datasetId
stringlengths 2
117
| author
stringlengths 2
42
⌀ | last_modified
timestamp[us, tz=UTC] | downloads
int64 0
9.36M
| likes
int64 0
3.89k
| tags
list | task_categories
list | createdAt
timestamp[us, tz=UTC] | card
stringlengths 19
1.01M
| embedding
list |
|---|---|---|---|---|---|---|---|---|---|
yuhsinchan/nmsqa_full-dev_test
|
yuhsinchan
| 2023-11-01T12:12:38Z | 41 | 0 |
[
"region:us"
] | null | 2023-11-01T10:27:53Z |
---
dataset_info:
features:
- name: case_id
dtype: string
- name: context_code
sequence: int16
- name: context_cnt
sequence: int16
- name: question_code
sequence: int16
- name: question_cnt
sequence: int16
- name: start_idx
dtype: int64
- name: end_idx
dtype: int64
- name: start_time
dtype: float64
- name: end_time
dtype: float64
splits:
- name: dev
num_bytes: 102442544
num_examples: 17155
- name: test
num_bytes: 2316076
num_examples: 267
download_size: 0
dataset_size: 104758620
---
# Dataset Card for "nmsqa_full-dev_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
maywell/ko_hh-rlhf-20k_filtered
|
maywell
| 2023-11-04T18:45:29Z | 41 | 2 |
[
"region:us"
] | null | 2023-11-04T03:38:26Z |
---
dataset_info:
features:
- name: chosen
dtype: string
- name: rejected
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 30828302
num_examples: 19363
download_size: 15034439
dataset_size: 30828302
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "ko_hh-rlhf-20k_filtered"
Synatra-Translation 모델로 번역된 20k rlhf셋입니다. 번역퀄이 뛰어나진 않습니다. 추가 대화문 등의 데이터 학습이 필요해보입니다.
## 베이스 데이터셋
[Anthropic/hh-rlhf](https://huggingface.co/datasets/Anthropic/hh-rlhf)
|
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anforsm/movie_posters-genres-80k-transformed
|
anforsm
| 2023-11-06T17:00:58Z | 41 | 0 |
[
"region:us"
] | null | 2023-11-06T16:42:07Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: id
dtype: int64
- name: image
sequence:
sequence:
sequence: float32
- name: genres
sequence: int64
splits:
- name: train
num_bytes: 23128566147.416473
num_examples: 78352
- name: test
num_bytes: 295187948.58352655
num_examples: 1000
download_size: 22030369211
dataset_size: 23423754096.0
---
# Dataset Card for "movie_posters-genres-80k-transformed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
tyzhu/squad_train500_eval100
|
tyzhu
| 2023-11-07T08:45:58Z | 41 | 0 |
[
"region:us"
] | null | 2023-11-07T07:45:05Z |
---
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
- name: inputs
dtype: string
- name: targets
dtype: string
splits:
- name: validation
num_bytes: 138876
num_examples: 100
- name: train
num_bytes: 1088215
num_examples: 500
download_size: 206819
dataset_size: 1227091
---
# Dataset Card for "squad_train500_eval100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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just097/wiki-comma-placement
|
just097
| 2023-11-07T11:18:49Z | 41 | 0 |
[
"task_categories:token-classification",
"language:en",
"license:mit",
"region:us"
] |
[
"token-classification"
] | 2023-11-07T11:13:26Z |
---
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: tokens
sequence: string
- name: tags
sequence: int64
splits:
- name: train
num_bytes: 35619014
num_examples: 82600
- name: validation
num_bytes: 8871452
num_examples: 20651
- name: test
num_bytes: 8462693
num_examples: 19667
download_size: 12620120
dataset_size: 52953159
license: mit
task_categories:
- token-classification
language:
- en
pretty_name: Wiki Comma Placement Dataset
---
# Dataset Card for "wiki-comma-placement"
Subset of Wikitext dataset suitable for fixing punctuation marks. For this particular case, main task is "comma placement"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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vilm/viet-pretrained-001
|
vilm
| 2023-11-11T13:36:34Z | 41 | 0 |
[
"license:mpl-2.0",
"region:us"
] | null | 2023-11-07T17:23:55Z |
---
license: mpl-2.0
---
|
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] |
erikaxenia/id_card_class
|
erikaxenia
| 2023-11-07T19:02:34Z | 41 | 0 |
[
"region:us"
] | null | 2023-11-07T19:02:20Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: int64
- name: ground_truth
dtype: string
splits:
- name: train
num_bytes: 130472837.0
num_examples: 470
- name: valid
num_bytes: 21179110.0
num_examples: 59
- name: test
num_bytes: 16112586.0
num_examples: 58
download_size: 166464862
dataset_size: 167764533.0
---
# Dataset Card for "id_card_class"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
Tural/MetaMathQA
|
Tural
| 2023-11-07T22:02:31Z | 41 | 0 |
[
"region:us"
] | null | 2023-11-07T22:02:21Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: instruction
dtype: string
- name: type
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 288517804
num_examples: 395000
download_size: 141855468
dataset_size: 288517804
---
# Dataset Card for "MetaMathQA"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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stas/openwebtext-synthetic-testing
|
stas
| 2023-11-14T07:31:20Z | 41 | 3 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-11-14T07:30:30Z |
---
license: apache-2.0
---
Using 10 records from [openwebtext-10k](https://huggingface.co/datasets/stas/openwebtext-10k) this dataset is written for very fast testing and can produce a repeat of these 10 records in a form of 1, 2, 3, 4, 5, 10, 100, 300 or 1k records splits, e.g.:
```
$ python -c 'from datasets import load_dataset; \
ds=load_dataset("stas/openwebtext-synthetic-testing", split="10.repeat"); print(len(ds))'
10
$ python -c 'from datasets import load_dataset; \
ds=load_dataset("stas/openwebtext-synthetic-testing", split="1k.repeat"); print(len(ds))'
1000
```
Each record is just a single `text` record of several paragraphs long - web articles.
As this is used for very fast functional testing on CI there is no `train` or `validation` splits, you can just repeat the same records.
|
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result-kand2-sdxl-wuerst-karlo/b50562e5
|
result-kand2-sdxl-wuerst-karlo
| 2023-11-23T03:38:23Z | 41 | 0 |
[
"region:us"
] | null | 2023-11-23T03:38:22Z |
---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 170
num_examples: 10
download_size: 1334
dataset_size: 170
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "b50562e5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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tbilisi-ai-lab/TinyStoriesInstruct
|
tbilisi-ai-lab
| 2023-11-24T17:29:41Z | 41 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-24T17:17:57Z |
---
license: mit
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
dataset_info:
features:
- name: 'Features:'
dtype: string
- name: 'Summary:'
dtype: string
- name: 'Random sentence:'
dtype: string
- name: 'Story:'
sequence: string
- name: 'Words:'
dtype: string
splits:
- name: train
num_bytes: 2342624089
num_examples: 2476532
- name: valid
num_bytes: 23620552
num_examples: 25027
download_size: 1276411379
dataset_size: 2366244641
---
|
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] |
lurosenb/medqa
|
lurosenb
| 2023-11-25T21:57:03Z | 41 | 0 |
[
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:en",
"license:mit",
"medical",
"region:us"
] |
[
"question-answering"
] | 2023-11-25T21:54:45Z |
---
license: mit
task_categories:
- question-answering
language:
- en
tags:
- medical
size_categories:
- 1K<n<10K
---
|
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] |
MaryDatascientist/en_dataset
|
MaryDatascientist
| 2023-11-29T08:00:32Z | 41 | 0 |
[
"region:us"
] | null | 2023-11-28T20:08:16Z |
---
dataset_info:
features:
- name: tokens
sequence: string
- name: ner_tags
sequence: int64
- name: lang
dtype: string
splits:
- name: train
num_bytes: 98357684.72831541
num_examples: 262560
- name: validation
num_bytes: 12407333.773835126
num_examples: 32820
- name: test
num_bytes: 12419125.385081522
num_examples: 32908
download_size: 30300039
dataset_size: 123184143.88723207
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
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persiannlp/parsinlu_query_paraphrasing
|
persiannlp
| 2022-10-22T15:13:22Z | 40 | 0 |
[
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|quora|google",
"language:fa",
"license:cc-by-nc-sa-4.0",
"arxiv:2012.06154",
"region:us"
] |
[
"query-paraphrasing"
] | 2022-03-02T23:29:22Z |
---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- fa
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|quora|google
task_categories:
- query-paraphrasing
task_ids:
- query-paraphrasing
---
# Dataset Card for PersiNLU (Query Paraphrasing)
## Table of Contents
- [Dataset Card for PersiNLU (Query Paraphrasing)](#dataset-card-for-persi_nlu_query_paraphrasing)
- [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:** [Github](https://github.com/persiannlp/parsinlu/)
- **Repository:** [Github](https://github.com/persiannlp/parsinlu/)
- **Paper:** [Arxiv](https://arxiv.org/abs/2012.06154)
- **Leaderboard:**
- **Point of Contact:** [email protected]
### Dataset Summary
A Persian query paraphrasng task (deciding whether two questions are paraphrases of each other).
The questions are partially generated from Google auto-complete, and partially translated from the Quora paraphrasing dataset.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The text dataset is in Persian (`fa`).
## Dataset Structure
### Data Instances
Here is an example from the dataset:
```json
{
"q1": "اعمال حج تمتع از چه روزی شروع میشود؟",
"q2": "ویار از چه روزی شروع میشود؟",
"label": "0",
"category": "natural"
}
```
### Data Fields
- `q1`: the first question.
- `q2`: the second question.
- `category`: whether the questions are mined from Quora (`qqp`) or they're extracted from Google auto-complete (`natural`).
- `label`: `1` if the questions are paraphrases; `0` otherwise.
### Data Splits
The train/dev/test splits contains 1830/898/1916 samples.
## Dataset Creation
### Curation Rationale
For details, check [the corresponding draft](https://arxiv.org/abs/2012.06154).
### 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
CC BY-NC-SA 4.0 License
### Citation Information
```bibtex
@article{huggingface:dataset,
title = {ParsiNLU: A Suite of Language Understanding Challenges for Persian},
authors = {Khashabi, Daniel and Cohan, Arman and Shakeri, Siamak and Hosseini, Pedram and Pezeshkpour, Pouya and Alikhani, Malihe and Aminnaseri, Moin and Bitaab, Marzieh and Brahman, Faeze and Ghazarian, Sarik and others},
year={2020}
journal = {arXiv e-prints},
eprint = {2012.06154},
}
```
### Contributions
Thanks to [@danyaljj](https://github.com/danyaljj) for adding this dataset.
|
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projecte-aina/casum
|
projecte-aina
| 2023-09-13T12:49:03Z | 40 | 0 |
[
"task_categories:summarization",
"annotations_creators:machine-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:unknown",
"language:ca",
"license:cc-by-nc-4.0",
"arxiv:2202.06871",
"region:us"
] |
[
"summarization"
] | 2022-03-02T23:29:22Z |
---
annotations_creators:
- machine-generated
language_creators:
- expert-generated
language:
- ca
license:
- cc-by-nc-4.0
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets: []
task_categories:
- summarization
task_ids: []
pretty_name: casum
---
# Dataset Card for CaSum
## 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
- **Paper:** [Sequence to Sequence Resources for Catalan](https://arxiv.org/pdf/2202.06871.pdf)
- **Point of Contact:** [Ona de Gibert Bonet](mailto:[email protected])
### Dataset Summary
CaSum is a summarization dataset. It is extracted from a newswire corpus crawled from the Catalan News Agency ([Agència Catalana de Notícies; ACN](https://www.acn.cat/)). The corpus consists of 217,735 instances that are composed by the headline and the body.
### Supported Tasks and Leaderboards
The dataset can be used to train a model for abstractive summarization. Success on this task is typically measured by achieving a high Rouge score. The [mbart-base-ca-casum](https://huggingface.co/projecte-aina/bart-base-ca-casum) model currently achieves a 41.39.
### Languages
The dataset is in Catalan (`ca-ES`).
## Dataset Structure
### Data Instances
```
{
'summary': 'Mapfre preveu ingressar 31.000 milions d’euros al tancament de 2018',
'text': 'L’asseguradora llançarà la seva filial Verti al mercat dels EUA a partir de 2017 ACN Madrid.-Mapfre preveu assolir uns ingressos de 31.000 milions d'euros al tancament de 2018 i destinarà a retribuir els seus accionistes com a mínim el 50% dels beneficis del grup durant el període 2016-2018, amb una rendibilitat mitjana a l’entorn del 5%, segons ha anunciat la companyia asseguradora durant la celebració aquest divendres de la seva junta general d’accionistes. La firma asseguradora també ha avançat que llançarà la seva filial d’automoció i llar al mercat dels EUA a partir de 2017. Mapfre ha recordat durant la junta que va pagar més de 540 milions d'euros en impostos el 2015, amb una taxa impositiva efectiva del 30,4 per cent. La companyia també ha posat en marxa el Pla de Sostenibilitat 2016-2018 i el Pla de Transparència Activa, “que han de contribuir a afermar la visió de Mapfre com a asseguradora global de confiança”, segons ha informat en un comunicat.'
}
```
### Data Fields
- `summary` (str): Summary of the piece of news
- `text` (str): The text of the piece of news
### Data Splits
We split our dataset into train, dev and test splits
- train: 197,735 examples
- validation: 10,000 examples
- test: 10,000 examples
## Dataset Creation
### Curation Rationale
We created this corpus to contribute to the development of language models in Catalan, a low-resource language. There exist few resources for summarization in Catalan.
### Source Data
#### Initial Data Collection and Normalization
We obtained each headline and its corresponding body of each news piece on the Catalan News Agency ([Agència Catalana de Notícies; ACN](https://www.acn.cat/)) website and applied the following cleaning pipeline: deduplicating the documents, removing the documents with empty attributes, and deleting some boilerplate sentences.
#### Who are the source language producers?
The news portal Catalan News Agency ([Agència Catalana de Notícies; ACN](https://www.acn.cat/)).
### Annotations
The dataset is unannotated.
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
Since all data comes from public websites, no anonymization process was performed.
## Considerations for Using the Data
### Social Impact of Dataset
We hope this corpus contributes to the development of summarization models in Catalan, a low-resource language.
### Discussion of Biases
We are aware that since the data comes from unreliable web pages, some biases may be present in the dataset. Nonetheless, we have not applied any steps to reduce their impact.
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
Text Mining Unit (TeMU) at the Barcelona Supercomputing Center ([email protected])
This work was funded by MT4All CEF project and [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
### Licensing information
[Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/).
### BibTeX citation
If you use any of these resources (datasets or models) in your work, please cite our latest preprint:
```bibtex
@misc{degibert2022sequencetosequence,
title={Sequence-to-Sequence Resources for Catalan},
author={Ona de Gibert and Ksenia Kharitonova and Blanca Calvo Figueras and Jordi Armengol-Estapé and Maite Melero},
year={2022},
eprint={2202.06871},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
[N/A]
|
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qanastek/WMT-16-PubMed
|
qanastek
| 2022-10-22T15:20:12Z | 40 | 2 |
[
"task_categories:translation",
"annotations_creators:machine-generated",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:extended",
"language:bg",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:en",
"language:es",
"language:et",
"language:fi",
"language:fr",
"language:hu",
"language:it",
"language:lt",
"language:lv",
"language:mt",
"language:nl",
"language:pl",
"language:pt",
"language:ro",
"language:sk",
"language:sl",
"language:sv",
"region:us"
] |
[
"translation",
"machine-translation"
] | 2022-03-02T23:29:22Z |
---
annotations_creators:
- machine-generated
- expert-generated
language_creators:
- found
language:
- bg
- cs
- da
- de
- el
- en
- es
- et
- fi
- fr
- hu
- it
- lt
- lv
- mt
- nl
- pl
- pt
- ro
- sk
- sl
- sv
multilinguality:
- multilingual
pretty_name: WMT-16-PubMed
size_categories:
- 100K<n<1M
source_datasets:
- extended
task_categories:
- translation
- machine-translation
task_ids:
- translation
- machine-translation
---
# WMT-16-PubMed : European parallel translation corpus from the European Medicines Agency
## Table of Contents
- [Dataset Card for [Needs More Information]](#dataset-card-for-needs-more-information)
- [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)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [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:** https://www.statmt.org/wmt16/biomedical-translation-task.html
- **Repository:** https://github.com/biomedical-translation-corpora/corpora
- **Paper:** https://aclanthology.org/W16-2301/
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Yanis Labrak](mailto:[email protected])
### Dataset Summary
`WMT-16-PubMed` is a parallel corpus for neural machine translation collected and aligned for ACL 2016 during the [WMT'16 Shared Task: Biomedical Translation Task](https://www.statmt.org/wmt16/biomedical-translation-task.html).
### Supported Tasks and Leaderboards
`translation`: The dataset can be used to train a model for translation.
### Languages
The corpora consists of a pair of source and target sentences for all 4 different languages :
**List of languages :** `English (en)`,`Spanish (es)`,`French (fr)`,`Portuguese (pt)`.
## Load the dataset with HuggingFace
```python
from datasets import load_dataset
dataset = load_dataset("qanastek/WMT-16-PubMed", split='train', download_mode='force_redownload')
print(dataset)
print(dataset[0])
```
## Dataset Structure
### Data Instances
```plain
lang doc_id workshop publisher source_text target_text
0 en-fr 26839447 WMT'16 Biomedical Translation Task - PubMed pubmed Global Health: Where Do Physiotherapy and Reha... La place des cheveux et des poils dans les rit...
1 en-fr 26837117 WMT'16 Biomedical Translation Task - PubMed pubmed Carabin Les Carabins
2 en-fr 26837116 WMT'16 Biomedical Translation Task - PubMed pubmed In Process Citation Le laboratoire d'Anatomie, Biomécanique et Org...
3 en-fr 26837115 WMT'16 Biomedical Translation Task - PubMed pubmed Comment on the misappropriation of bibliograph... Du détournement des références bibliographique...
4 en-fr 26837114 WMT'16 Biomedical Translation Task - PubMed pubmed Anti-aging medicine, a science-based, essentia... La médecine anti-âge, une médecine scientifiqu...
... ... ... ... ... ... ...
973972 en-pt 20274330 WMT'16 Biomedical Translation Task - PubMed pubmed Myocardial infarction, diagnosis and treatment Infarto do miocárdio; diagnóstico e tratamento
973973 en-pt 20274329 WMT'16 Biomedical Translation Task - PubMed pubmed The health areas politics A política dos campos de saúde
973974 en-pt 20274328 WMT'16 Biomedical Translation Task - PubMed pubmed The role in tissue edema and liquid exchanges ... O papel dos tecidos nos edemas e nas trocas lí...
973975 en-pt 20274327 WMT'16 Biomedical Translation Task - PubMed pubmed About suppuration of the wound after thoracopl... Sôbre as supurações da ferida operatória após ...
973976 en-pt 20274326 WMT'16 Biomedical Translation Task - PubMed pubmed Experimental study of liver lesions in the tre... Estudo experimental das lesões hepáticas no tr...
```
### Data Fields
**lang** : The pair of source and target language of type `String`.
**source_text** : The source text of type `String`.
**target_text** : The target text of type `String`.
### Data Splits
`en-es` : 285,584
`en-fr` : 614,093
`en-pt` : 74,300
## Dataset Creation
### Curation Rationale
For details, check the corresponding [pages](https://www.statmt.org/wmt16/biomedical-translation-task.html).
### Source Data
<!-- #### Initial Data Collection and Normalization
ddd -->
#### Who are the source language producers?
The shared task as been organized by :
* Antonio Jimeno Yepes (IBM Research Australia)
* Aurélie Névéol (LIMSI, CNRS, France)
* Mariana Neves (Hasso-Plattner Institute, Germany)
* Karin Verspoor (University of Melbourne, Australia)
### Personal and Sensitive Information
The corpora is free of personal or sensitive information.
## Considerations for Using the Data
### Other Known Limitations
The nature of the task introduce a variability in the quality of the target translations.
## Additional Information
### Dataset Curators
__Hugging Face WMT-16-PubMed__: Labrak Yanis, Dufour Richard (Not affiliated with the original corpus)
__WMT'16 Shared Task: Biomedical Translation Task__:
* Antonio Jimeno Yepes (IBM Research Australia)
* Aurélie Névéol (LIMSI, CNRS, France)
* Mariana Neves (Hasso-Plattner Institute, Germany)
* Karin Verspoor (University of Melbourne, Australia)
<!-- ### Licensing Information
ddd -->
### Citation Information
Please cite the following paper when using this dataset.
```latex
@inproceedings{bojar-etal-2016-findings,
title = Findings of the 2016 Conference on Machine Translation,
author = {
Bojar, Ondrej and
Chatterjee, Rajen and
Federmann, Christian and
Graham, Yvette and
Haddow, Barry and
Huck, Matthias and
Jimeno Yepes, Antonio and
Koehn, Philipp and
Logacheva, Varvara and
Monz, Christof and
Negri, Matteo and
Neveol, Aurelie and
Neves, Mariana and
Popel, Martin and
Post, Matt and
Rubino, Raphael and
Scarton, Carolina and
Specia, Lucia and
Turchi, Marco and
Verspoor, Karin and
Zampieri, Marcos,
},
booktitle = Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers,
month = aug,
year = 2016,
address = Berlin, Germany,
publisher = Association for Computational Linguistics,
url = https://aclanthology.org/W16-2301,
doi = 10.18653/v1/W16-2301,
pages = 131--198,
}
```
|
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] |
NbAiLab/NST
|
NbAiLab
| 2022-08-12T14:09:29Z | 40 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2022-04-20T12:06:56Z |
---
license: apache-2.0
---
|
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nbroad/mediasum
|
nbroad
| 2022-10-25T10:40:11Z | 40 | 1 |
[
"task_categories:summarization",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-nc-sa-4.0",
"arxiv:2103.06410",
"region:us"
] |
[
"summarization"
] | 2022-07-15T21:42:51Z |
---
language:
- en
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
task_categories:
- summarization
---
# MediaSum
## Description
This large-scale media interview dataset contains 463.6K transcripts with abstractive summaries,
collected from interview transcripts and overview / topic descriptions from NPR and CNN.
### **NOTE: The authors have requested that this dataset be used for research purposes only**
## Homepage
https://github.com/zcgzcgzcg1/MediaSum
## Paper
https://arxiv.org/abs/2103.06410
## Authors
### Chenguang Zhu*, Yang Liu*, Jie Mei, Michael Zeng
#### Microsoft Cognitive Services Research Group
{chezhu,yaliu10,jimei,nzeng}@microsoft.com
## Citation
@article{zhu2021mediasum,
title={MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization},
author={Zhu, Chenguang and Liu, Yang and Mei, Jie and Zeng, Michael},
journal={arXiv preprint arXiv:2103.06410},
year={2021}
}
## Dataset size
Train: 443,596
Validation: 10,000
Test: 10,000
The splits were made by using the file located here: https://github.com/zcgzcgzcg1/MediaSum/tree/main/data
## Data details
- id (string): unique identifier
- program (string): the program this transcript came from
- date (string): date of program
- url (string): link to where audio and transcript are located
- title (string): title of the program. some datapoints do not have a title
- summary (string): summary of the program
- utt (list of string): list of utterances by the speakers in the program. corresponds with `speaker`
- speaker (list of string): list of speakers, corresponds with `utt`
Example:
```
{
"id": "NPR-11",
"program": "Day to Day",
"date": "2008-06-10",
"url": "https://www.npr.org/templates/story/story.php?storyId=91356794",
"title": "Researchers Find Discriminating Plants",
"summary": "The \"sea rocket\" shows preferential treatment to plants that are its kin. Evolutionary plant ecologist Susan Dudley of McMaster University in Ontario discusses her discovery.",
"utt": [
"This is Day to Day. I'm Madeleine Brand.",
"And I'm Alex Cohen.",
"Coming up, the question of who wrote a famous religious poem turns into a very unchristian battle.",
"First, remember the 1970s? People talked to their houseplants, played them classical music. They were convinced plants were sensuous beings and there was that 1979 movie, \"The Secret Life of Plants.\"",
"Only a few daring individuals, from the scientific establishment, have come forward with offers to replicate his experiments, or test his results. The great majority are content simply to condemn his efforts without taking the trouble to investigate their validity.",
...
"OK. Thank you.",
"That's Susan Dudley. She's an associate professor of biology at McMaster University in Hamilt on Ontario. She discovered that there is a social life of plants."
],
"speaker": [
"MADELEINE BRAND, host",
"ALEX COHEN, host",
"ALEX COHEN, host",
"MADELEINE BRAND, host",
"Unidentified Male",
..."
Professor SUSAN DUDLEY (Biology, McMaster University)",
"MADELEINE BRAND, host"
]
}
```
## Using the dataset
```python
from datasets import load_dataset
ds = load_dataset("nbroad/mediasum")
```
## Data location
https://drive.google.com/file/d/1ZAKZM1cGhEw2A4_n4bGGMYyF8iPjLZni/view?usp=sharing
## License
No license specified, but the authors have requested that this dataset be used for research purposes only.
|
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Artificio/WikiArt
|
Artificio
| 2023-01-18T17:13:54Z | 40 | 5 |
[
"region:us"
] | null | 2022-07-21T21:18:50Z |
---
dataset_info:
features:
- name: title
dtype: string
- name: artist
dtype: string
- name: date
dtype: string
- name: genre
dtype: string
- name: style
dtype: string
- name: description
dtype: string
- name: filename
dtype: string
- name: image
dtype: image
- name: embeddings_pca512
sequence: float32
splits:
- name: train
num_bytes: 1659296285.75
num_examples: 103250
download_size: 1711766693
dataset_size: 1659296285.75
---
# Dataset Card for "WikiArt"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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Kirili4ik/yandex_jobs
|
Kirili4ik
| 2022-09-03T17:55:00Z | 40 | 4 |
[
"task_categories:text-generation",
"task_categories:summarization",
"task_categories:multiple-choice",
"task_ids:language-modeling",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:ru",
"license:unknown",
"vacancies",
"jobs",
"ru",
"yandex",
"region:us"
] |
[
"text-generation",
"summarization",
"multiple-choice"
] | 2022-09-03T17:22:02Z |
---
annotations_creators:
- expert-generated
language:
- ru
language_creators:
- found
license:
- unknown
multilinguality:
- monolingual
paperswithcode_id: climate-fever
pretty_name: yandex_jobs
size_categories:
- n<1K
source_datasets:
- original
tags:
- vacancies
- jobs
- ru
- yandex
task_categories:
- text-generation
- summarization
- multiple-choice
task_ids:
- language-modeling
---
# Dataset Card for Yandex_Jobs
## 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)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Contributions](#contributions)
## Dataset Description
### Dataset Summary
This is a dataset of more than 600 IT vacancies in Russian from parsing telegram channel https://t.me/ya_jobs. All the texts are perfectly structured, no missing values.
### Supported Tasks and Leaderboards
`text-generation` with the 'Raw text column'.
`summarization` as for getting from all the info the header.
`multiple-choice` as for the hashtags (to choose multiple from all available in the dataset)
### Languages
The text in the dataset is in only in Russian. The associated BCP-47 code is `ru`.
## Dataset Structure
### Data Instances
The data is parsed from a vacancy of Russian IT company [Yandex](https://ya.ru/).
An example from the set looks as follows:
```
{'Header': 'Разработчик интерфейсов в группу разработки спецпроектов',
'Emoji': '🎳',
'Description': 'Конструктор лендингов — это инструмент Яндекса, который позволяет пользователям создавать лендинги и турбо-лендинги для Яндекс.Директа. Турбо — режим ускоренной загрузки страниц для показа на мобильных. У нас современный стек, смелые планы и высокая динамика.\nМы ищем опытного и открытого новому фронтенд-разработчика.',
'Requirements': '• отлично знаете JavaScript
• разрабатывали на Node.js, применяли фреймворк Express
• умеете создавать веб-приложения на React + Redux
• знаете HTML и CSS, особенности их отображения в браузерах',
'Tasks': '• разрабатывать интерфейсы',
'Pluses': '• писали интеграционные, модульные, функциональные или браузерные тесты
• умеете разворачивать и администрировать веб-сервисы: собирать Docker-образы, настраивать мониторинги, выкладывать в облачные системы, отлаживать в продакшене
• работали с реляционными БД PostgreSQL',
'Hashtags': '#фронтенд #турбо #JS',
'Link': 'https://ya.cc/t/t7E3UsmVSKs6L',
'Raw text': 'Разработчик интерфейсов в группу разработки спецпроектов🎳
Конструктор лендингов — это инструмент Яндекса, который позволяет пользователям создавать лендинги и турбо-лендинги для Яндекс.Директа. Турбо — режим ускоренной загрузки страниц для показа на мобильных. У нас современный стек, смелые планы и высокая динамика.
Мы ищем опытного и открытого новому фронтенд-разработчика.
Мы ждем, что вы:
• отлично знаете JavaScript
• разрабатывали на Node.js, применяли фреймворк Express
• умеете создавать веб-приложения на React + Redux
• знаете HTML и CSS, особенности их отображения в браузерах
Что нужно делать:
• разрабатывать интерфейсы
Будет плюсом, если вы:
• писали интеграционные, модульные, функциональные или браузерные тесты
• умеете разворачивать и администрировать веб-сервисы: собирать Docker-образы, настраивать мониторинги, выкладывать в облачные системы, отлаживать в продакшене
• работали с реляционными БД PostgreSQL
https://ya.cc/t/t7E3UsmVSKs6L
#фронтенд #турбо #JS'
}
```
### Data Fields
- `Header`: A string with a position title (str)
- `Emoji`: Emoji that is used at the end of the title position (usually asosiated with the position) (str)
- `Description`: Short description of the vacancy (str)
- `Requirements`: A couple of required technologies/programming languages/experience (str)
- `Tasks`: Examples of the tasks of the job position (str)
- `Pluses`: A couple of great points for the applicant to have (technologies/experience/etc)
- `Hashtags`: A list of hashtags assosiated with the job (usually programming languages) (str)
- `Link`: A link to a job description (there may be more information, but it is not checked) (str)
- `Raw text`: Raw text with all the formatiing from the channel. Created with other fields. (str)
### Data Splits
There is not enough examples yet to split it to train/test/val in my opinion.
## Dataset Creation
It downloaded and parsed from telegram channel https://t.me/ya_jobs 03.09.2022. All the unparsed examples and the ones missing any field are deleted (from 1600 vacancies to only 600 without any missing fields like emojis or links)
## Considerations for Using the Data
These vacancies are for only one IT company (yandex). This means they can be pretty specific and probably can not be generalized as any vacancies or even any IT vacancies.
## Contributions
- **Point of Contact and Author:** [Kirill Gelvan](telegram: @kirili4ik)
|
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TheGreatRambler/mm2_level
|
TheGreatRambler
| 2022-11-11T08:07:34Z | 40 | 5 |
[
"task_categories:other",
"task_categories:object-detection",
"task_categories:text-retrieval",
"task_categories:token-classification",
"task_categories:text-generation",
"multilinguality:multilingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:multilingual",
"license:cc-by-nc-sa-4.0",
"text-mining",
"region:us"
] |
[
"other",
"object-detection",
"text-retrieval",
"token-classification",
"text-generation"
] | 2022-09-18T20:15:00Z |
---
language:
- multilingual
license:
- cc-by-nc-sa-4.0
multilinguality:
- multilingual
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- other
- object-detection
- text-retrieval
- token-classification
- text-generation
task_ids: []
pretty_name: Mario Maker 2 levels
tags:
- text-mining
---
# Mario Maker 2 levels
Part of the [Mario Maker 2 Dataset Collection](https://tgrcode.com/posts/mario_maker_2_datasets)
## Dataset Description
The Mario Maker 2 levels dataset consists of 26.6 million levels from Nintendo's online service totaling around 100GB of data. The dataset was created using the self-hosted [Mario Maker 2 api](https://tgrcode.com/posts/mario_maker_2_api) over the course of 1 month in February 2022.
### How to use it
The Mario Maker 2 levels dataset is a very large dataset so for most use cases it is recommended to make use of the streaming API of `datasets`. You can load and iterate through the dataset with the following code:
```python
from datasets import load_dataset
ds = load_dataset("TheGreatRambler/mm2_level", streaming=True, split="train")
print(next(iter(ds)))
#OUTPUT:
{
'data_id': 3000004,
'name': 'カベキック',
'description': 'カベキックをとにかくするコースです。',
'uploaded': 1561644329,
'created': 1561674240,
'gamestyle': 4,
'theme': 0,
'difficulty': 0,
'tag1': 7,
'tag2': 10,
'game_version': 1,
'world_record': 8049,
'upload_time': 193540,
'upload_attempts': 1,
'num_comments': 60,
'clear_condition': 0,
'clear_condition_magnitude': 0,
'timer': 300,
'autoscroll_speed': 0,
'clears': 1646,
'attempts': 3168,
'clear_rate': 51.957070707070706,
'plays': 1704,
'versus_matches': 80,
'coop_matches': 27,
'likes': 152,
'boos': 118,
'unique_players_and_versus': 1391,
'weekly_likes': 0,
'weekly_plays': 1,
'uploader_pid': '5218390885570355093',
'first_completer_pid': '16824392528839047213',
'record_holder_pid': '5411258160547085075',
'level_data': [some binary data],
'unk2': 0,
'unk3': [some binary data],
'unk9': 3,
'unk10': 4,
'unk11': 1,
'unk12': 1
}
```
Level data is a binary blob describing the actual level and is equivalent to the level format Nintendo uses in-game. It is gzip compressed and needs to be decompressed to be read. To read it you only need to use the provided `level.ksy` kaitai struct file and install the kaitai struct runtime to parse it into an object:
```python
from datasets import load_dataset
from kaitaistruct import KaitaiStream
from io import BytesIO
from level import Level
import zlib
ds = load_dataset("TheGreatRambler/mm2_level", streaming=True, split="train")
level_data = next(iter(ds))["level_data"]
level = Level(KaitaiStream(BytesIO(zlib.decompress(level_data))))
# NOTE level.overworld.objects is a fixed size (limitation of Kaitai struct)
# must iterate by object_count or null objects will be included
for i in range(level.overworld.object_count):
obj = level.overworld.objects[i]
print("X: %d Y: %d ID: %s" % (obj.x, obj.y, obj.id))
#OUTPUT:
X: 1200 Y: 400 ID: ObjId.block
X: 1360 Y: 400 ID: ObjId.block
X: 1360 Y: 240 ID: ObjId.block
X: 1520 Y: 240 ID: ObjId.block
X: 1680 Y: 240 ID: ObjId.block
X: 1680 Y: 400 ID: ObjId.block
X: 1840 Y: 400 ID: ObjId.block
X: 2000 Y: 400 ID: ObjId.block
X: 2160 Y: 400 ID: ObjId.block
X: 2320 Y: 400 ID: ObjId.block
X: 2480 Y: 560 ID: ObjId.block
X: 2480 Y: 720 ID: ObjId.block
X: 2480 Y: 880 ID: ObjId.block
X: 2160 Y: 880 ID: ObjId.block
```
Rendering the level data into an image can be done using [Toost](https://github.com/TheGreatRambler/toost) if desired.
You can also download the full dataset. Note that this will download ~100GB:
```python
ds = load_dataset("TheGreatRambler/mm2_level", split="train")
```
## Data Structure
### Data Instances
```python
{
'data_id': 3000004,
'name': 'カベキック',
'description': 'カベキックをとにかくするコースです。',
'uploaded': 1561644329,
'created': 1561674240,
'gamestyle': 4,
'theme': 0,
'difficulty': 0,
'tag1': 7,
'tag2': 10,
'game_version': 1,
'world_record': 8049,
'upload_time': 193540,
'upload_attempts': 1,
'num_comments': 60,
'clear_condition': 0,
'clear_condition_magnitude': 0,
'timer': 300,
'autoscroll_speed': 0,
'clears': 1646,
'attempts': 3168,
'clear_rate': 51.957070707070706,
'plays': 1704,
'versus_matches': 80,
'coop_matches': 27,
'likes': 152,
'boos': 118,
'unique_players_and_versus': 1391,
'weekly_likes': 0,
'weekly_plays': 1,
'uploader_pid': '5218390885570355093',
'first_completer_pid': '16824392528839047213',
'record_holder_pid': '5411258160547085075',
'level_data': [some binary data],
'unk2': 0,
'unk3': [some binary data],
'unk9': 3,
'unk10': 4,
'unk11': 1,
'unk12': 1
}
```
### Data Fields
|Field|Type|Description|
|---|---|---|
|data_id|int|Data IDs are unique identifiers, gaps in the table are due to levels deleted by users or Nintendo|
|name|string|Course name|
|description|string|Course description|
|uploaded|int|UTC timestamp for when the level was uploaded|
|created|int|Local timestamp for when the level was created|
|gamestyle|int|Gamestyle, enum below|
|theme|int|Theme, enum below|
|difficulty|int|Difficulty, enum below|
|tag1|int|The first tag, if it exists, enum below|
|tag2|int|The second tag, if it exists, enum below|
|game_version|int|The version of the game this level was made on|
|world_record|int|The world record in milliseconds|
|upload_time|int|The upload time in milliseconds|
|upload_attempts|int|The number of attempts it took the uploader to upload|
|num_comments|int|Number of comments, may not reflect the archived comments if there were more than 1000 comments|
|clear_condition|int|Clear condition, enum below|
|clear_condition_magnitude|int|If applicable, the magnitude of the clear condition|
|timer|int|The timer of the level|
|autoscroll_speed|int|A unit of how fast the configured autoscroll speed is for the level|
|clears|int|Course clears|
|attempts|int|Course attempts|
|clear_rate|float|Course clear rate as a float between 0 and 1|
|plays|int|Course plays, or "footprints"|
|versus_matches|int|Course versus matches|
|coop_matches|int|Course coop matches|
|likes|int|Course likes|
|boos|int|Course boos|
|unique_players_and_versus|int|All unique players that have ever played this level, including the number of versus matches|
|weekly_likes|int|The weekly likes on this course|
|weekly_plays|int|The weekly plays on this course|
|uploader_pid|string|The player ID of the uploader|
|first_completer_pid|string|The player ID of the user who first cleared this course|
|record_holder_pid|string|The player ID of the user who held the world record at time of archival |
|level_data|bytes|The GZIP compressed decrypted level data, kaitai struct file is provided for reading|
|unk2|int|Unknown|
|unk3|bytes|Unknown|
|unk9|int|Unknown|
|unk10|int|Unknown|
|unk11|int|Unknown|
|unk12|int|Unknown|
### Data Splits
The dataset only contains a train split.
## Enums
The dataset contains some enum integer fields. This can be used to convert back to their string equivalents:
```python
GameStyles = {
0: "SMB1",
1: "SMB3",
2: "SMW",
3: "NSMBU",
4: "SM3DW"
}
Difficulties = {
0: "Easy",
1: "Normal",
2: "Expert",
3: "Super expert"
}
CourseThemes = {
0: "Overworld",
1: "Underground",
2: "Castle",
3: "Airship",
4: "Underwater",
5: "Ghost house",
6: "Snow",
7: "Desert",
8: "Sky",
9: "Forest"
}
TagNames = {
0: "None",
1: "Standard",
2: "Puzzle solving",
3: "Speedrun",
4: "Autoscroll",
5: "Auto mario",
6: "Short and sweet",
7: "Multiplayer versus",
8: "Themed",
9: "Music",
10: "Art",
11: "Technical",
12: "Shooter",
13: "Boss battle",
14: "Single player",
15: "Link"
}
ClearConditions = {
137525990: "Reach the goal without landing after leaving the ground.",
199585683: "Reach the goal after defeating at least/all (n) Mechakoopa(s).",
272349836: "Reach the goal after defeating at least/all (n) Cheep Cheep(s).",
375673178: "Reach the goal without taking damage.",
426197923: "Reach the goal as Boomerang Mario.",
436833616: "Reach the goal while wearing a Shoe.",
713979835: "Reach the goal as Fire Mario.",
744927294: "Reach the goal as Frog Mario.",
751004331: "Reach the goal after defeating at least/all (n) Larry(s).",
900050759: "Reach the goal as Raccoon Mario.",
947659466: "Reach the goal after defeating at least/all (n) Blooper(s).",
976173462: "Reach the goal as Propeller Mario.",
994686866: "Reach the goal while wearing a Propeller Box.",
998904081: "Reach the goal after defeating at least/all (n) Spike(s).",
1008094897: "Reach the goal after defeating at least/all (n) Boom Boom(s).",
1051433633: "Reach the goal while holding a Koopa Shell.",
1061233896: "Reach the goal after defeating at least/all (n) Porcupuffer(s).",
1062253843: "Reach the goal after defeating at least/all (n) Charvaargh(s).",
1079889509: "Reach the goal after defeating at least/all (n) Bullet Bill(s).",
1080535886: "Reach the goal after defeating at least/all (n) Bully/Bullies.",
1151250770: "Reach the goal while wearing a Goomba Mask.",
1182464856: "Reach the goal after defeating at least/all (n) Hop-Chops.",
1219761531: "Reach the goal while holding a Red POW Block. OR Reach the goal after activating at least/all (n) Red POW Block(s).",
1221661152: "Reach the goal after defeating at least/all (n) Bob-omb(s).",
1259427138: "Reach the goal after defeating at least/all (n) Spiny/Spinies.",
1268255615: "Reach the goal after defeating at least/all (n) Bowser(s)/Meowser(s).",
1279580818: "Reach the goal after defeating at least/all (n) Ant Trooper(s).",
1283945123: "Reach the goal on a Lakitu's Cloud.",
1344044032: "Reach the goal after defeating at least/all (n) Boo(s).",
1425973877: "Reach the goal after defeating at least/all (n) Roy(s).",
1429902736: "Reach the goal while holding a Trampoline.",
1431944825: "Reach the goal after defeating at least/all (n) Morton(s).",
1446467058: "Reach the goal after defeating at least/all (n) Fish Bone(s).",
1510495760: "Reach the goal after defeating at least/all (n) Monty Mole(s).",
1656179347: "Reach the goal after picking up at least/all (n) 1-Up Mushroom(s).",
1665820273: "Reach the goal after defeating at least/all (n) Hammer Bro(s.).",
1676924210: "Reach the goal after hitting at least/all (n) P Switch(es). OR Reach the goal while holding a P Switch.",
1715960804: "Reach the goal after activating at least/all (n) POW Block(s). OR Reach the goal while holding a POW Block.",
1724036958: "Reach the goal after defeating at least/all (n) Angry Sun(s).",
1730095541: "Reach the goal after defeating at least/all (n) Pokey(s).",
1780278293: "Reach the goal as Superball Mario.",
1839897151: "Reach the goal after defeating at least/all (n) Pom Pom(s).",
1969299694: "Reach the goal after defeating at least/all (n) Peepa(s).",
2035052211: "Reach the goal after defeating at least/all (n) Lakitu(s).",
2038503215: "Reach the goal after defeating at least/all (n) Lemmy(s).",
2048033177: "Reach the goal after defeating at least/all (n) Lava Bubble(s).",
2076496776: "Reach the goal while wearing a Bullet Bill Mask.",
2089161429: "Reach the goal as Big Mario.",
2111528319: "Reach the goal as Cat Mario.",
2131209407: "Reach the goal after defeating at least/all (n) Goomba(s)/Galoomba(s).",
2139645066: "Reach the goal after defeating at least/all (n) Thwomp(s).",
2259346429: "Reach the goal after defeating at least/all (n) Iggy(s).",
2549654281: "Reach the goal while wearing a Dry Bones Shell.",
2694559007: "Reach the goal after defeating at least/all (n) Sledge Bro(s.).",
2746139466: "Reach the goal after defeating at least/all (n) Rocky Wrench(es).",
2749601092: "Reach the goal after grabbing at least/all (n) 50-Coin(s).",
2855236681: "Reach the goal as Flying Squirrel Mario.",
3036298571: "Reach the goal as Buzzy Mario.",
3074433106: "Reach the goal as Builder Mario.",
3146932243: "Reach the goal as Cape Mario.",
3174413484: "Reach the goal after defeating at least/all (n) Wendy(s).",
3206222275: "Reach the goal while wearing a Cannon Box.",
3314955857: "Reach the goal as Link.",
3342591980: "Reach the goal while you have Super Star invincibility.",
3346433512: "Reach the goal after defeating at least/all (n) Goombrat(s)/Goombud(s).",
3348058176: "Reach the goal after grabbing at least/all (n) 10-Coin(s).",
3353006607: "Reach the goal after defeating at least/all (n) Buzzy Beetle(s).",
3392229961: "Reach the goal after defeating at least/all (n) Bowser Jr.(s).",
3437308486: "Reach the goal after defeating at least/all (n) Koopa Troopa(s).",
3459144213: "Reach the goal after defeating at least/all (n) Chain Chomp(s).",
3466227835: "Reach the goal after defeating at least/all (n) Muncher(s).",
3481362698: "Reach the goal after defeating at least/all (n) Wiggler(s).",
3513732174: "Reach the goal as SMB2 Mario.",
3649647177: "Reach the goal in a Koopa Clown Car/Junior Clown Car.",
3725246406: "Reach the goal as Spiny Mario.",
3730243509: "Reach the goal in a Koopa Troopa Car.",
3748075486: "Reach the goal after defeating at least/all (n) Piranha Plant(s)/Jumping Piranha Plant(s).",
3797704544: "Reach the goal after defeating at least/all (n) Dry Bones.",
3824561269: "Reach the goal after defeating at least/all (n) Stingby/Stingbies.",
3833342952: "Reach the goal after defeating at least/all (n) Piranha Creeper(s).",
3842179831: "Reach the goal after defeating at least/all (n) Fire Piranha Plant(s).",
3874680510: "Reach the goal after breaking at least/all (n) Crates(s).",
3974581191: "Reach the goal after defeating at least/all (n) Ludwig(s).",
3977257962: "Reach the goal as Super Mario.",
4042480826: "Reach the goal after defeating at least/all (n) Skipsqueak(s).",
4116396131: "Reach the goal after grabbing at least/all (n) Coin(s).",
4117878280: "Reach the goal after defeating at least/all (n) Magikoopa(s).",
4122555074: "Reach the goal after grabbing at least/all (n) 30-Coin(s).",
4153835197: "Reach the goal as Balloon Mario.",
4172105156: "Reach the goal while wearing a Red POW Box.",
4209535561: "Reach the Goal while riding Yoshi.",
4269094462: "Reach the goal after defeating at least/all (n) Spike Top(s).",
4293354249: "Reach the goal after defeating at least/all (n) Banzai Bill(s)."
}
```
<!-- TODO create detailed statistics -->
## Dataset Creation
The dataset was created over a little more than a month in Febuary 2022 using the self hosted [Mario Maker 2 api](https://tgrcode.com/posts/mario_maker_2_api). As requests made to Nintendo's servers require authentication the process had to be done with upmost care and limiting download speed as to not overload the API and risk a ban. There are no intentions to create an updated release of this dataset.
## Considerations for Using the Data
The dataset consists of levels from many different Mario Maker 2 players globally and as such their titles and descriptions could contain harmful language. Harmful depictions could also be present in the level data, should you choose to render it.
|
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] |
jamescalam/channel-metadata
|
jamescalam
| 2022-10-26T01:05:55Z | 40 | 1 |
[
"task_categories:other",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:afl-3.0",
"youtube",
"video",
"video metadata",
"tech",
"science and tech",
"region:us"
] |
[
"other"
] | 2022-10-14T05:29:45Z |
---
annotations_creators:
- no-annotation
language:
- en
language_creators:
- found
license:
- afl-3.0
multilinguality:
- monolingual
pretty_name: Tech Channels Metadata
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- youtube
- video
- video metadata
- tech
- science and tech
task_categories:
- other
task_ids: []
---
Dataset containing video metadata from a few tech channels, i.e.
* [James Briggs](https://youtube.com/c/JamesBriggs)
* [Yannic Kilcher](https://www.youtube.com/c/YannicKilcher)
* [sentdex](https://www.youtube.com/c/sentdex)
* [Daniel Bourke](https://www.youtube.com/channel/UCr8O8l5cCX85Oem1d18EezQ)
* [AI Coffee Break with Letitia](https://www.youtube.com/c/AICoffeeBreak)
* [Alex Ziskind](https://youtube.com/channel/UCajiMK_CY9icRhLepS8_3ug)
|
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pszemraj/text2image-multi-prompt
|
pszemraj
| 2023-11-21T13:19:29Z | 40 | 8 |
[
"task_categories:text-generation",
"task_categories:feature-extraction",
"multilinguality:monolingual",
"source_datasets:bartman081523/stable-diffusion-discord-prompts",
"source_datasets:succinctly/midjourney-prompts",
"source_datasets:Gustavosta/Stable-Diffusion-Prompts",
"language:en",
"license:apache-2.0",
"text generation",
"region:us"
] |
[
"text-generation",
"feature-extraction"
] | 2022-11-09T22:47:39Z |
---
language:
- en
license: apache-2.0
multilinguality:
- monolingual
source_datasets:
- bartman081523/stable-diffusion-discord-prompts
- succinctly/midjourney-prompts
- Gustavosta/Stable-Diffusion-Prompts
pretty_name: multi text2image prompts a dataset collection
tags:
- text generation
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- config_name: original
data_files:
- split: train
path: original/train-*
- split: test
path: original/test-*
dataset_info:
- config_name: default
features:
- name: text
dtype: string
- name: src_dataset
dtype: string
splits:
- name: train
num_bytes: 262736830
num_examples: 1677221
- name: test
num_bytes: 56294291
num_examples: 292876
download_size: 151054782
dataset_size: 319031121
- config_name: original
features:
- name: text
dtype: string
- name: src_dataset
dtype: string
splits:
- name: train
num_bytes: 741427383
num_examples: 3551734
- name: test
num_bytes: 83615440
num_examples: 399393
download_size: 402186258
dataset_size: 825042823
task_categories:
- text-generation
- feature-extraction
---
# text2image multi-prompt(s): a dataset collection
- collection of several text2image prompt datasets
- data was cleaned/normalized with the goal of removing "model specific APIs" like the "--ar" for Midjourney and so on
- data de-duplicated on a basic level: exactly duplicate prompts were dropped (_after cleaning and normalization_)
## updates
- Oct 2023: the `default` config has been updated with better deduplication. It was deduplicated with minhash (_params: n-gram size set to 3, deduplication threshold at 0.6, hash function chosen as xxh3 with 32-bit hash bits, and 128 permutations with a batch size of 10,000._) which drops 2+ million rows.
- original version is still available under `config_name="original"`
## contents
default:
```
DatasetDict({
train: Dataset({
features: ['text', 'src_dataset'],
num_rows: 1677221
})
test: Dataset({
features: ['text', 'src_dataset'],
num_rows: 292876
})
})
```
For `original` config:
```
DatasetDict({
train: Dataset({
features: ['text', 'src_dataset'],
num_rows: 3551734
})
test: Dataset({
features: ['text', 'src_dataset'],
num_rows: 399393
})
})
```
_NOTE: as the other two datasets did not have a `validation` split, the validation split of `succinctly/midjourney-prompts` was merged into `train`._
|
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bigbio/progene
|
bigbio
| 2022-12-22T15:46:19Z | 40 | 2 |
[
"multilinguality:monolingual",
"language:en",
"license:cc-by-4.0",
"region:us"
] | null | 2022-11-13T22:11:35Z |
---
language:
- en
bigbio_language:
- English
license: cc-by-4.0
multilinguality: monolingual
bigbio_license_shortname: CC_BY_4p0
pretty_name: ProGene
homepage: https://zenodo.org/record/3698568#.YlVHqdNBxeg
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
---
# Dataset Card for ProGene
## Dataset Description
- **Homepage:** https://zenodo.org/record/3698568#.YlVHqdNBxeg
- **Pubmed:** True
- **Public:** True
- **Tasks:** NER
The Protein/Gene corpus was developed at the JULIE Lab Jena under supervision of Prof. Udo Hahn.
The executing scientist was Dr. Joachim Wermter.
The main annotator was Dr. Rico Pusch who is an expert in biology.
The corpus was developed in the context of the StemNet project (http://www.stemnet.de/).
## Citation Information
```
@inproceedings{faessler-etal-2020-progene,
title = "{P}ro{G}ene - A Large-scale, High-Quality Protein-Gene Annotated Benchmark Corpus",
author = "Faessler, Erik and
Modersohn, Luise and
Lohr, Christina and
Hahn, Udo",
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.564",
pages = "4585--4596",
abstract = "Genes and proteins constitute the fundamental entities of molecular genetics. We here introduce ProGene (formerly called FSU-PRGE), a corpus that reflects our efforts to cope with this important class of named entities within the framework of a long-lasting large-scale annotation campaign at the Jena University Language {\&} Information Engineering (JULIE) Lab. We assembled the entire corpus from 11 subcorpora covering various biological domains to achieve an overall subdomain-independent corpus. It consists of 3,308 MEDLINE abstracts with over 36k sentences and more than 960k tokens annotated with nearly 60k named entity mentions. Two annotators strove for carefully assigning entity mentions to classes of genes/proteins as well as families/groups, complexes, variants and enumerations of those where genes and proteins are represented by a single class. The main purpose of the corpus is to provide a large body of consistent and reliable annotations for supervised training and evaluation of machine learning algorithms in this relevant domain. Furthermore, we provide an evaluation of two state-of-the-art baseline systems {---} BioBert and flair {---} on the ProGene corpus. We make the evaluation datasets and the trained models available to encourage comparable evaluations of new methods in the future.",
language = "English",
ISBN = "979-10-95546-34-4",
}
```
|
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] |
bigbio/swedish_medical_ner
|
bigbio
| 2022-12-22T15:46:59Z | 40 | 2 |
[
"multilinguality:monolingual",
"language:sv",
"license:cc-by-sa-4.0",
"region:us"
] | null | 2022-11-13T22:12:24Z |
---
language:
- sv
bigbio_language:
- Swedish
license: cc-by-sa-4.0
multilinguality: monolingual
bigbio_license_shortname: CC_BY_SA_4p0
pretty_name: Swedish Medical NER
homepage: https://github.com/olofmogren/biomedical-ner-data-swedish/
bigbio_pubmed: False
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
---
# Dataset Card for Swedish Medical NER
## Dataset Description
- **Homepage:** https://github.com/olofmogren/biomedical-ner-data-swedish/
- **Pubmed:** False
- **Public:** True
- **Tasks:** NER
swedish_medical_ner is Named Entity Recognition dataset on medical text in Swedish.
It consists three subsets which are in turn derived from three different sources
respectively: the Swedish Wikipedia (a.k.a. wiki), Läkartidningen (a.k.a. lt),
and 1177 Vårdguiden (a.k.a. 1177). While the Swedish Wikipedia and Läkartidningen
subsets in total contains over 790000 sequences with 60 characters each,
the 1177 Vårdguiden subset is manually annotated and contains 927 sentences,
2740 annotations, out of which 1574 are disorder and findings, 546 are
pharmaceutical drug, and 620 are body structure.
Texts from both Swedish Wikipedia and Läkartidningen were automatically annotated
using a list of medical seed terms. Sentences from 1177 Vårdguiden were manuually
annotated.
## Citation Information
```
@inproceedings{almgren-etal-2016-named,
author = {
Almgren, Simon and
Pavlov, Sean and
Mogren, Olof
},
title = {Named Entity Recognition in Swedish Medical Journals with Deep Bidirectional Character-Based LSTMs},
booktitle = {Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining (BioTxtM 2016)},
publisher = {The COLING 2016 Organizing Committee},
pages = {30-39},
year = {2016},
month = {12},
url = {https://aclanthology.org/W16-5104},
eprint = {https://aclanthology.org/W16-5104.pdf}
}
```
|
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] |
NeelNanda/code-tokenized
|
NeelNanda
| 2022-11-14T00:05:01Z | 40 | 0 |
[
"region:us"
] | null | 2022-11-14T00:04:10Z |
---
dataset_info:
features:
- name: tokens
sequence: int64
splits:
- name: train
num_bytes: 2436318372
num_examples: 297257
download_size: 501062424
dataset_size: 2436318372
---
# Dataset Card for "code-tokenized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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memray/openkp
|
memray
| 2022-12-31T06:04:30Z | 40 | 0 |
[
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | 2022-12-31T05:57:58Z |
---
license: cc-by-nc-sa-4.0
---
|
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DFKI-SLT/knowledge_net
|
DFKI-SLT
| 2023-01-19T09:16:32Z | 40 | 2 |
[
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:entity-linking-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:en",
"knowledgenet",
"region:us"
] |
[
"text-classification"
] | 2023-01-19T09:15:44Z |
---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- found
license: []
multilinguality:
- monolingual
pretty_name: KnowledgeNet is a dataset for automatically populating a knowledge base
size_categories:
- 10K<n<100K
source_datasets: []
tags:
- knowledgenet
task_categories:
- text-classification
task_ids:
- multi-class-classification
- entity-linking-classification
dataset_info:
- config_name: knet
features:
- name: fold
dtype: int32
- name: documentId
dtype: string
- name: source
dtype: string
- name: documentText
dtype: string
- name: passages
sequence:
- name: passageId
dtype: string
- name: passageStart
dtype: int32
- name: passageEnd
dtype: int32
- name: passageText
dtype: string
- name: exhaustivelyAnnotatedProperties
sequence:
- name: propertyId
dtype: string
- name: propertyName
dtype: string
- name: propertyDescription
dtype: string
- name: facts
sequence:
- name: factId
dtype: string
- name: propertyId
dtype: string
- name: humanReadable
dtype: string
- name: annotatedPassage
dtype: string
- name: subjectStart
dtype: int32
- name: subjectEnd
dtype: int32
- name: subjectText
dtype: string
- name: subjectUri
dtype: string
- name: objectStart
dtype: int32
- name: objectEnd
dtype: int32
- name: objectText
dtype: string
- name: objectUri
dtype: string
splits:
- name: train
num_bytes: 10161415
num_examples: 3977
download_size: 14119313
dataset_size: 10161415
- config_name: knet_tokenized
features:
- name: doc_id
dtype: string
- name: passage_id
dtype: string
- name: fact_id
dtype: string
- name: tokens
sequence: string
- name: subj_start
dtype: int32
- name: subj_end
dtype: int32
- name: subj_type
dtype:
class_label:
names:
'0': O
'1': PER
'2': ORG
'3': LOC
'4': DATE
- name: subj_uri
dtype: string
- name: obj_start
dtype: int32
- name: obj_end
dtype: int32
- name: obj_type
dtype:
class_label:
names:
'0': O
'1': PER
'2': ORG
'3': LOC
'4': DATE
- name: obj_uri
dtype: string
- name: relation
dtype:
class_label:
names:
'0': NO_RELATION
'1': DATE_OF_BIRTH
'2': DATE_OF_DEATH
'3': PLACE_OF_RESIDENCE
'4': PLACE_OF_BIRTH
'5': NATIONALITY
'6': EMPLOYEE_OR_MEMBER_OF
'7': EDUCATED_AT
'8': POLITICAL_AFFILIATION
'9': CHILD_OF
'10': SPOUSE
'11': DATE_FOUNDED
'12': HEADQUARTERS
'13': SUBSIDIARY_OF
'14': FOUNDED_BY
'15': CEO
splits:
- name: train
num_bytes: 4511963
num_examples: 10895
download_size: 14119313
dataset_size: 4511963
- config_name: knet_re
features:
- name: documentId
dtype: string
- name: passageId
dtype: string
- name: factId
dtype: string
- name: passageText
dtype: string
- name: humanReadable
dtype: string
- name: annotatedPassage
dtype: string
- name: subjectStart
dtype: int32
- name: subjectEnd
dtype: int32
- name: subjectText
dtype: string
- name: subjectType
dtype:
class_label:
names:
'0': O
'1': PER
'2': ORG
'3': LOC
'4': DATE
- name: subjectUri
dtype: string
- name: objectStart
dtype: int32
- name: objectEnd
dtype: int32
- name: objectText
dtype: string
- name: objectType
dtype:
class_label:
names:
'0': O
'1': PER
'2': ORG
'3': LOC
'4': DATE
- name: objectUri
dtype: string
- name: relation
dtype:
class_label:
names:
'0': NO_RELATION
'1': DATE_OF_BIRTH
'2': DATE_OF_DEATH
'3': PLACE_OF_RESIDENCE
'4': PLACE_OF_BIRTH
'5': NATIONALITY
'6': EMPLOYEE_OR_MEMBER_OF
'7': EDUCATED_AT
'8': POLITICAL_AFFILIATION
'9': CHILD_OF
'10': SPOUSE
'11': DATE_FOUNDED
'12': HEADQUARTERS
'13': SUBSIDIARY_OF
'14': FOUNDED_BY
'15': CEO
splits:
- name: train
num_bytes: 6098219
num_examples: 10895
download_size: 14119313
dataset_size: 6098219
---
# Dataset Card for "KnowledgeNet"
## 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:** [knowledge-net](https://github.com/diffbot/knowledge-net)
- **Paper:** [KnowledgeNet: A Benchmark Dataset for Knowledge Base Population](https://aclanthology.org/D19-1069/)
- **Size of downloaded dataset files:** 12.59 MB
- **Size of the generated dataset:** 6.1 MB
### Dataset Summary
KnowledgeNet is a benchmark dataset for the task of automatically populating a knowledge base (Wikidata) with facts
expressed in natural language text on the web. KnowledgeNet provides text exhaustively annotated with facts, thus
enabling the holistic end-to-end evaluation of knowledge base population systems as a whole, unlike previous benchmarks
that are more suitable for the evaluation of individual subcomponents (e.g., entity linking, relation extraction).
For instance, the dataset contains text expressing the fact (Gennaro Basile; RESIDENCE; Moravia), in the passage:
"Gennaro Basile was an Italian painter, born in Naples but active in the German-speaking countries. He settled at Brünn,
in Moravia, and lived about 1756..."
For a description of the dataset and baseline systems, please refer to their
[EMNLP paper](https://github.com/diffbot/knowledge-net/blob/master/knowledgenet-emnlp-cameraready.pdf).
Note: This Datasetreader currently only supports the `train` split and does not contain negative examples.
In addition to the original format this repository also provides two version (`knet_re`, `knet_tokenized`) that are
easier to use for simple relation extraction. You can load them with
`datasets.load_dataset("DFKI-SLT/knowledge_net", name="<config>")`.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
The language in the dataset is English.
## Dataset Structure
### Data Instances
#### knet
- **Size of downloaded dataset files:** 12.59 MB
- **Size of the generated dataset:** 10.16 MB
An example of 'train' looks as follows:
```json
{
"fold": 2,
"documentId": "8313",
"source": "DBpedia Abstract",
"documentText": "Gennaro Basile\n\nGennaro Basile was an Italian painter, born in Naples but active in the German-speaking countries. He settled at Brünn, in Moravia, and lived about 1756. His best picture is the altar-piece in the chapel of the chateau at Seeberg, in Salzburg. Most of his works remained in Moravia.",
"passages": [
{
"passageId": "8313:16:114",
"passageStart": 16,
"passageEnd": 114,
"passageText": "Gennaro Basile was an Italian painter, born in Naples but active in the German-speaking countries.",
"exhaustivelyAnnotatedProperties": [
{
"propertyId": "12",
"propertyName": "PLACE_OF_BIRTH",
"propertyDescription": "Describes the relationship between a person and the location where she/he was born."
}
],
"facts": [
{
"factId": "8313:16:30:63:69:12",
"propertyId": "12",
"humanReadable": "<Gennaro Basile> <PLACE_OF_BIRTH> <Naples>",
"annotatedPassage": "<Gennaro Basile> was an Italian painter, born in <Naples> but active in the German-speaking countries.",
"subjectStart": 16,
"subjectEnd": 30,
"subjectText": "Gennaro Basile",
"subjectUri": "http://www.wikidata.org/entity/Q19517888",
"objectStart": 63,
"objectEnd": 69,
"objectText": "Naples",
"objectUri": "http://www.wikidata.org/entity/Q2634"
}
]
},
{
"passageId": "8313:115:169",
"passageStart": 115,
"passageEnd": 169,
"passageText": "He settled at Brünn, in Moravia, and lived about 1756.",
"exhaustivelyAnnotatedProperties": [
{
"propertyId": "11",
"propertyName": "PLACE_OF_RESIDENCE",
"propertyDescription": "Describes the relationship between a person and the location where she/he lives/lived."
},
{
"propertyId": "12",
"propertyName": "PLACE_OF_BIRTH",
"propertyDescription": "Describes the relationship between a person and the location where she/he was born."
}
],
"facts": [
{
"factId": "8313:115:117:129:134:11",
"propertyId": "11",
"humanReadable": "<He> <PLACE_OF_RESIDENCE> <Brünn>",
"annotatedPassage": "<He> settled at <Brünn>, in Moravia, and lived about 1756.",
"subjectStart": 115,
"subjectEnd": 117,
"subjectText": "He",
"subjectUri": "http://www.wikidata.org/entity/Q19517888",
"objectStart": 129,
"objectEnd": 134,
"objectText": "Brünn",
"objectUri": "http://www.wikidata.org/entity/Q14960"
},
{
"factId": "8313:115:117:139:146:11",
"propertyId": "11",
"humanReadable": "<He> <PLACE_OF_RESIDENCE> <Moravia>",
"annotatedPassage": "<He> settled at Brünn, in <Moravia>, and lived about 1756.",
"subjectStart": 115,
"subjectEnd": 117,
"subjectText": "He",
"subjectUri": "http://www.wikidata.org/entity/Q19517888",
"objectStart": 139,
"objectEnd": 146,
"objectText": "Moravia",
"objectUri": "http://www.wikidata.org/entity/Q43266"
}
]
}
]
}
```
#### knet_re
- **Size of downloaded dataset files:** 12.59 MB
- **Size of the generated dataset:** 6.1 MB
An example of 'train' looks as follows:
```json
{
"documentId": "7",
"passageId": "7:23:206",
"factId": "7:23:44:138:160:1",
"passageText": "Tata Chemicals Europe (formerly Brunner Mond (UK) Limited) is a UK-based chemicals company that is a subsidiary of Tata Chemicals Limited, itself a part of the India-based Tata Group.",
"humanReadable": "<Tata Chemicals Europe> <SUBSIDIARY_OF> <Tata Chemicals Limited>",
"annotatedPassage": "<Tata Chemicals Europe> (formerly Brunner Mond (UK) Limited) is a UK-based chemicals company that is a subsidiary of <Tata Chemicals Limited>, itself a part of the India-based Tata Group.",
"subjectStart": 0,
"subjectEnd": 21,
"subjectText": "Tata Chemicals Europe",
"subjectType": 2,
"subjectUri": "",
"objectStart": 115,
"objectEnd": 137,
"objectText": "Tata Chemicals Limited",
"objectType": 2,
"objectUri": "http://www.wikidata.org/entity/Q2331365",
"relation": 13
}
```
#### knet_tokenized
- **Size of downloaded dataset files:** 12.59 MB
- **Size of the generated dataset:** 4.5 MB
An example of 'train' looks as follows:
```json
{
"doc_id": "7",
"passage_id": "7:23:206",
"fact_id": "7:162:168:183:205:1",
"tokens": ["Tata", "Chemicals", "Europe", "(", "formerly", "Brunner", "Mond", "(", "UK", ")", "Limited", ")", "is", "a", "UK", "-", "based", "chemicals", "company", "that", "is", "a", "subsidiary", "of", "Tata", "Chemicals", "Limited", ",", "itself", "a", "part", "of", "the", "India", "-", "based", "Tata", "Group", "."],
"subj_start": 28,
"subj_end": 29,
"subj_type": 2,
"subj_uri": "http://www.wikidata.org/entity/Q2331365",
"obj_start": 33,
"obj_end": 38,
"obj_type": 2,
"obj_uri": "http://www.wikidata.org/entity/Q331715",
"relation": 13
}
```
### Data Fields
#### knet
- `fold`: the fold, a `int` feature.
- `documentId`: the document id, a `string` feature.
- `source`: the source, a `string` feature.
- `documenText`: the document text, a `string` feature.
- `passages`: the list of passages, a `list` of `dict`.
- `passageId`: the passage id, a `string` feature.
- `passageStart`: the passage start, a `int` feature.
- `passageEnd`: the passage end, a `int` feature.
- `passageText`: the passage text, a `string` feature.
- `exhaustivelyAnnotatedProperties`: the list of exhaustively annotated properties, a `list` of `dict`.
- `propertyId`: the property id, a `string` feature.
- `propertyName`: the property name, a `string` feature.
- `propertyDescription`: the property description, a `string` feature.
- `facts`: the list of facts, a `list` of `dict`.
- `factId`: the fact id, a `string` feature.
- `propertyId`: the property id, a `string` feature.
- `humanReadable`: the human readable annotation, a `string` feature.
- `annotatedPassage`: the annotated passage, a `string` feature.
- `subjectStart`: the subject start, a `int` feature.
- `subjectEnd`: the subject end, a `int` feature.
- `subjectText`: the subject text, a `string` feature.
- `subjectUri`: the subject uri, a `string` feature.
- `objectStart`: the object start, a `int` feature.
- `objectEnd`: the object end, a `int` feature.
- `objectText`: the object text, a `string` feature.
- `objectUri`: the object uri, a `string` feature.
#### knet_re
- `documentId`: the document id, a `string` feature.
- `passageId`: the passage id, a `string` feature.
- `passageText`: the passage text, a `string` feature.
- `factId`: the fact id, a `string` feature.
- `humanReadable`: human-readable annotation, a `string` features.
- `annotatedPassage`: annotated passage, a `string` feature.
- `subjectStart`: the index of the start character of the relation subject mention, an `ìnt` feature.
- `subjectEnd`: the index of the end character of the relation subject mention, exclusive, an `ìnt` feature.
- `subjectText`: the text the subject mention, a `string` feature.
- `subjectType`: the NER type of the subject mention, a `string` classification label.
```json
{"O": 0, "PER": 1, "ORG": 2, "LOC": 3, "DATE": 4}
```
- `subjectUri`: the Wikidata URI of the subject mention, a `string` feature.
- `objectStart`: the index of the start character of the relation object mention, an `ìnt` feature.
- `objectEnd`: the index of the end character of the relation object mention, exclusive, an `ìnt` feature.
- `objectText`: the text the object mention, a `string` feature.
- `objectType`: the NER type of the object mention, a `string` classification label.
```json
{"O": 0, "PER": 1, "ORG": 2, "LOC": 3, "DATE": 4}
```
- `objectUri`: the Wikidata URI of the object mention, a `string` feature.
- `relation`: the relation label of this instance, a `string` classification label.
```json
{"NO_RELATION": 0, "DATE_OF_BIRTH": 1, "DATE_OF_DEATH": 2, "PLACE_OF_RESIDENCE": 3, "PLACE_OF_BIRTH": 4, "NATIONALITY": 5, "EMPLOYEE_OR_MEMBER_OF": 6, "EDUCATED_AT": 7, "POLITICAL_AFFILIATION": 8, "CHILD_OF": 9, "SPOUSE": 10, "DATE_FOUNDED": 11, "HEADQUARTERS": 12, "SUBSIDIARY_OF": 13, "FOUNDED_BY": 14, "CEO": 15}
```
#### knet_tokenized
- `doc_id`: the document id, a `string` feature.
- `passage_id`: the passage id, a `string` feature.
- `factId`: the fact id, a `string` feature.
- `tokens`: the list of tokens of this passage, obtained with spaCy, a `list` of `string` features.
- `subj_start`: the index of the start token of the relation subject mention, an `ìnt` feature.
- `subj_end`: the index of the end token of the relation subject mention, exclusive, an `ìnt` feature.
- `subj_type`: the NER type of the subject mention, a `string` classification label.
```json
{"O": 0, "PER": 1, "ORG": 2, "LOC": 3, "DATE": 4}
```
- `subj_uri`: the Wikidata URI of the subject mention, a `string` feature.
- `obj_start`: the index of the start token of the relation object mention, an `ìnt` feature.
- `obj_end`: the index of the end token of the relation object mention, exclusive, an `ìnt` feature.
- `obj_type`: the NER type of the object mention, a `string` classification label.
```json
{"O": 0, "PER": 1, "ORG": 2, "LOC": 3, "DATE": 4}
```
- `obj_uri`: the Wikidata URI of the object mention, a `string` feature.
- `relation`: the relation label of this instance, a `string` classification label.
```json
{"NO_RELATION": 0, "DATE_OF_BIRTH": 1, "DATE_OF_DEATH": 2, "PLACE_OF_RESIDENCE": 3, "PLACE_OF_BIRTH": 4, "NATIONALITY": 5, "EMPLOYEE_OR_MEMBER_OF": 6, "EDUCATED_AT": 7, "POLITICAL_AFFILIATION": 8, "CHILD_OF": 9, "SPOUSE": 10, "DATE_FOUNDED": 11, "HEADQUARTERS": 12, "SUBSIDIARY_OF": 13, "FOUNDED_BY": 14, "CEO": 15}
```
### Data Splits
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## 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)
are labeled as no_relation.
[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{mesquita-etal-2019-knowledgenet,
title = "{K}nowledge{N}et: A Benchmark Dataset for Knowledge Base Population",
author = "Mesquita, Filipe and
Cannaviccio, Matteo and
Schmidek, Jordan and
Mirza, Paramita and
Barbosa, Denilson",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1069",
doi = "10.18653/v1/D19-1069",
pages = "749--758",}
```
### Contributions
Thanks to [@phucdev](https://github.com/phucdev) for adding this dataset.
|
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Multimodal-Fatima/VQAv2_sample_train
|
Multimodal-Fatima
| 2023-02-12T00:01:41Z | 40 | 0 |
[
"region:us"
] | null | 2023-02-10T17:59:46Z |
---
dataset_info:
features:
- name: question_type
dtype: string
- name: multiple_choice_answer
dtype: string
- name: answers
sequence: string
- name: answers_original
list:
- name: answer
dtype: string
- name: answer_confidence
dtype: string
- name: answer_id
dtype: int64
- name: id_image
dtype: int64
- name: answer_type
dtype: string
- name: question_id
dtype: int64
- name: question
dtype: string
- name: image
dtype: image
- name: id
dtype: int64
- name: clip_tags_ViT_L_14
sequence: string
- name: blip_caption
dtype: string
- name: DETA_detections_deta_swin_large_o365_coco_classes
list:
- name: attribute
dtype: string
- name: box
sequence: float32
- name: label
dtype: string
- name: location
dtype: string
- name: ratio
dtype: float32
- name: size
dtype: string
- name: tag
dtype: string
splits:
- name: train
num_bytes: 158100925.0
num_examples: 1000
download_size: 155253264
dataset_size: 158100925.0
---
# Dataset Card for "VQAv2_sample_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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mrm8488/chatdoctor200k
|
mrm8488
| 2023-03-29T19:57:18Z | 40 | 0 |
[
"region:us"
] | null | 2023-03-29T19:57:01Z |
---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 207526604
num_examples: 207407
download_size: 115879622
dataset_size: 207526604
---
# Dataset Card for "chatdoctor200k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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Francesco/road-traffic
|
Francesco
| 2023-03-30T09:12:18Z | 40 | 1 |
[
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
] |
[
"object-detection"
] | 2023-03-30T09:11:50Z |
---
dataset_info:
features:
- name: image_id
dtype: int64
- name: image
dtype: image
- name: width
dtype: int32
- name: height
dtype: int32
- name: objects
sequence:
- name: id
dtype: int64
- name: area
dtype: int64
- name: bbox
sequence: float32
length: 4
- name: category
dtype:
class_label:
names:
'0': road-traffic
'1': bicycles
'2': buses
'3': crosswalks
'4': fire hydrants
'5': motorcycles
'6': traffic lights
'7': vehicles
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- object-detection
task_ids: []
pretty_name: road-traffic
tags:
- rf100
---
# Dataset Card for road-traffic
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/road-traffic
- **Point of Contact:** [email protected]
### Dataset Summary
road-traffic
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `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]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/road-traffic
### Citation Information
```
@misc{ road-traffic,
title = { road traffic Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/road-traffic } },
url = { https://universe.roboflow.com/object-detection/road-traffic },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset.
|
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] |
norabelrose/truthful_qa
|
norabelrose
| 2023-04-24T22:32:59Z | 40 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-04-24T22:25:30Z |
---
license: apache-2.0
---
|
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] |
yanchao/cifar10buqi
|
yanchao
| 2023-05-19T07:00:52Z | 40 | 0 |
[
"size_categories:1K<n<10K",
"language:en",
"license:apache-2.0",
"chemistry",
"region:us"
] | null | 2023-05-19T05:56:55Z |
---
license: apache-2.0
language:
- en
tags:
- chemistry
pretty_name: buqi
size_categories:
- 1K<n<10K
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
buqi
### Supported Tasks and Leaderboards
buqi
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
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results-sd-v1-5-sd-v2-1-if-v1-0-karlo/87834474
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo
| 2023-05-23T05:17:56Z | 40 | 0 |
[
"region:us"
] | null | 2023-05-23T05:17:55Z |
---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 180
num_examples: 10
download_size: 1340
dataset_size: 180
---
# Dataset Card for "87834474"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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Brand24/mms
|
Brand24
| 2023-08-23T21:49:55Z | 40 | 4 |
[
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:mixed",
"multilinguality:multi-lingual",
"size_categories:1M<n<10M",
"language:ar",
"language:bg",
"language:bs",
"language:cs",
"language:de",
"language:el",
"language:en",
"language:es",
"language:fa",
"language:fr",
"language:he",
"language:hi",
"language:hr",
"language:hu",
"language:it",
"language:ja",
"language:lv",
"language:pl",
"language:pt",
"language:ru",
"language:sk",
"language:sl",
"language:sq",
"language:sr",
"language:sv",
"language:th",
"language:ur",
"language:zh",
"license:other",
"arxiv:2306.07902",
"region:us"
] |
[
"text-classification"
] | 2023-05-24T12:07:06Z |
---
annotations_creators:
- mixed
language:
- ar
- bg
- bs
- cs
- de
- el
- en
- es
- fa
- fr
- he
- hi
- hr
- hu
- it
- ja
- lv
- pl
- pt
- ru
- sk
- sl
- sq
- sr
- sv
- th
- ur
- zh
license:
- other
multilinguality:
- multi-lingual
size_categories:
- 1M<n<10M
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: Massive-Multilingual-Sentiment
---
# Massive Multilingual Sentiment Corpora (MMS)
## Corpora Summary
Despite impressive advancements in multilingual corpora collection and model training, developing large-scale deployments of multilingual models still presents a significant challenge. This is particularly true for language tasks that are culture-dependent. One such example is the area of multilingual sentiment analysis, where affective markers can be subtle and deeply ensconced in culture.
This work presents the most extensive open massively multilingual corpus of datasets for training sentiment models. The corpus consists of 79 manually selected from over 350 datasets reported in the scientific literature based on strict quality criteria and covers 27 languages. Datasets can be queried using several linguistic and functional features. In addition, we present a multi-faceted sentiment classification benchmark summarizing hundreds of experiments conducted on different base models, training objectives, dataset collections, and fine-tuning strategies.
More about dataset here [https://brand24-ai.github.io/mms_benchmark](https://brand24-ai.github.io/mms_benchmark).
## General licenses information
This is a library of the open-sourced datasets that we gathered. We provide citations or links to sources of these datasets. It is essential to mention that these datasets could have different licenses, and we encourage everybody to check the permissions of each dataset separately. It is critical because, for example, not all datasets will be available for commercial purposes. This ensures that proper consent and permissions are obtained for the use and curation of the data, respecting the rights and privacy of the individuals whose data is included in the datasets. You will cite our library and the authors of each dataset you want to use.
## Usage
```python
import datasets
# whole dataset will be downloaded and cached
mms_dataset = datasets.load_dataset("Brand24/mms")
# filter only texts in Polish
pl = mms_dataset.filter(lambda row: row['language'] == 'pl')
```
## Corpora statistics
### Per language
| language | label_name | count |
|:-----------|:-------------|--------:|
| ar | negative | 138899 |
| ar | neutral | 192774 |
| ar | positive | 600402 |
| bg | negative | 13930 |
| bg | neutral | 28657 |
| bg | positive | 19563 |
| bs | negative | 11974 |
| bs | neutral | 11145 |
| bs | positive | 13064 |
| cs | negative | 39674 |
| cs | neutral | 59200 |
| cs | positive | 97413 |
| de | negative | 104667 |
| de | neutral | 100071 |
| de | positive | 111149 |
| el | negative | 230 |
| el | neutral | 38 |
| el | positive | 232 |
| en | negative | 304939 |
| en | neutral | 290823 |
| en | positive | 1734724 |
| es | negative | 108733 |
| es | neutral | 122493 |
| es | positive | 187486 |
| fa | negative | 1602 |
| fa | neutral | 5091 |
| fa | positive | 6832 |
| fr | negative | 84187 |
| fr | neutral | 43245 |
| fr | positive | 83199 |
| he | negative | 2279 |
| he | neutral | 243 |
| he | positive | 6097 |
| hi | negative | 4992 |
| hi | neutral | 6392 |
| hi | positive | 5615 |
| hr | negative | 19757 |
| hr | neutral | 19470 |
| hr | positive | 38367 |
| hu | negative | 8974 |
| hu | neutral | 17621 |
| hu | positive | 30087 |
| it | negative | 4043 |
| it | neutral | 4193 |
| it | positive | 3829 |
| ja | negative | 83982 |
| ja | neutral | 41979 |
| ja | positive | 83819 |
| lv | negative | 1378 |
| lv | neutral | 2618 |
| lv | positive | 1794 |
| pl | negative | 77422 |
| pl | neutral | 62074 |
| pl | positive | 97192 |
| pt | negative | 56827 |
| pt | neutral | 55165 |
| pt | positive | 45842 |
| ru | negative | 31770 |
| ru | neutral | 48106 |
| ru | positive | 31054 |
| sk | negative | 14431 |
| sk | neutral | 12842 |
| sk | positive | 29350 |
| sl | negative | 33694 |
| sl | neutral | 50553 |
| sl | positive | 29296 |
| sq | negative | 6889 |
| sq | neutral | 14757 |
| sq | positive | 22638 |
| sr | negative | 25089 |
| sr | neutral | 32283 |
| sr | positive | 18996 |
| sv | negative | 16266 |
| sv | neutral | 13342 |
| sv | positive | 11738 |
| th | negative | 9326 |
| th | neutral | 28616 |
| th | positive | 34377 |
| ur | negative | 5239 |
| ur | neutral | 8585 |
| ur | positive | 5836 |
| zh | negative | 117967 |
| zh | neutral | 69016 |
| zh | positive | 144719 |
## Dataset Structure
### Linguistic Typology
The field of language typology focuses on studying the similarities and differences among languages. These differences can be categorized into phonological (sounds), syntactic (structures), lexical (vocabulary), and theoretical aspects. Linguistic typology analyzes the current state of languages, contrasting with genealogical linguistics, which examines historical relationships between languages.
Genealogical linguistics studies language families and genera. A language family consists of languages that share a common ancestral language, while genera are branches within a language family. The Indo-European family, for example, includes genera such as Slavic, Romance, Germanic, and Indic. Over 7000 languages are categorized into approximately 150 language families, with Indo-European, Sino-Tibetan, Turkic, Afro-Asiatic, Nilo-Saharan, Niger-Congo, and Eskimo-Aleut being some of the largest families.
Within linguistic typology, languages are described using various linguistic features. Our work focuses on sentiment classification and selects ten relevant features:
- `text`: The feature text represents the actual text of the sentiment dataset. It is of type string and contains the text samples or sentences for sentiment analysis.
- `label`: The feature label corresponds to the sentiment labels of the text samples. It is of type ClassLabel and has three possible values: negative, neutral, and positive. These labels indicate the sentiment or emotional polarity associated with the text.
- `original_dataset`: The feature original_dataset refers to the name or identifier of the original dataset from which the text samples were extracted. It is of type string and provides information about the source dataset.
- `domain`: The feature domain represents the domain or topic of the sentiment dataset. It is of type string and provides context regarding the subject matter of the text samples.
- `language`: The feature language indicates the language of the text samples in the sentiment dataset. It is of type string and specifies the language in which the text is written.
- `Family`: The feature Family represents the language family to which a specific language belongs. It is of type string and provides information about the broader categorization of languages into language families.
- `Genus`: The feature Genus corresponds to the genus or branch within a language family. It is of type string and indicates the specific subgrouping of languages within a language family.
- `Definite article`: Half of the languages do not use the definite article, which signals uniqueness or definiteness of a concept.
- `Indefinite article`: Half of the languages do not use the indefinite article, with some languages using a separate article or the numeral "one."
- `Number of cases`: Languages vary greatly in the number of morphological cases used.
- `Order of subject, verb, and object`: Different languages have different word orderings, with variations like SOV, SVO, VSO, VOS, OVS, and OSV.
- `Negative morphemes`: Negative morphemes indicate clausal negation in declarative sentences.
- `Polar questions`: Questions with yes/no answers, which can be formed using question particles, interrogative morphology, or intonation.
- `Position of the negative morpheme`: The position of the negative morpheme can vary in relation to subjects and objects.
- `Prefixing vs. suffixing`: Languages differ in their use of prefixes and suffixes in inflectional morphology.
- `Coding of nominal plurals`: Plurals can be expressed through morphological changes or the use of plurality indicator morphemes.
- `Grammatical genders`: Languages vary in the number of grammatical genders used, or may not use the concept at all.
These language features are available as filtering options in our library. Users can download specific facets of the collection, such as datasets in Slavic languages with interrogative word order for polar questions or datasets from the Afro-Asiatic language family without morphological case-making.
### Usage
Code example for loading and filtering Slavic language in which polar questions are formed using the interrogative word order
```python
import datasets
mms_dataset = datasets.load_dataset("Brand24/mms")
slavic = mms_dataset.filter(lambda row: row["Genus"] == "Slavic" and row["Polar questions"] == "interrogative word order")
```
Filtering sentiment datasets from the Afro-Asiatic language family without morphological case-making
```python
afro_asiatic = mms_dataset.filter(lambda row: row["Family"] == "Afro-Asiatic" and row["Number of cases"] == "no morphological case-making")
```
## Dataset Creation
### Who are the source language producers?
The data comes from multiple papers and covers a large variety of languages. For the specific dataset information, please check out the companion paper.
### Annotations
Similarly, like for data producers, you should check papers that propose the specific datasets you are interested in.
#### Annotation process
We describe the annotations process of our internally created dataset in this corpus.
## Considerations for Using the Data
### Social Impact and Limitations
Corpus is intended to bring more sentiment annotated data to a wide variety of lanuages, the aim of the corpus is to make large amounts of data available to lower resource languages in order to facilitate the training of state-of-the-art ML models for sentiment analysis.
## Additional Information
### Dataset Curators
The corpus was put together by
- [@laugustyniak](https://www.linkedin.com/in/lukaszaugustyniak/)
- [@swozniak](https://www.linkedin.com/in/wscode/)
- [@mgruza](https://www.linkedin.com/in/marcin-gruza-276b2512b/)
- [@pgramacki](https://www.linkedin.com/in/piotrgramacki/)
- [@krajda](https://www.linkedin.com/in/krzysztof-rajda/)
- [@mmorzy](https://www.linkedin.com/in/mikolajmorzy/)
- [@tkajdanowicz](https://www.linkedin.com/in/kajdanowicz/)
### Licensing Information
These data are released under this licensing scheme.
We do not own any text from which these data and datasets have been extracted.
We license the actual packaging of these data under the Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) https://creativecommons.org/licenses/by-nc/4.0/
This work is published from Poland.
Should you consider that our data contains material that is owned by you and should, therefore not be reproduced here, please:
* Clearly identify yourself with detailed contact data such as an address, telephone number, or email address at which you can be contacted.
* Clearly identify the copyrighted work claimed to be infringed.
* Clearly identify the material claimed to be infringing and the information reasonably sufficient to allow us to locate the material.
We will comply with legitimate requests by removing the affected sources from the next release of the corpus.
### Citation Information
### The main corpus citation
```bibtex
@misc{augustyniak2023massively,
title={Massively Multilingual Corpus of Sentiment Datasets and Multi-faceted Sentiment Classification Benchmark},
author={Łukasz Augustyniak and Szymon Woźniak and Marcin Gruza and Piotr Gramacki and Krzysztof Rajda and Mikołaj Morzy and Tomasz Kajdanowicz},
year={2023},
eprint={2306.07902},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### All datasets in corpus
[https://brand24-ai.github.io/mms_benchmark/citations.html](https://brand24-ai.github.io/mms_benchmark/citations.html)
## Acknowledgements
- BRAND24 - https://brand24.com
- CLARIN-PL-Biz - https://clarin.biz
|
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Binaryy/travel_sample
|
Binaryy
| 2023-06-09T11:53:34Z | 40 | 1 |
[
"region:us"
] | null | 2023-06-09T09:05:26Z |
---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 41063
num_examples: 20
download_size: 29530
dataset_size: 41063
---
# Dataset Card for "travel_sample"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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ChanceFocus/flare-sm-cikm
|
ChanceFocus
| 2023-06-25T18:16:45Z | 40 | 1 |
[
"region:us"
] | null | 2023-06-25T17:56:12Z |
---
dataset_info:
features:
- name: id
dtype: string
- name: query
dtype: string
- name: answer
dtype: string
- name: text
dtype: string
- name: choices
sequence: string
- name: gold
dtype: int64
splits:
- name: train
num_bytes: 26082681
num_examples: 3396
- name: valid
num_bytes: 3231915
num_examples: 431
- name: test
num_bytes: 8123670
num_examples: 1143
download_size: 19175558
dataset_size: 37438266
---
# Dataset Card for "flare-sm-cikm"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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wu981526092/MGSD
|
wu981526092
| 2023-08-26T06:22:56Z | 40 | 1 |
[
"task_categories:text-classification",
"task_categories:token-classification",
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"region:us"
] |
[
"text-classification",
"token-classification"
] | 2023-06-29T18:05:33Z |
---
license: mit
task_categories:
- text-classification
- token-classification
language:
- en
size_categories:
- 10K<n<100K
---
# MULTI-GRAIN STEREOTYPE DATASET (MGSD)
The MULTI-GRAIN STEREOTYPE DATASET (MGSD) is a comprehensive dataset designed for the research and analysis of stereotypes in natural language processing. It provides granular annotations at both the sentence and token levels, enabling various studies and applications in stereotype detection.
## Dataset Structure
The dataset contains the following columns:
- **text_with_marker**: Contains the original text with markers (`===`) highlighting potential stereotype tokens.
- **text_no_marker**: The text without any markers, suitable for models that operate at the sentence level.
- **label**: Indicates if the sentence is a stereotype, anti-stereotype, or unrelated.
- **stereotype_type**: Describes the type of stereotype e.g., race, gender, profession.
- **binary_class**: A binary classification of the stereotype e.g., stereotype_race, unrelated.
- **multi_class**: A multi-class classification label e.g., stereotype_race, stereotype_gender.
- **original_dataset**: Source of the data.
## Usage
This dataset can be used to train models for various tasks:
1. **Sentence-level Stereotype Detection**: Using the `text_no_marker` column as input and `binary_label` or `multi_label` as target.
2. **Token-level Stereotype Detection**: Using the `text_with_marker` to identify the position of the token in the sentence.
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rai-sandeep/test_ds_1
|
rai-sandeep
| 2023-07-03T11:13:46Z | 40 | 0 |
[
"region:us"
] | null | 2023-07-03T11:13:44Z |
---
dataset_info:
features:
- name: category
dtype: string
- name: topic
dtype: string
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 4689
num_examples: 4
download_size: 11810
dataset_size: 4689
---
# Dataset Card for "test_ds_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
d0rj/dolphin-ru
|
d0rj
| 2023-07-26T14:54:29Z | 40 | 3 |
[
"task_categories:conversational",
"task_categories:text-classification",
"task_categories:token-classification",
"task_categories:table-question-answering",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:summarization",
"task_categories:feature-extraction",
"task_categories:text-generation",
"task_categories:text2text-generation",
"language_creators:translated",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:ehartford/dolphin",
"language:ru",
"license:apache-2.0",
"ChatGPT",
"instruct",
"instruct-tune",
"region:us"
] |
[
"conversational",
"text-classification",
"token-classification",
"table-question-answering",
"question-answering",
"zero-shot-classification",
"summarization",
"feature-extraction",
"text-generation",
"text2text-generation"
] | 2023-07-20T22:49:00Z |
---
language_creators:
- translated
language:
- ru
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
pretty_name: Dolphin (ru)
source_datasets:
- ehartford/dolphin
license: apache-2.0
tags:
- ChatGPT
- instruct
- instruct-tune
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 8037639673
num_examples: 2840090
download_size: 3900911155
dataset_size: 8037639673
task_categories:
- conversational
- text-classification
- token-classification
- table-question-answering
- question-answering
- zero-shot-classification
- summarization
- feature-extraction
- text-generation
- text2text-generation
---
# Dolphin-ru 🐬
## Dataset Description
- **Homepage:** https://erichartford.com/dolphin
This is translated version of [ehartford/dolphin](https://huggingface.co/datasets/ehartford/dolphin) into Russian.
|
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] |
PL-MTEB/8tags-clustering
|
PL-MTEB
| 2023-09-11T12:39:58Z | 40 | 0 |
[
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | 2023-08-11T11:26:31Z |
---
license: cc-by-nc-sa-4.0
---
|
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-0.6869441866874695
] |
squarelike/ko_medical_chat
|
squarelike
| 2023-08-19T06:45:48Z | 40 | 5 |
[
"language:ko",
"medical",
"region:us"
] | null | 2023-08-18T18:24:58Z |
---
language:
- ko
tags:
- medical
---
[https://github.com/jwj7140/ko-medical-chat](https://github.com/jwj7140/ko-medical-chat)
Korean medical conversation dataset from converting [MedText](https://huggingface.co/datasets/BI55/MedText) and [ChatDoctor](https://github.com/Kent0n-Li/ChatDoctor)
|
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] |
raygx/NepaliTweets
|
raygx
| 2023-08-24T03:49:49Z | 40 | 0 |
[
"region:us"
] | null | 2023-08-24T03:48:49Z |
---
dataset_info:
features:
- name: Sentiment
dtype: int64
- name: Sentences
dtype: string
splits:
- name: train
num_bytes: 14803386
num_examples: 44269
download_size: 5521101
dataset_size: 14803386
---
# Dataset Card for "NepaliTweets"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
Sakil/DPO_dataset
|
Sakil
| 2023-08-25T12:36:57Z | 40 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-08-25T12:19:09Z |
---
license: apache-2.0
---
|
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cesarali/test_ocean_clients
|
cesarali
| 2023-08-28T17:26:38Z | 40 | 0 |
[
"region:us"
] | null | 2023-08-28T17:26:35Z |
---
dataset_info:
features:
- name: o
dtype: int64
- name: c
dtype: int64
- name: e
dtype: int64
- name: a
dtype: int64
- name: n
dtype: int64
- name: education
dtype: string
- name: current_work
dtype: string
- name: ethnicity
dtype: string
- name: income_bracket
dtype: string
- name: age
dtype: int64
- name: sexual_orientation
dtype: string
- name: favorite_movies
sequence: string
- name: favorite_tv_shows
sequence: string
- name: comment
dtype: string
- name: id
dtype: string
- name: location
dtype: string
splits:
- name: train
num_bytes: 6755
num_examples: 20
download_size: 12166
dataset_size: 6755
---
# Dataset Card for "test_ocean_clients"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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EgilKarlsen/AA_DistilRoBERTa_FT
|
EgilKarlsen
| 2023-09-04T07:35:37Z | 40 | 0 |
[
"region:us"
] | null | 2023-09-04T07:35:12Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
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splits:
- name: train
num_bytes: 80318765
num_examples: 26057
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num_examples: 8686
download_size: 147154939
dataset_size: 107092821
---
# Dataset Card for "AA_DistilRoBERTa_FT"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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MU-NLPC/Calc-svamp
|
MU-NLPC
| 2023-10-30T15:05:26Z | 40 | 0 |
[
"task_categories:text-generation",
"size_categories:n<1K",
"language:en",
"license:mit",
"math world problems",
"math",
"arithmetics",
"arxiv:2305.15017",
"region:us"
] |
[
"text-generation"
] | 2023-09-08T14:56:46Z |
---
language:
- en
license: mit
size_categories:
- n<1K
task_categories:
- text-generation
tags:
- math world problems
- math
- arithmetics
dataset_info:
- config_name: default
features:
- name: id
dtype: string
- name: question
dtype: string
- name: chain
dtype: string
- name: result
dtype: string
- name: result_float
dtype: float64
- name: equation
dtype: string
- name: problem_type
dtype: string
splits:
- name: test
num_bytes: 335744
num_examples: 1000
download_size: 116449
dataset_size: 335744
- config_name: original-splits
features:
- name: id
dtype: string
- name: question
dtype: string
- name: chain
dtype: string
- name: result
dtype: string
- name: result_float
dtype: float64
- name: equation
dtype: string
- name: problem_type
dtype: string
splits:
- name: test
num_bytes: 335744
num_examples: 1000
download_size: 116449
dataset_size: 335744
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- config_name: original-splits
data_files:
- split: test
path: original-splits/test-*
---
# Dataset Card for Calc-SVAMP
## Summary
The dataset is a collection of simple math word problems focused on arithmetics. It is derived from <https://github.com/arkilpatel/SVAMP/>.
The main addition in this dataset variant is the `chain` column. It was created by converting the solution to a simple html-like language that can be easily
parsed (e.g. by BeautifulSoup). The data contains 3 types of tags:
- gadget: A tag whose content is intended to be evaluated by calling an external tool (sympy-based calculator in this case)
- output: An output of the external tool
- result: The final answer to the mathematical problem (a number)
## Supported Tasks
This variant of the dataset is intended for training Chain-of-Thought reasoning models able to use external tools to enhance the factuality of their responses.
This dataset presents in-context scenarios where models can outsource the computations in the reasoning chain to a calculator.
## Construction process
We created the dataset by converting the **equation** attribute in the original dataset to a sequence (chain) of calculations, with final one being the result to the math problem.
We also perform in-dataset and cross-dataset data-leak detection within the [Calc-X collection](https://huggingface.co/collections/MU-NLPC/calc-x-652fee9a6b838fd820055483).
However, for SVAMP specifically, we detected no data leaks and filtered no data.
## Content and data splits
The dataset contains the same data instances as the original dataset except for a correction of inconsistency between `equation` and `answer` in one data instance.
To the best of our knowledge, the original dataset does not contain an official train-test split. We treat the whole dataset as a testing benchmark.
## Attributes:
- **id**: problem id from the original dataset
- **question**: the question intended to answer
- **chain**: series of simple operations (derived from `equation`) that leads to the solution
- **result**: the result (number) as a string
- **result_float**: result converted to a floating point
- **equation**: a nested expression that evaluates to the correct result
- **problem_type**: a category of the problem
Attributes **id**, **question**, **chain**, and **result** are present in all datasets in [Calc-X collection](https://huggingface.co/collections/MU-NLPC/calc-x-652fee9a6b838fd820055483).
## Related work
This dataset was created as a part of a larger effort in training models capable of using a calculator during inference, which we call Calcformers.
- [**Calc-X collection**](https://huggingface.co/collections/MU-NLPC/calc-x-652fee9a6b838fd820055483) - datasets for training Calcformers
- [**Calcformers collection**](https://huggingface.co/collections/MU-NLPC/calcformers-65367392badc497807b3caf5) - calculator-using models we trained and published on HF
- [**Calc-X and Calcformers paper**](https://arxiv.org/abs/2305.15017)
- [**Calc-X and Calcformers repo**](https://github.com/prompteus/calc-x)
Here are links to the original dataset:
- [**original SVAMP dataset and repo**](https://github.com/arkilpatel/SVAMP/)
- [**original SVAMP paper**](https://www.semanticscholar.org/paper/Are-NLP-Models-really-able-to-Solve-Simple-Math-Patel-Bhattamishra/13c4e5a6122f3fa2663f63e49537091da6532f35)
## Licence
MIT, consistent with the original source dataset linked above.
## Cite
If you use this version of dataset in research, please cite the original [SVAMP paper](https://www.semanticscholar.org/paper/Are-NLP-Models-really-able-to-Solve-Simple-Math-Patel-Bhattamishra/13c4e5a6122f3fa2663f63e49537091da6532f35), and [Calc-X collection](https://arxiv.org/abs/2305.15017) as follows:
```bibtex
@inproceedings{kadlcik-etal-2023-soft,
title = "Calc-X and Calcformers: Empowering Arithmetical Chain-of-Thought through Interaction with Symbolic Systems",
author = "Marek Kadlčík and Michal Štefánik and Ondřej Sotolář and Vlastimil Martinek",
booktitle = "Proceedings of the The 2023 Conference on Empirical Methods in Natural Language Processing: Main track",
month = dec,
year = "2023",
address = "Singapore, Singapore",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2305.15017",
}
```
|
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mychen76/ds_receipts_v2_train
|
mychen76
| 2023-09-20T21:38:03Z | 40 | 0 |
[
"region:us"
] | null | 2023-09-20T08:56:43Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 102670815.483
num_examples: 1137
download_size: 102731891
dataset_size: 102670815.483
---
# Dataset Card for "ds_receipts_v2_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
TurkuNLP/genre-6
|
TurkuNLP
| 2023-09-26T06:42:00Z | 40 | 0 |
[
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:en",
"region:us"
] |
[
"text-classification"
] | 2023-09-25T13:08:04Z |
---
task_categories:
- text-classification
language:
- en
size_categories:
- 10K<n<100K
---
# Dataset Card for Dataset Name
## Dataset Summary
Genre-6 dataset is an English dataset based on Kindletrends (UK & US). It contains more than 20k books and associated categories with ready-made binary classification and multilabel classification labels.
## Dataset Structure
### Data Instances
`` {"text": "...", "categories": "Engineering & Transportation;Science & Math", "fiction": "non-fiction", "split1": ['Science & Math'], "split2" : ['Engineering & Transportation', 'Science & Math'], "split3": ['Science & Math']} ``
### Data Fields
- text: Kindletrends text
- categories: Kidletrends categories (1 to 2 categories per book)
- fiction: binary label for fiction and non-fiction books
- splits 1,2,3: multilabel for different subsets of the categories
### Data Splits
The dataset contains train (80%), validation (10%) and test (10%) splits.
The splits for multilabels are following:
- split1: 'Biology & Nature & Biological Sciences','Computer Science', 'Fantasy','Medicine & Health Sciences','Philosophy','Science & Math'.
- split2: 'Biology & Nature & Biological Sciences','Computer Science', 'Engineering & Transportation','Fantasy','Medicine & Health Sciences','Science & Math'.
- split3: 'Biology & Nature & Biological Sciences','Computer Science', 'Fantasy','Medicine & Health Sciences', 'Poetry', 'Politics & Social Sciences', 'Science & Math'.
More splits can be generated from the field "categories".
### Source Data
[Kindletrends](https://kindletrends.com/categories/)
|
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DykeF/NCTCRCHE100K
|
DykeF
| 2023-10-04T19:37:15Z | 40 | 0 |
[
"license:cc-by-4.0",
"region:us"
] | null | 2023-10-03T07:42:24Z |
---
license: cc-by-4.0
---
# NCTCRCHE100K Dataset Card
# Citation
```bash
Kather, Jakob Nikolas, Halama, Niels, & Marx, Alexander. (2018). 100,000 histological images of human colorectal cancer and healthy tissue (v0.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.1214456
```
# Description
This is a set of 100,000 non-overlapping image patches from hematoxylin & eosin (H&E) stained histological images of human colorectal cancer (CRC) and normal tissue.
All images are 224x224 pixels (px) at 0.5 microns per pixel (MPP). All images are color-normalized using Macenko's method (http://ieeexplore.ieee.org/abstract/document/5193250/, DOI 10.1109/ISBI.2009.5193250).
Tissue classes are: Adipose (ADI), background (BACK), debris (DEB), lymphocytes (LYM), mucus (MUC), smooth muscle (MUS), normal colon mucosa (NORM), cancer-associated stroma (STR), colorectal adenocarcinoma epithelium (TUM).
These images were manually extracted from N=86 H&E stained human cancer tissue slides from formalin-fixed paraffin-embedded (FFPE) samples from the NCT Biobank (National Center for Tumor Diseases, Heidelberg, Germany) and the UMM pathology archive (University Medical Center Mannheim, Mannheim, Germany). Tissue samples contained CRC primary tumor slides and tumor tissue from CRC liver metastases; normal tissue classes were augmented with non-tumorous regions from gastrectomy specimen to increase variability.
### Data Structure
The dataset is structured into training splits (100,000 "train" and 100,000 "train_nonorm" samples) as well as a validation split of 7180 samples.
## Setup Instructions
```bash
from torch.utils.data import DataLoader
from torchvision.transforms import ToTensor
def transform(data):
data["image"] = [ToTensor()(img) for img in data["image"]] # convert to torch.Tensor
return data
from datasets import load_dataset
ds_train = load_dataset("DykeF/NCTCRCHE100K", split="train") # or train_nonorm or validation
ds_train.set_transform(transform)
|
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ckiplab/traditional-chinese-dolly-15k
|
ckiplab
| 2023-10-05T16:16:41Z | 40 | 5 |
[
"license:cc-by-sa-3.0",
"region:us"
] | null | 2023-10-05T16:01:01Z |
---
license: cc-by-sa-3.0
---
|
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matthewfranglen/aste-v2
|
matthewfranglen
| 2023-10-09T10:05:10Z | 40 | 0 |
[
"task_categories:token-classification",
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:en",
"arxiv:1911.01616",
"arxiv:2107.12214",
"arxiv:2010.02609",
"region:us"
] |
[
"token-classification",
"text-classification"
] | 2023-10-07T18:45:31Z |
---
pretty_name: "Aspect Sentiment Triplet Extraction v2"
language:
- en
arxiv:
- 2107.12214
- 2010.02609
- 1911.01616
size_categories:
- 1K<n<10K
task_categories:
- token-classification
- text-classification
configs:
- config_name: 2014-laptop-sem-eval
data_files:
- split: train
path: "data/2014/laptop/sem-eval/train.gz.parquet"
- split: valid
path: "data/2014/laptop/sem-eval/valid.gz.parquet"
- split: test
path: "data/2014/laptop/sem-eval/test.gz.parquet"
- config_name: 2014-laptop-aste-v2
data_files:
- split: train
path: "data/2014/laptop/aste/train.gz.parquet"
- split: valid
path: "data/2014/laptop/aste/valid.gz.parquet"
- split: test
path: "data/2014/laptop/aste/test.gz.parquet"
- config_name: 2014-restaurant-sem-eval
data_files:
- split: train
path: "data/2014/restaurant/sem-eval/train.gz.parquet"
- split: valid
path: "data/2014/restaurant/sem-eval/valid.gz.parquet"
- split: test
path: "data/2014/restaurant/sem-eval/test.gz.parquet"
- config_name: 2014-restaurant-aste-v2
data_files:
- split: train
path: "data/2014/restaurant/aste/train.gz.parquet"
- split: valid
path: "data/2014/restaurant/aste/valid.gz.parquet"
- split: test
path: "data/2014/restaurant/aste/test.gz.parquet"
- config_name: 2015-restaurant-sem-eval
data_files:
- split: train
path: "data/2015/restaurant/sem-eval/train.gz.parquet"
- split: valid
path: "data/2015/restaurant/sem-eval/valid.gz.parquet"
- split: test
path: "data/2015/restaurant/sem-eval/test.gz.parquet"
- config_name: 2015-restaurant-aste-v2
data_files:
- split: train
path: "data/2015/restaurant/aste/train.gz.parquet"
- split: valid
path: "data/2015/restaurant/aste/valid.gz.parquet"
- split: test
path: "data/2015/restaurant/aste/test.gz.parquet"
- config_name: 2016-restaurant-sem-eval
data_files:
- split: train
path: "data/2016/restaurant/sem-eval/train.gz.parquet"
- split: valid
path: "data/2016/restaurant/sem-eval/valid.gz.parquet"
- split: test
path: "data/2016/restaurant/sem-eval/test.gz.parquet"
- config_name: 2016-restaurant-aste-v2
data_files:
- split: train
path: "data/2016/restaurant/aste/train.gz.parquet"
- split: valid
path: "data/2016/restaurant/aste/valid.gz.parquet"
- split: test
path: "data/2016/restaurant/aste/test.gz.parquet"
---
## Dataset Description
### Task Summary
Aspect Sentiment Triplet Extraction (ASTE) is the task of extracting the triplets of target entities, their associated sentiment, and opinion spans explaining the reason for the sentiment.
This task is firstly proposed by (Peng et al., 2020) in the paper [Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis (In AAAI 2020)](https://arxiv.org/abs/1911.01616).
For Example, given the sentence:
> The screen is very large and crystal clear with amazing colors and resolution .
The objective of the Aspect Sentiment Triplet Extraction (ASTE) task is to predict the triplets:
> [('screen', 'large', 'Positive'), ('screen', 'clear', 'Positive'), ('colors', 'amazing', 'Positive'), ('resolution', 'amazing', 'Positive')]
where a triplet consists of (target, opinion, sentiment).
### Dataset Summary
Sentiment analysis is increasingly viewed as a vital task both from an academic and a commercial standpoint.
The majority of current approaches, however, attempt to detect the overall polarity of a sentence, paragraph, or text span, regardless of the entities mentioned (e.g., laptops, restaurants) and their aspects (e.g., battery, screen; food, service).
By contrast, this task is concerned with aspect based sentiment analysis (ABSA), where the goal is to identify the aspects of given target entities and the sentiment expressed towards each aspect.
This dataset consists of customer reviews with human-authored annotations identifying the mentioned aspects of the target entities and the sentiment polarity of each aspect.
### Dataset Source
The ASTE dataset is from the [xuuuluuu/SemEval-Triplet-data](https://github.com/xuuuluuu/SemEval-Triplet-data) repository.
It is based on the Sem Eval 2014, 2015 and 2016 datasets, with some preprocessing applied to the text.
* [Sem Eval 2014 Task 4](https://alt.qcri.org/semeval2014/task4/)
* [Sem Eval 2015 Task 12](https://alt.qcri.org/semeval2015/task12/)
* [Sem Eval 2016 Task 5](https://alt.qcri.org/semeval2016/task5/)
### Dataset Details
The train, validation and test splits come from the ASTE dataset.
There are the following columns:
* index
The ASTE and Sem Eval datasets had multiple annotations per document.
This dataset has a single annotation per row.
To make it easier to collect all annotations for a document the index can be used to group them.
All annotations for a given document will have the same index.
* text
This is the document that is annotated, either in the ASTE form or in the Sem Eval form (see below for details).
* aspect_start_index
The zero based character index for the first letter of the aspect term
* aspect_end_index
The zero based character index for the last letter of the aspect term
* aspect_term
The aspect term as it appears in the text
* opinion_start_index
The zero based character index for the first letter of the opinion term
* opinion_end_index
The zero based character index for the last letter of the opinion term
* opinion_term
The opinion term as it appears in the text
* sentiment
The sentiment class for the opinion about the aspect.
One of _negative_, _neutral_ or _positive_.
The ASTE dataset involved preprocessing the SemEval text.
This preprocessing fixed some of the spelling mistakes, for example:
> Keyboard good sized and wasy to use.
(easy misspelt as wasy).
The preprocessing also includes tokenization of the text and then separating the tokens with whitespace, for example:
> It 's just as fast with one program open as it is with sixteen open .
Since the added whitespace can lead to unnatrual text I have provided two forms of the dataset.
Subsets that end with `aste-v2` have the preprocessed text with spelling correction and additional whitespace.
Subsets that end with `sem-eval` have the original Sem Eval text.
### Citation Information
```
@misc{xu2021learning,
title={Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction},
author={Lu Xu and Yew Ken Chia and Lidong Bing},
year={2021},
eprint={2107.12214},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{xu2021positionaware,
title={Position-Aware Tagging for Aspect Sentiment Triplet Extraction},
author={Lu Xu and Hao Li and Wei Lu and Lidong Bing},
year={2021},
eprint={2010.02609},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{peng2019knowing,
title={Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis},
author={Haiyun Peng and Lu Xu and Lidong Bing and Fei Huang and Wei Lu and Luo Si},
year={2019},
eprint={1911.01616},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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ai2lumos/lumos_complex_qa_plan_onetime
|
ai2lumos
| 2023-10-23T22:34:12Z | 40 | 0 |
[
"task_categories:conversational",
"task_categories:text-generation",
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"language-agent",
"reasoning",
"question-answering",
"planning",
"region:us"
] |
[
"conversational",
"text-generation",
"question-answering"
] | 2023-10-23T05:36:48Z |
---
license: apache-2.0
task_categories:
- conversational
- text-generation
- question-answering
language:
- en
tags:
- language-agent
- reasoning
- question-answering
- planning
size_categories:
- 10K<n<100K
---
# 🪄 Lumos: Language Agents with Unified Formats, Modular Design, and Open-Source LLMs
<p align="center">
🌐<a href="https://allenai.github.io/lumos">[Website]</a>
📝<a href="">[Paper]</a>
🤗<a href="https://huggingface.co/datasets?sort=trending&search=ai2lumos">[Data]</a>
🤗<a href="https://huggingface.co/models?sort=trending&search=ai2lumos">[Model]</a>
</p>
We introduce 🪄**Lumos**, Language Agents with **Unified** Formats, **Modular** Design, and **Open-Source** LLMs. **Lumos** unifies a suite of complex interactive tasks and achieves competitive performance with GPT-4/3.5-based and larger open-source agents.
**Lumos** has following features:
* 🧩 **Modular Architecture**:
- **Lumos** consists of planning, grounding, and execution modules built based on LLAMA-2-7B.
* 🌍 **Diverse Training Data**:
- **Lumos** is trained with ~40K high-quality annotations from ground-truth reasoning steps in existing benchmarks with GPT-4.
* 🚀 **Competitive Performance**:
- 🚀 **Lumos** outperforms **GPT-4/3.5-based** agents on complex QA and web agent tasks, and **larger open agents** on maths tasks.
- 🚀 **Lumos** performs better than open agent baseline formulations including **chain-of-thoughts** and **unmodularized** training.
- 🚀 **Lumos** surpasses larger open LLM agents and domain-specific agents on an unseen task, WebShop.
## Data Overview
`lumos_complex_qa_plan_onetime` is the data for training **planning** module on **complex QA** task in **Lumos-Onetime (Lumos-O)** formulation.
The source of the training annotation training data is shown below:
| Datasets | Number |
|---|---|
|StrategyQA|1777|
|Musique|17632|
## Models Trained with the Data
`lumos_complex_qa_plan_onetime` is used to train the following models.
|Model|Huggingface Repo|
|---|---|
|`lumos_complex_qa_plan_onetime`| [🤗Huggingface Repo](https://huggingface.co/ai2lumos/lumos_complex_qa_plan_onetime) |
|`lumos_unified_plan_onetime`| [🤗Huggingface Repo](https://huggingface.co/ai2lumos/lumos_unified_plan_onetime) |
## Citation
If you find this work is relevant with your research, please feel free to cite our work!
```
@article{yin2023lumos,
title={Lumos: Towards Language Agents that are Unified, Modular, and Open Source},
author={Yin, Da and Brahman, Faeze and Ravichander, Abhilasha and Chandu, Khyathi and Chang, Kai-Wei and Choi, Yejin and Lin, Bill Yuchen},
year={2023}
}
```
|
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roborovski/upsampled-prompts-parti
|
roborovski
| 2023-11-01T13:25:03Z | 40 | 0 |
[
"region:us"
] | null | 2023-10-29T17:32:37Z |
---
dataset_info:
features:
- name: Prompt
dtype: string
- name: Category
dtype: string
- name: Upsampled
dtype: string
splits:
- name: train
num_bytes: 10258852
num_examples: 23318
download_size: 5483101
dataset_size: 10258852
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "upsampled-prompts-parti"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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btt-mining-coalation/red_pajama_random_5000
|
btt-mining-coalation
| 2023-11-09T14:45:49Z | 40 | 0 |
[
"region:us"
] | null | 2023-11-05T01:42:27Z |
---
dataset_info:
features:
- name: text
dtype: string
- name: summary
dtype: string
- name: reward_dpo
dtype: float64
splits:
- name: train
num_bytes: 304835574
num_examples: 5000
download_size: 137390511
dataset_size: 304835574
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "red_pajama_random_5000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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ostapeno/qa-openai_batched_long_icl0_clen512_maxD-1_maxC2000_08000_cleaned_train
|
ostapeno
| 2023-11-06T14:50:24Z | 40 | 0 |
[
"region:us"
] | null | 2023-11-06T14:45:47Z |
---
configs:
- config_name: default
data_files:
- split: abstract_algebra
path: data/abstract_algebra-*
- split: college_biology
path: data/college_biology-*
- split: formal_logic
path: data/formal_logic-*
- split: global_facts
path: data/global_facts-*
- split: high_school_government_and_politics
path: data/high_school_government_and_politics-*
- split: high_school_physics
path: data/high_school_physics-*
- split: machine_learning
path: data/machine_learning-*
- split: prehistory
path: data/prehistory-*
- split: security_studies
path: data/security_studies-*
- split: sociology
path: data/sociology-*
dataset_info:
features:
- name: id
dtype: string
- name: context
dtype: string
- name: docno
dtype: string
- name: subject
dtype: string
- name: icl_examples
dtype: 'null'
- name: author_instr
dtype: string
- name: instruction
dtype: string
- name: response
dtype: string
splits:
- name: abstract_algebra
num_bytes: 23879722.12837838
num_examples: 8000
- name: college_biology
num_bytes: 25235647.10957722
num_examples: 8000
- name: formal_logic
num_bytes: 24553218.679549113
num_examples: 8000
- name: global_facts
num_bytes: 24477439.56043956
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- name: machine_learning
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- name: prehistory
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- name: sociology
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download_size: 140394435
dataset_size: 251509378.8798793
---
# Dataset Card for "qa-openai_batched_long_icl0_clen512_maxD-1_maxC2000_08000_cleaned_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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evanfrick/lichess
|
evanfrick
| 2023-11-08T08:36:24Z | 40 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-08T05:48:31Z |
---
license: mit
---
|
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umarbutler/open-australian-legal-embeddings
|
umarbutler
| 2023-12-01T05:29:52Z | 40 | 4 |
[
"task_categories:text-retrieval",
"task_ids:document-retrieval",
"annotations_creators:no-annotation",
"language_creators:found",
"size_categories:1M<n<10M",
"source_datasets:umarbutler/open-australian-legal-corpus",
"language:en",
"license:other",
"law",
"legal",
"australia",
"embeddings",
"doi:10.57967/hf/1347",
"region:us"
] |
[
"text-retrieval"
] | 2023-11-14T00:14:21Z |
---
language:
- en
license: other
license_name: open-australian-legal-corpus
license_link: https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus/blob/main/LICENCE.md
tags:
- law
- legal
- australia
- embeddings
annotations_creators:
- no-annotation
language_creators:
- found
language_details: en-AU, en-GB
pretty_name: Open Australian Legal Embeddings
size_categories:
- 1M<n<10M
source_datasets:
- umarbutler/open-australian-legal-corpus
task_categories:
- text-retrieval
task_ids:
- document-retrieval
viewer: true
dataset_info:
features:
- name: version_id
dtype: string
- name: type
dtype: string
- name: jurisdiction
dtype: string
- name: source
dtype: string
- name: citation
dtype: string
- name: url
dtype: string
- name: is_last_chunk
dtype: bool
- name: text
dtype: string
- name: embedding
list: float32
config_name: train
splits:
- name: train
num_bytes: 28500857221
num_examples: 5208238
download_size: 45586801753
dataset_size: 28500857221
---
<!-- To update the above `dataset_info` section, please run the following command: `datasets-cli test open_australian_legal_embeddings.py --save_info --all_configs`. -->
# **Open Australian Legal Embeddings ⚖️**
<a href="https://huggingface.co/datasets/umarbutler/open-australian-legal-embeddings" alt="Release"><img src="https://img.shields.io/badge/release-v1.0.0-green"></a>
The Open Australian Legal Embeddings are the first open-source embeddings of Australian legislative and judicial documents.
Trained on the largest open database of Australian law, the [Open Australian Legal Corpus](https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus), the Embeddings consist of roughly 5.2 million 384-dimensional vectors embedded with [`BAAI/bge-small-en-v1.5`](https://huggingface.co/BAAI/bge-small-en-v1.5).
The Embeddings open the door to a wide range of possibilities in the field of Australian legal AI, including the development of document classifiers, search engines and chatbots.
To ensure their accessibility to as wide an audience as possible, the Embeddings are distributed under the same licence as the [Open Australian Legal Corpus](https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus/blob/main/LICENCE.md).
## Usage 👩💻
The below code snippet illustrates how the Embeddings may be loaded and queried via the [Hugging Face Datasets](https://huggingface.co/docs/datasets/index) Python library:
```python
import itertools
import sklearn.metrics.pairwise
from datasets import load_dataset
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('BAAI/bge-small-en-v1.5')
instruction = 'Represent this sentence for searching relevant passages: '
# Load the embeddings.
oale = load_dataset('open_australian_legal_embeddings.py', split='train')
# Sample the first 100,000 embeddings.
sample = list(itertools.islice(oale, 100000))
# Embed a query.
query = model.encode(instruction + 'Who is the Governor-General of Australia?', normalize_embeddings=True)
# Identify the most similar embedding to the query.
similarities = sklearn.metrics.pairwise.cosine_similarity([query], [embedding['embedding'] for embedding in sample])
most_similar_index = similarities.argmax()
most_similar = sample[most_similar_index]
# Print the most similar text.
print(most_similar['text'])
```
To speed up the loading of the Embeddings, you may wish to install [`orjson`](https://github.com/ijl/orjson).
## Structure 🗂️
The Embeddings are stored in [`data/embeddings.jsonl`](https://huggingface.co/datasets/umarbutler/open-australian-legal-embeddings/blob/main/data/embeddings.jsonl), a json lines file where each line is a list of 384 32-bit floating point numbers. Associated metadata is stored in [`data/metadatas.jsonl`](https://huggingface.co/datasets/umarbutler/open-australian-legal-embeddings/blob/main/data/metadatas.jsonl) and the corresponding texts are located in [`data/texts.jsonl`](https://huggingface.co/datasets/umarbutler/open-australian-legal-embeddings/blob/main/data/texts.jsonl).
The metadata fields are the same as those used for the [Open Australian Legal Corpus](https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus#structure-%F0%9F%97%82%EF%B8%8F), barring the `text` field, which was removed, and with the addition of the `is_last_chunk` key, which is a boolean flag for whether a text is the last chunk of a document (used to detect and remove corrupted documents when creating and updating the Embeddings).
## Creation 🧪
All documents in the [Open Australian Legal Corpus](https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus#statistics-%F0%9F%93%8A) were split into semantically meaningful chunks up to 512-tokens-long (as determined by [`bge-small-en-v1.5`](https://huggingface.co/BAAI/bge-small-en-v1.5)'s tokeniser) with the [`semchunk`](https://github.com/umarbutler/semchunk) Python library. These chunks included a header embedding documents' titles, jurisdictions and types in the following format:
```perl
Title: {title}
Jurisdiction: {jurisdiction}
Type: {type}
{text}
```
When embedded into the above header, the names of jurisdictions were capitalised and stripped of hyphens. The `commonwealth` jurisdiction was also renamed to 'Commonwealth of Australia'. In the cases of types, `primary_legislation` became 'Act', `secondary_legislation` became 'Regulation', `bill` became 'Bill' and `decision` became 'Judgment'.
The chunks were then vectorised by [`bge-small-en-v1.5`](https://huggingface.co/BAAI/bge-small-en-v1.5) on a single GeForce RTX 2080 Ti with a batch size of 32 via the [`SentenceTransformers`](https://www.sbert.net/) library.
The resulting embeddings were serialised as json-encoded lists of floats by [`orjson`](https://github.com/ijl/orjson) and stored in [`data/embeddings.jsonl`](https://huggingface.co/datasets/umarbutler/open-australian-legal-embeddings/blob/main/data/embeddings.jsonl). The corresponding metadata and texts (with their headers removed) were saved to [`data/metadatas.jsonl`](https://huggingface.co/datasets/umarbutler/open-australian-legal-embeddings/blob/main/data/metadatas.jsonl) and [`data/texts.jsonl`](https://huggingface.co/datasets/umarbutler/open-australian-legal-embeddings/blob/main/data/texts.jsonl), respectively.
The code used to create and update the Embeddings may be found [here](https://github.com/umarbutler/open-australian-legal-embeddings-creator).
## Changelog 🔄
All notable changes to the Embeddings are documented in its [Changelog 🔄](https://huggingface.co/datasets/umarbutler/open-australian-legal-embeddings/blob/main/CHANGELOG.md).
This project adheres to [Keep a Changelog](https://keepachangelog.com/en/1.0.0/) and [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
## Licence 📜
The Embeddings are distributed under the same licence as the [Open Australian Legal Corpus](https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus/blob/main/LICENCE.md).
## Citation 🔖
If you've relied on the Embeddings for your work, please cite:
```latex
@misc{butler-2023-open-australian-legal-embeddings,
author = {Butler, Umar},
year = {2023},
title = {Open Australian Legal Embeddings},
publisher = {Hugging Face},
version = {1.0.0},
doi = {10.57967/hf/1347},
url = {https://huggingface.co/datasets/umarbutler/open-australian-legal-embeddings}
}
```
## Acknowledgements 🙏
In the spirit of reconciliation, the author acknowledges the Traditional Custodians of Country throughout Australia and their connections to land, sea and community. He pays his respect to their Elders past and present and extends that respect to all Aboriginal and Torres Strait Islander peoples today.
The author thanks the creators of the many Python libraries relied upon in the creation of the Embeddings.
Finally, the author is eternally grateful for the endless support of his wife and her willingness to put up with many a late night spent writing code and quashing bugs.
|
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] |
jlbaker361/addition_decimal
|
jlbaker361
| 2023-11-15T13:00:20Z | 40 | 0 |
[
"region:us"
] | null | 2023-11-14T23:39:16Z |
---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: float64
- name: text
dtype: string
splits:
- name: train
num_bytes: 2145709.8
num_examples: 29376
- name: test
num_bytes: 238412.2
num_examples: 3264
download_size: 884683
dataset_size: 2384122.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Dataset Card for "addition_decimal"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
atmallen/qm_bob_hard_4_mixture_1.0e
|
atmallen
| 2023-11-16T18:18:42Z | 40 | 0 |
[
"region:us"
] | null | 2023-11-16T03:34:20Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: alice_label
dtype: bool
- name: bob_label
dtype: bool
- name: difficulty
dtype: int64
- name: statement
dtype: string
- name: choices
sequence: string
- name: character
dtype: string
- name: label
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
splits:
- name: train
num_bytes: 4578170.5
num_examples: 37091
- name: validation
num_bytes: 487083.5
num_examples: 3969
- name: test
num_bytes: 477119.5
num_examples: 3926
download_size: 1539574
dataset_size: 5542373.5
---
# Dataset Card for "qm_bob_hard_4_mixture_1.0e"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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rjaiswal/tubogas-dataset
|
rjaiswal
| 2023-11-20T09:42:33Z | 40 | 0 |
[
"region:us"
] | null | 2023-11-16T15:57:04Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 4389802.0
num_examples: 42
download_size: 2186125
dataset_size: 4389802.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "tubogas-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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liminghao1630/API-Bank
|
liminghao1630
| 2023-11-17T06:41:39Z | 40 | 3 |
[
"license:mit",
"region:us"
] | null | 2023-11-17T06:39:46Z |
---
license: mit
---
|
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] |
ngoxuanphong/zalo
|
ngoxuanphong
| 2023-11-21T10:00:50Z | 40 | 0 |
[
"size_categories:1K<n<10K",
"language:en",
"license:apache-2.0",
"region:us"
] | null | 2023-11-20T04:46:01Z |
---
license: apache-2.0
language:
- en
size_categories:
- 1K<n<10K
---
|
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] |
SUSTech/mt_bench_judge
|
SUSTech
| 2023-11-23T10:18:55Z | 40 | 0 |
[
"region:us"
] | null | 2023-11-23T10:03:09Z |
---
dataset_info:
features:
- name: question_id
dtype: int64
- name: model
dtype: string
- name: conversation
list:
- name: content
dtype: string
- name: role
dtype: string
- name: turn
dtype: int64
- name: judge
sequence: string
- name: user_prompt
dtype: string
- name: judgment
dtype: string
- name: score
dtype: float64
- name: tstamp
dtype: float64
- name: category
dtype: string
- name: reference
sequence: string
splits:
- name: train
num_bytes: 4409406
num_examples: 800
download_size: 949262
dataset_size: 4409406
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "mt_bench_judge"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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RIW/butterfly_wm_100
|
RIW
| 2023-11-27T18:21:20Z | 40 | 0 |
[
"region:us"
] | null | 2023-11-27T18:21:05Z |
---
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 275312249.0
num_examples: 973
download_size: 275334504
dataset_size: 275312249.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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michelcarroll/llama2-earnings-stock-prediction-fine-tune-binary
|
michelcarroll
| 2023-12-02T17:30:59Z | 40 | 0 |
[
"region:us"
] | null | 2023-12-02T17:30:50Z |
---
dataset_info:
features:
- name: completion
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 83958219
num_examples: 111752
- name: development
num_bytes: 25508622
num_examples: 33558
- name: test
num_bytes: 771489
num_examples: 1000
download_size: 47616738
dataset_size: 110238330
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: development
path: data/development-*
- split: test
path: data/test-*
---
|
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] |
ju-bezdek/conll2003-SK-NER
|
ju-bezdek
| 2023-03-21T08:13:05Z | 39 | 0 |
[
"task_categories:other",
"task_ids:named-entity-recognition",
"task_ids:part-of-speech",
"annotations_creators:machine-generated",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|conll2003",
"language:sk",
"license:unknown",
"structure-prediction",
"region:us"
] |
[
"other"
] | 2022-03-02T23:29:22Z |
---
annotations_creators:
- machine-generated
- expert-generated
language_creators:
- found
language:
- sk
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|conll2003
task_categories:
- other
task_ids:
- named-entity-recognition
- part-of-speech
pretty_name: conll-2003-sk-ner
tags:
- structure-prediction
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Dataset Card for [Dataset Name]](#dataset-card-for-dataset-name)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
## Dataset Description
This is translated version of the original CONLL2003 dataset (translated from English to Slovak via Google translate) Annotation was done mostly automatically with word matching scripts. Records where some tags were not matched, were annotated manually (10%) Unlike the original Conll2003 dataset, this one contains only NER tags
- **Point of Contact: [@ju-bezdek](https://github.com/ju-bezdek) **
### Supported Tasks and Leaderboards
NER
labels:
- 0: O
- 1: B-PER
- 2: I-PER
- 3: B-ORG
- 4: I-ORG
- 5: B-LOC
- 6: I-LOC
- 7: B-MISC
- 8: I-MISC
### Languages
sk
## Dataset Structure
### Data Splits
train, test, val
## Dataset Creation
### Source Data
https://huggingface.co/datasets/conll2003
### Annotations
#### Annotation process
- Machine Translation
- Machine pairing tags with reverse translation, and hardcoded rules (including phrase regex matching etc.)
- Manual annotation of records that couldn't be automatically matched
|
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] |
AhmedSSabir/Japanese-wiki-dump-sentence-dataset
|
AhmedSSabir
| 2023-07-11T12:22:09Z | 39 | 3 |
[
"task_categories:sentence-similarity",
"task_categories:text-classification",
"task_categories:text-generation",
"size_categories:1M<n<10M",
"language:ja",
"region:us"
] |
[
"sentence-similarity",
"text-classification",
"text-generation"
] | 2022-06-08T11:34:04Z |
---
task_categories:
- sentence-similarity
- text-classification
- text-generation
language:
- ja
size_categories:
- 1M<n<10M
---
# Dataset
5M (5121625) clean Japanese full sentence with the context. This dataset can be used to learn unsupervised semantic similarity, etc.
|
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] |
mounikaiiith/Telugu_Sentiment
|
mounikaiiith
| 2022-07-04T15:05:31Z | 39 | 1 |
[
"license:cc-by-4.0",
"region:us"
] | null | 2022-06-19T12:06:15Z |
---
license: cc-by-4.0
---
Do cite the below reference for using the dataset:
@article{marreddy2022resource, title={Am I a Resource-Poor Language? Data Sets, Embeddings, Models and Analysis for four different NLP tasks in Telugu Language},
author={Marreddy, Mounika and Oota, Subba Reddy and Vakada, Lakshmi Sireesha and Chinni, Venkata Charan and Mamidi, Radhika},
journal={Transactions on Asian and Low-Resource Language Information Processing}, publisher={ACM New York, NY} }
If you want to use the two classes (positive and negative) from the dataset, do cite the below reference:
@article{marreddy2022multi,
title={Multi-Task Text Classification using Graph Convolutional Networks for Large-Scale Low Resource Language},
author={Marreddy, Mounika and Oota, Subba Reddy and Vakada, Lakshmi Sireesha and Chinni, Venkata Charan and Mamidi, Radhika},
journal={arXiv preprint arXiv:2205.01204},
year={2022}
}
|
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] |
arize-ai/xtreme_en
|
arize-ai
| 2022-07-01T17:23:29Z | 39 | 0 |
[
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|xtreme",
"language:en",
"license:mit",
"region:us"
] |
[
"token-classification"
] | 2022-06-30T19:48:47Z |
---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
pretty_name: named-entity-recognition-en-no-drift
size_categories:
- 10K<n<100K
source_datasets:
- extended|xtreme
task_categories:
- token-classification
task_ids:
- named-entity-recognition
---
# Dataset Card for `reviews_with_drift`
## 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
### Dataset Summary
This dataset was crafted to be used in our tutorial [Link to the tutorial when ready]. It consists on a large Movie Review Dataset mixed with some reviews from a Hotel Review Dataset. The training/validation set are purely obtained from the Movie Review Dataset while the production set is mixed. Some other features have been added (`age`, `gender`, `context`) as well as a made up timestamp `prediction_ts` of when the inference took place.
### Supported Tasks and Leaderboards
`text-classification`, `sentiment-classification`: The dataset is mainly used for text classification: given the text, predict the sentiment (positive or negative).
### Languages
Text is mainly written in english.
## 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 [@fjcasti1](https://github.com/fjcasti1) for adding this dataset.
|
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] |
arize-ai/xtreme_en_language_drift_es
|
arize-ai
| 2022-07-01T17:25:51Z | 39 | 0 |
[
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|xtreme",
"language:en",
"license:mit",
"region:us"
] |
[
"token-classification"
] | 2022-06-30T21:07:38Z |
---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
pretty_name: named-entity-recognition-en-no-drift
size_categories:
- 10K<n<100K
source_datasets:
- extended|xtreme
task_categories:
- token-classification
task_ids:
- named-entity-recognition
---
# Dataset Card for `reviews_with_drift`
## 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
### Dataset Summary
This dataset was crafted to be used in our tutorial [Link to the tutorial when ready]. It consists on a large Movie Review Dataset mixed with some reviews from a Hotel Review Dataset. The training/validation set are purely obtained from the Movie Review Dataset while the production set is mixed. Some other features have been added (`age`, `gender`, `context`) as well as a made up timestamp `prediction_ts` of when the inference took place.
### Supported Tasks and Leaderboards
`text-classification`, `sentiment-classification`: The dataset is mainly used for text classification: given the text, predict the sentiment (positive or negative).
### Languages
Text is mainly written in english.
## 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 [@fjcasti1](https://github.com/fjcasti1) for adding this dataset.
|
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] |
arize-ai/xtreme_en_token_drift
|
arize-ai
| 2022-07-01T17:25:34Z | 39 | 1 |
[
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|xtreme",
"language:en",
"license:mit",
"region:us"
] |
[
"token-classification"
] | 2022-06-30T21:08:01Z |
---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
pretty_name: named-entity-recognition-en-no-drift
size_categories:
- 10K<n<100K
source_datasets:
- extended|xtreme
task_categories:
- token-classification
task_ids:
- named-entity-recognition
---
# Dataset Card for `reviews_with_drift`
## 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
### Dataset Summary
This dataset was crafted to be used in our tutorial [Link to the tutorial when ready]. It consists on a large Movie Review Dataset mixed with some reviews from a Hotel Review Dataset. The training/validation set are purely obtained from the Movie Review Dataset while the production set is mixed. Some other features have been added (`age`, `gender`, `context`) as well as a made up timestamp `prediction_ts` of when the inference took place.
### Supported Tasks and Leaderboards
`text-classification`, `sentiment-classification`: The dataset is mainly used for text classification: given the text, predict the sentiment (positive or negative).
### Languages
Text is mainly written in english.
## 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 [@fjcasti1](https://github.com/fjcasti1) for adding this dataset.
|
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helena-balabin/pereira_fMRI_passages
|
helena-balabin
| 2023-07-27T13:34:57Z | 39 | 0 |
[
"region:us"
] | null | 2022-10-19T14:45:37Z |
---
dataset_info:
features:
- name: language_lh
sequence:
sequence: float64
- name: language_rh
sequence:
sequence: float64
- name: vision_body
sequence:
sequence: float64
- name: vision_face
sequence:
sequence: float64
- name: vision_object
sequence:
sequence: float64
- name: vision_scene
sequence:
sequence: float64
- name: vision
sequence:
sequence: float64
- name: dmn
sequence:
sequence: float64
- name: task
sequence:
sequence: float64
- name: all
sequence:
sequence: float64
- name: paragraphs
sequence: string
- name: topic_indices
sequence: uint8
- name: permuted_paragraphs
sequence: string
splits:
- name: train
num_bytes: 1649652912
num_examples: 8
download_size: 1658872446
dataset_size: 1649652912
---
# Dataset Card for "pereira_fMRI_passages"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
Rosenberg/genia
|
Rosenberg
| 2022-10-23T12:08:03Z | 39 | 2 |
[
"license:mit",
"region:us"
] | null | 2022-10-23T12:07:06Z |
---
license: mit
---
|
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Guizmus/AnimeChanStyle
|
Guizmus
| 2022-11-14T23:45:20Z | 39 | 3 |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2022-11-13T21:13:37Z |
---
license: creativeml-openrail-m
thumbnail: "https://huggingface.co/datasets/Guizmus/AnimeChanStyle/resolve/main/showcase_dataset.jpg"
---
This is the dataset used for making the model : https://huggingface.co/Guizmus/AnimeChanStyle
The images were made by the users of Stable Diffusion discord using CreativeML-OpenRail-M licenced models, in the intent to make this dataset.
90 pictures captioned with their content by hand, with the suffix ",AnimeChan Style"
The collection process was made public during less than a day, until enough variety was introduced to train through a Dreambooth method a style corresponding to the different members of this community
The picture captioned are available in [this zip file](https://huggingface.co/datasets/Guizmus/AnimeChanStyle/resolve/main/AnimeChanStyle%20v2.3.zip)
|
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] |
bigbio/mayosrs
|
bigbio
| 2022-12-22T15:44:58Z | 39 | 0 |
[
"multilinguality:monolingual",
"language:en",
"license:cc0-1.0",
"region:us"
] | null | 2022-11-13T22:09:14Z |
---
language:
- en
bigbio_language:
- English
license: cc0-1.0
multilinguality: monolingual
bigbio_license_shortname: CC0_1p0
pretty_name: MayoSRS
homepage: https://conservancy.umn.edu/handle/11299/196265
bigbio_pubmed: False
bigbio_public: True
bigbio_tasks:
- SEMANTIC_SIMILARITY
---
# Dataset Card for MayoSRS
## Dataset Description
- **Homepage:** https://conservancy.umn.edu/handle/11299/196265
- **Pubmed:** False
- **Public:** True
- **Tasks:** STS
MayoSRS consists of 101 clinical term pairs whose relatedness was determined by nine medical coders and three physicians from the Mayo Clinic.
## Citation Information
```
@article{pedersen2007measures,
title={Measures of semantic similarity and relatedness in the biomedical domain},
author={Pedersen, Ted and Pakhomov, Serguei VS and Patwardhan, Siddharth and Chute, Christopher G},
journal={Journal of biomedical informatics},
volume={40},
number={3},
pages={288--299},
year={2007},
publisher={Elsevier}
}
```
|
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] |
jonathan-roberts1/Satellite-Images-of-Hurricane-Damage
|
jonathan-roberts1
| 2023-03-31T14:53:28Z | 39 | 1 |
[
"license:cc-by-4.0",
"arxiv:1807.01688",
"region:us"
] | null | 2023-02-17T17:22:30Z |
---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': flooded or damaged buildings
'1': undamaged buildings
splits:
- name: train
num_bytes: 25588780
num_examples: 10000
download_size: 26998688
dataset_size: 25588780
license: cc-by-4.0
---
# Dataset Card for "Satellite-Images-of-Hurricane-Damage"
## Dataset Description
- **Paper** [Deep learning based damage detection on post-hurricane satellite imagery](https://arxiv.org/pdf/1807.01688.pdf)
- **Data** [IEEE-Dataport](https://ieee-dataport.org/open-access/detecting-damaged-buildings-post-hurricane-satellite-imagery-based-customized)
- **Split** Train_another
- **GitHub** [DamageDetection](https://github.com/qcao10/DamageDetection)
## Split Information
This HuggingFace dataset repository contains just the Train_another split.
### Licensing Information
[CC BY 4.0](https://ieee-dataport.org/open-access/detecting-damaged-buildings-post-hurricane-satellite-imagery-based-customized)
## Citation Information
[Deep learning based damage detection on post-hurricane satellite imagery](https://arxiv.org/pdf/1807.01688.pdf)
[IEEE-Dataport](https://ieee-dataport.org/open-access/detecting-damaged-buildings-post-hurricane-satellite-imagery-based-customized)
```
@misc{sdad-1e56-18,
title = {Detecting Damaged Buildings on Post-Hurricane Satellite Imagery Based on Customized Convolutional Neural Networks},
author = {Cao, Quoc Dung and Choe, Youngjun},
year = 2018,
publisher = {IEEE Dataport},
doi = {10.21227/sdad-1e56},
url = {https://dx.doi.org/10.21227/sdad-1e56}
}
@article{cao2018deep,
title={Deep learning based damage detection on post-hurricane satellite imagery},
author={Cao, Quoc Dung and Choe, Youngjun},
journal={arXiv preprint arXiv:1807.01688},
year={2018}
}
```
|
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] |
sunzeyeah/chinese_chatgpt_corpus
|
sunzeyeah
| 2023-03-23T16:53:47Z | 39 | 72 |
[
"task_categories:text-generation",
"task_categories:text2text-generation",
"task_categories:question-answering",
"task_categories:reinforcement-learning",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:unknown",
"multilinguality:monolingual",
"size_categories:5M<n<10M",
"language:zh",
"license:unknown",
"region:us"
] |
[
"text-generation",
"text2text-generation",
"question-answering",
"reinforcement-learning"
] | 2023-03-21T09:16:21Z |
---
annotations_creators:
- no-annotation
language_creators:
- unknown
language:
- zh
license:
- unknown
multilinguality:
- monolingual
pretty_name: Chinese-ChatGPT-Corpus
size_categories:
- 5M<n<10M
task_categories:
- text-generation
- text2text-generation
- question-answering
- reinforcement-learning
task_ids:
- language-modeling
- masked-language-modeling
---
# Dataset Card for chinese_chatgpt_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
- **Size of downloaded dataset files:** 5.05 GB
- **Size of the generated dataset:** 0 GB
- **Total amount of disk used:** 5.05 GB
### Dataset Summary
This repo collects chinese corpus for Supervised Finetuning (SFT) and Reinforcement Learning From Human Feedback (RLHF).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
Chinese
## Dataset Structure
### Data Instances
#### train_data_external_v1.jsonl
- **Size of downloaded dataset files:** 5.04 GB
- **Size of the generated dataset:** 0 GB
- **Total amount of disk used:** 5.04 GB
An example looks as follows:
```
{
"prompt": "问题：有没有给未成年贷款的有的联系",
"answers":
[
{
"answer": "若通过招行办理，我行规定，贷款人年龄需年满18岁，且年龄加贷款年限不得超过70岁。如果您持有我行信用卡附属卡，可尝试办理预借现金。",
"score": 1
}
],
"prefix": "回答："
}
```
#### dev_data_external_v1.jsonl
- **Size of downloaded dataset files:** 9.55 MB
- **Size of the generated dataset:** 0 MB
- **Total amount of disk used:** 9.55 MB
An example looks as follows:
```
{
"prompt": "初学纹发现1/2\"的管螺纹并不是1\"的一半。不知道其中的原因，请各位指点。",
"answers":
[
{
"answer": "管螺纹的名义尺寸是“管子”的孔（内）径，而管子的壁厚不是两倍。所以，1/2\"的管螺纹并不是1\"的一半，",
"score": 1
}
],
"prefix": "回答："
}
```
### Data Fields
The data fields are the same among all splits.
#### train_data_external_v1.jsonl
- `prompt`: prompt, `string`
- `answers`: list of answers
- `answer`: answer, `string`
- `score`: score of answer, `int`
- `prefix`: prefix to the answer, `string`
#### dev_data_external_v1.jsonl
- `prompt`: prompt, `string`
- `answers`: list of answers
- `answer`: answer, `string`
- `score`: score of answer, `int`
- `prefix`: prefix to the answer, `string`
### Data Splits
| name | train |
|----------|-------:|
|train_data_external_v1.jsonl|5477982|
|dev_data_external_v1.jsonl|10000|
## Dataset Creation
### Curation Rationale
Link to github: [data_prepare](https://github.com/sunzeyeah/RLHF/blob/master/src/data_prepare.py)
### Source Data
#### Initial Data Collection and Normalization
- [百科](https://github.com/brightmart/nlp_chinese_corpus)
- [知道问答](https://github.com/SophonPlus/ChineseNlpCorpus)
- [对联](https://github.com/wb14123/couplet-dataset/releases/download/1.0/couplet.tar.gz)
- [古文](https://github.com/NiuTrans/Classical-Modern)
- [古诗词](https://github.com/chinese-poetry/chinese-poetry)
- 微博新闻评论
#### 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)
|
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] |
mstz/steel_plates
|
mstz
| 2023-04-16T18:03:07Z | 39 | 1 |
[
"task_categories:tabular-classification",
"size_categories:1K<n<5K",
"language:en",
"license:cc",
"steel_plates",
"tabular_classification",
"binary_classification",
"multiclass_classification",
"UCI",
"region:us"
] |
[
"tabular-classification"
] | 2023-04-14T15:26:35Z |
---
language:
- en
tags:
- steel_plates
- tabular_classification
- binary_classification
- multiclass_classification
- UCI
pretty_name: Landsat
size_categories:
- 1K<n<5K
task_categories:
- tabular-classification
configs:
- steel_plates
- steel_plates_0
- steel_plates_1
- steel_plates_2
- steel_plates_3
- steel_plates_4
- steel_plates_5
- steel_plates_6
license: cc
---
# Landsat
The [Steel Plates dataset](https://archive-beta.ics.uci.edu/dataset/198/steel+plates+faults) from the [UCI repository](https://archive-beta.ics.uci.edu/).
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-----------------------|---------------------------|-------------------------|
| steel_plates | Multiclass classification.| |
| steel_plates_0 | Binary classification. | Is the input of class 0? |
| steel_plates_1 | Binary classification. | Is the input of class 1? |
| steel_plates_2 | Binary classification. | Is the input of class 2? |
| steel_plates_3 | Binary classification. | Is the input of class 3? |
| steel_plates_4 | Binary classification. | Is the input of class 4? |
| steel_plates_5 | Binary classification. | Is the input of class 5? |
| steel_plates_6 | Binary classification. | Is the input of class 6? |
|
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houck2040/engineering
|
houck2040
| 2023-04-18T15:06:30Z | 39 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-04-18T14:56:31Z |
---
license: mit
---
Data comes from Published Texas A&M Engineering News and was used to train a MLM @3epochs 500
This messages was not generated by AI
|
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sandhyasachidanandan/workshop-path-dataset
|
sandhyasachidanandan
| 2023-04-26T19:29:25Z | 39 | 0 |
[
"region:us"
] | null | 2023-04-26T19:29:21Z |
---
dataset_info:
features:
- name: product_name
dtype: string
- name: product_description
dtype: string
- name: category_path
dtype: string
splits:
- name: train
num_bytes: 26820
num_examples: 100
download_size: 6735
dataset_size: 26820
---
# Dataset Card for "workshop-path-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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yuchenlin/G-PlanET
|
yuchenlin
| 2023-07-15T07:33:33Z | 39 | 3 |
[
"task_categories:text-generation",
"task_categories:table-to-text",
"task_categories:table-question-answering",
"language:en",
"license:apache-2.0",
"arxiv:2209.00465",
"region:us"
] |
[
"text-generation",
"table-to-text",
"table-question-answering"
] | 2023-05-11T00:54:50Z |
---
task_categories:
- text-generation
- table-to-text
- table-question-answering
language:
- en
license: apache-2.0
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:** https://arxiv.org/abs/2209.00465
- **Leaderboard:**
- **Point of Contact:** [email protected]
### Dataset Summary
This **G-PlanET** dataset is built on AI2 [ALFRED](https://leaderboard.allenai.org/alfred/submissions/get-started).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
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wyxu/dataset_copied
|
wyxu
| 2023-05-25T07:45:47Z | 39 | 0 |
[
"task_categories:image-classification",
"size_categories:1K<n<10K",
"language:en",
"region:us"
] |
[
"image-classification"
] | 2023-05-23T03:55:20Z |
---
task_categories:
- image-classification
language:
- en
size_categories:
- 1K<n<10K
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
A copied data set from CIFAR10 as a demonstration
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
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] |
FanChen0116/syn_few7_7100_chat_all_data_pvi
|
FanChen0116
| 2023-06-01T02:38:40Z | 39 | 0 |
[
"region:us"
] | null | 2023-05-31T08:34:59Z |
---
dataset_info:
features:
- name: id
dtype: int64
- name: tokens
sequence: string
- name: labels
sequence:
class_label:
names:
'0': O
'1': I-time
'2': B-date
'3': B-last_name
'4': B-people
'5': I-date
'6': I-people
'7': I-last_name
'8': I-first_name
'9': B-first_name
'10': B-time
- name: request_slot
sequence: string
splits:
- name: train
num_bytes: 558759
num_examples: 3335
- name: validation
num_bytes: 646729
num_examples: 3731
- name: test
num_bytes: 646729
num_examples: 3731
download_size: 92716
dataset_size: 1852217
---
# Dataset Card for "syn_few7_7100_chat_all_data_pvi"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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CIRAL/ciral-corpus
|
CIRAL
| 2023-06-27T19:01:03Z | 39 | 0 |
[
"language:ha",
"language:so",
"language:sw",
"language:yo",
"license:apache-2.0",
"region:us"
] | null | 2023-06-01T20:05:01Z |
---
language:
- ha
- so
- sw
- yo
mutilinguality:
- multilingual
task-categories:
- text-retrieval
license: apache-2.0
viewer: true
---
# Dataset Summary
CIRAL is a collection for cross-lingual information retrieval research across four (4) African languages. The collection comprises English queries and query-passage relevance judgements manually annotated by native speakers.
This dataset stores passages which have been culled from news websites for CIRAL.
## Dataset Structure
This dataset is configured by language. An example of a passage data entry is
```json
{
'docid': 'DOCID#0#0',
'title': 'This is the title of a sample passage',
'text': 'This is the content of a sample passage',
'url': 'https:/\/\this-is-a-sample-url.com'
}
```
## Load Dataset
An example to load the dataset
```python
language = "hausa"
dataset = load_dataset("ciral/ciral-corpus", language)
```
## Citation
...
|
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] |
openaccess-ai-collective/chatbot-arena-elo-scores
|
openaccess-ai-collective
| 2023-06-23T19:57:16Z | 39 | 0 |
[
"region:us"
] | null | 2023-06-17T23:31:18Z |
---
dataset_info:
features:
- name: elo_score
dtype: float64
- name: chatbot_name
dtype: string
splits:
- name: train
num_bytes: 359
num_examples: 14
download_size: 1669
dataset_size: 359
---
# Dataset Card for "chatbot-arena-elo-scores"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
mcipriano/stackoverflow-kubernetes-questions
|
mcipriano
| 2023-10-10T18:21:03Z | 39 | 11 |
[
"task_categories:question-answering",
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-sa-4.0",
"Kubernetes",
"Stackoverflow",
"region:us"
] |
[
"question-answering",
"text-generation"
] | 2023-06-19T23:31:32Z |
---
license: cc-by-sa-4.0
task_categories:
- question-answering
- text-generation
language:
- en
tags:
- Kubernetes
- Stackoverflow
size_categories:
- 10K<n<100K
---
The purpose of this dataset is to provide the opportunity to perform any training, fine-tuning, etc. for any Language Model. In the 'data' folder, you will find the dataset in Parquet format, which is one of the formats used for these processes.
In case it may be useful for other purposes, I have also included the dataset in CSV format.
All data in this dataset were retrieved from the Stack Exchange network using the Stack Exchange Data explorer tool (https://github.com/StackExchange/StackExchange.DataExplorer). Specifically, the dataset contains all the Question-Answer pairs from Stack Overflow with Kubernetes tags. Specifically, in each Question-Answer pair, the Answer is the one with a positive and maximum score. Posts on Stack Overflow with negative scores have been excluded from the dataset.
|
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Romjiik/Russian_bank_reviews
|
Romjiik
| 2023-06-22T21:29:37Z | 39 | 1 |
[
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:ru",
"finance",
"region:us"
] |
[
"text-classification"
] | 2023-06-22T21:15:12Z |
---
task_categories:
- text-classification
language:
- ru
tags:
- finance
pretty_name: bank reviews
size_categories:
- 10K<n<100K
---
# Dataset Card for bank reviews dataset
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The dataset is collected from the [banki.ru](https://www.banki.ru/services/responses/list/?is_countable=on) website.
It contains customer reviews of various banks. In total, the dataset contains 12399 reviews.
The dataset is suitable for sentiment classification.
The dataset contains this fields - bank name, username, review title, review text, review time, number of views,
number of comments, review rating set by the user, as well as ratings for special categories
### Languages
Russian
|
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] |
zxbsmk/webnovel_cn
|
zxbsmk
| 2023-08-09T09:39:49Z | 39 | 42 |
[
"task_categories:text2text-generation",
"size_categories:10M<n<100M",
"language:zh",
"license:mit",
"doi:10.57967/hf/0877",
"region:us"
] |
[
"text2text-generation"
] | 2023-07-09T14:33:25Z |
---
license: mit
task_categories:
- text2text-generation
language:
- zh
size_categories:
- 10M<n<100M
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
---
## 内容
包含从12560本网文提取的约**21.7M**条可用于训练小说生成的中文指令数据(novel_json_tokens512.zip)。~~下载链接：https://pan.baidu.com/s/1TorBMbrqxrn6odRF0PJBVw
提取码：jlh3~~
以及从中提取出的包含**50k**条数据的子集(novel_cn_token512_50k.json)。其中输入和输出都不多于 512 tokens。
## 样例
在原有小说文本基础上，依据下列五种指令生成数据。
其中，文本由小说中随机抽取的连续句子组成。
1. 给定标题，直接生成简介。
2. 给定标题和简介，生成开头。
3. 给定简介和一段文本，生成后续文本。
4. 给定标题和一段文本，生成后续文本。
5. 给定一段文本，生成后续文本。
```
{
"instruction": "小说名：无限恐怖\n节选正文：\n“不行，中校，我们必须把这里的情况和这些虫族的拍摄画面传回地球去，是的，我们人类已经到了最危险的关头，地球上所有的工业重工业完全应该按照战时情况进行任务布置，我们人类的工业力根本不是虫族能够想象的，一年，不，半年我们就能有一万艘宇宙战舰升空，如果全球一共进行建造的话，我们一定……”一名少校参谋长看着那密密麻麻的宇宙虫海，他神色激动的对楚轩说道。\n楚轩默默咬了一口苹果，他说道：“问题一……一万艘宇宙战舰需要多少熟练的战舰船员？需要多少士兵在战舰上服役，半年里我们可以聚集齐全吗？问题二……虫族会给地球联邦政府留下半年的时间吗？一旦我们被消灭干净，则联邦政府将会随即遭受到这些宇宙虫族的太空袭击，直到我们的太空优势完全丧失之后，虫族就能放心移动陨石进行攻击，甚至是将大量陆地虫子带上地球，那时我们人类能够凭肉体与虫子对抗吗？问题三……我们现在该如何把信息传给地球？”\n这名参谋长顿时就不说话了，而旁边一名上尉忽然说道：“在探戈幽灵星上的七座要塞中，每一座要塞都有大功率粒子喷射联络器，这种联络器连我们自己的科技都无法屏蔽，我想虫子也应该不可能继续屏蔽吧，毕竟那只是粒子而已，不是电流信号这些……”续篇：",
"input": "",
"output": "楚轩咬了一口苹果说道：“没错，所以我们才要登陆到探戈幽灵星，这是我们的目的，同时为了保存宇宙舰队的战斗力，以最大的平原军事基地，风暴要塞为中心，所有舰队都要降落到地面上，然后层层布防，在援军到来前，在地球联邦政府做好了战争准备前，我们必须像颗钉子一样钉死在这里，让虫族无法忽视我们而去进攻地球……怎么样？我们很可能会死在这里，面对数十万，数百万，甚至数千万的虫子前扑后续的攻击，但是我们的名字一定会载入史册……把刚才记录下来的我的话发向所有舰队，然后告诉他们，作为逃命被自己人杀掉，或者是成为英雄回到地球，让他们明白我们的处境，然后自己去选择吧。”\n周围的参谋和军官们同时向楚轩四人敬了一礼，接着那参谋长激动的向某处平台走了过去。\n郑吒默默走到了楚轩身边，他小声的说道：“楚轩……你什么时候变得了那么会煽情了啊？”\n楚轩却是理所当然的说道：“将自己归于多数人的一边，以前的你不是这样评价我的吗？没错，将自己归于多数人的一边，这是做任何大事都要先完成的第一步……已经让他们知道命运和我们连接在了一起，接着就只需要好好的安排下局面与等待‘主神’的任务就可以了，时间还有三天……”\n时间还有三天，在当天中午的时候，舰队群的预警舰果然发现了在探戈幽灵星后方徘徊着另一颗巨大圆球，它仿佛卫星一样座落在探戈幽灵星的近地轨道上，而随着联合舰队的到来，这只巨大圆球上果然也飞出了数以万计的宇宙虫子，这下联合舰队果然却如楚轩的预言那般了，前有埋伏，后有追兵，唯一的一条路就只剩下降落到探戈幽灵星上了。"
},
{
"instruction": "给定小说简介和节选，续写小说",
"input": "小说简介：主人公郑吒自从失去了自己最亲密的青梅竹马后，对这种反复而又无聊的现代生活已经感到十分的厌倦。正在这时，他发现电脑屏幕上弹出了一段信息：“想明白生命的意义吗？想真正的……活着吗？”在按下YES后，一切都改变了。他进入了一个恐怖片的轮回世界——主神空间……在主神空间里，只有不停地变强、不停地进化，才能闯过那一关关的恐怖片，才能活下去。郑吒，怎样才能活下去？是杀死与自己对抗的所有人，走向孤独的王者之道？还是和自己的伙伴奋战到死，以仁义之道来度过劫难？其实，所有的一切也只是为了活下去。直到他们发现了主神空间的秘密……究竟……谁才是暗中真正的威胁？一切尽在无限恐怖！\n\n\n\n上半部：初始\n节选篇章：“什么叫作事情已经发展到了这一步！”郑吒大声的喧哗吼叫着，他冲着楚轩吼道：“你这个白痴！我一直都在重复着同样一句话，我们是同伴啊，为什么不可以事先把你的猜测或者布局告诉我们呢？每次都把我当成白痴一样，每次都在算计我之后轻描淡写的说一句我骗你的，你究竟把我当成什么人？”\n“呃……大概是可以欺骗的凡人智慧者吧。”楚轩想了一下直接说道。\n“……不要拦着我！王侠，伊莫顿，你们放开我！我要干掉这个白痴！”\n喧哗又一次发生了，片刻之后再次安静下来时，楚轩本已略微肿胀的脸终于是彻底肿胀了起来，而看他的样子依然是一丁点疼痛都感觉不到，依然悠闲的吃着黄瓜。\n“……总之，事情已经发展到了这一步……”郑吒摸着额头，很痛苦的说道：“那么我们必须先要得到王女鄂亚的关押位置所在，当然了，直接打进首都也其实也不是不可以，但是从原剧情来看，似乎只有那个名叫邪影者的魔法师与国王歌巴塔尔知道她的关押位置，为了不打草惊蛇，还是花些时间慢慢渗透进去查找线索吧。”\n“赵樱空，你负责潜行进王城内，能不杀人尽量不杀人，若是遇到危险就转换人格，或者联络我们……伊莫顿，你负责到王城外的村庄去打探消息，尽量不要引人注意，若是可以，使用你的法术控制些人也行，照刚才我说的，遇到危险可以杀人，也可以联络我们，楚轩，王侠，你们两人就在附近待机，一旦发现有任何情况都要提供支援。”",
"output": "众人都点着头，王侠忽然好奇的问道：“你呢？你打算干什么去？”\n“我会把整个王城的情况给摸清楚，关键是王城里可能隐藏着的力量，我很在意啊，伊莫顿的感觉从某些方面来讲比我们都更出色，毕竟他是死亡的生物……或许，这场恐怖片世界并不像我们所想的那样轻松呢。”郑吒说到这里时叹了口气，他接着又看向了那首都位置。\n“时间还有两天多，我们尽量在三天内完成这部恐怖片世界吧……希望另一边的幼龙能够赶快成长。”郑吒边说话，边驾驶绿魔滑板就向地面飞去，渐渐的，他离众人已经越来越远了。\n此刻，在离王城首都极遥远外的小村处，主角伊拉贡正极其狼狈的奔跑在树丛中，跟随在他身边的还有他的舅舅……非常不幸的，逃跑没多久，他的表哥就失散在了这片森林中，或者说是被那些士兵们给抓住了也说不定。\n更加不幸的是，那名中年武士明显已经落败，不然不会多出那么多士兵紧紧追着他们，比起在村庄的时候，士兵的数量又更加的多了，至少有十多名士兵在他们不远处紧紧追赶。\n“你到底偷了什么东西啊！为什么会有这么多士兵来追赶你呢？”伊拉贡的舅舅气喘吁吁的问道，他已经跑得没什么精力去发怒了。\n“……一个龙蛋，不是偷的，这是我从森林里拣来的！”伊拉贡虽然也是跑得筋疲力尽，但他还在坚持着最后的底线，依然不停辩解着。\n“龙蛋？那可是国王的东西啊！而且还是孵化出幼龙的龙蛋！你这个白痴，你这样会害死大家的！”伊拉贡的舅舅一听此话就气急败坏的叫道，但他还是不停向前跑去，不敢有丁点停顿，因为在他们背后不停的追赶着十多名士兵。\n“在那里！看到他们了！他们在那里！”"
}
```
## 字段：
```
instruction: 指令
input: 输入
output: 输出
```
## 使用限制
仅允许将此数据集及使用此数据集生成的衍生物用于研究目的，不得用于商业，以及其他会对社会带来危害的用途。
本数据集不代表任何一方的立场、利益或想法，无关任何团体的任何类型的主张。因使用本数据集带来的任何损害、纠纷，本项目不承担任何责任。
Join group via https://t.me/+JbovpBG6-gBiNDI1
|
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] |
pytorch-survival/gbsg_pycox
|
pytorch-survival
| 2023-07-12T01:53:49Z | 39 | 0 |
[
"region:us"
] | null | 2023-07-12T00:32:24Z |
---
dataset_info:
features:
- name: x0
dtype: float32
- name: x1
dtype: float32
- name: x2
dtype: float32
- name: x3
dtype: float32
- name: x4
dtype: float32
- name: x5
dtype: float32
- name: x6
dtype: float32
- name: event_time
dtype: float32
- name: event_indicator
dtype: int32
splits:
- name: train
num_bytes: 80352
num_examples: 2232
download_size: 34711
dataset_size: 80352
---
# Dataset Card for "gbsg_pycox"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
nlpkevinl/whatsthatbook
|
nlpkevinl
| 2023-08-15T07:29:24Z | 39 | 0 |
[
"task_categories:text-retrieval",
"language:en",
"license:odc-by",
"arxiv:2305.15053",
"region:us"
] |
[
"text-retrieval"
] | 2023-07-26T15:29:14Z |
---
license: odc-by
task_categories:
- text-retrieval
language:
- en
pretty_name: whatsthatbook
extra_gated_prompt: "To access this dataset, you agree to the terms and conditions from the GoodReads website stated here: https://www.goodreads.com/about/terms"
extra_gated_fields:
I agree to use to the terms and conditions: checkbox
---
# Dataset Card for WhatsThatBook
## Dataset Description
- **Paper: https://arxiv.org/abs/2305.15053**
- **Point of Contact: [email protected]**
### Dataset Summary
A collection of tip-of-the-tongue queries for book searches. The dataset was curated from GoodReads community forum user queries. It seves as a training and evaluation
resource for tip-of-the-tongue book queries. The user queries contain the interactions on the community forum and the documents are books with associated metadata.
### Supported Tasks and Leaderboards
WhatsThatBook is intended for information retrieval tasks including but not limited to standard retrieval, using just the original query posted by the user
and interactive settings, where the system asks clarification queries to narrow down the user's information needs.
### Languages
The dataset is primary in English, some book descriptions may contain other languages.
## Dataset Structure
### Data Fields
Data fields for WhatsThatBook queries:
- `question`: Inital query posted to the community forum
- `question_posted_date`: The date that the query was posted in YYYY-MM-DD format
- `book_id`: ID of the gold book used for evaluation
- `answers`: List of the gold book descriptions
The fields for the books:
- `title`: The title of the book
- `author`: The author of the book
- `author_url`: Link to the author page
- `description` The blurb of the book that contains description of the plot or
- `isbn_13`: The ISBN 13 number
- `date`: String representation of the date from the book webpage
- `parsed_dates`:A list of the publication date parsed out in YYYY-MM-DD format
- `image_link`: original link to image
- `ratings`: Total number of ratings
- `reviews`: Total number of reviews
- `genres`: Dictionary of genre tags to number of times tagged with that genre
- `id`: ID of the book, corresponding to the query file
### Data Splits
The dataset is comprised of two parts, WTB (WhatsThatBook), as well as TOMT (tip-of-my-tongue). WhatsThatBook contains standard train, dev, and test splits, and TOMT serves as an evaluation set.
## Dataset Creation
### Source Data
## Additional Information
### Dataset Curators
1. Kevin Lin, UC Berkeley, [email protected]
2. Kyle Lo, Allen Institue For Artificial Intelligence, [email protected]
### Citation Information
```
@article{lin2023decomposing,
title={Decomposing Complex Queries for Tip-of-the-tongue Retrieval},
author={Lin, Kevin and Lo, Kyle and Gonzalez, Joseph E and Klein, Dan},
journal={arXiv preprint arXiv:2305.15053},
year={2023}
}
```
|
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] |
lamini/text_to_sql_finetune
|
lamini
| 2023-09-01T05:39:56Z | 39 | 3 |
[
"region:us"
] | null | 2023-08-30T02:19:28Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 34818227
num_examples: 16428
- name: test
num_bytes: 1050788
num_examples: 1034
download_size: 3691335
dataset_size: 35869015
---
# Dataset Card for "text_to_sql_finetune"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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mickume/harry_potter_tiny
|
mickume
| 2023-08-30T12:46:15Z | 39 | 0 |
[
"region:us"
] | null | 2023-08-30T12:46:08Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1234764
num_examples: 7481
download_size: 747534
dataset_size: 1234764
---
# Dataset Card for "harrypotter_tiny"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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ZhenDOS/alpha_bank_data
|
ZhenDOS
| 2023-09-18T17:53:13Z | 39 | 0 |
[
"task_categories:conversational",
"language:ru",
"license:afl-3.0",
"finance",
"region:us"
] |
[
"conversational"
] | 2023-09-14T15:27:51Z |
---
license: afl-3.0
task_categories:
- conversational
language:
- ru
tags:
- finance
---
|
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mhenrichsen/context-aware-splits
|
mhenrichsen
| 2023-09-17T11:00:59Z | 39 | 3 |
[
"region:us"
] | null | 2023-09-17T11:00:56Z |
---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 35235257
num_examples: 12255
download_size: 20336185
dataset_size: 35235257
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "context-aware-splits"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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shossain/govreport-summarization-tokenized
|
shossain
| 2023-09-20T07:04:40Z | 39 | 0 |
[
"region:us"
] | null | 2023-09-20T06:19:21Z |
---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 69604
num_examples: 973
download_size: 22673
dataset_size: 69604
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "govreport-summarization-tokenized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
Aples/FineTune_Dataset_Aples_1K
|
Aples
| 2023-09-27T19:26:31Z | 39 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-09-27T19:23:20Z |
---
license: mit
---
|
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AlexHung29629/stack-exchange-paired-128K
|
AlexHung29629
| 2023-10-13T05:42:06Z | 39 | 0 |
[
"region:us"
] | null | 2023-10-13T04:07:53Z |
---
dataset_info:
features:
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 243412260
num_examples: 128000
download_size: 82603750
dataset_size: 243412260
---
# Dataset Card for "stack-exchange-paired-128K"
## token數
llama2: 97868021
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
paul-w-qs/churn_prediction
|
paul-w-qs
| 2023-10-13T11:13:38Z | 39 | 0 |
[
"region:us"
] | null | 2023-10-13T11:13:26Z |
---
dataset_info:
features:
- name: SALESFORCEACCOUNTID
dtype: string
- name: ACCOUNTING_MONTH
dtype: string
- name: CHURN
dtype: int64
- name: DOWNSELL
dtype: int64
- name: RENEWAL_MONTH
dtype: string
- name: CHURN_SUM
dtype: float64
- name: DOWNSELL_SUM
dtype: float64
- name: CONTRACT_START_DATE
dtype: string
- name: CONTRACT_END_DATE
dtype: string
- name: REGION
dtype: string
- name: TENURE_MONTHS
dtype: int64
- name: MONTHS_UNTIL_EVENT
dtype: int64
- name: DNB_GLOBAL_SALES_REVENUE
dtype: float64
- name: DNB_GLOBAL_EMPLOYEE_COUNT
dtype: int64
- name: DETECTEDSEATSCOUNT
dtype: float64
- name: PRODUCT_ONE
dtype: int64
- name: NUM_PRODUCTS_DAYS_LATE_PREV_90
dtype: int64
- name: LICENSINGSPECIALIST_CHANGE
dtype: int64
- name: CAR_HEALTH_CHECK
dtype: int64
- name: CROSS_SELL_RECENCY
dtype: int64
- name: SEATS_DOWNSELL_RECENCY
dtype: int64
- name: PRODUCT_TWO
dtype: int64
- name: PCT_PRODUCT_THREE_ENABLED
dtype: int64
- name: OTHER
dtype: int64
- name: CUSTOMER_BEHAVIOUR_ONE
dtype: int64
- name: CUSTOMER_BEHAVIOUR_TWO
dtype: int64
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dtype: int64
- name: PRODUCT_FIVE
dtype: int64
- name: PCT_PRODUCT_FIVE_ENABLED
dtype: int64
- name: MAX_SUPPORT_CASE_DAYSTOCLOSE
dtype: int64
- name: SUM_P4FLAG
dtype: int64
- name: PRODUCT_SIX
dtype: int64
- name: CUSTOMER_BEHAVIOUR_THREE
dtype: int64
- name: PRODUCT_THREE
dtype: int64
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dtype: int64
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dtype: float64
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dtype: int64
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dtype: float64
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- name: IMPLEMENTATION_MONTHS_RUNNING_TOTAL
dtype: int64
- name: CAR_CHURN_OR_RISK_DISCUSSION
dtype: int64
- name: PRODUCT_SIXTEEN
dtype: int64
- name: PRODUCT_SEVENTEEN
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- name: RATIO_SEATS_ACTIVE
dtype: float64
- name: MONTHLY_PRODUCT_COUNT
dtype: int64
- name: ARR
dtype: float64
- name: SUPPORT_CASE_NUMBEROFSLABREACHES
dtype: int64
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dtype: int64
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dtype: int64
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dtype: int64
- name: PRODUCT_TWENTYONE
dtype: int64
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dtype: int64
- name: PRODUCT_TWENTYTHREE
dtype: int64
- name: SUM_P0FLAG
dtype: int64
- name: SUM_P1FLAG
dtype: int64
- name: SUM_P2FLAG
dtype: int64
- name: SUM_P3FLAG
dtype: int64
- name: BACKLOG
dtype: int64
- name: AVG_SUPPORT_CASE_PRIORITY_SCORE
dtype: float64
- name: COMPETITOR_SEATS
dtype: int64
- name: RPU
dtype: float64
- name: SECTOR
dtype: string
- name: P0123_FLAGS
dtype: int64
- name: ARR_DIV_SEATS
dtype: float64
splits:
- name: train
num_bytes: 85464346
num_examples: 100000
download_size: 7102198
dataset_size: 85464346
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "churn_prediction"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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cis-lmu/GlotStoryBook
|
cis-lmu
| 2023-11-02T00:45:13Z | 39 | 1 |
[
"language:kwn",
"language:nno",
"language:mlg",
"language:miu",
"language:mhi",
"language:yua",
"language:dga",
"language:pol",
"language:sck",
"language:nuj",
"language:ben",
"language:san",
"language:luo",
"language:guz",
"language:hus",
"language:adh",
"language:lwg",
"language:lue",
"language:nhw",
"language:mer",
"language:lug",
"language:xsm",
"language:ell",
"language:rus",
"language:afr",
"language:ewe",
"language:yue",
"language:mnw",
"language:laj",
"language:myx",
"language:fra",
"language:adx",
"language:teo",
"language:cce",
"language:kln",
"language:hat",
"language:zne",
"language:srp",
"language:mmc",
"language:mal",
"language:fat",
"language:nyu",
"language:ndo",
"language:ven",
"language:hch",
"language:ssw",
"language:kqn",
"language:mhw",
"language:koo",
"language:prs",
"language:nso",
"language:yor",
"language:zho",
"language:naq",
"language:nle",
"language:mqu",
"language:lun",
"language:tuv",
"language:ocu",
"language:sme",
"language:kdj",
"language:alz",
"language:lit",
"language:spa",
"language:mfe",
"language:maz",
"language:tum",
"language:nhe",
"language:hun",
"language:dje",
"language:ori",
"language:swa",
"language:ron",
"language:her",
"language:urd",
"language:ttj",
"language:ktz",
"language:tur",
"language:kam",
"language:sag",
"language:kru",
"language:kok",
"language:toi",
"language:jpn",
"language:orm",
"language:rki",
"language:tsn",
"language:nep",
"language:tha",
"language:zul",
"language:ctu",
"language:khg",
"language:dag",
"language:pcm",
"language:keo",
"language:lko",
"language:amh",
"language:saq",
"language:jam",
"language:ara",
"language:kik",
"language:toh",
"language:kan",
"language:lgg",
"language:tam",
"language:aeb",
"language:ckb",
"language:deu",
"language:guj",
"language:ukr",
"language:tir",
"language:tet",
"language:mar",
"language:bxk",
"language:gur",
"language:vie",
"language:old",
"language:nch",
"language:kpz",
"language:xho",
"language:crk",
"language:ita",
"language:kmr",
"language:nyn",
"language:por",
"language:kri",
"language:gaa",
"language:hin",
"language:asm",
"language:mas",
"language:xog",
"language:khm",
"language:csw",
"language:nor",
"language:tgl",
"language:kin",
"language:luc",
"language:ful",
"language:sqi",
"language:kua",
"language:cat",
"language:tsc",
"language:pus",
"language:nld",
"language:kor",
"language:sot",
"language:mya",
"language:lat",
"language:bod",
"language:eng",
"language:nob",
"language:nzi",
"language:twi",
"language:hau",
"language:dan",
"language:kau",
"language:pan",
"language:swe",
"language:fas",
"language:som",
"language:tso",
"language:loz",
"language:anu",
"language:tel",
"language:ada",
"language:nbl",
"language:lsm",
"language:ach",
"language:bem",
"language:pmq",
"language:mat",
"language:gjn",
"language:nya",
"language:epo",
"license:cc",
"story ",
"storybook",
"language-identification",
"arxiv:2310.16248",
"arxiv:2309.13320",
"region:us"
] | null | 2023-10-13T11:13:40Z |
---
license: cc
language:
- kwn
- nno
- mlg
- miu
- mhi
- yua
- dga
- pol
- sck
- nuj
- ben
- san
- luo
- guz
- hus
- adh
- lwg
- lue
- nhw
- mer
- lug
- xsm
- ell
- rus
- afr
- ewe
- yue
- mnw
- laj
- myx
- fra
- adx
- teo
- cce
- kln
- hat
- zne
- srp
- mmc
- mal
- fat
- nyu
- ndo
- ven
- hch
- ssw
- kqn
- mhw
- koo
- prs
- nso
- yor
- zho
- naq
- nle
- mqu
- lun
- tuv
- ocu
- sme
- kdj
- alz
- lit
- spa
- mfe
- maz
- tum
- nhe
- hun
- dje
- ori
- swa
- ron
- her
- urd
- ttj
- ktz
- tur
- kam
- sag
- kru
- kok
- toi
- jpn
- orm
- rki
- tsn
- nep
- tha
- zul
- ctu
- khg
- dag
- pcm
- keo
- lko
- amh
- saq
- jam
- ara
- kik
- toh
- kan
- lgg
- tam
- aeb
- ckb
- deu
- guj
- ukr
- tir
- tet
- mar
- bxk
- gur
- vie
- old
- nch
- kpz
- xho
- crk
- ita
- kmr
- nyn
- por
- kri
- gaa
- hin
- asm
- mas
- xog
- khm
- csw
- nor
- tgl
- kin
- luc
- ful
- sqi
- kua
- cat
- tsc
- pus
- nld
- kor
- sot
- mya
- lat
- bod
- eng
- nob
- nzi
- twi
- hau
- dan
- kau
- pan
- swe
- fas
- som
- tso
- loz
- anu
- tel
- ada
- nbl
- lsm
- ach
- bem
- pmq
- mat
- gjn
- nya
- epo
pretty_name: GlotStoryBook Corpus
tags:
- 'story '
- storybook
- language-identification
---
## Dataset Description
Story Books for 180 ISO-639-3 script pairs (174 unique ISO-639-3 codes).
- **Homepage:** [homepage](https://github.com/cisnlp/GlotStoryBook)
- **Repository:** [github](https://github.com/cisnlp/GlotStoryBook)
- **Paper:** [paper](https://arxiv.org/abs/2310.16248)
- **Point of Contact:** [email protected]
## Usage (HF Loader)
```python
from datasets import load_dataset
dataset = load_dataset('cis-lmu/GlotStoryBook')
print(dataset['train'][0]) # First row data
```
## Download
If you are not a fan of the HF dataloader, download it directly:
```python
! wget https://huggingface.co/datasets/cis-lmu/GlotStoryBook/resolve/main/GlotStoryBook.csv
```
# Tools
To compute the script of each text we used Glotscript ([code](https://github.com/cisnlp/GlotScript) and [paper](https://arxiv.org/abs/2309.13320)).
## License and Copyright
We do not own any of the text from which these data has been extracted.
All the files are collected from the repository located at https://github.com/global-asp/.
The source repository for each text and file is stored in the dataset.
Each file in the dataset is associated with one license from the CC family.
The licenses include 'CC BY', 'CC BY-NC', 'CC BY-NC-SA', 'CC-BY', 'CC-BY-NC', and 'Public Domain'.
We also license the code, actual packaging and the metadata of these data under the cc0-1.0.
## Github
We additionally provide a GitHub version that openly shares the source code for processing this dataset:
https://github.com/cisnlp/GlotStoryBook
## Citation
If you use any part of this code and data in your research, please cite it (along with https://github.com/global-asp/) using the following BibTeX entry.
This work is part of the [GlotLID](https://github.com/cisnlp/GlotLID) project.
```
@inproceedings{
kargaran2023glotlid,
title={{GlotLID}: Language Identification for Low-Resource Languages},
author={Kargaran, Amir Hossein and Imani, Ayyoob and Yvon, Fran{\c{c}}ois and Sch{\"u}tze, Hinrich},
booktitle={The 2023 Conference on Empirical Methods in Natural Language Processing},
year={2023},
url={https://openreview.net/forum?id=dl4e3EBz5j}
}
```
|
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