': liquor_store_outdoor
'506': loading_dock
'507': lobby
'508': lock_chamber
'509': loft
'510': lookout_station_indoor
'511': lookout_station_outdoor
'512': lumberyard_indoor
'513': lumberyard_outdoor
'514': machine_shop
'515': manhole
'516': mansion
'517': manufactured_home
'518': market_indoor
'519': marsh
'520': martial_arts_gym
'521': mastaba
'522': maternity_ward
'523': mausoleum
'524': medina
'525': menhir
'526': mesa
'527': mess_hall
'528': mezzanine
'529': military_hospital
'530': military_hut
'531': military_tent
'532': mine
'533': mineshaft
'534': mini_golf_course_indoor
'535': mini_golf_course_outdoor
'536': mission
'537': dry
'538': water
'539': mobile_home
'540': monastery_indoor
'541': monastery_outdoor
'542': moon_bounce
'543': moor
'544': morgue
'545': mosque_indoor
'546': mosque_outdoor
'547': motel
'548': mountain
'549': mountain_path
'550': mountain_road
'551': movie_theater_indoor
'552': movie_theater_outdoor
'553': mudflat
'554': museum_indoor
'555': museum_outdoor
'556': music_store
'557': music_studio
'558': misc
'559': natural_history_museum
'560': naval_base
'561': newsroom
'562': newsstand_indoor
'563': newsstand_outdoor
'564': nightclub
'565': nuclear_power_plant_indoor
'566': nuclear_power_plant_outdoor
'567': nunnery
'568': nursery
'569': nursing_home
'570': oasis
'571': oast_house
'572': observatory_indoor
'573': observatory_outdoor
'574': observatory_post
'575': ocean
'576': office_building
'577': office_cubicles
'578': oil_refinery_indoor
'579': oil_refinery_outdoor
'580': oilrig
'581': operating_room
'582': optician
'583': organ_loft_interior
'584': orlop_deck
'585': ossuary
'586': outcropping
'587': outhouse_indoor
'588': outhouse_outdoor
'589': overpass
'590': oyster_bar
'591': oyster_farm
'592': acropolis
'593': aircraft_carrier_object
'594': amphitheater_indoor
'595': archipelago
'596': questionable
'597': assembly_hall
'598': assembly_plant
'599': awning_deck
'600': back_porch
'601': backdrop
'602': backroom
'603': backstage_outdoor
'604': backstairs_indoor
'605': backwoods
'606': ballet
'607': balustrade
'608': barbeque
'609': basin_outdoor
'610': bath_indoor
'611': bath_outdoor
'612': bathhouse_outdoor
'613': battlefield
'614': bay
'615': booth_outdoor
'616': bottomland
'617': breakfast_table
'618': bric-a-brac
'619': brooklet
'620': bubble_chamber
'621': buffet
'622': bulkhead
'623': bunk_bed
'624': bypass
'625': byroad
'626': cabin_cruiser
'627': cargo_helicopter
'628': cellar
'629': chair_lift
'630': cocktail_lounge
'631': corner
'632': country_house
'633': country_road
'634': customhouse
'635': dance_floor
'636': deck-house_boat_deck_house
'637': deck-house_deck_house
'638': dining_area
'639': diving_board
'640': embrasure
'641': entranceway_indoor
'642': entranceway_outdoor
'643': entryway_outdoor
'644': estaminet
'645': farm_building
'646': farmhouse
'647': feed_bunk
'648': field_house
'649': field_tent_indoor
'650': field_tent_outdoor
'651': fire_trench
'652': fireplace
'653': flashflood
'654': flatlet
'655': floating_dock
'656': flood_plain
'657': flowerbed
'658': flume_indoor
'659': flying_buttress
'660': foothill
'661': forecourt
'662': foreshore
'663': front_porch
'664': garden
'665': gas_well
'666': glen
'667': grape_arbor
'668': grove
'669': guardroom
'670': guesthouse
'671': gymnasium_outdoor
'672': head_shop
'673': hen_yard
'674': hillock
'675': housing_estate
'676': housing_project
'677': howdah
'678': inlet
'679': insane_asylum
'680': outside
'681': juke_joint
'682': jungle
'683': kraal
'684': laboratorywet
'685': landing_strip
'686': layby
'687': lean-to_tent
'688': loge
'689': loggia_outdoor
'690': lower_deck
'691': luggage_van
'692': mansard
'693': meadow
'694': meat_house
'695': megalith
'696': mens_store_outdoor
'697': mental_institution_indoor
'698': mental_institution_outdoor
'699': military_headquarters
'700': millpond
'701': millrace
'702': natural_spring
'703': nursing_home_outdoor
'704': observation_station
'705': open-hearth_furnace
'706': operating_table
'707': outbuilding
'708': palestra
'709': parkway
'710': patio_indoor
'711': pavement
'712': pawnshop_outdoor
'713': pinetum
'714': piste_road
'715': pizzeria_outdoor
'716': powder_room
'717': pumping_station
'718': reception_room
'719': rest_stop
'720': retaining_wall
'721': rift_valley
'722': road
'723': rock_garden
'724': rotisserie
'725': safari_park
'726': salon
'727': saloon
'728': sanatorium
'729': science_laboratory
'730': scrubland
'731': scullery
'732': seaside
'733': semidesert
'734': shelter
'735': shelter_deck
'736': shelter_tent
'737': shore
'738': shrubbery
'739': sidewalk
'740': snack_bar
'741': snowbank
'742': stage_set
'743': stall
'744': stateroom
'745': store
'746': streetcar_track
'747': student_center
'748': study_hall
'749': sugar_refinery
'750': sunroom
'751': supply_chamber
'752': t-bar_lift
'753': tannery
'754': teahouse
'755': threshing_floor
'756': ticket_window_indoor
'757': tidal_basin
'758': tidal_river
'759': tiltyard
'760': tollgate
'761': tomb
'762': tract_housing
'763': trellis
'764': truck_stop
'765': upper_balcony
'766': vestibule
'767': vinery
'768': walkway
'769': war_room
'770': washroom
'771': water_fountain
'772': water_gate
'773': waterscape
'774': waterway
'775': wetland
'776': widows_walk_indoor
'777': windstorm
'778': packaging_plant
'779': pagoda
'780': paper_mill
'781': park
'782': parking_garage_indoor
'783': parking_garage_outdoor
'784': parking_lot
'785': parlor
'786': particle_accelerator
'787': party_tent_indoor
'788': party_tent_outdoor
'789': pasture
'790': pavilion
'791': pawnshop
'792': pedestrian_overpass_indoor
'793': penalty_box
'794': pet_shop
'795': pharmacy
'796': physics_laboratory
'797': piano_store
'798': picnic_area
'799': pier
'800': pig_farm
'801': pilothouse_indoor
'802': pilothouse_outdoor
'803': pitchers_mound
'804': pizzeria
'805': planetarium_indoor
'806': planetarium_outdoor
'807': plantation_house
'808': playground
'809': playroom
'810': plaza
'811': podium_indoor
'812': podium_outdoor
'813': police_station
'814': pond
'815': pontoon_bridge
'816': poop_deck
'817': porch
'818': portico
'819': portrait_studio
'820': postern
'821': power_plant_outdoor
'822': print_shop
'823': priory
'824': promenade
'825': promenade_deck
'826': pub_indoor
'827': pub_outdoor
'828': pulpit
'829': putting_green
'830': quadrangle
'831': quicksand
'832': quonset_hut_indoor
'833': racecourse
'834': raceway
'835': raft
'836': railroad_track
'837': railway_yard
'838': rainforest
'839': ramp
'840': ranch
'841': ranch_house
'842': reading_room
'843': reception
'844': recreation_room
'845': rectory
'846': recycling_plant_indoor
'847': refectory
'848': repair_shop
'849': residential_neighborhood
'850': resort
'851': rest_area
'852': restaurant
'853': restaurant_kitchen
'854': restaurant_patio
'855': restroom_indoor
'856': restroom_outdoor
'857': revolving_door
'858': riding_arena
'859': river
'860': road_cut
'861': rock_arch
'862': roller_skating_rink_indoor
'863': roller_skating_rink_outdoor
'864': rolling_mill
'865': roof
'866': roof_garden
'867': root_cellar
'868': rope_bridge
'869': roundabout
'870': roundhouse
'871': rubble
'872': ruin
'873': runway
'874': sacristy
'875': salt_plain
'876': sand_trap
'877': sandbar
'878': sauna
'879': savanna
'880': sawmill
'881': schoolhouse
'882': schoolyard
'883': science_museum
'884': scriptorium
'885': sea_cliff
'886': seawall
'887': security_check_point
'888': server_room
'889': sewer
'890': sewing_room
'891': shed
'892': shipping_room
'893': shipyard_outdoor
'894': shoe_shop
'895': shopping_mall_indoor
'896': shopping_mall_outdoor
'897': shower
'898': shower_room
'899': shrine
'900': signal_box
'901': sinkhole
'902': ski_jump
'903': ski_lodge
'904': ski_resort
'905': ski_slope
'906': sky
'907': skywalk_indoor
'908': skywalk_outdoor
'909': slum
'910': snowfield
'911': massage_room
'912': mineral_bath
'913': spillway
'914': sporting_goods_store
'915': squash_court
'916': stable
'917': baseball
'918': stadium_outdoor
'919': stage_indoor
'920': stage_outdoor
'921': staircase
'922': starting_gate
'923': steam_plant_outdoor
'924': steel_mill_indoor
'925': storage_room
'926': storm_cellar
'927': street
'928': strip_mall
'929': strip_mine
'930': student_residence
'931': submarine_interior
'932': sun_deck
'933': sushi_bar
'934': swamp
'935': swimming_hole
'936': swimming_pool_indoor
'937': synagogue_indoor
'938': synagogue_outdoor
'939': taxistand
'940': taxiway
'941': tea_garden
'942': tearoom
'943': teashop
'944': television_room
'945': east_asia
'946': mesoamerican
'947': south_asia
'948': western
'949': tennis_court_indoor
'950': tennis_court_outdoor
'951': tent_outdoor
'952': terrace_farm
'953': indoor_round
'954': indoor_seats
'955': theater_outdoor
'956': thriftshop
'957': throne_room
'958': ticket_booth
'959': tobacco_shop_indoor
'960': toll_plaza
'961': tollbooth
'962': topiary_garden
'963': tower
'964': town_house
'965': toyshop
'966': track_outdoor
'967': trading_floor
'968': trailer_park
'969': train_interior
'970': train_station_outdoor
'971': station
'972': tree_farm
'973': tree_house
'974': trench
'975': trestle_bridge
'976': tundra
'977': rail_indoor
'978': rail_outdoor
'979': road_indoor
'980': road_outdoor
'981': turkish_bath
'982': ocean_deep
'983': ocean_shallow
'984': utility_room
'985': valley
'986': van_interior
'987': vegetable_garden
'988': velodrome_indoor
'989': velodrome_outdoor
'990': ventilation_shaft
'991': veranda
'992': vestry
'993': veterinarians_office
'994': videostore
'995': village
'996': vineyard
'997': volcano
'998': volleyball_court_indoor
'999': volleyball_court_outdoor
'1000': voting_booth
'1001': waiting_room
'1002': walk_in_freezer
'1003': warehouse_indoor
'1004': warehouse_outdoor
'1005': washhouse_indoor
'1006': washhouse_outdoor
'1007': watchtower
'1008': water_mill
'1009': water_park
'1010': water_tower
'1011': water_treatment_plant_indoor
'1012': water_treatment_plant_outdoor
'1013': block
'1014': cascade
'1015': cataract
'1016': fan
'1017': plunge
'1018': watering_hole
'1019': weighbridge
'1020': wet_bar
'1021': wharf
'1022': wheat_field
'1023': whispering_gallery
'1024': widows_walk_interior
'1025': windmill
'1026': window_seat
'1027': barrel_storage
'1028': winery
'1029': witness_stand
'1030': woodland
'1031': workroom
'1032': workshop
'1033': wrestling_ring_indoor
'1034': wrestling_ring_outdoor
'1035': yard
'1036': youth_hostel
'1037': zen_garden
'1038': ziggurat
'1039': zoo
'1040': forklift
'1041': hollow
'1042': hutment
'1043': pueblo
'1044': vat
'1045': perfume_shop
'1046': steel_mill_outdoor
'1047': orchestra_pit
'1048': bridle_path
'1049': lyceum
'1050': one-way_street
'1051': parade_ground
'1052': pump_room
'1053': recycling_plant_outdoor
'1054': chuck_wagon
splits:
- name: train
num_bytes: 8468086
num_examples: 20210
- name: test
num_bytes: 744607
num_examples: 3352
- name: validation
num_bytes: 838032
num_examples: 2000
download_size: 1179202534
dataset_size: 10050725
- config_name: instance_segmentation
features:
- name: image
dtype: image
- name: annotation
dtype: image
splits:
- name: train
num_bytes: 862611544
num_examples: 20210
- name: test
num_bytes: 212493928
num_examples: 3352
- name: validation
num_bytes: 87502294
num_examples: 2000
download_size: 1197393920
dataset_size: 1162607766
---
# Dataset Card for MIT Scene Parsing Benchmark
## 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:** [MIT Scene Parsing Benchmark homepage](http://sceneparsing.csail.mit.edu/)
- **Repository:** [Scene Parsing repository (Caffe/Torch7)](https://github.com/CSAILVision/sceneparsing),[Scene Parsing repository (PyTorch)](https://github.com/CSAILVision/semantic-segmentation-pytorch) and [Instance Segmentation repository](https://github.com/CSAILVision/placeschallenge/tree/master/instancesegmentation)
- **Paper:** [Scene Parsing through ADE20K Dataset](http://people.csail.mit.edu/bzhou/publication/scene-parse-camera-ready.pdf) and [Semantic Understanding of Scenes through ADE20K Dataset](https://arxiv.org/abs/1608.05442)
- **Leaderboard:** [MIT Scene Parsing Benchmark leaderboard](http://sceneparsing.csail.mit.edu/#:~:text=twice%20per%20week.-,leaderboard,-Organizers)
- **Point of Contact:** [Bolei Zhou](mailto:bzhou@ie.cuhk.edu.hk)
### Dataset Summary
Scene parsing is the task of segmenting and parsing an image into different image regions associated with semantic categories, such as sky, road, person, and bed. MIT Scene Parsing Benchmark (SceneParse150) provides a standard training and evaluation platform for the algorithms of scene parsing. The data for this benchmark comes from ADE20K Dataset which contains more than 20K scene-centric images exhaustively annotated with objects and object parts. Specifically, the benchmark is divided into 20K images for training, 2K images for validation, and another batch of held-out images for testing. There are in total 150 semantic categories included for evaluation, which include e.g. sky, road, grass, and discrete objects like person, car, bed. Note that there are non-uniform distribution of objects occuring in the images, mimicking a more natural object occurrence in daily scene.
The goal of this benchmark is to segment and parse an image into different image regions associated with semantic categories, such as sky, road, person, and bedThis benchamark is similar to semantic segmentation tasks in COCO and Pascal Dataset, but the data is more scene-centric and with a diverse range of object categories. The data for this benchmark comes from ADE20K Dataset which contains more than 20K scene-centric images exhaustively annotated with objects and object parts.
### Supported Tasks and Leaderboards
- `scene-parsing`: The goal of this task is to segment the whole image densely into semantic classes (image regions), where each pixel is assigned a class label such as the region of *tree* and the region of *building*.
[The leaderboard](http://sceneparsing.csail.mit.edu/#:~:text=twice%20per%20week.-,leaderboard,-Organizers) for this task ranks the models by considering the mean of the pixel-wise accuracy and class-wise IoU as the final score. Pixel-wise accuracy indicates the ratio of pixels which are correctly predicted, while class-wise IoU indicates the Intersection of Union of pixels averaged over all the 150 semantic categories. Refer to the [Development Kit](https://github.com/CSAILVision/sceneparsing) for the detail.
- `instance-segmentation`: The goal of this task is to detect the object instances inside an image and further generate the precise segmentation masks of the objects. Its difference compared to the task of scene parsing is that in scene parsing there is no instance concept for the segmented regions, instead in instance segmentation if there are three persons in the scene, the network is required to segment each one of the person regions. This task doesn't have an active leaderboard. The performance of the instance segmentation algorithms is evaluated by Average Precision (AP, or mAP), following COCO evaluation metrics. For each image, at most 255 top-scoring instance masks are taken across all categories. Each instance mask prediction is only considered if its IoU with ground truth is above a certain threshold. There are 10 IoU thresholds of 0.50:0.05:0.95 for evaluation. The final AP is averaged across 10 IoU thresholds and 100 categories. You can refer to COCO evaluation page for more explanation: http://mscoco.org/dataset/#detections-eval
### Languages
English.
## Dataset Structure
### Data Instances
A data point comprises an image and its annotation mask, which is `None` in the testing set. The `scene_parsing` configuration has an additional `scene_category` field.
#### `scene_parsing`
```
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=683x512 at 0x1FF32A3EDA0>,
'annotation': <PIL.PngImagePlugin.PngImageFile image mode=L size=683x512 at 0x1FF32E5B978>,
'scene_category': 0
}
```
#### `instance_segmentation`
```
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=256x256 at 0x20B51B5C400>,
'annotation': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256 at 0x20B57051B38>
}
```
### Data Fields
#### `scene_parsing`
- `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`.
- `annotation`: A `PIL.Image.Image` object containing the annotation mask.
- `scene_category`: A scene category for the image (e.g. `airport_terminal`, `canyon`, `mobile_home`).
> **Note**: annotation masks contain labels ranging from 0 to 150, where 0 refers to "other objects". Those pixels are not considered in the official evaluation. Refer to [this file](https://github.com/CSAILVision/sceneparsing/blob/master/objectInfo150.csv) for the information about the labels of the 150 semantic categories, including indices, pixel ratios and names.
#### `instance_segmentation`
- `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`.
- `annotation`: A `PIL.Image.Image` object containing the annotation mask.
> **Note**: in the instance annotation masks, the R(ed) channel encodes category ID, and the G(reen) channel encodes instance ID. Each object instance has a unique instance ID regardless of its category ID. In the dataset, all images have <256 object instances. Refer to [this file (train split)](https://github.com/CSAILVision/placeschallenge/blob/master/instancesegmentation/instanceInfo100_train.txt) and to [this file (validation split)](https://github.com/CSAILVision/placeschallenge/blob/master/instancesegmentation/instanceInfo100_val.txt) for the information about the labels of the 100 semantic categories. To find the mapping between the semantic categories for `instance_segmentation` and `scene_parsing`, refer to [this file](https://github.com/CSAILVision/placeschallenge/blob/master/instancesegmentation/categoryMapping.txt).
### Data Splits
The data is split into training, test and validation set. The training data contains 20210 images, the testing data contains 3352 images and the validation data contains 2000 images.
## Dataset Creation
### Curation Rationale
The rationale from the paper for the ADE20K dataset from which this benchmark originates:
> Semantic understanding of visual scenes is one of the holy grails of computer vision. Despite efforts of the community in data collection, there are still few image datasets covering a wide range of scenes and object categories with pixel-wise annotations for scene understanding. In this work, we present a densely annotated dataset ADE20K, which spans diverse annotations of scenes, objects, parts of objects, and
in some cases even parts of parts.
> The motivation of this work is to collect a dataset that has densely annotated images (every pixel has a semantic label) with a large and an unrestricted open vocabulary. The
images in our dataset are manually segmented in great detail, covering a diverse set of scenes, object and object part categories. The challenge for collecting such annotations is finding reliable annotators, as well as the fact that labeling is difficult if the class list is not defined in advance. On the other hand, open vocabulary naming also suffers from naming inconsistencies across different annotators. In contrast,
our dataset was annotated by a single expert annotator, providing extremely detailed and exhaustive image annotations. On average, our annotator labeled 29 annotation segments per image, compared to the 16 segments per image labeled by external annotators (like workers from Amazon Mechanical Turk). Furthermore, the data consistency and quality are much higher than that of external annotators.
### Source Data
#### Initial Data Collection and Normalization
Images come from the LabelMe, SUN datasets, and Places and were selected to cover the 900 scene categories defined in the SUN database.
This benchmark was built by selecting the top 150 objects ranked by their total pixel ratios from the ADE20K dataset. As the original images in the ADE20K dataset have various sizes, for simplicity those large-sized images were rescaled to make their minimum heights or widths as 512. Among the 150 objects, there are 35 stuff classes (i.e., wall, sky, road) and 115 discrete objects (i.e., car, person, table). The annotated pixels of the 150 objects occupy 92.75% of all the pixels in the dataset, where the stuff classes occupy 60.92%, and discrete objects occupy 31.83%.
#### Who are the source language producers?
The same as in the LabelMe, SUN datasets, and Places datasets.
### Annotations
#### Annotation process
Annotation process for the ADE20K dataset:
> **Image Annotation.** For our dataset, we are interested in having a diverse set of scenes with dense annotations of all the objects present. Images come from the LabelMe, SUN datasets, and Places and were selected to cover the 900 scene categories defined in the SUN database. Images were annotated by a single expert worker using the LabelMe interface. Fig. 2 shows a snapshot of the annotation interface and one fully segmented image. The worker provided three types of annotations: object segments with names, object parts, and attributes. All object instances are segmented independently so that the dataset could be used to train and evaluate detection or segmentation algorithms. Datasets such as COCO, Pascal or Cityscape start by defining a set of object categories of interest. However, when labeling all the objects in a scene, working with a predefined list of objects is not possible as new categories
appear frequently (see fig. 5.d). Here, the annotator created a dictionary of visual concepts where new classes were added constantly to ensure consistency in object naming. Object parts are associated with object instances. Note that parts can have parts too, and we label these associations as well. For example, the ‘rim’ is a part of a ‘wheel’, which in turn is part of a ‘car’. A ‘knob’ is a part of a ‘door’
that can be part of a ‘cabinet’. The total part hierarchy has a depth of 3. The object and part hierarchy is in the supplementary materials.
> **Annotation Consistency.** Defining a labeling protocol is relatively easy when the labeling task is restricted to a fixed list of object classes, however it becomes challenging when the class list is openended. As the goal is to label all the objects within each image, the list of classes grows unbounded. >Many object classes appear only a few times across the entire collection of images. However, those rare >object classes cannot be ignored as they might be important elements for the interpretation of the scene. >Labeling in these conditions becomes difficult because we need to keep a growing list of all the object >classes in order to have a consistent naming across the entire dataset. Despite the annotator’s best effort, >the process is not free of noise. To analyze the annotation consistency we took a subset of 61 randomly >chosen images from the validation set, then asked our annotator to annotate them again (there is a time difference of six months). One expects that there are some differences between the two annotations. A few examples are shown in Fig 3. On average, 82.4% of the pixels got the same label. The remaining 17.6% of pixels had some errors for which we grouped into three error types as follows:
>
> • Segmentation quality: Variations in the quality of segmentation and outlining of the object boundary. One typical source of error arises when segmenting complex objects such as buildings and trees, which can be segmented with different degrees of precision. 5.7% of the pixels had this type of error.
>
> • Object naming: Differences in object naming (due to ambiguity or similarity between concepts, for instance calling a big car a ‘car’ in one segmentation and a ‘truck’ in the another one, or a ‘palm tree’ a‘tree’. 6.0% of the pixels had naming issues. These errors can be reduced by defining a very precise terminology, but this becomes much harder with a large growing vocabulary.
>
> • Segmentation quantity: Missing objects in one of the two segmentations. There is a very large number of objects in each image and some images might be annotated more thoroughly than others. For example, in the third column of Fig 3 the annotator missed some small objects in different annotations. 5.9% of the pixels are due to missing labels. A similar issue existed in segmentation datasets such as the Berkeley Image segmentation dataset.
>
> The median error values for the three error types are: 4.8%, 0.3% and 2.6% showing that the mean value is dominated by a few images, and that the most common type of error is segmentation quality.
To further compare the annotation done by our single expert annotator and the AMT-like annotators, 20 images
from the validation set are annotated by two invited external annotators, both with prior experience in image labeling. The first external annotator had 58.5% of inconsistent pixels compared to the segmentation provided by our annotator, and the second external annotator had 75% of the inconsistent pixels. Many of these inconsistencies are due to the poor quality of the segmentations provided by external annotators (as it has been observed with AMT which requires multiple verification steps for quality control). For the
best external annotator (the first one), 7.9% of pixels have inconsistent segmentations (just slightly worse than our annotator), 14.9% have inconsistent object naming and 35.8% of the pixels correspond to missing objects, which is due to the much smaller number of objects annotated by the external annotator in comparison with the ones annotated by our expert annotator. The external annotators labeled on average 16 segments per image while our annotator provided 29 segments per image.
#### Who are the annotators?
Three expert annotators and the AMT-like annotators.
### 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
Refer to the `Annotation Consistency` subsection of `Annotation Process`.
## Additional Information
### Dataset Curators
Bolei Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso and Antonio Torralba.
### Licensing Information
The MIT Scene Parsing Benchmark dataset is licensed under a [BSD 3-Clause License](https://github.com/CSAILVision/sceneparsing/blob/master/LICENSE).
### Citation Information
```bibtex
@inproceedings{zhou2017scene,
title={Scene Parsing through ADE20K Dataset},
author={Zhou, Bolei and Zhao, Hang and Puig, Xavier and Fidler, Sanja and Barriuso, Adela and Torralba, Antonio},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
year={2017}
}
@article{zhou2016semantic,
title={Semantic understanding of scenes through the ade20k dataset},
author={Zhou, Bolei and Zhao, Hang and Puig, Xavier and Fidler, Sanja and Barriuso, Adela and Torralba, Antonio},
journal={arXiv preprint arXiv:1608.05442},
year={2016}
}
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. | [
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danbider/codegen | danbider | 2023-07-21T01:53:30Z | 2,330 | 0 | null | [
"region:us"
] | 2023-07-21T01:53:30Z | 2023-07-20T23:14:53.000Z | 2023-07-20T23:14:53 | Entry not found | [
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indonlp/indonlu | indonlp | 2023-02-03T05:49:02Z | 2,325 | 24 | indonlu-benchmark | [
"task_categories:question-answering",
"task_categories:text-classification",
"task_categories:token-classification",
"task_ids:closed-domain-qa",
"task_ids:multi-class-classification",
"task_ids:named-entity-recognition",
"task_ids:part-of-speech",
"task_ids:semantic-similarity-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"size_categories:n<1K",
"source_datasets:original",
"language:id",
"license:mit",
"keyphrase-extraction",
"span-extraction",
"aspect-based-sentiment-analysis",
"arxiv:1809.03391",
"region:us"
] | 2023-02-03T05:49:02Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- id
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
- 1K<n<10K
- n<1K
source_datasets:
- original
task_categories:
- question-answering
- text-classification
- token-classification
task_ids:
- closed-domain-qa
- multi-class-classification
- named-entity-recognition
- part-of-speech
- semantic-similarity-classification
- sentiment-classification
paperswithcode_id: indonlu-benchmark
pretty_name: IndoNLU
configs:
- bapos
- casa
- emot
- facqa
- hoasa
- keps
- nergrit
- nerp
- posp
- smsa
- terma
- wrete
tags:
- keyphrase-extraction
- span-extraction
- aspect-based-sentiment-analysis
dataset_info:
- config_name: emot
features:
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dtype: string
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dtype:
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names:
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1: anger
2: love
3: fear
4: happy
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download_size: 840917
dataset_size: 855356
- config_name: smsa
features:
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num_examples: 500
download_size: 2509229
dataset_size: 2536544
- config_name: casa
features:
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splits:
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num_examples: 180
download_size: 144903
dataset_size: 145961
- config_name: hoasa
features:
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dtype:
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- name: air_panas
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- name: sunrise_meal
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- name: tv
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- name: wifi
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splits:
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- name: test
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download_size: 477314
dataset_size: 572824
- config_name: wrete
features:
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dtype: string
- name: hypothesis
dtype: string
- name: category
dtype: string
- name: label
dtype:
class_label:
names:
0: NotEntail
1: Entail_or_Paraphrase
splits:
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num_examples: 300
- name: validation
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num_examples: 50
- name: test
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num_examples: 100
download_size: 151018
dataset_size: 150665
- config_name: posp
features:
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sequence: string
- name: pos_tags
sequence:
class_label:
names:
0: B-PPO
1: B-KUA
2: B-ADV
3: B-PRN
4: B-VBI
5: B-PAR
6: B-VBP
7: B-NNP
8: B-UNS
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splits:
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num_examples: 840
download_size: 2407206
dataset_size: 3445992
- config_name: bapos
features:
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sequence: string
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sequence:
class_label:
names:
0: B-PR
1: B-CD
2: I-PR
3: B-SYM
4: B-JJ
5: B-DT
6: I-UH
7: I-NND
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26: B-VB
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29: B-UH
30: I-NN
31: B-PRP
32: I-SC
33: B-Z
34: I-PRP
35: I-OD
36: I-SYM
37: B-WH
38: B-FW
39: I-CC
40: B-X
splits:
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- name: test
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dataset_size: 4706885
- config_name: terma
features:
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sequence: string
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sequence:
class_label:
names:
0: I-SENTIMENT
1: O
2: I-ASPECT
3: B-SENTIMENT
4: B-ASPECT
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- name: test
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num_examples: 1000
download_size: 816822
dataset_size: 1360240
- config_name: keps
features:
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sequence: string
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sequence:
class_label:
names:
0: O
1: B
2: I
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- name: test
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num_examples: 247
download_size: 134042
dataset_size: 283684
- config_name: nergrit
features:
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
0: I-PERSON
1: B-ORGANISATION
2: I-ORGANISATION
3: B-PLACE
4: I-PLACE
5: O
6: B-PERSON
splits:
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- name: validation
num_bytes: 119567
num_examples: 209
- name: test
num_bytes: 117274
num_examples: 209
download_size: 641265
dataset_size: 1197551
- config_name: nerp
features:
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
0: I-PPL
1: B-EVT
2: B-PLC
3: I-IND
4: B-IND
5: B-FNB
6: I-EVT
7: B-PPL
8: I-PLC
9: O
10: I-FNB
splits:
- name: train
num_bytes: 2751348
num_examples: 6720
- name: validation
num_bytes: 343924
num_examples: 840
- name: test
num_bytes: 350720
num_examples: 840
download_size: 1725986
dataset_size: 3445992
- config_name: facqa
features:
- name: question
sequence: string
- name: passage
sequence: string
- name: seq_label
sequence:
class_label:
names:
0: O
1: B
2: I
splits:
- name: train
num_bytes: 2454368
num_examples: 2495
- name: validation
num_bytes: 306249
num_examples: 311
- name: test
num_bytes: 306831
num_examples: 311
download_size: 2591968
dataset_size: 3067448
---
# Dataset Card for IndoNLU
## 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:** [IndoNLU Website](https://www.indobenchmark.com/)
- **Repository:** [IndoNLU GitHub](https://github.com/indobenchmark/indonlu)
- **Paper:** [IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding](https://www.aclweb.org/anthology/2020aacl-main.85.pdf)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
The IndoNLU benchmark is a collection of resources for training, evaluating, and analyzing natural language understanding systems for Bahasa Indonesia (Indonesian language).
There are 12 datasets in IndoNLU benchmark for Indonesian natural language understanding.
1. `EmoT`: An emotion classification dataset collected from the social media platform Twitter. The dataset consists of around 4000 Indonesian colloquial language tweets, covering five different emotion labels: anger, fear, happy, love, and sadness
2. `SmSA`: This sentence-level sentiment analysis dataset is a collection of comments and reviews in Indonesian obtained from multiple online platforms. The text was crawled and then annotated by several Indonesian linguists to construct this dataset. There are three possible sentiments on the `SmSA` dataset: positive, negative, and neutral
3. `CASA`: An aspect-based sentiment analysis dataset consisting of around a thousand car reviews collected from multiple Indonesian online automobile platforms. The dataset covers six aspects of car quality. We define the task to be a multi-label classification task, where each label represents a sentiment for a single aspect with three possible values: positive, negative, and neutral.
4. `HoASA`: An aspect-based sentiment analysis dataset consisting of hotel reviews collected from the hotel aggregator platform, [AiryRooms](https://github.com/annisanurulazhar/absa-playground). The dataset covers ten different aspects of hotel quality. Similar to the `CASA` dataset, each review is labeled with a single sentiment label for each aspect. There are four possible sentiment classes for each sentiment label: positive, negative, neutral, and positive-negative. The positivenegative label is given to a review that contains multiple sentiments of the same aspect but for different objects (e.g., cleanliness of bed and toilet).
5. `WReTE`: The Wiki Revision Edits Textual Entailment dataset consists of 450 sentence pairs constructed from Wikipedia revision history. The dataset contains pairs of sentences and binary semantic relations between the pairs. The data are labeled as entailed when the meaning of the second sentence can be derived from the first one, and not entailed otherwise.
6. `POSP`: This Indonesian part-of-speech tagging (POS) dataset is collected from Indonesian news websites. The dataset consists of around 8000 sentences with 26 POS tags. The POS tag labels follow the [Indonesian Association of Computational Linguistics (INACL) POS Tagging Convention](http://inacl.id/inacl/wp-content/uploads/2017/06/INACL-POS-Tagging-Convention-26-Mei.pdf).
7. `BaPOS`: This POS tagging dataset contains about 1000 sentences, collected from the [PAN Localization Project](http://www.panl10n.net/). In this dataset, each word is tagged by one of [23 POS tag classes](https://bahasa.cs.ui.ac.id/postag/downloads/Tagset.pdf). Data splitting used in this benchmark follows the experimental setting used by [Kurniawan and Aji (2018)](https://arxiv.org/abs/1809.03391).
8. `TermA`: This span-extraction dataset is collected from the hotel aggregator platform, [AiryRooms](https://github.com/jordhy97/final_project). The dataset consists of thousands of hotel reviews, which each contain a span label for aspect and sentiment words representing the opinion of the reviewer on the corresponding aspect. The labels use Inside-Outside-Beginning (IOB) tagging representation with two kinds of tags, aspect and sentiment.
9. `KEPS`: This keyphrase extraction dataset consists of text from Twitter discussing banking products and services and is written in the Indonesian language. A phrase containing important information is considered a keyphrase. Text may contain one or more keyphrases since important phrases can be located at different positions. The dataset follows the IOB chunking format, which represents the position of the keyphrase.
10. `NERGrit`: This NER dataset is taken from the [Grit-ID repository](https://github.com/grit-id/nergrit-corpus), and the labels are spans in IOB chunking representation. The dataset consists of three kinds of named entity tags, PERSON (name of person), PLACE (name of location), and ORGANIZATION (name of organization).
11. `NERP`: This NER dataset (Hoesen and Purwarianti, 2018) contains texts collected from several Indonesian news websites. There are five labels available in this dataset, PER (name of person), LOC (name of location), IND (name of product or brand), EVT (name of the event), and FNB (name of food and beverage). Similar to the `TermA` dataset, the `NERP` dataset uses the IOB chunking format.
12. `FacQA`: The goal of the FacQA dataset is to find the answer to a question from a provided short passage from a news article. Each row in the FacQA dataset consists of a question, a short passage, and a label phrase, which can be found inside the corresponding short passage. There are six categories of questions: date, location, name, organization, person, and quantitative.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
Indonesian
## Dataset Structure
### Data Instances
1. `EmoT` dataset
A data point consists of `tweet` and `label`. An example from the train set looks as follows:
```
{
'tweet': 'Ini adalah hal yang paling membahagiakan saat biasku foto bersama ELF #ReturnOfTheLittlePrince #HappyHeeChulDay'
'label': 4,
}
```
2. `SmSA` dataset
A data point consists of `text` and `label`. An example from the train set looks as follows:
```
{
'text': 'warung ini dimiliki oleh pengusaha pabrik tahu yang sudah puluhan tahun terkenal membuat tahu putih di bandung . tahu berkualitas , dipadu keahlian memasak , dipadu kretivitas , jadilah warung yang menyajikan menu utama berbahan tahu , ditambah menu umum lain seperti ayam . semuanya selera indonesia . harga cukup terjangkau . jangan lewatkan tahu bletoka nya , tidak kalah dengan yang asli dari tegal !'
'label': 0,
}
```
3. `CASA` dataset
A data point consists of `sentence` and multi-label `feature`, `machine`, `others`, `part`, `price`, and `service`. An example from the train set looks as follows:
```
{
'sentence': 'Saya memakai Honda Jazz GK5 tahun 2014 ( pertama meluncur ) . Mobil nya bagus dan enak sesuai moto nya menyenangkan untuk dikendarai',
'fuel': 1,
'machine': 1,
'others': 2,
'part': 1,
'price': 1,
'service': 1
}
```
4. `HoASA` dataset
A data point consists of `sentence` and multi-label `ac`, `air_panas`, `bau`, `general`, `kebersihan`, `linen`, `service`, `sunrise_meal`, `tv`, and `wifi`. An example from the train set looks as follows:
```
{
'sentence': 'kebersihan kurang...',
'ac': 1,
'air_panas': 1,
'bau': 1,
'general': 1,
'kebersihan': 0,
'linen': 1,
'service': 1,
'sunrise_meal': 1,
'tv': 1,
'wifi': 1
}
```
5. `WreTE` dataset
A data point consists of `premise`, `hypothesis`, `category`, and `label`. An example from the train set looks as follows:
```
{
'premise': 'Pada awalnya bangsa Israel hanya terdiri dari satu kelompok keluarga di antara banyak kelompok keluarga yang hidup di tanah Kanan pada abad 18 SM .',
'hypothesis': 'Pada awalnya bangsa Yahudi hanya terdiri dari satu kelompok keluarga di antara banyak kelompok keluarga yang hidup di tanah Kanan pada abad 18 SM .'
'category': 'menolak perubahan teks terakhir oleh istimewa kontribusi pengguna 141 109 98 87 141 109 98 87 dan mengembalikan revisi 6958053 oleh johnthorne',
'label': 0,
}
```
6. `POSP` dataset
A data point consists of `tokens` and `pos_tags`. An example from the train set looks as follows:
```
{
'tokens': ['kepala', 'dinas', 'tata', 'kota', 'manado', 'amos', 'kenda', 'menyatakan', 'tidak', 'tahu', '-', 'menahu', 'soal', 'pencabutan', 'baliho', '.', 'ia', 'enggan', 'berkomentar', 'banyak', 'karena', 'merasa', 'bukan', 'kewenangannya', '.'],
'pos_tags': [11, 6, 11, 11, 7, 7, 7, 9, 23, 4, 21, 9, 11, 11, 11, 21, 3, 2, 4, 1, 19, 9, 23, 11, 21]
}
```
7. `BaPOS` dataset
A data point consists of `tokens` and `pos_tags`. An example from the train set looks as follows:
```
{
'tokens': ['Kera', 'untuk', 'amankan', 'pesta', 'olahraga'],
'pos_tags': [27, 8, 26, 27, 30]
}
```
8. `TermA` dataset
A data point consists of `tokens` and `seq_label`. An example from the train set looks as follows:
```
{
'tokens': ['kamar', 'saya', 'ada', 'kendala', 'di', 'ac', 'tidak', 'berfungsi', 'optimal', '.', 'dan', 'juga', 'wifi', 'koneksi', 'kurang', 'stabil', '.'],
'seq_label': [1, 1, 1, 1, 1, 4, 3, 0, 0, 1, 1, 1, 4, 2, 3, 0, 1]
}
```
9. `KEPS` dataset
A data point consists of `tokens` and `seq_label`. An example from the train set looks as follows:
```
{
'tokens': ['Setelah', 'melalui', 'proses', 'telepon', 'yang', 'panjang', 'tutup', 'sudah', 'kartu', 'kredit', 'bca', 'Ribet'],
'seq_label': [0, 1, 1, 2, 0, 0, 1, 0, 1, 2, 2, 1]
}
```
10. `NERGrit` dataset
A data point consists of `tokens` and `ner_tags`. An example from the train set looks as follows:
```
{
'tokens': ['Kontribusinya', 'terhadap', 'industri', 'musik', 'telah', 'mengumpulkan', 'banyak', 'prestasi', 'termasuk', 'lima', 'Grammy', 'Awards', ',', 'serta', 'dua', 'belas', 'nominasi', ';', 'dua', 'Guinness', 'World', 'Records', ';', 'dan', 'penjualannya', 'diperkirakan', 'sekitar', '64', 'juta', 'rekaman', '.'],
'ner_tags': [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5]}
```
11. `NERP` dataset
A data point consists of `tokens` and `ner_tags`. An example from the train set looks as follows:
```
{
'tokens': ['kepala', 'dinas', 'tata', 'kota', 'manado', 'amos', 'kenda', 'menyatakan', 'tidak', 'tahu', '-', 'menahu', 'soal', 'pencabutan', 'baliho', '.', 'ia', 'enggan', 'berkomentar', 'banyak', 'karena', 'merasa', 'bukan', 'kewenangannya', '.'],
'ner_tags': [9, 9, 9, 9, 2, 7, 0, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9]
}
```
12. `FacQA` dataset
A data point consists of `question`, `passage`, and `seq_label`. An example from the train set looks as follows:
```
{
'passage': ['Lewat', 'telepon', 'ke', 'kantor', 'berita', 'lokal', 'Current', 'News', 'Service', ',', 'Hezb-ul', 'Mujahedeen', ',', 'kelompok', 'militan', 'Kashmir', 'yang', 'terbesar', ',', 'menyatakan', 'bertanggung', 'jawab', 'atas', 'ledakan', 'di', 'Srinagar', '.'],
'question': ['Kelompok', 'apakah', 'yang', 'menyatakan', 'bertanggung', 'jawab', 'atas', 'ledakan', 'di', 'Srinagar', '?'],
'seq_label': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
}
```
### Data Fields
1. `EmoT` dataset
- `tweet`: a `string` feature.
- `label`: an emotion label, with possible values including `sadness`, `anger`, `love`, `fear`, `happy`.
2. `SmSA` dataset
- `text`: a `string` feature.
- `label`: a sentiment label, with possible values including `positive`, `neutral`, `negative`.
3. `CASA` dataset
- `sentence`: a `string` feature.
- `fuel`: a sentiment label, with possible values including `negative`, `neutral`, `positive`.
- `machine`: a sentiment label, with possible values including `negative`, `neutral`, `positive`.
- `others`: a sentiment label, with possible values including `negative`, `neutral`, `positive`.
- `part`: a sentiment label, with possible values including `negative`, `neutral`, `positive`.
- `price`: a sentiment label, with possible values including `negative`, `neutral`, `positive`.
- `service`: a sentiment label, with possible values including `negative`, `neutral`, `positive`.
4. `HoASA` dataset
- `sentence`: a `string` feature.
- `ac`: a sentiment label, with possible values including `neg`, `neut`, `pos`, `neg_pos`.
- `air_panas`: a sentiment label, with possible values including `neg`, `neut`, `pos`, `neg_pos`.
- `bau`: a sentiment label, with possible values including `neg`, `neut`, `pos`, `neg_pos`.
- `general`: a sentiment label, with possible values including `neg`, `neut`, `pos`, `neg_pos`.
- `kebersihan`: a sentiment label, with possible values including `neg`, `neut`, `pos`, `neg_pos`.
- `linen`: a sentiment label, with possible values including `neg`, `neut`, `pos`, `neg_pos`.
- `service`: a sentiment label, with possible values including `neg`, `neut`, `pos`, `neg_pos`.
- `sunrise_meal`: a sentiment label, with possible values including `neg`, `neut`, `pos`, `neg_pos`.
- `tv`: a sentiment label, with possible values including `neg`, `neut`, `pos`, `neg_pos`.
- `wifi`: a sentiment label, with possible values including `neg`, `neut`, `pos`, `neg_pos`.
5. `WReTE` dataset
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `category`: a `string` feature.
- `label`: a classification label, with possible values including `NotEntail`, `Entail_or_Paraphrase`.
6. `POSP` dataset
- `tokens`: a `list` of `string` features.
- `pos_tags`: a `list` of POS tag labels, with possible values including `B-PPO`, `B-KUA`, `B-ADV`, `B-PRN`, `B-VBI`.
The POS tag labels follow the [Indonesian Association of Computational Linguistics (INACL) POS Tagging Convention](http://inacl.id/inacl/wp-content/uploads/2017/06/INACLPOS-Tagging-Convention-26-Mei.pdf).
7. `BaPOS` dataset
- `tokens`: a `list` of `string` features.
- `pos_tags`: a `list` of POS tag labels, with possible values including `B-PR`, `B-CD`, `I-PR`, `B-SYM`, `B-JJ`.
The POS tag labels from [Tagset UI](https://bahasa.cs.ui.ac.id/postag/downloads/Tagset.pdf).
8. `TermA` dataset
- `tokens`: a `list` of `string` features.
- `seq_label`: a `list` of classification labels, with possible values including `I-SENTIMENT`, `O`, `I-ASPECT`, `B-SENTIMENT`, `B-ASPECT`.
9. `KEPS` dataset
- `tokens`: a `list` of `string` features.
- `seq_label`: a `list` of classification labels, with possible values including `O`, `B`, `I`.
The labels use Inside-Outside-Beginning (IOB) tagging.
10. `NERGrit` dataset
- `tokens`: a `list` of `string` features.
- `ner_tags`: a `list` of NER tag labels, with possible values including `I-PERSON`, `B-ORGANISATION`, `I-ORGANISATION`, `B-PLACE`, `I-PLACE`.
The labels use Inside-Outside-Beginning (IOB) tagging.
11. `NERP` dataset
- `tokens`: a `list` of `string` features.
- `ner_tags`: a `list` of NER tag labels, with possible values including `I-PPL`, `B-EVT`, `B-PLC`, `I-IND`, `B-IND`.
12. `FacQA` dataset
- `question`: a `list` of `string` features.
- `passage`: a `list` of `string` features.
- `seq_label`: a `list` of classification labels, with possible values including `O`, `B`, `I`.
### Data Splits
The data is split into a training, validation and test set.
| | dataset | Train | Valid | Test |
|----|---------|-------|-------|------|
| 1 | EmoT | 3521 | 440 | 440 |
| 2 | SmSA | 11000 | 1260 | 500 |
| 3 | CASA | 810 | 90 | 180 |
| 4 | HoASA | 2283 | 285 | 286 |
| 5 | WReTE | 300 | 50 | 100 |
| 6 | POSP | 6720 | 840 | 840 |
| 7 | BaPOS | 8000 | 1000 | 1029 |
| 8 | TermA | 3000 | 1000 | 1000 |
| 9 | KEPS | 800 | 200 | 247 |
| 10 | NERGrit | 1672 | 209 | 209 |
| 11 | NERP | 6720 | 840 | 840 |
| 12 | FacQA | 2495 | 311 | 311 |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
The licensing status of the IndoNLU benchmark datasets is under MIT License.
### Citation Information
IndoNLU citation
```
@inproceedings{wilie2020indonlu,
title={IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding},
author={Bryan Wilie and Karissa Vincentio and Genta Indra Winata and Samuel Cahyawijaya and X. Li and Zhi Yuan Lim and S. Soleman and R. Mahendra and Pascale Fung and Syafri Bahar and A. Purwarianti},
booktitle={Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing},
year={2020}
}
```
`EmoT` dataset citation
```
@inproceedings{saputri2018emotion,
title={Emotion Classification on Indonesian Twitter Dataset},
author={Mei Silviana Saputri, Rahmad Mahendra, and Mirna Adriani},
booktitle={Proceedings of the 2018 International Conference on Asian Language Processing(IALP)},
pages={90--95},
year={2018},
organization={IEEE}
}
```
`SmSA` dataset citation
```
@inproceedings{purwarianti2019improving,
title={Improving Bi-LSTM Performance for Indonesian Sentiment Analysis Using Paragraph Vector},
author={Ayu Purwarianti and Ida Ayu Putu Ari Crisdayanti},
booktitle={Proceedings of the 2019 International Conference of Advanced Informatics: Concepts, Theory and Applications (ICAICTA)},
pages={1--5},
year={2019},
organization={IEEE}
}
```
`CASA` dataset citation
```
@inproceedings{ilmania2018aspect,
title={Aspect Detection and Sentiment Classification Using Deep Neural Network for Indonesian Aspect-based Sentiment Analysis},
author={Arfinda Ilmania, Abdurrahman, Samuel Cahyawijaya, Ayu Purwarianti},
booktitle={Proceedings of the 2018 International Conference on Asian Language Processing(IALP)},
pages={62--67},
year={2018},
organization={IEEE}
}
```
`HoASA` dataset citation
```
@inproceedings{azhar2019multi,
title={Multi-label Aspect Categorization with Convolutional Neural Networks and Extreme Gradient Boosting},
author={A. N. Azhar, M. L. Khodra, and A. P. Sutiono}
booktitle={Proceedings of the 2019 International Conference on Electrical Engineering and Informatics (ICEEI)},
pages={35--40},
year={2019}
}
```
`WReTE` dataset citation
```
@inproceedings{setya2018semi,
title={Semi-supervised Textual Entailment on Indonesian Wikipedia Data},
author={Ken Nabila Setya and Rahmad Mahendra},
booktitle={Proceedings of the 2018 International Conference on Computational Linguistics and Intelligent Text Processing (CICLing)},
year={2018}
}
```
`POSP` dataset citation
```
@inproceedings{hoesen2018investigating,
title={Investigating Bi-LSTM and CRF with POS Tag Embedding for Indonesian Named Entity Tagger},
author={Devin Hoesen and Ayu Purwarianti},
booktitle={Proceedings of the 2018 International Conference on Asian Language Processing (IALP)},
pages={35--38},
year={2018},
organization={IEEE}
}
```
`BaPOS` dataset citation
```
@inproceedings{dinakaramani2014designing,
title={Designing an Indonesian Part of Speech Tagset and Manually Tagged Indonesian Corpus},
author={Arawinda Dinakaramani, Fam Rashel, Andry Luthfi, and Ruli Manurung},
booktitle={Proceedings of the 2014 International Conference on Asian Language Processing (IALP)},
pages={66--69},
year={2014},
organization={IEEE}
}
@inproceedings{kurniawan2018toward,
title={Toward a Standardized and More Accurate Indonesian Part-of-Speech Tagging},
author={Kemal Kurniawan and Alham Fikri Aji},
booktitle={Proceedings of the 2018 International Conference on Asian Language Processing (IALP)},
pages={303--307},
year={2018},
organization={IEEE}
}
```
`TermA` dataset citation
```
@article{winatmoko2019aspect,
title={Aspect and Opinion Term Extraction for Hotel Reviews Using Transfer Learning and Auxiliary Labels},
author={Yosef Ardhito Winatmoko, Ali Akbar Septiandri, Arie Pratama Sutiono},
journal={arXiv preprint arXiv:1909.11879},
year={2019}
}
@article{fernando2019aspect,
title={Aspect and Opinion Terms Extraction Using Double Embeddings and Attention Mechanism for Indonesian Hotel Reviews},
author={Jordhy Fernando, Masayu Leylia Khodra, Ali Akbar Septiandri},
journal={arXiv preprint arXiv:1908.04899},
year={2019}
}
```
`KEPS` dataset citation
```
@inproceedings{mahfuzh2019improving,
title={Improving Joint Layer RNN based Keyphrase Extraction by Using Syntactical Features},
author={Miftahul Mahfuzh, Sidik Soleman, and Ayu Purwarianti},
booktitle={Proceedings of the 2019 International Conference of Advanced Informatics: Concepts, Theory and Applications (ICAICTA)},
pages={1--6},
year={2019},
organization={IEEE}
}
```
`NERGrit` dataset citation
```
@online{nergrit2019,
title={NERGrit Corpus},
author={NERGrit Developers},
year={2019},
url={https://github.com/grit-id/nergrit-corpus}
}
```
`NERP` dataset citation
```
@inproceedings{hoesen2018investigating,
title={Investigating Bi-LSTM and CRF with POS Tag Embedding for Indonesian Named Entity Tagger},
author={Devin Hoesen and Ayu Purwarianti},
booktitle={Proceedings of the 2018 International Conference on Asian Language Processing (IALP)},
pages={35--38},
year={2018},
organization={IEEE}
}
```
`FacQA` dataset citation
```
@inproceedings{purwarianti2007machine,
title={A Machine Learning Approach for Indonesian Question Answering System},
author={Ayu Purwarianti, Masatoshi Tsuchiya, and Seiichi Nakagawa},
booktitle={Proceedings of Artificial Intelligence and Applications },
pages={573--578},
year={2007}
}
```
### Contributions
Thanks to [@yasirabd](https://github.com/yasirabd) for adding this dataset. | [
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subjqa | null | 2023-03-16T13:27:54Z | 2,319 | 7 | subjqa | [
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] | 2023-03-16T13:27:54Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
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---
# Dataset Card for subjqa
## 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/lewtun/SubjQA
- **Paper:** https://arxiv.org/abs/2004.14283
- **Point of Contact:** [Lewis Tunstall](mailto:lewis.c.tunstall@gmail.com)
### Dataset Summary
SubjQA is a question answering dataset that focuses on subjective (as opposed to factual) questions and answers. The dataset consists of roughly **10,000** questions over reviews from 6 different domains: books, movies, grocery, electronics, TripAdvisor (i.e. hotels), and restaurants. Each question is paired with a review and a span is highlighted as the answer to the question (with some questions having no answer). Moreover, both questions and answer spans are assigned a _subjectivity_ label by annotators. Questions such as _"How much does this product weigh?"_ is a factual question (i.e., low subjectivity), while "Is this easy to use?" is a subjective question (i.e., high subjectivity).
In short, SubjQA provides a setting to study how well extractive QA systems perform on finding answer that are less factual and to what extent modeling subjectivity can improve the performance of QA systems.
_Note:_ Much of the information provided on this dataset card is taken from the README provided by the authors in their GitHub repository ([link](https://github.com/megagonlabs/SubjQA)).
To load a domain with `datasets` you can run the following:
```python
from datasets import load_dataset
# other options include: electronics, grocery, movies, restaurants, tripadvisor
dataset = load_dataset("subjqa", "books")
```
### Supported Tasks and Leaderboards
* `question-answering`: The dataset can be used to train a model for extractive question answering, which involves questions whose answer can be identified as a span of text in a review. Success on this task is typically measured by achieving a high Exact Match or F1 score. The BERT model that is first fine-tuned on SQuAD 2.0 and then further fine-tuned on SubjQA achieves the scores shown in the figure below.
### Languages
The text in the dataset is in English and the associated BCP-47 code is `en`.
## Dataset Structure
### Data Instances
An example from `books` domain is shown below:
```json
{
"answers": {
"ans_subj_score": [1.0],
"answer_start": [324],
"answer_subj_level": [2],
"is_ans_subjective": [true],
"text": ["This is a wonderfully written book"],
},
"context": "While I would not recommend this book to a young reader due to a couple pretty explicate scenes I would recommend it to any adult who just loves a good book. Once I started reading it I could not put it down. I hesitated reading it because I didn't think that the subject matter would be interesting, but I was so wrong. This is a wonderfully written book.",
"domain": "books",
"id": "0255768496a256c5ed7caed9d4e47e4c",
"is_ques_subjective": false,
"nn_asp": "matter",
"nn_mod": "interesting",
"q_reviews_id": "a907837bafe847039c8da374a144bff9",
"query_asp": "part",
"query_mod": "fascinating",
"ques_subj_score": 0.0,
"question": "What are the parts like?",
"question_subj_level": 2,
"review_id": "a7f1a2503eac2580a0ebbc1d24fffca1",
"title": "0002007770",
}
```
### Data Fields
Each domain and split consists of the following columns:
* ```title```: The id of the item/business discussed in the review.
* ```question```: The question (written based on a query opinion).
* ```id```: A unique id assigned to the question-review pair.
* ```q_reviews_id```: A unique id assigned to all question-review pairs with a shared question.
* ```question_subj_level```: The subjectiviy level of the question (on a 1 to 5 scale with 1 being the most subjective).
* ```ques_subj_score```: The subjectivity score of the question computed using the [TextBlob](https://textblob.readthedocs.io/en/dev/) package.
* ```context```: The review (that mentions the neighboring opinion).
* ```review_id```: A unique id associated with the review.
* ```answers.text```: The span labeled by annotators as the answer.
* ```answers.answer_start```: The (character-level) start index of the answer span highlighted by annotators.
* ```is_ques_subjective```: A boolean subjectivity label derived from ```question_subj_level``` (i.e., scores below 4 are considered as subjective)
* ```answers.answer_subj_level```: The subjectiviy level of the answer span (on a 1 to 5 scale with 5 being the most subjective).
* ```answers.ans_subj_score```: The subjectivity score of the answer span computed usign the [TextBlob](https://textblob.readthedocs.io/en/dev/) package.
* ```answers.is_ans_subjective```: A boolean subjectivity label derived from ```answer_subj_level``` (i.e., scores below 4 are considered as subjective)
* ```domain```: The category/domain of the review (e.g., hotels, books, ...).
* ```nn_mod```: The modifier of the neighboring opinion (which appears in the review).
* ```nn_asp```: The aspect of the neighboring opinion (which appears in the review).
* ```query_mod```: The modifier of the query opinion (around which a question is manually written).
* ```query_asp```: The aspect of the query opinion (around which a question is manually written).
### Data Splits
The question-review pairs from each domain are split into training, development, and test sets. The table below shows the size of the dataset per each domain and split.
| Domain | Train | Dev | Test | Total |
|-------------|-------|-----|------|-------|
| TripAdvisor | 1165 | 230 | 512 | 1686 |
| Restaurants | 1400 | 267 | 266 | 1683 |
| Movies | 1369 | 261 | 291 | 1677 |
| Books | 1314 | 256 | 345 | 1668 |
| Electronics | 1295 | 255 | 358 | 1659 |
| Grocery | 1124 | 218 | 591 | 1725 |
Based on the subjectivity labels provided by annotators, one observes that 73% of the questions and 74% of the answers in the dataset are subjective. This provides a substantial number of subjective QA pairs as well as a reasonable number of factual questions to compare and constrast the performance of QA systems on each type of QA pairs.
Finally, the next table summarizes the average length of the question, the review, and the highlighted answer span for each category.
| Domain | Review Len | Question Len | Answer Len | % answerable |
|-------------|------------|--------------|------------|--------------|
| TripAdvisor | 187.25 | 5.66 | 6.71 | 78.17 |
| Restaurants | 185.40 | 5.44 | 6.67 | 60.72 |
| Movies | 331.56 | 5.59 | 7.32 | 55.69 |
| Books | 285.47 | 5.78 | 7.78 | 52.99 |
| Electronics | 249.44 | 5.56 | 6.98 | 58.89 |
| Grocery | 164.75 | 5.44 | 7.25 | 64.69 |
## Dataset Creation
### Curation Rationale
Most question-answering datasets like SQuAD and Natural Questions focus on answering questions over factual data such as Wikipedia and news articles. However, in domains like e-commerce the questions and answers are often _subjective_, that is, they depend on the personal experience of the users. For example, a customer on Amazon may ask "Is the sound quality any good?", which is more difficult to answer than a factoid question like "What is the capital of Australia?" These considerations motivate the creation of SubjQA as a tool to investigate the relationship between subjectivity and question-answering.
### Source Data
#### Initial Data Collection and Normalization
The SubjQA dataset is constructed based on publicly available review datasets. Specifically, the _movies_, _books_, _electronics_, and _grocery_ categories are constructed using reviews from the [Amazon Review dataset](http://jmcauley.ucsd.edu/data/amazon/links.html). The _TripAdvisor_ category, as the name suggests, is constructed using reviews from TripAdvisor which can be found [here](http://times.cs.uiuc.edu/~wang296/Data/). Finally, the _restaurants_ category is constructed using the [Yelp Dataset](https://www.yelp.com/dataset) which is also publicly available.
The process of constructing SubjQA is discussed in detail in the [paper](https://arxiv.org/abs/2004.14283). In a nutshell, the dataset construction consists of the following steps:
1. First, all _opinions_ expressed in reviews are extracted. In the pipeline, each opinion is modeled as a (_modifier_, _aspect_) pair which is a pair of spans where the former describes the latter. (good, hotel), and (terrible, acting) are a few examples of extracted opinions.
2. Using Matrix Factorization techniques, implication relationships between different expressed opinions are mined. For instance, the system mines that "responsive keys" implies "good keyboard". In our pipeline, we refer to the conclusion of an implication (i.e., "good keyboard" in this examples) as the _query_ opinion, and we refer to the premise (i.e., "responsive keys") as its _neighboring_ opinion.
3. Annotators are then asked to write a question based on _query_ opinions. For instance given "good keyboard" as the query opinion, they might write "Is this keyboard any good?"
4. Each question written based on a _query_ opinion is then paired with a review that mentions its _neighboring_ opinion. In our example, that would be a review that mentions "responsive keys".
5. The question and review pairs are presented to annotators to select the correct answer span, and rate the subjectivity level of the question as well as the subjectivity level of the highlighted answer span.
A visualisation of the data collection pipeline is shown in the image below.
#### Who are the source language producers?
As described above, the source data for SubjQA is customer reviews of products and services on e-commerce websites like Amazon and TripAdvisor.
### Annotations
#### Annotation process
The generation of questions and answer span labels were obtained through the [Appen](https://appen.com/) platform. From the SubjQA paper:
> The platform provides quality control by showing the workers 5 questions at a time, out of which one is labeled by the experts. A worker who fails to maintain 70% accuracy is kicked out by the platform and his judgements are ignored ... To ensure good quality labels, we paid each worker 5 cents per annotation.
The instructions for generating a question are shown in the following figure:
<img width="874" alt="ques_gen" src="https://user-images.githubusercontent.com/26859204/117259092-03d67300-ae4e-11eb-81f2-9077fee1085f.png">
Similarly, the interface for the answer span and subjectivity labelling tasks is shown below:
As described in the SubjQA paper, the workers assign subjectivity scores (1-5) to each question and the selected answer span. They can also indicate if a question cannot be answered from the given review.
#### Who are the annotators?
Workers on the Appen platform.
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
The SubjQA dataset can be used to develop question-answering systems that can provide better on-demand answers to e-commerce customers who are interested in subjective questions about products and services.
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
The people involved in creating the SubjQA dataset are the authors of the accompanying paper:
* Johannes Bjerva1, Department of Computer Science, University of Copenhagen, Department of Computer Science, Aalborg University
* Nikita Bhutani, Megagon Labs, Mountain View
* Behzad Golshan, Megagon Labs, Mountain View
* Wang-Chiew Tan, Megagon Labs, Mountain View
* Isabelle Augenstein, Department of Computer Science, University of Copenhagen
### Licensing Information
The SubjQA dataset is provided "as-is", and its creators make no representation as to its accuracy.
The SubjQA dataset is constructed based on the following datasets and thus contains subsets of their data:
* [Amazon Review Dataset](http://jmcauley.ucsd.edu/data/amazon/links.html) from UCSD
* Used for _books_, _movies_, _grocery_, and _electronics_ domains
* [The TripAdvisor Dataset](http://times.cs.uiuc.edu/~wang296/Data/) from UIUC's Database and Information Systems Laboratory
* Used for the _TripAdvisor_ domain
* [The Yelp Dataset](https://www.yelp.com/dataset)
* Used for the _restaurants_ domain
Consequently, the data within each domain of the SubjQA dataset should be considered under the same license as the dataset it was built upon.
### Citation Information
If you are using the dataset, please cite the following in your work:
```
@inproceedings{bjerva20subjqa,
title = "SubjQA: A Dataset for Subjectivity and Review Comprehension",
author = "Bjerva, Johannes and
Bhutani, Nikita and
Golahn, Behzad and
Tan, Wang-Chiew and
Augenstein, Isabelle",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
month = November,
year = "2020",
publisher = "Association for Computational Linguistics",
}
```
### Contributions
Thanks to [@lewtun](https://github.com/lewtun) for adding this dataset. | [
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nielsr/funsd | nielsr | 2021-07-27T07:59:20Z | 2,312 | 9 | null | [
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ted_hrlr | null | 2023-04-05T13:41:24Z | 2,308 | 0 | null | [
"task_categories:translation",
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
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"language:ru",
"language:tr",
"license:cc-by-nc-nd-4.0",
"region:us"
] | 2023-04-05T13:41:24Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
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license:
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multilinguality:
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pretty_name: TEDHrlr
size_categories:
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source_datasets:
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task_categories:
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task_ids: []
paperswithcode_id: null
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---
# Dataset Card for "ted_hrlr"
## 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/neulab/word-embeddings-for-nmt
- **Paper:** [When and Why Are Pre-Trained Word Embeddings Useful for Neural Machine Translation?](https://aclanthology.org/N18-2084/)
- **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:** 1.83 GB
- **Size of the generated dataset:** 281.66 MB
- **Total amount of disk used:** 2.12 GB
### Dataset Summary
Data sets derived from TED talk transcripts for comparing similar language pairs
where one is high resource and the other is low resource.
### 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
#### az_to_en
- **Size of downloaded dataset files:** 131.01 MB
- **Size of the generated dataset:** 1.53 MB
- **Total amount of disk used:** 132.54 MB
An example of 'train' looks as follows.
```
{
"translation": {
"az": "zəhmət olmasa , sizə xitab edən sözlər eşidəndə əlinizi qaldırın .",
"en": "please raise your hand if something applies to you ."
}
}
```
#### aztr_to_en
- **Size of downloaded dataset files:** 131.01 MB
- **Size of the generated dataset:** 40.14 MB
- **Total amount of disk used:** 171.15 MB
An example of 'train' looks as follows.
```
{
"translation": {
"az_tr": "zəhmət olmasa , sizə xitab edən sözlər eşidəndə əlinizi qaldırın .",
"en": "please raise your hand if something applies to you ."
}
}
```
#### be_to_en
- **Size of downloaded dataset files:** 131.01 MB
- **Size of the generated dataset:** 1.43 MB
- **Total amount of disk used:** 132.42 MB
An example of 'train' looks as follows.
```
{
"translation": {
"be": "zəhmət olmasa , sizə xitab edən sözlər eşidəndə əlinizi qaldırın .",
"en": "please raise your hand if something applies to you ."
}
}
```
#### beru_to_en
- **Size of downloaded dataset files:** 131.01 MB
- **Size of the generated dataset:** 60.20 MB
- **Total amount of disk used:** 191.21 MB
An example of 'validation' looks as follows.
```
This example was too long and was cropped:
{
"translation": "{\"be_ru\": \"11 yaşımdaydım . səhərin birində , evimizdəki sevinc səslərinə oyandığım indiki kimi yadımdadır .\", \"en\": \"when i was..."
}
```
#### es_to_pt
- **Size of downloaded dataset files:** 131.01 MB
- **Size of the generated dataset:** 9.13 MB
- **Total amount of disk used:** 140.14 MB
An example of 'validation' looks as follows.
```
This example was too long and was cropped:
{
"translation": "{\"es\": \"11 yaşımdaydım . səhərin birində , evimizdəki sevinc səslərinə oyandığım indiki kimi yadımdadır .\", \"pt\": \"when i was 11..."
}
```
### Data Fields
The data fields are the same among all splits.
#### az_to_en
- `translation`: a multilingual `string` variable, with possible languages including `az`, `en`.
#### aztr_to_en
- `translation`: a multilingual `string` variable, with possible languages including `az_tr`, `en`.
#### be_to_en
- `translation`: a multilingual `string` variable, with possible languages including `be`, `en`.
#### beru_to_en
- `translation`: a multilingual `string` variable, with possible languages including `be_ru`, `en`.
#### es_to_pt
- `translation`: a multilingual `string` variable, with possible languages including `es`, `pt`.
### Data Splits
| name |train |validation|test|
|----------|-----:|---------:|---:|
|az_to_en | 5947| 672| 904|
|aztr_to_en|188397| 672| 904|
|be_to_en | 4510| 249| 665|
|beru_to_en|212615| 249| 665|
|es_to_pt | 44939| 1017|1764|
## 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{qi-etal-2018-pre,
title = "When and Why Are Pre-Trained Word Embeddings Useful for Neural Machine Translation?",
author = "Qi, Ye and
Sachan, Devendra and
Felix, Matthieu and
Padmanabhan, Sarguna and
Neubig, Graham",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-2084",
doi = "10.18653/v1/N18-2084",
pages = "529--535",
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset. | [
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wiqa | null | 2023-04-05T13:43:43Z | 2,295 | 2 | wiqa | [
"language:en",
"region:us"
] | 2023-04-05T13:43:43Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- en
paperswithcode_id: wiqa
pretty_name: What-If Question Answering
dataset_info:
features:
- name: question_stem
dtype: string
- name: question_para_step
sequence: string
- name: answer_label
dtype: string
- name: answer_label_as_choice
dtype: string
- name: choices
sequence:
- name: text
dtype: string
- name: label
dtype: string
- name: metadata_question_id
dtype: string
- name: metadata_graph_id
dtype: string
- name: metadata_para_id
dtype: string
- name: metadata_question_type
dtype: string
- name: metadata_path_len
dtype: int32
splits:
- name: train
num_bytes: 17089298
num_examples: 29808
- name: test
num_bytes: 1532223
num_examples: 3003
- name: validation
num_bytes: 3779584
num_examples: 6894
download_size: 5247733
dataset_size: 22401105
---
# Dataset Card for "wiqa"
## 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://allenai.org/data/wiqa](https://allenai.org/data/wiqa)
- **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:** 5.24 MB
- **Size of the generated dataset:** 22.40 MB
- **Total amount of disk used:** 27.65 MB
### Dataset Summary
The WIQA dataset V1 has 39705 questions containing a perturbation and a possible effect in the context of a paragraph.
The dataset is split into 29808 train questions, 6894 dev questions and 3003 test questions.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 5.24 MB
- **Size of the generated dataset:** 22.40 MB
- **Total amount of disk used:** 27.65 MB
An example of 'validation' looks as follows.
```
{
"answer_label": "more",
"answer_label_as_choice": "A",
"choices": {
"label": ["A", "B", "C"],
"text": ["more", "less", "no effect"]
},
"metadata_graph_id": "481",
"metadata_para_id": "528",
"metadata_path_len": 3,
"metadata_question_id": "influence_graph:528:481:77#0",
"metadata_question_type": "INPARA_EFFECT",
"question_para_step": ["A male and female rabbit mate", "The female rabbit becomes pregnant", "Baby rabbits form inside of the mother rabbit", "The female rabbit gives birth to a litter", "The newborn rabbits grow up to become adults", "The adult rabbits find mates."],
"question_stem": "suppose the female is sterile happens, how will it affect LESS rabbits."
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `question_stem`: a `string` feature.
- `question_para_step`: a `list` of `string` features.
- `answer_label`: a `string` feature.
- `answer_label_as_choice`: a `string` feature.
- `choices`: a dictionary feature containing:
- `text`: a `string` feature.
- `label`: a `string` feature.
- `metadata_question_id`: a `string` feature.
- `metadata_graph_id`: a `string` feature.
- `metadata_para_id`: a `string` feature.
- `metadata_question_type`: a `string` feature.
- `metadata_path_len`: a `int32` feature.
### Data Splits
| name |train|validation|test|
|-------|----:|---------:|---:|
|default|29808| 6894|3003|
## 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
```
@article{wiqa,
author = {Niket Tandon and Bhavana Dalvi Mishra and Keisuke Sakaguchi and Antoine Bosselut and Peter Clark}
title = {WIQA: A dataset for "What if..." reasoning over procedural text},
journal = {arXiv:1909.04739v1},
year = {2019},
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf), [@mariamabarham](https://github.com/mariamabarham), [@lhoestq](https://github.com/lhoestq) for adding this dataset. | [
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DFKI-SLT/few-nerd | DFKI-SLT | 2023-06-21T09:59:09Z | 2,281 | 13 | few-nerd | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|wikipedia",
"language:en",
"license:cc-by-sa-4.0",
"structure-prediction",
"region:us"
] | 2023-06-21T09:59:09Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|wikipedia
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: few-nerd
pretty_name: Few-NERD
tags:
- structure-prediction
---
# Dataset Card for "Few-NERD"
## 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://ningding97.github.io/fewnerd/](https://ningding97.github.io/fewnerd/)
- **Repository:** [https://github.com/thunlp/Few-NERD](https://github.com/thunlp/Few-NERD)
- **Paper:** [https://aclanthology.org/2021.acl-long.248/](https://aclanthology.org/2021.acl-long.248/)
- **Point of Contact:** See [https://ningding97.github.io/fewnerd/](https://ningding97.github.io/fewnerd/)
### Dataset Summary
This script is for loading the Few-NERD dataset from https://ningding97.github.io/fewnerd/.
Few-NERD is a large-scale, fine-grained manually annotated named entity recognition dataset, which contains 8 coarse-grained types, 66 fine-grained types, 188,200 sentences, 491,711 entities, and 4,601,223 tokens. Three benchmark tasks are built, one is supervised (Few-NERD (SUP)) and the other two are few-shot (Few-NERD (INTRA) and Few-NERD (INTER)).
NER tags use the `IO` tagging scheme. The original data uses a 2-column CoNLL-style format, with empty lines to separate sentences. DOCSTART information is not provided since the sentences are randomly ordered.
For more details see https://ningding97.github.io/fewnerd/ and https://aclanthology.org/2021.acl-long.248/.
### Supported Tasks and Leaderboards
- **Tasks:** Named Entity Recognition, Few-shot NER
- **Leaderboards:**
- https://ningding97.github.io/fewnerd/
- named-entity-recognition:https://paperswithcode.com/sota/named-entity-recognition-on-few-nerd-sup
- other-few-shot-ner:https://paperswithcode.com/sota/few-shot-ner-on-few-nerd-intra
- other-few-shot-ner:https://paperswithcode.com/sota/few-shot-ner-on-few-nerd-inter
### Languages
English
## Dataset Structure
### Data Instances
- **Size of downloaded dataset files:**
- `super`: 14.6 MB
- `intra`: 11.4 MB
- `inter`: 11.5 MB
- **Size of the generated dataset:**
- `super`: 116.9 MB
- `intra`: 106.2 MB
- `inter`: 106.2 MB
- **Total amount of disk used:** 366.8 MB
An example of 'train' looks as follows.
```json
{
'id': '1',
'tokens': ['It', 'starred', 'Hicks', "'s", 'wife', ',', 'Ellaline', 'Terriss', 'and', 'Edmund', 'Payne', '.'],
'ner_tags': [0, 0, 7, 0, 0, 0, 7, 7, 0, 7, 7, 0],
'fine_ner_tags': [0, 0, 51, 0, 0, 0, 50, 50, 0, 50, 50, 0]
}
```
### Data Fields
The data fields are the same among all splits.
- `id`: a `string` feature.
- `tokens`: a `list` of `string` features.
- `ner_tags`: a `list` of classification labels, with possible values including `O` (0), `art` (1), `building` (2), `event` (3), `location` (4), `organization` (5), `other`(6), `person` (7), `product` (8)
- `fine_ner_tags`: a `list` of fine-grained classification labels, with possible values including `O` (0), `art-broadcastprogram` (1), `art-film` (2), ...
### Data Splits
| Task | Train | Dev | Test |
| ----- | ------ | ----- | ---- |
| SUP | 131767 | 18824 | 37648 |
| INTRA | 99519 | 19358 | 44059 |
| INTER | 130112 | 18817 | 14007 |
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[CC BY-SA 4.0 license](https://creativecommons.org/licenses/by-sa/4.0/)
### Citation Information
```
@inproceedings{ding-etal-2021-nerd,
title = "Few-{NERD}: A Few-shot Named Entity Recognition Dataset",
author = "Ding, Ning and
Xu, Guangwei and
Chen, Yulin and
Wang, Xiaobin and
Han, Xu and
Xie, Pengjun and
Zheng, Haitao and
Liu, Zhiyuan",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.248",
doi = "10.18653/v1/2021.acl-long.248",
pages = "3198--3213",
}
```
### Contributions | [
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wiki_hop | null | 2022-11-03T16:47:35Z | 2,260 | 1 | wikihop | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0",
"multi-hop",
"arxiv:1710.06481",
"region:us"
] | 2022-11-03T16:47:35Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- expert-generated
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: wikihop
pretty_name: WikiHop
tags:
- multi-hop
dataset_info:
- config_name: original
features:
- name: id
dtype: string
- name: query
dtype: string
- name: answer
dtype: string
- name: candidates
sequence: string
- name: supports
sequence: string
- name: annotations
sequence:
sequence: string
splits:
- name: train
num_bytes: 325952974
num_examples: 43738
- name: validation
num_bytes: 41246536
num_examples: 5129
download_size: 339843061
dataset_size: 367199510
- config_name: masked
features:
- name: id
dtype: string
- name: question
dtype: string
- name: answer
dtype: string
- name: candidates
sequence: string
- name: supports
sequence: string
- name: annotations
sequence:
sequence: string
splits:
- name: train
num_bytes: 348249138
num_examples: 43738
- name: validation
num_bytes: 44066862
num_examples: 5129
download_size: 339843061
dataset_size: 392316000
---
# Dataset Card for WikiHop
## 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:** [QAngaroo](http://qangaroo.cs.ucl.ac.uk/)
- **Repository:** [If the dataset is hosted on github or has a github homepage, add URL here]()
- **Paper:** [Constructing Datasets for Multi-hop Reading Comprehension Across Documents](https://arxiv.org/abs/1710.06481)
- **Leaderboard:** [leaderboard](http://qangaroo.cs.ucl.ac.uk/leaderboard.html)
- **Point of Contact:** [Johannes Welbl](j.welbl@cs.ucl.ac.uk)
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
C-MTEB/T2Retrieval-qrels | C-MTEB | 2023-07-28T10:11:11Z | 2,246 | 0 | null | [
"region:us"
] | 2023-07-28T10:11:11Z | 2023-07-28T10:11:07.000Z | 2023-07-28T10:11:07 | ---
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*
dataset_info:
features:
- name: qid
dtype: string
- name: pid
dtype: string
- name: score
dtype: int64
splits:
- name: dev
num_bytes: 3133383
num_examples: 118932
download_size: 1146734
dataset_size: 3133383
---
# Dataset Card for "T2Retrieval-qrels"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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jamescalam/llama-2-arxiv-papers-chunked | jamescalam | 2023-07-25T03:12:24Z | 2,244 | 13 | null | [
"language:en",
"arxiv:2307.09288",
"region:us"
] | 2023-07-25T03:12:24Z | 2023-07-25T03:06:58.000Z | 2023-07-25T03:06:58 | ---
language:
- en
pretty_name: Chunked Arxiv Papers for Llama 2
---
This dataset contains chunked extracts (of ~300 tokens) from papers related to (and including) the [Llama 2 research paper](https://arxiv.org/abs/2307.09288). Related papers were identified by following a trail of references, extracting those papers with the [`arxiv-bot`](https://github.com/aurelio-labs/arxiv-bot) package, and repeating. | [
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Graphcore/gqa | Graphcore | 2022-10-25T08:59:27Z | 2,240 | 0 | null | [
"language:en",
"license:cc-by-4.0",
"region:us"
] | 2022-10-25T08:59:27Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- en
license:
- cc-by-4.0
---
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
bigbio/med_qa | bigbio | 2023-09-26T13:00:32Z | 2,236 | 24 | null | [
"multilinguality:multilingual",
"language:en",
"language:zh",
"license:unknown",
"region:us"
] | 2023-09-26T13:00:32Z | 2022-11-13T22:09:18.000Z | 2022-11-13T22:09:18 | ---
language:
- en
- zh
bigbio_language:
- English
- Chinese (Simplified)
- Chinese (Traditional, Taiwan)
license: unknown
multilinguality: multilingual
bigbio_license_shortname: UNKNOWN
pretty_name: MedQA
homepage: https://github.com/jind11/MedQA
bigbio_pubmed: False
bigbio_public: True
bigbio_tasks:
- QUESTION_ANSWERING
---
# Dataset Card for MedQA
## Dataset Description
- **Homepage:** https://github.com/jind11/MedQA
- **Pubmed:** False
- **Public:** True
- **Tasks:** QA
In this work, we present the first free-form multiple-choice OpenQA dataset for solving medical problems, MedQA,
collected from the professional medical board exams. It covers three languages: English, simplified Chinese, and
traditional Chinese, and contains 12,723, 34,251, and 14,123 questions for the three languages, respectively. Together
with the question data, we also collect and release a large-scale corpus from medical textbooks from which the reading
comprehension models can obtain necessary knowledge for answering the questions.
## Citation Information
```
@article{jin2021disease,
title={What disease does this patient have? a large-scale open domain question answering dataset from medical exams},
author={Jin, Di and Pan, Eileen and Oufattole, Nassim and Weng, Wei-Hung and Fang, Hanyi and Szolovits, Peter},
journal={Applied Sciences},
volume={11},
number={14},
pages={6421},
year={2021},
publisher={MDPI}
}
```
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sem_eval_2018_task_1 | null | 2022-11-18T21:45:06Z | 2,235 | 10 | null | [
"task_categories:text-classification",
"task_ids:multi-label-classification",
"annotations_creators:crowdsourced",
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"language:en",
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"license:unknown",
"emotion-classification",
"region:us"
] | 2022-11-18T21:45:06Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- ar
- en
- es
license:
- unknown
multilinguality:
- multilingual
pretty_name: 'SemEval-2018 Task 1: Affect in Tweets'
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-label-classification
tags:
- emotion-classification
dataset_info:
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dtype: string
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download_size: 5975590
dataset_size: 1298947
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dataset_size: 798625
---
# Dataset Card for SemEval-2018 Task 1: Affect in Tweets
## 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://competitions.codalab.org/competitions/17751
- **Repository:**
- **Paper:** http://saifmohammad.com/WebDocs/semeval2018-task1.pdf
- **Leaderboard:**
- **Point of Contact:** https://www.saifmohammad.com/
### Dataset Summary
Tasks: We present an array of tasks where systems have to automatically determine the intensity of emotions (E) and intensity of sentiment (aka valence V) of the tweeters from their tweets. (The term tweeter refers to the person who has posted the tweet.) We also include a multi-label emotion classification task for tweets. For each task, we provide separate training and test datasets for English, Arabic, and Spanish tweets. The individual tasks are described below:
1. EI-reg (an emotion intensity regression task): Given a tweet and an emotion E, determine the intensity of E that best represents the mental state of the tweeter—a real-valued score between 0 (least E) and 1 (most E).
Separate datasets are provided for anger, fear, joy, and sadness.
2. EI-oc (an emotion intensity ordinal classification task): Given a tweet and an emotion E, classify the tweet into one of four ordinal classes of intensity of E that best represents the mental state of the tweeter.
Separate datasets are provided for anger, fear, joy, and sadness.
3. V-reg (a sentiment intensity regression task): Given a tweet, determine the intensity of sentiment or valence (V) that best represents the mental state of the tweeter—a real-valued score between 0 (most negative) and 1 (most positive).
4. V-oc (a sentiment analysis, ordinal classification, task): Given a tweet, classify it into one of seven ordinal classes, corresponding to various levels of positive and negative sentiment intensity, that best represents the mental state of the tweeter.
5. E-c (an emotion classification task): Given a tweet, classify it as 'neutral or no emotion' or as one, or more, of eleven given emotions that best represent the mental state of the tweeter.
Here, E refers to emotion, EI refers to emotion intensity, V refers to valence or sentiment intensity, reg refers to regression, oc refers to ordinal classification, c refers to classification.
Together, these tasks encompass various emotion and sentiment analysis tasks. You are free to participate in any number of tasks and on any of the datasets.
**Currently only the subtask 5 (E-c) is available on the Hugging Face Dataset Hub.**
### Supported Tasks and Leaderboards
### Languages
English, Arabic and Spanish
## Dataset Structure
### Data Instances
An example from the `subtask5.english` config is:
```
{'ID': '2017-En-21441',
'Tweet': "“Worry is a down payment on a problem you may never have'. \xa0Joyce Meyer. #motivation #leadership #worry",
'anger': False,
'anticipation': True,
'disgust': False,
'fear': False,
'joy': False,
'love': False,
'optimism': True,
'pessimism': False,
'sadness': False,
'surprise': False,
'trust': True}
```
### Data Fields
For any config of the subtask 5:
- ID: string id of the tweet
- Tweet: text content of the tweet as a string
- anger: boolean, True if anger represents the mental state of the tweeter
- anticipation: boolean, True if anticipation represents the mental state of the tweeter
- disgust: boolean, True if disgust represents the mental state of the tweeter
- fear: boolean, True if fear represents the mental state of the tweeter
- joy: boolean, True if joy represents the mental state of the tweeter
- love: boolean, True if love represents the mental state of the tweeter
- optimism: boolean, True if optimism represents the mental state of the tweeter
- pessimism: boolean, True if pessimism represents the mental state of the tweeter
- sadness: boolean, True if sadness represents the mental state of the tweeter
- surprise: boolean, True if surprise represents the mental state of the tweeter
- trust: boolean, True if trust represents the mental state of the tweeter
Note that the test set has no labels, and therefore all labels are set to False.
### Data Splits
| | train | validation | test |
|---------|------:|-----------:|------:|
| English | 6,838 | 886 | 3,259 |
| Arabic | 2,278 | 585 | 1,518 |
| Spanish | 3,561 | 679 | 2,854 |
## Dataset Creation
### Curation Rationale
### Source Data
Tweets
#### Initial Data Collection and Normalization
#### Who are the source language producers?
Twitter users.
### Annotations
#### Annotation process
We presented one tweet at a time to the annotators
and asked which of the following options best de-
scribed the emotional state of the tweeter:
– anger (also includes annoyance, rage)
– anticipation (also includes interest, vigilance)
– disgust (also includes disinterest, dislike, loathing)
– fear (also includes apprehension, anxiety, terror)
– joy (also includes serenity, ecstasy)
– love (also includes affection)
– optimism (also includes hopefulness, confidence)
– pessimism (also includes cynicism, no confidence)
– sadness (also includes pensiveness, grief)
– surprise (also includes distraction, amazement)
– trust (also includes acceptance, liking, admiration)
– neutral or no emotion
Example tweets were provided in advance with ex-
amples of suitable responses.
On the Figure Eight task settings, we specified
that we needed annotations from seven people for
each tweet. However, because of the way the gold
tweets were set up, they were annotated by more
than seven people. The median number of anno-
tations was still seven. In total, 303 people anno-
tated between 10 and 4,670 tweets each. A total of
174,356 responses were obtained.
Mohammad, S., Bravo-Marquez, F., Salameh, M., & Kiritchenko, S. (2018). SemEval-2018 task 1: Affect in tweets. Proceedings of the 12th International Workshop on Semantic Evaluation, 1–17. https://doi.org/10.18653/v1/S18-1001
#### Who are the annotators?
Crowdworkers on Figure Eight.
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
Saif M. Mohammad, Felipe Bravo-Marquez, Mohammad Salameh and Svetlana Kiritchenko
### Licensing Information
See the official [Terms and Conditions](https://competitions.codalab.org/competitions/17751#learn_the_details-terms_and_conditions)
### Citation Information
@InProceedings{SemEval2018Task1,
author = {Mohammad, Saif M. and Bravo-Marquez, Felipe and Salameh, Mohammad and Kiritchenko, Svetlana},
title = {SemEval-2018 {T}ask 1: {A}ffect in Tweets},
booktitle = {Proceedings of International Workshop on Semantic Evaluation (SemEval-2018)},
address = {New Orleans, LA, USA},
year = {2018}}
### Contributions
Thanks to [@maxpel](https://github.com/maxpel) for adding this dataset. | [
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ncbi_disease | null | 2023-01-25T14:41:18Z | 2,225 | 23 | ncbi-disease-1 | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | 2023-01-25T14:41:18Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: ncbi-disease-1
pretty_name: NCBI Disease
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-Disease
'2': I-Disease
config_name: ncbi_disease
splits:
- name: train
num_bytes: 2355516
num_examples: 5433
- name: validation
num_bytes: 413900
num_examples: 924
- name: test
num_bytes: 422842
num_examples: 941
download_size: 1546492
dataset_size: 3192258
train-eval-index:
- config: ncbi_disease
task: token-classification
task_id: multi_class_classification
splits:
train_split: train
eval_split: test
col_mapping:
tokens: text
ner_tags: target
metrics:
- type: accuracy
name: Accuracy
- type: f1
name: F1 macro
args:
average: macro
- type: f1
name: F1 micro
args:
average: micro
- type: f1
name: F1 weighted
args:
average: weighted
- type: precision
name: Precision macro
args:
average: macro
- type: precision
name: Precision micro
args:
average: micro
- type: precision
name: Precision weighted
args:
average: weighted
- type: recall
name: Recall macro
args:
average: macro
- type: recall
name: Recall micro
args:
average: micro
- type: recall
name: Recall weighted
args:
average: weighted
---
# Dataset Card for NCBI Disease
## Table of Contents
- [Dataset Card for NCBI Disease](#dataset-card-for-ncbi-disease)
- [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)
- [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:** [NCBI](https://www.ncbi.nlm.nih.gov/research/bionlp/Data/disease)
- **Repository:** [Github](https://github.com/spyysalo/ncbi-disease)
- **Paper:** [NCBI disease corpus: A resource for disease name recognition and concept normalization](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3951655)
- **Leaderboard:** [Named Entity Recognition on NCBI-disease](https://paperswithcode.com/sota/named-entity-recognition-ner-on-ncbi-disease)
- **Point of Contact:** [email](zhiyong.lu@nih.gov)
### Dataset Summary
This dataset contains the disease name and concept annotations of the NCBI disease corpus, a collection of 793 PubMed abstracts fully annotated at the mention and concept level to serve as a research resource for the biomedical natural language processing community.
### Supported Tasks and Leaderboards
Named Entity Recognition: [Leaderboard](https://paperswithcode.com/sota/named-entity-recognition-ner-on-ncbi-disease)
### Languages
The text in the dataset is in English. The associated BCP-47 code is `en`.
## Dataset Structure
### Data Instances
Instances of the dataset contain an array of `tokens`, `ner_tags` and an `id`. An example of an instance of the dataset:
```
{
'tokens': ['Identification', 'of', 'APC2', ',', 'a', 'homologue', 'of', 'the', 'adenomatous', 'polyposis', 'coli', 'tumour', 'suppressor', '.'],
'ner_tags': [0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0],
'id': '0'
}
```
### Data Fields
- `id`: Sentence identifier.
- `tokens`: Array of tokens composing a sentence.
- `ner_tags`: Array of tags, where `0` indicates no disease mentioned, `1` signals the first token of a disease and `2` the subsequent disease tokens.
### Data Splits
The data is split into a train (5433 instances), validation (924 instances) and test set (941 instances).
## Dataset Creation
### Curation Rationale
The goal of the dataset consists on improving the state-of-the-art in disease name recognition and normalization research, by providing a high-quality gold standard thus enabling the development of machine-learning based approaches for such tasks.
### Source Data
#### Initial Data Collection and Normalization
The dataset consists on abstracts extracted from PubMed.
#### Who are the source language producers?
The source language producers are the authors of publication abstracts hosted in PubMed.
### Annotations
#### Annotation process
Each PubMed abstract was manually annotated by two annotators with disease mentions and their corresponding concepts in Medical Subject Headings (MeSH®) or Online Mendelian Inheritance in Man (OMIM®). Manual curation was performed using PubTator, which allowed the use of pre-annotations as a pre-step to manual annotations. Fourteen annotators were randomly paired and differing annotations were discussed for reaching a consensus in two annotation phases. Finally, all results were checked against annotations of the rest of the corpus to assure corpus-wide consistency.
#### Who are the annotators?
The annotator group consisted of 14 people with backgrounds in biomedical informatics research and experience in biomedical text corpus annotation.
### Personal and Sensitive Information
[N/A]
## Considerations for Using the Data
### Social Impact of Dataset
Information encoded in natural language in biomedical literature publications is only useful if efficient and reliable ways of accessing and analyzing that information are available. Natural language processing and text mining tools are therefore essential for extracting valuable information. This dataset provides an annotated corpora that can be used to develop highly effective tools to automatically detect central biomedical concepts such as diseases.
### Discussion of Biases
To avoid annotator bias, pairs of annotators were chosen randomly for each set, so that each pair of annotators overlapped for at most two sets.
### Other Known Limitations
A handful of disease concepts were discovered that were not included in MEDIC. For those, we decided to include the appropriate OMIM identifiers.
In addition, certain disease mentions were found to not be easily represented using the standard categorizations.
Also, each PMID document was pre-annotated using the Inference Method developed for disease name normalization, which properly handles abbreviation recognition, robust string matching, etc. As such, human annotators were given the pre-annotated documents as a starting point and allowed to see each pre-annotation with a computed confidence.
## Additional Information
### Dataset Curators
Rezarta Islamaj Doğan, Robert Leaman, Zhiyong Lu
### Licensing Information
```
PUBLIC DOMAIN NOTICE
This work is a "United States Government Work" under the terms of the
United States Copyright Act. It was written as part of the authors'
official duties as a United States Government employee and thus cannot
be copyrighted within the United States. The data is freely available
to the public for use. The National Library of Medicine and the
U.S. Government have not placed any restriction on its use or
reproduction.
Although all reasonable efforts have been taken to ensure the accuracy
and reliability of the data and its source code, the NLM and the
U.S. Government do not and cannot warrant the performance or results
that may be obtained by using it. The NLM and the U.S. Government
disclaim all warranties, express or implied, including warranties of
performance, merchantability or fitness for any particular purpose.
Please cite the authors in any work or product based on this material:
An improved corpus of disease mentions in PubMed citations
http://aclweb.org/anthology-new/W/W12/W12-2411.pdf
NCBI Disease Corpus: A Resource for Disease Name Recognition and
Normalization http://www.ncbi.nlm.nih.gov/pubmed/24393765
Disease Name Normalization with Pairwise Learning to Rank
http://www.ncbi.nlm.nih.gov/pubmed/23969135
```
### Citation Information
```
@article{dougan2014ncbi,
title={NCBI disease corpus: a resource for disease name recognition and concept normalization},
author={Do{\u{g}}an, Rezarta Islamaj and Leaman, Robert and Lu, Zhiyong},
journal={Journal of biomedical informatics},
volume={47},
pages={1--10},
year={2014},
publisher={Elsevier}
}
```
### Contributions
Thanks to [@edugp](https://github.com/edugp) for adding this dataset. | [
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