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yangwang825/sst2-pwws | 2023-10-09T22:08:55.000Z | [
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kor_nli | 2023-04-05T10:09:06.000Z | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"task_ids:multi-input-text-classification",
"annotations_creators:crowdsourced",
"language_creators:machine-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|multi_nli",
"source_datasets:extended|snli",
"source_datasets:extended|xnli",
"language:ko",
"license:cc-by-sa-4.0",
"region:us"
] | null | Korean Natural Language Inference datasets | @article{ham2020kornli,
title={KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding},
author={Ham, Jiyeon and Choe, Yo Joong and Park, Kyubyong and Choi, Ilji and Soh, Hyungjoon},
journal={arXiv preprint arXiv:2004.03289},
year={2020}
} | 4 | 360 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- machine-generated
- expert-generated
language:
- ko
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|multi_nli
- extended|snli
- extended|xnli
task_categories:
- text-classification
task_ids:
- natural-language-inference
- multi-input-text-classification
paperswithcode_id: kornli
pretty_name: KorNLI
dataset_info:
- config_name: multi_nli
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
splits:
- name: train
num_bytes: 84729207
num_examples: 392702
download_size: 42113232
dataset_size: 84729207
- config_name: snli
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
splits:
- name: train
num_bytes: 80137097
num_examples: 550152
download_size: 42113232
dataset_size: 80137097
- config_name: xnli
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
splits:
- name: validation
num_bytes: 518830
num_examples: 2490
- name: test
num_bytes: 1047437
num_examples: 5010
download_size: 42113232
dataset_size: 1566267
---
# Dataset Card for "kor_nli"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/kakaobrain/KorNLUDatasets](https://github.com/kakaobrain/KorNLUDatasets)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 126.34 MB
- **Size of the generated dataset:** 166.43 MB
- **Total amount of disk used:** 292.77 MB
### Dataset Summary
Korean Natural Language Inference datasets.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### multi_nli
- **Size of downloaded dataset files:** 42.11 MB
- **Size of the generated dataset:** 84.72 MB
- **Total amount of disk used:** 126.85 MB
An example of 'train' looks as follows.
```
```
#### snli
- **Size of downloaded dataset files:** 42.11 MB
- **Size of the generated dataset:** 80.13 MB
- **Total amount of disk used:** 122.25 MB
An example of 'train' looks as follows.
```
```
#### xnli
- **Size of downloaded dataset files:** 42.11 MB
- **Size of the generated dataset:** 1.56 MB
- **Total amount of disk used:** 43.68 MB
An example of 'validation' looks as follows.
```
```
### Data Fields
The data fields are the same among all splits.
#### multi_nli
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
#### snli
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
#### xnli
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
### Data Splits
#### multi_nli
| |train |
|---------|-----:|
|multi_nli|392702|
#### snli
| |train |
|----|-----:|
|snli|550152|
#### xnli
| |validation|test|
|----|---------:|---:|
|xnli| 2490|5010|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
The dataset is licensed under Creative Commons [Attribution-ShareAlike license (CC BY-SA 4.0)](http://creativecommons.org/licenses/by-sa/4.0/).
### Citation Information
```
@article{ham2020kornli,
title={KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding},
author={Ham, Jiyeon and Choe, Yo Joong and Park, Kyubyong and Choi, Ilji and Soh, Hyungjoon},
journal={arXiv preprint arXiv:2004.03289},
year={2020}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset. | 7,879 | [
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THUIR/T2Ranking | 2023-07-03T08:46:32.000Z | [
"task_categories:text-retrieval",
"size_categories:1M<n<10M",
"language:zh",
"license:apache-2.0",
"arxiv:2304.03679",
"region:us"
] | THUIR | null | @misc{xie2023t2ranking,
title={T2Ranking: A large-scale Chinese Benchmark for Passage Ranking},
author={Xiaohui Xie and Qian Dong and Bingning Wang and Feiyang Lv and Ting Yao and Weinan Gan and Zhijing Wu and Xiangsheng Li and Haitao Li and Yiqun Liu and Jin Ma},
year={2023},
eprint={2304.03679},
archivePrefix={arXiv},
primaryClass={cs.IR}
} | 13 | 360 | 2023-04-04T07:15:08 | ---
license: apache-2.0
task_categories:
- text-retrieval
language:
- zh
size_categories:
- 1M<n<10M
---
# T<sup>2</sup>Ranking
## Introduction
T<sup>2</sup>Ranking is a large-scale Chinese benchmark for passage ranking. The details about T<sup>2</sup>Ranking are elaborated in [this paper](https://arxiv.org/abs/2304.03679#).
Passage ranking are important and challenging topics for both academics and industries in the area of Information Retrieval (IR). The goal of passage ranking is to compile a search result list ordered in terms of relevance to the query from a large passage collection. Typically, Passage ranking involves two stages: passage retrieval and passage re-ranking.
To support the passage ranking research, various benchmark datasets are constructed. However, the commonly-used datasets for passage ranking usually focus on the English language. For non-English scenarios, such as Chinese, the existing datasets are limited in terms of data scale, fine-grained relevance annotation and false negative issues.
To address this problem, we introduce T<sup>2</sup>Ranking, a large-scale Chinese benchmark for passage ranking. T<sup>2</sup>Ranking comprises more than 300K queries and over 2M unique passages from real- world search engines. Specifically, we sample question-based search queries from user logs of the Sogou search engine, a popular search system in China. For each query, we extract the content of corresponding documents from different search engines. After model-based passage segmentation and clustering-based passage de-duplication, a large-scale passage corpus is obtained. For a given query and its corresponding passages, we hire expert annotators to provide 4-level relevance judgments of each query-passage pair.
<div align=center><img width="600" height="200" src="https://github.com/THUIR/T2Ranking/blob/main/pic/stat.png?raw=true"/></div>
<div align=center>Table 1: The data statistics of datasets commonly used in passage ranking. FR(SR): First (Second)- stage of passage ranking, i.e., passage Retrieval (Re-ranking).</div>
Compared with existing datasets, T<sup>2</sup>Ranking dataset has the following characteristics and advantages:
* The proposed dataset focus on the Chinese search scenario, and has advantages in data scale compared with existing Chinese passage ranking datasets, which can better support the design of deep learning algorithms
* The proposed dataset has a large number of fine-grained relevance annotations, which is helpful for mining fine-grained relationship between queries and passages and constructing more accurate ranking algorithms.
* By retrieving passage results from multiple commercial search engines and providing complete annotation, we ease the false negative problem to some extent, which is beneficial to providing more accurate evaluation.
* We design multiple strategies to ensure the high quality of our dataset, such as using a passage segment model and a passage clustering model to enhance the semantic integrity and diversity of passages and employing active learning for annotation method to improve the efficiency and quality of data annotation.
## Data Download
The whole dataset is placed in [huggingface](https://huggingface.co/datasets/THUIR/T2Ranking), and the data formats are presented in the following table.
<div class="center">
| Description| Filename|Num Records|Format|
|-------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------|----------:|-----------------------------------:|
| Collection | collection.tsv | 2,303,643 | tsv: pid, passage |
| Queries Train | queries.train.tsv | 258,042 | tsv: qid, query |
| Queries Dev | queries.dev.tsv | 24,832 | tsv: qid, query |
| Queries Test | queries.test.tsv | 24,832 | tsv: qid, query |
| Qrels Train for re-ranking | qrels.train.tsv | 1,613,421 | TREC qrels format |
| Qrels Dev for re-ranking | qrels.dev.tsv | 400,536 | TREC qrels format |
| Qrels Retrieval Train | qrels.retrieval.train.tsv | 744,663 | tsv: qid, pid |
| Qrels Retrieval Dev | qrels.retrieval.dev.tsv | 118,933 | tsv: qid, pid |
| BM25 Negatives | train.bm25.tsv | 200,359,731 | tsv: qid, pid, index |
| Hard Negatives | train.mined.tsv | 200,376,001 | tsv: qid, pid, index, score |
</div>
You can download the dataset by running the following command:
```bash
git lfs install
git clone https://huggingface.co/datasets/THUIR/T2Ranking
```
After downloading, you can find the following files in the folder:
```
├── data
│ ├── collection.tsv
│ ├── qrels.dev.tsv
│ ├── qrels.retrieval.dev.tsv
│ ├── qrels.retrieval.train.tsv
│ ├── qrels.train.tsv
│ ├── queries.dev.tsv
│ ├── queries.test.tsv
│ ├── queries.train.tsv
│ ├── train.bm25.tsv
│ └── train.mined.tsv
├── script
│ ├── train_cross_encoder.sh
│ └── train_dual_encoder.sh
└── src
├── convert2trec.py
├── dataset_factory.py
├── modeling.py
├── msmarco_eval.py
├── train_cross_encoder.py
├── train_dual_encoder.py
└── utils.py
```
## Training and Evaluation
The dual-encoder can be trained by running the following command:
```bash
sh script/train_dual_encoder.sh
```
After training the model, you can evaluate the model by running the following command:
```bash
python src/msmarco_eval.py data/qrels.retrieval.dev.tsv output/res.top1000.step20
```
The cross-encoder can be trained by running the following command:
```bash
sh script/train_cross_encoder.sh
```
After training the model, you can evaluate the model by running the following command:
```bash
python src/convert2trec.py output/res.step-20 && python src/msmarco_eval.py data/qrels.retrieval.dev.tsv output/res.step-20.trec && path_to/trec_eval -m ndcg_cut.5 data/qrels.dev.tsv res.step-20.trec
```
BM25 on DEV set
```bash
#####################
MRR @10: 0.35894801237316354
QueriesRanked: 24831
recall@1: 0.05098711868967141
recall@1000: 0.7464097131133757
recall@50: 0.4942572226146033
#####################
```
DPR w/o hard negatives on DEV set
```bash
#####################
MRR @10: 0.4856112079562753
QueriesRanked: 24831
recall@1: 0.07367235058688999
recall@1000: 0.9082753169878586
recall@50: 0.7099350889583964
#####################
```
DPR w/ hard negatives on DEV set
```bash
#####################
MRR @10: 0.5166915171959451
QueriesRanked: 24831
recall@1: 0.08047455688965123
recall@1000: 0.9135220125786163
recall@50: 0.7327044025157232
#####################
```
BM25 retrieved+CE reranked on DEV set
```bash
#####################
MRR @10: 0.5188107959009376
QueriesRanked: 24831
recall@1: 0.08545219116806242
recall@1000: 0.7464097131133757
recall@50: 0.595298153566744
#####################
ndcg_cut_20 all 0.4405
ndcg_cut_100 all 0.4705
#####################
```
DPR retrieved+CE reranked on DEV set
```bash
#####################
MRR @10: 0.5508822816845231
QueriesRanked: 24831
recall@1: 0.08903406988867588
recall@1000: 0.9135220125786163
recall@50: 0.7393720781623112
#####################
ndcg_cut_20 all 0.5131
ndcg_cut_100 all 0.5564
#####################
```
## License
The dataset is licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0.html).
## Citation
If you use this dataset in your research, please cite our paper:
```
@misc{xie2023t2ranking,
title={T2Ranking: A large-scale Chinese Benchmark for Passage Ranking},
author={Xiaohui Xie and Qian Dong and Bingning Wang and Feiyang Lv and Ting Yao and Weinan Gan and Zhijing Wu and Xiangsheng Li and Haitao Li and Yiqun Liu and Jin Ma},
year={2023},
eprint={2304.03679},
archivePrefix={arXiv},
primaryClass={cs.IR}
}
```
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akoksal/LongForm | 2023-09-19T20:20:39.000Z | [
"task_categories:text2text-generation",
"task_categories:text-generation",
"task_categories:question-answering",
"task_categories:conversational",
"task_categories:summarization",
"task_categories:table-to-text",
"size_categories:10K<n<100K",
"language:en",
"instruction-tuning",
"arxiv:2304.08460",
"region:us"
] | akoksal | null | null | 30 | 360 | 2023-04-16T21:41:37 | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
- name: source
dtype: string
- name: subset
dtype: string
splits:
- name: train
num_bytes: 63759065
num_examples: 23652
- name: validation
num_bytes: 6190242
num_examples: 2042
- name: test
num_bytes: 6080212
num_examples: 2045
download_size: 45525146
dataset_size: 76029519
task_categories:
- text2text-generation
- text-generation
- question-answering
- conversational
- summarization
- table-to-text
language:
- en
tags:
- instruction-tuning
pretty_name: longform
size_categories:
- 10K<n<100K
---
# LongForm
The LongForm dataset is created by leveraging English corpus
examples with augmented instructions. We select a
diverse set of human-written
documents from existing corpora such as C4 and
Wikipedia and generate instructions for the given
documents via LLMs. Then, we extend these examples with structured corpora examples such as Stack Exchange and WikiHow and task examples such as question answering, email writing, grammar error correction, story/poem generation, and text summarization.
## Distribution
The distribution of the LongForm dataset in terms of the source of examples is below. It contains examples generated from raw text corpora via LLMs, structured corpus examples, as well as various NLP task examples such as email writing, grammar error correction, story/poem generation, and text summarization.
| **Type** | **Source** | **Number of Examples** |
|------------------------|----------------|------------------------|
| **Corpora** | C4 | 10,000 |
| | Wikipedia | 5,000 |
| **Structured Corpora** | Stack Exchange | 4,380 |
| | WikiHow | 2,500 |
| **Tasks** | NIv2 | 3,684 |
| | Big Bench | 600 |
| | BEA-GEC | 1,203 |
| | Enron | 372 |
| **Total** | | 27,739 |
| | | |
| **Train** | | 23,652 |
| **Validation** | | 2,042 |
| **Test** | | 2,045 |
## Models
| | **All** | **Recipe Generation** | **ELI5** | **Writing Prompts** |
|-----------------------|---------|-----------------------------------|----------|---------------------|
| **T0++** | 10.9 | 18.7 | 3.8 | 10.2 |
| **Tk-Instruct** | 6.3 | 12.9* | 3.6 | 2.4 |
| **Flan-T5** | 10.6 | 20.9* | 3.5 | 7.4 |
| **Alpaca-LLaMA-7B** | 14.6 | 19.5 | 12.5 | 11.8 |
| **OPT-30B** | 11.1 | 18.6 | 12.2 | 2.6 |
| **[LongForm-T5-XL](https://huggingface.co/akoksal/LongForm-T5-XL)** | 16.3 | 20.2 | 18.3 | 10.6 |
| **[LongForm-OPT-2.7B](https://huggingface.co/akoksal/LongForm-OPT-2.7B)** | 17.8 | 15.5 | 17.9 | **19.9** |
| **[LongForm-OPT-6.7B](https://huggingface.co/akoksal/LongForm-OPT-6.7B)** | 17.7 | 16.9 | 17.2 | 19.0 |
| [**LongForm-LLaMA-7B**](https://huggingface.co/akoksal/LongForm-LLaMA-7B-diff)‡ | **19.7** | **21.7** | **18.6** | 18.9 |
‡: We can just release the difference between LongForm-LLaMA-7B and pretrained LLaMA-7B publicly due to restrictions of LLaMA models.
## Examples
| **Source** | **Instruction** | **Output**|
|------------------------|----------------|------------------------|
| C4 | What is the likelihood that Donald Trump's HHS secretary nominee, Alex Azar, will push policies that benefit pharmaceutical companies' pocketbooks? | His record suggests he’s unlikely to push pricing policies that would hit pharmaceutical companies’ pocketbooks.<br />President Donald Trump tweeted Monday that his nominee for HHS secretary, Alex Azar will “be a star for … lower drug prices!” But the record of the former top executive for Eli Lilly, which tripled the price of a top-selling insulin drug while he led its U.S. operation, suggests a different story.<br />Lilly is one of three drug companies targeted by a class-action lawsuit that accuses the company[...]<br />“This nominee has impeccable big pharma credentials but non-existent credentials when it comes to concerns on drug prices,” said Peter Welch, co-chair of the House Democrats Drug Pricing Task Force.
|Wikipedia | "Where is San Saba located in Rome, Italy?" Respond in 2 sentences. |San Saba is an ancient basilica church in Rome, Italy. It lies on the so-called "Piccolo Aventino", which is an area close to the ancient Aurelian Walls next to the Aventine Hill and Caelian Hill.|
## Dataset Description
- **Repository:** https://github.com/akoksal/LongForm
- **Paper:** https://arxiv.org/abs/2304.08460
- **Version:** v1.0 - April 18, 2023
- **Contact:** [Abdullatif Köksal](https://twitter.com/akoksal_)
## License
The LongForm project is subject to a MIT License with custom limitations for restrictions imposed by OpenAI (for the instruction generation part), as well as the license of language models (OPT, LLaMA, and T5).
## Citation
```
@misc{koksal2023longform,
title={LongForm: Optimizing Instruction Tuning for Long Text Generation with Corpus Extraction},
author={Abdullatif Köksal and Timo Schick and Anna Korhonen and Hinrich Schütze},
year={2023},
eprint={2304.08460},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | 6,005 | [
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llm-book/livedoor-news-corpus | 2023-09-30T08:44:39.000Z | [
"task_categories:summarization",
"size_categories:1K<n<10K",
"language:ja",
"news",
"region:us"
] | llm-book | null | null | 1 | 360 | 2023-06-21T07:16:52 | ---
task_categories:
- summarization
language:
- ja
tags:
- news
pretty_name: livedoor-news-corpus
size_categories:
- 1K<n<10K
---
# Dataset Card for llm-book/ner-wikinews-dataset
書籍『大規模言語モデル入門』で使用する、株式会社ロンウイットが提供する「livedoorニュースコーパス」によるデータセットです。
[オリジナルのサイト](https://www.rondhuit.com/download.html)と同じものを使用しています。
本コーパスは、NHN Japan株式会社が運営する「livedoor ニュース」のうち、下記のクリエイティブ・コモンズライセンスが適用されるニュース記事を収集し、可能な限りHTMLタグを取り除いて作成したものです。
### Licence
Attribution-NoDerivs 2.1 Japan (CC BY-ND 2.1 JP) License | 493 | [
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result-kand2-sdxl-wuerst-karlo/e395fcfb | 2023-09-15T15:42:19.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 360 | 2023-09-15T15:42:18 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 152
num_examples: 10
download_size: 1308
dataset_size: 152
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "e395fcfb"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/94daaaa5 | 2023-09-15T16:14:38.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 360 | 2023-09-15T16:14:36 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 198
num_examples: 10
download_size: 1363
dataset_size: 198
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "94daaaa5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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] |
pawlo2013/one_piece_dataset | 2023-10-28T13:45:08.000Z | [
"region:us"
] | pawlo2013 | null | null | 0 | 360 | 2023-10-27T06:34:37 | ---
dataset_info:
features:
- name: full_colour
dtype: image
- name: sketch
dtype: image
splits:
- name: train
num_bytes: 170204480.0
num_examples: 922
download_size: 170225532
dataset_size: 170204480.0
---
# Dataset Card for "one_piece_dataset"
<h1> This dataset contains 922 images taken from the one piece anime, with each row containing the coloured image and the sketch one. </h1>
<img alt='example images' src="./combined.png"/ >
<h2> Example Setup (the images are not normalized) </h2>
<code>
transform = Compose([
transforms.Resize((128, 128)),
transforms.ToTensor(),
transforms.Lambda(lambda t: (t * 2) - 1)
])
def train():
def transforms(examples):
examples["sketch_pixel_values"] = [transform(image.convert("RGB")) for image in examples["sketch"]]
examples["full_colour_pixel_values"] = [ transform(image.convert("RGB")) for image in examples["full_colour"]]
del examples["sketch"]
del examples["full_colour"]
return examples
dataset = load_dataset("pawlo2013/one_piece_dataset", split="train")
transformed_full_colour_dataset = dataset.with_transform(transforms)
dataloader = DataLoader(transformed_full_colour_dataset, batch_size=16, shuffle=True, num_workers=0)
</code> | 1,349 | [
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result-kand2-sdxl-wuerst-karlo/a350d62a | 2023-09-15T11:08:21.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 359 | 2023-09-15T11:08:20 | ---
dataset_info:
features:
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splits:
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num_examples: 10
download_size: 1365
dataset_size: 179
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "a350d62a"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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] |
result-kand2-sdxl-wuerst-karlo/e06f76e8 | 2023-09-15T18:17:10.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 359 | 2023-09-15T18:17:09 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 169
num_examples: 10
download_size: 1323
dataset_size: 169
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "e06f76e8"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/bbe01f48 | 2023-09-15T18:27:46.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 359 | 2023-09-15T18:27:45 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 217
num_examples: 10
download_size: 1377
dataset_size: 217
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "bbe01f48"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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aslg_pc12 | 2023-04-05T09:36:28.000Z | [
"task_categories:translation",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:translation",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ase",
"language:en",
"license:cc-by-nc-4.0",
"region:us"
] | null | A large synthetic collection of parallel English and ASL-Gloss texts.
There are two string features: text, and gloss. | @inproceedings{othman2012english,
title={English-asl gloss parallel corpus 2012: Aslg-pc12},
author={Othman, Achraf and Jemni, Mohamed},
booktitle={5th Workshop on the Representation and Processing of Sign Languages: Interactions between Corpus and Lexicon LREC},
year={2012}
} | 2 | 357 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
- expert-generated
language:
- ase
- en
language_creators:
- found
license:
- cc-by-nc-4.0
multilinguality:
- translation
pretty_name: English-ASL Gloss Parallel Corpus 2012
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: aslg-pc12
dataset_info:
features:
- name: gloss
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 13497111
num_examples: 87710
download_size: 12773431
dataset_size: 13497111
---
# Dataset Card for "aslg_pc12"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://achrafothman.net/site/asl-smt/](https://achrafothman.net/site/asl-smt/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 12.77 MB
- **Size of the generated dataset:** 13.50 MB
- **Total amount of disk used:** 26.27 MB
### Dataset Summary
Synthetic English-ASL Gloss Parallel Corpus 2012
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 12.77 MB
- **Size of the generated dataset:** 13.50 MB
- **Total amount of disk used:** 26.27 MB
An example of 'train' looks as follows.
```
{
"gloss": "WRITE STATEMENT AND DESC-ORAL QUESTION TABLE SEE MINUTE\n",
"text": "written statements and oral questions tabling see minutes\n"
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `gloss`: a `string` feature.
- `text`: a `string` feature.
### Data Splits
| name |train|
|-------|----:|
|default|87710|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@inproceedings{othman2012english,
title={English-asl gloss parallel corpus 2012: Aslg-pc12},
author={Othman, Achraf and Jemni, Mohamed},
booktitle={5th Workshop on the Representation and Processing of Sign Languages: Interactions between Corpus and Lexicon LREC},
year={2012}
}
```
### Contributions
Thanks to [@AmitMY](https://github.com/AmitMY) for adding this dataset. | 5,728 | [
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] |
mteb/summeval | 2022-09-27T19:14:10.000Z | [
"language:en",
"region:us"
] | mteb | null | null | 2 | 357 | 2022-06-21T13:37:10 | ---
language:
- en
---
# SummEval
The annotations include summaries generated by 16 models from 100 source news articles (1600 examples in total).
Each of the summaries was annotated by 5 indepedent crowdsource workers and 3 independent experts (8 annotations in total).
Summaries were evaluated across 4 dimensions: coherence, consistency, fluency, relevance.
Each source news article comes with the original reference from the CNN/DailyMail dataset and 10 additional crowdsources reference summaries.
For this dataset, we averaged the 3 **expert** annotations to get the human scores.
source: https://github.com/Yale-LILY/SummEval | 635 | [
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result-kand2-sdxl-wuerst-karlo/1d35978a | 2023-09-16T01:30:03.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 357 | 2023-09-16T01:30:02 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 163
num_examples: 10
download_size: 1301
dataset_size: 163
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "1d35978a"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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] |
result-kand2-sdxl-wuerst-karlo/8e18a25b | 2023-09-16T15:18:38.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 357 | 2023-09-16T15:18:37 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 191
num_examples: 10
download_size: 1358
dataset_size: 191
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "8e18a25b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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asset | 2023-06-01T14:59:51.000Z | [
"task_categories:text-classification",
"task_categories:text2text-generation",
"task_ids:text-simplification",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"source_datasets:extended|other-turkcorpus",
"language:en",
"license:cc-by-sa-4.0",
"simplification-evaluation",
"region:us"
] | null | ASSET is a dataset for evaluating Sentence Simplification systems with multiple rewriting transformations,
as described in "ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations".
The corpus is composed of 2000 validation and 359 test original sentences that were each simplified 10 times by different annotators.
The corpus also contains human judgments of meaning preservation, fluency and simplicity for the outputs of several automatic text simplification systems. | @inproceedings{alva-manchego-etal-2020-asset,
title = "{ASSET}: {A} Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations",
author = "Alva-Manchego, Fernando and
Martin, Louis and
Bordes, Antoine and
Scarton, Carolina and
Sagot, Benoit and
Specia, Lucia",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.424",
pages = "4668--4679",
} | 9 | 356 | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- found
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
- extended|other-turkcorpus
task_categories:
- text-classification
- text2text-generation
task_ids:
- text-simplification
paperswithcode_id: asset
pretty_name: ASSET
tags:
- simplification-evaluation
dataset_info:
- config_name: simplification
features:
- name: original
dtype: string
- name: simplifications
sequence: string
splits:
- name: validation
num_bytes: 2303496
num_examples: 2000
- name: test
num_bytes: 411031
num_examples: 359
download_size: 3639353
dataset_size: 2714527
- config_name: ratings
features:
- name: original
dtype: string
- name: simplification
dtype: string
- name: original_sentence_id
dtype: int32
- name: aspect
dtype:
class_label:
names:
'0': meaning
'1': fluency
'2': simplicity
- name: worker_id
dtype: int32
- name: rating
dtype: int32
splits:
- name: full
num_bytes: 1036853
num_examples: 4500
download_size: 3639353
dataset_size: 1036853
config_names:
- ratings
- simplification
---
# Dataset Card for ASSET
## 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:** [ASSET Github repository](https://github.com/facebookresearch/asset)
- **Paper:** [ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations](https://www.aclweb.org/anthology/2020.acl-main.424/)
- **Point of Contact:** [Louis Martin](louismartincs@gmail.com)
### Dataset Summary
[ASSET](https://github.com/facebookresearch/asset) [(Alva-Manchego et al., 2020)](https://www.aclweb.org/anthology/2020.acl-main.424.pdf) is multi-reference dataset for the evaluation of sentence simplification in English. The dataset uses the same 2,359 sentences from [TurkCorpus]( https://github.com/cocoxu/simplification/) [(Xu et al., 2016)](https://www.aclweb.org/anthology/Q16-1029.pdf) and each sentence is associated with 10 crowdsourced simplifications. Unlike previous simplification datasets, which contain a single transformation (e.g., lexical paraphrasing in TurkCorpus or sentence
splitting in [HSplit](https://www.aclweb.org/anthology/D18-1081.pdf)), the simplifications in ASSET encompass a variety of rewriting transformations.
### Supported Tasks and Leaderboards
The dataset supports the evaluation of `text-simplification` systems. Success in this tasks is typically measured using the [SARI](https://huggingface.co/metrics/sari) and [FKBLEU](https://huggingface.co/metrics/fkbleu) metrics described in the paper [Optimizing Statistical Machine Translation for Text Simplification](https://www.aclweb.org/anthology/Q16-1029.pdf).
### Languages
The text in this dataset is in English (`en`).
## Dataset Structure
### Data Instances
- `simplification` configuration: an instance consists in an original sentence and 10 possible reference simplifications.
- `ratings` configuration: a data instance consists in an original sentence, a simplification obtained by an automated system, and a judgment of quality along one of three axes by a crowd worker.
### Data Fields
- `original`: an original sentence from the source datasets
- `simplifications`: in the `simplification` config, a set of reference simplifications produced by crowd workers.
- `simplification`: in the `ratings` config, a simplification of the original obtained by an automated system
- `aspect`: in the `ratings` config, the aspect on which the simplification is evaluated, one of `meaning`, `fluency`, `simplicity`
- `rating`: a quality rating between 0 and 100
### Data Splits
ASSET does not contain a training set; many models use [WikiLarge](https://github.com/XingxingZhang/dress) (Zhang and Lapata, 2017) for training.
Each input sentence has 10 associated reference simplified sentences. The statistics of ASSET are given below.
| | Dev | Test | Total |
| ----- | ------ | ---- | ----- |
| Input Sentences | 2000 | 359 | 2359 |
| Reference Simplifications | 20000 | 3590 | 23590 |
The test and validation sets are the same as those of TurkCorpus. The split was random.
There are 19.04 tokens per reference on average (lower than 21.29 and 25.49 for TurkCorpus and HSplit, respectively). Most (17,245) of the referece sentences do not involve sentence splitting.
## Dataset Creation
### Curation Rationale
ASSET was created in order to improve the evaluation of sentence simplification. It uses the same input sentences as the [TurkCorpus]( https://github.com/cocoxu/simplification/) dataset from [(Xu et al., 2016)](https://www.aclweb.org/anthology/Q16-1029.pdf). The 2,359 input sentences of TurkCorpus are a sample of "standard" (not simple) sentences from the [Parallel Wikipedia Simplification (PWKP)](https://www.informatik.tu-darmstadt.de/ukp/research_6/data/sentence_simplification/simple_complex_sentence_pairs/index.en.jsp) dataset [(Zhu et al., 2010)](https://www.aclweb.org/anthology/C10-1152.pdf), which come from the August 22, 2009 version of Wikipedia. The sentences of TurkCorpus were chosen to be of similar length [(Xu et al., 2016)](https://www.aclweb.org/anthology/Q16-1029.pdf). No further information is provided on the sampling strategy.
The TurkCorpus dataset was developed in order to overcome some of the problems with sentence pairs from Standard and Simple Wikipedia: a large fraction of sentences were misaligned, or not actually simpler [(Xu et al., 2016)](https://www.aclweb.org/anthology/Q16-1029.pdf). However, TurkCorpus mainly focused on *lexical paraphrasing*, and so cannot be used to evaluate simplifications involving *compression* (deletion) or *sentence splitting*. HSplit [(Sulem et al., 2018)](https://www.aclweb.org/anthology/D18-1081.pdf), on the other hand, can only be used to evaluate sentence splitting. The reference sentences in ASSET include a wider variety of sentence rewriting strategies, combining splitting, compression and paraphrasing. Annotators were given examples of each kind of transformation individually, as well as all three transformations used at once, but were allowed to decide which transformations to use for any given sentence.
An example illustrating the differences between TurkCorpus, HSplit and ASSET is given below:
> **Original:** He settled in London, devoting himself chiefly to practical teaching.
>
> **TurkCorpus:** He rooted in London, devoting himself mainly to practical teaching.
>
> **HSplit:** He settled in London. He devoted himself chiefly to practical teaching.
>
> **ASSET:** He lived in London. He was a teacher.
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
The input sentences are from English Wikipedia (August 22, 2009 version). No demographic information is available for the writers of these sentences. However, most Wikipedia editors are male (Lam, 2011; Graells-Garrido, 2015), which has an impact on the topics covered (see also [the Wikipedia page on Wikipedia gender bias](https://en.wikipedia.org/wiki/Gender_bias_on_Wikipedia)). In addition, Wikipedia editors are mostly white, young, and from the Northern Hemisphere [(Wikipedia: Systemic bias)](https://en.wikipedia.org/wiki/Wikipedia:Systemic_bias).
Reference sentences were written by 42 workers on Amazon Mechanical Turk (AMT). The requirements for being an annotator were:
- Passing a Qualification Test (appropriately simplifying sentences). Out of 100 workers, 42 passed the test.
- Being a resident of the United States, United Kingdom or Canada.
- Having a HIT approval rate over 95%, and over 1000 HITs approved.
No other demographic or compensation information is provided in the ASSET paper.
### Annotations
#### Annotation process
The instructions given to the annotators are available [here](https://github.com/facebookresearch/asset/blob/master/crowdsourcing/AMT_AnnotationInstructions.pdf).
#### 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
The dataset may contain some social biases, as the input sentences are based on Wikipedia. Studies have shown that the English Wikipedia contains both gender biases (Schmahl et al., 2020) and racial biases (Adams et al., 2019).
> Adams, Julia, Hannah Brückner, and Cambria Naslund. "Who Counts as a Notable Sociologist on Wikipedia? Gender, Race, and the “Professor Test”." Socius 5 (2019): 2378023118823946.
> Schmahl, Katja Geertruida, et al. "Is Wikipedia succeeding in reducing gender bias? Assessing changes in gender bias in Wikipedia using word embeddings." Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science. 2020.
### Other Known Limitations
Dataset provided for research purposes only. Please check dataset license for additional information.
## Additional Information
### Dataset Curators
ASSET was developed by researchers at the University of Sheffield, Inria,
Facebook AI Research, and Imperial College London. The work was partly supported by Benoît Sagot's chair in the PRAIRIE institute, funded by the French National Research Agency (ANR) as part of the "Investissements d’avenir" program (reference ANR-19-P3IA-0001).
### Licensing Information
[Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/)
### Citation Information
```
@inproceedings{alva-manchego-etal-2020-asset,
title = "{ASSET}: {A} Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations",
author = "Alva-Manchego, Fernando and
Martin, Louis and
Bordes, Antoine and
Scarton, Carolina and
Sagot, Beno{\^\i}t and
Specia, Lucia",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.424",
pages = "4668--4679",
}
```
This dataset card uses material written by [Juan Diego Rodriguez](https://github.com/juand-r).
### Contributions
Thanks to [@yjernite](https://github.com/yjernite) for adding this dataset. | 11,647 | [
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result-kand2-sdxl-wuerst-karlo/a48196ad | 2023-09-16T10:13:26.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 356 | 2023-09-16T10:13:25 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 155
num_examples: 10
download_size: 1306
dataset_size: 155
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "a48196ad"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/a2d1bcf0 | 2023-09-16T15:16:31.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 356 | 2023-09-16T15:16:30 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 220
num_examples: 10
download_size: 1379
dataset_size: 220
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "a2d1bcf0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/fbc48c23 | 2023-09-16T20:33:59.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 356 | 2023-09-16T20:33:58 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 169
num_examples: 10
download_size: 1322
dataset_size: 169
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "fbc48c23"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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] |
result-kand2-sdxl-wuerst-karlo/0dc6521d | 2023-09-16T23:57:15.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 356 | 2023-09-16T23:57:14 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 166
num_examples: 10
download_size: 1304
dataset_size: 166
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "0dc6521d"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/76e05263 | 2023-09-17T02:45:19.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 356 | 2023-09-17T02:45:19 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 197
num_examples: 10
download_size: 1361
dataset_size: 197
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "76e05263"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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smilegate-ai/kor_unsmile | 2022-03-27T12:29:28.000Z | [
"region:us"
] | smilegate-ai | null | null | 0 | 355 | 2022-03-27T12:24:17 | Entry not found | 15 | [
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cardiffnlp/tweet_topic_single | 2022-11-27T11:25:34.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"multilinguality:monolingual",
"size_categories:1k<10K",
"language:en",
"license:other",
"arxiv:2209.09824",
"region:us"
] | cardiffnlp | [TweetTopic](https://arxiv.org/abs/2209.09824) | @inproceedings{dimosthenis-etal-2022-twitter,
title = "{T}witter {T}opic {C}lassification",
author = "Antypas, Dimosthenis and
Ushio, Asahi and
Camacho-Collados, Jose and
Neves, Leonardo and
Silva, Vitor and
Barbieri, Francesco",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics"
} | 3 | 355 | 2022-09-02T00:20:17 | ---
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 1k<10K
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: TweetTopicSingle
---
# Dataset Card for "cardiffnlp/tweet_topic_single"
## Dataset Description
- **Paper:** [https://arxiv.org/abs/2209.09824](https://arxiv.org/abs/2209.09824)
- **Dataset:** Tweet Topic Dataset
- **Domain:** Twitter
- **Number of Class:** 6
### Dataset Summary
This is the official repository of TweetTopic (["Twitter Topic Classification
, COLING main conference 2022"](https://arxiv.org/abs/2209.09824)), a topic classification dataset on Twitter with 6 labels.
Each instance of TweetTopic comes with a timestamp which distributes from September 2019 to August 2021.
See [cardiffnlp/tweet_topic_multi](https://huggingface.co/datasets/cardiffnlp/tweet_topic_multi) for multi label version of TweetTopic.
The tweet collection used in TweetTopic is same as what used in [TweetNER7](https://huggingface.co/datasets/tner/tweetner7).
The dataset is integrated in [TweetNLP](https://tweetnlp.org/) too.
### Preprocessing
We pre-process tweets before the annotation to normalize some artifacts, converting URLs into a special token `{{URL}}` and non-verified usernames into `{{USERNAME}}`.
For verified usernames, we replace its display name (or account name) with symbols `{@}`.
For example, a tweet
```
Get the all-analog Classic Vinyl Edition
of "Takin' Off" Album from @herbiehancock
via @bluenoterecords link below:
http://bluenote.lnk.to/AlbumOfTheWeek
```
is transformed into the following text.
```
Get the all-analog Classic Vinyl Edition
of "Takin' Off" Album from {@herbiehancock@}
via {@bluenoterecords@} link below: {{URL}}
```
A simple function to format tweet follows below.
```python
import re
from urlextract import URLExtract
extractor = URLExtract()
def format_tweet(tweet):
# mask web urls
urls = extractor.find_urls(tweet)
for url in urls:
tweet = tweet.replace(url, "{{URL}}")
# format twitter account
tweet = re.sub(r"\b(\s*)(@[\S]+)\b", r'\1{\2@}', tweet)
return tweet
target = """Get the all-analog Classic Vinyl Edition of "Takin' Off" Album from @herbiehancock via @bluenoterecords link below: http://bluenote.lnk.to/AlbumOfTheWeek"""
target_format = format_tweet(target)
print(target_format)
'Get the all-analog Classic Vinyl Edition of "Takin\' Off" Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}'
```
### Data Splits
| split | number of texts | description |
|:------------------------|-----:|------:|
| test_2020 | 376 | test dataset from September 2019 to August 2020 |
| test_2021 | 1693 | test dataset from September 2020 to August 2021 |
| train_2020 | 2858 | training dataset from September 2019 to August 2020 |
| train_2021 | 1516 | training dataset from September 2020 to August 2021 |
| train_all | 4374 | combined training dataset of `train_2020` and `train_2021` |
| validation_2020 | 352 | validation dataset from September 2019 to August 2020 |
| validation_2021 | 189 | validation dataset from September 2020 to August 2021 |
| train_random | 2830 | randomly sampled training dataset with the same size as `train_2020` from `train_all` |
| validation_random | 354 | randomly sampled training dataset with the same size as `validation_2020` from `validation_all` |
| test_coling2022_random | 3399 | random split used in the COLING 2022 paper |
| train_coling2022_random | 3598 | random split used in the COLING 2022 paper |
| test_coling2022 | 3399 | temporal split used in the COLING 2022 paper |
| train_coling2022 | 3598 | temporal split used in the COLING 2022 paper |
For the temporal-shift setting, model should be trained on `train_2020` with `validation_2020` and evaluate on `test_2021`.
In general, model would be trained on `train_all`, the most representative training set with `validation_2021` and evaluate on `test_2021`.
**IMPORTANT NOTE:** To get a result that is comparable with the results of the COLING 2022 Tweet Topic paper, please use `train_coling2022` and `test_coling2022` for temporal-shift, and `train_coling2022_random` and `test_coling2022_random` fir random split (the coling2022 split does not have validation set).
### Models
| model | training data | F1 | F1 (macro) | Accuracy |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------|---------:|-------------:|-----------:|
| [cardiffnlp/roberta-large-tweet-topic-single-all](https://huggingface.co/cardiffnlp/roberta-large-tweet-topic-single-all) | all (2020 + 2021) | 0.896043 | 0.800061 | 0.896043 |
| [cardiffnlp/roberta-base-tweet-topic-single-all](https://huggingface.co/cardiffnlp/roberta-base-tweet-topic-single-all) | all (2020 + 2021) | 0.887773 | 0.79793 | 0.887773 |
| [cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-single-all](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-single-all) | all (2020 + 2021) | 0.892499 | 0.774494 | 0.892499 |
| [cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-single-all](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-single-all) | all (2020 + 2021) | 0.890136 | 0.776025 | 0.890136 |
| [cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-single-all](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-single-all) | all (2020 + 2021) | 0.894861 | 0.800952 | 0.894861 |
| [cardiffnlp/roberta-large-tweet-topic-single-2020](https://huggingface.co/cardiffnlp/roberta-large-tweet-topic-single-2020) | 2020 only | 0.878913 | 0.70565 | 0.878913 |
| [cardiffnlp/roberta-base-tweet-topic-single-2020](https://huggingface.co/cardiffnlp/roberta-base-tweet-topic-single-2020) | 2020 only | 0.868281 | 0.729667 | 0.868281 |
| [cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-single-2020](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m-tweet-topic-single-2020) | 2020 only | 0.882457 | 0.740187 | 0.882457 |
| [cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-single-2020](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2020-tweet-topic-single-2020) | 2020 only | 0.87596 | 0.746275 | 0.87596 |
| [cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-single-2020](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021-tweet-topic-single-2020) | 2020 only | 0.877732 | 0.746119 | 0.877732 |
Model fine-tuning script can be found [here](https://huggingface.co/datasets/cardiffnlp/tweet_topic_single/blob/main/lm_finetuning.py).
## Dataset Structure
### Data Instances
An example of `train` looks as follows.
```python
{
"text": "Game day for {{USERNAME}} U18\u2019s against {{USERNAME}} U18\u2019s. Even though it\u2019s a \u2018home\u2019 game for the people that have settled in Mid Wales it\u2019s still a 4 hour round trip for us up to Colwyn Bay. Still enjoy it though!",
"date": "2019-09-08",
"label": 4,
"id": "1170606779568463874",
"label_name": "sports_&_gaming"
}
```
### Label ID
The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/tweet_topic_single/raw/main/dataset/label.single.json).
```python
{
"arts_&_culture": 0,
"business_&_entrepreneurs": 1,
"pop_culture": 2,
"daily_life": 3,
"sports_&_gaming": 4,
"science_&_technology": 5
}
```
### Citation Information
```
@inproceedings{dimosthenis-etal-2022-twitter,
title = "{T}witter {T}opic {C}lassification",
author = "Antypas, Dimosthenis and
Ushio, Asahi and
Camacho-Collados, Jose and
Neves, Leonardo and
Silva, Vitor and
Barbieri, Francesco",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics"
}
``` | 8,526 | [
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result-kand2-sdxl-wuerst-karlo/b73eb60b | 2023-09-17T14:04:09.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 355 | 2023-09-17T14:04:08 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 206
num_examples: 10
download_size: 1380
dataset_size: 206
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "b73eb60b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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] |
result-kand2-sdxl-wuerst-karlo/00dbfb2c | 2023-09-17T14:41:51.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-17T14:41:51 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 240
num_examples: 10
download_size: 1450
dataset_size: 240
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "00dbfb2c"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/8edb1fe9 | 2023-09-18T04:48:26.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-18T04:48:25 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 258
num_examples: 10
download_size: 1429
dataset_size: 258
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "8edb1fe9"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/b2489367 | 2023-09-18T15:20:18.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-18T15:20:17 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 254
num_examples: 10
download_size: 1431
dataset_size: 254
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "b2489367"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/9bc865b4 | 2023-09-18T17:00:59.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-18T17:00:58 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 188
num_examples: 10
download_size: 1354
dataset_size: 188
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "9bc865b4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/4e6d4d01 | 2023-09-18T17:02:52.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-18T17:02:52 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 176
num_examples: 10
download_size: 1328
dataset_size: 176
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "4e6d4d01"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/b6ea8c05 | 2023-09-18T17:02:55.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-18T17:02:55 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 176
num_examples: 10
download_size: 1328
dataset_size: 176
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "b6ea8c05"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/908725e5 | 2023-09-19T00:19:24.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-19T00:19:23 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 161
num_examples: 10
download_size: 1318
dataset_size: 161
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "908725e5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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] |
result-kand2-sdxl-wuerst-karlo/e87ec3b2 | 2023-09-19T00:21:50.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-19T00:21:50 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 153
num_examples: 10
download_size: 1306
dataset_size: 153
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "e87ec3b2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/9e7f6f37 | 2023-09-19T00:24:03.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-19T00:24:02 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 152
num_examples: 10
download_size: 1303
dataset_size: 152
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "9e7f6f37"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/ad45b2bb | 2023-09-19T01:34:46.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-19T01:34:45 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 188
num_examples: 10
download_size: 1388
dataset_size: 188
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "ad45b2bb"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/36e1d427 | 2023-09-19T14:17:01.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-19T14:17:00 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 232
num_examples: 10
download_size: 1385
dataset_size: 232
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "36e1d427"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/a77d2949 | 2023-09-20T09:17:02.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-20T09:17:01 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 168
num_examples: 10
download_size: 1322
dataset_size: 168
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "a77d2949"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/df2d5286 | 2023-09-20T21:15:39.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-20T21:15:37 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 215
num_examples: 10
download_size: 1374
dataset_size: 215
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "df2d5286"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/9cc99eaf | 2023-09-20T21:15:42.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-20T21:15:41 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 215
num_examples: 10
download_size: 1374
dataset_size: 215
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "9cc99eaf"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/6845e847 | 2023-09-20T22:33:18.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-20T22:33:17 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 211
num_examples: 10
download_size: 1393
dataset_size: 211
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "6845e847"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/04554133 | 2023-09-21T19:46:51.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-21T19:46:51 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 167
num_examples: 10
download_size: 1328
dataset_size: 167
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "04554133"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/103deca7 | 2023-09-22T05:57:32.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-22T05:57:31 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 210
num_examples: 10
download_size: 1367
dataset_size: 210
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "103deca7"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/54ae8a8b | 2023-09-22T08:45:06.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-22T08:45:05 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 200
num_examples: 10
download_size: 1374
dataset_size: 200
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "54ae8a8b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/92f7fec0 | 2023-09-22T14:16:39.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-22T14:16:38 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 251
num_examples: 10
download_size: 1446
dataset_size: 251
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "92f7fec0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/d50de234 | 2023-09-22T15:13:31.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-22T15:13:30 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 198
num_examples: 10
download_size: 1368
dataset_size: 198
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "d50de234"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/af730738 | 2023-09-22T15:13:33.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-22T15:13:33 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 198
num_examples: 10
download_size: 1368
dataset_size: 198
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "af730738"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/03ada2d6 | 2023-09-22T15:13:35.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-22T15:13:35 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 198
num_examples: 10
download_size: 1368
dataset_size: 198
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "03ada2d6"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/837a21b8 | 2023-09-22T20:38:57.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-22T20:38:56 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 168
num_examples: 10
download_size: 1307
dataset_size: 168
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "837a21b8"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/9537a11b | 2023-09-23T00:58:26.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-23T00:58:25 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 168
num_examples: 10
download_size: 1356
dataset_size: 168
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "9537a11b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/083be228 | 2023-09-24T02:55:45.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-24T02:55:44 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 176
num_examples: 10
download_size: 1349
dataset_size: 176
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "083be228"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/0c7ab6c4 | 2023-09-25T03:27:54.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-25T03:27:53 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 166
num_examples: 10
download_size: 1324
dataset_size: 166
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "0c7ab6c4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/d39ae2e0 | 2023-09-26T01:14:35.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-26T01:14:34 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 169
num_examples: 10
download_size: 1317
dataset_size: 169
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "d39ae2e0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/6400c282 | 2023-09-26T01:34:38.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-26T01:34:37 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 165
num_examples: 10
download_size: 1313
dataset_size: 165
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "6400c282"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/db197d09 | 2023-09-26T15:16:30.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-26T15:16:30 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 170
num_examples: 10
download_size: 1327
dataset_size: 170
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "db197d09"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/7aa2df49 | 2023-09-26T16:34:15.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-26T16:34:14 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 172
num_examples: 10
download_size: 1339
dataset_size: 172
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "7aa2df49"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/1b874213 | 2023-09-26T16:50:27.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-26T16:50:26 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 161
num_examples: 10
download_size: 1306
dataset_size: 161
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "1b874213"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/02511ac7 | 2023-09-26T22:38:28.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-26T22:38:28 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 187
num_examples: 10
download_size: 1369
dataset_size: 187
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "02511ac7"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/c2696aec | 2023-09-26T23:48:54.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-26T23:48:54 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 169
num_examples: 10
download_size: 1324
dataset_size: 169
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "c2696aec"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/2af00e50 | 2023-09-27T02:03:14.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-27T02:03:14 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 206
num_examples: 10
download_size: 1404
dataset_size: 206
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "2af00e50"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/043bd500 | 2023-09-27T02:37:30.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-27T02:37:29 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 164
num_examples: 10
download_size: 1319
dataset_size: 164
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "043bd500"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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] |
result-kand2-sdxl-wuerst-karlo/812b079e | 2023-09-27T02:37:32.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-27T02:37:31 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 164
num_examples: 10
download_size: 1319
dataset_size: 164
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "812b079e"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/c9242d3b | 2023-09-27T04:27:02.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-27T04:27:01 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 220
num_examples: 10
download_size: 1395
dataset_size: 220
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "c9242d3b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/cc93a78b | 2023-09-27T06:17:25.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-27T06:17:24 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 239
num_examples: 10
download_size: 1420
dataset_size: 239
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "cc93a78b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/6155933b | 2023-09-27T13:21:58.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-27T13:21:57 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 215
num_examples: 10
download_size: 1402
dataset_size: 215
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "6155933b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/94c40829 | 2023-09-27T15:58:35.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-27T15:58:34 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 271
num_examples: 10
download_size: 1428
dataset_size: 271
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "94c40829"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/3ec30f64 | 2023-09-27T16:06:02.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-27T16:06:02 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 218
num_examples: 10
download_size: 1399
dataset_size: 218
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "3ec30f64"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/52b331a6 | 2023-09-27T18:08:07.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-27T18:08:07 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 189
num_examples: 10
download_size: 1383
dataset_size: 189
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "52b331a6"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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] |
result-kand2-sdxl-wuerst-karlo/7f0dfe44 | 2023-09-27T19:12:09.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-27T19:12:08 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 212
num_examples: 10
download_size: 1370
dataset_size: 212
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "7f0dfe44"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/463b7b19 | 2023-09-28T06:54:08.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-28T06:54:07 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 161
num_examples: 10
download_size: 1299
dataset_size: 161
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "463b7b19"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/694df328 | 2023-09-28T17:05:56.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-28T17:05:55 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 162
num_examples: 10
download_size: 1318
dataset_size: 162
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "694df328"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/80bca589 | 2023-09-28T22:53:52.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-28T22:53:51 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 242
num_examples: 10
download_size: 1409
dataset_size: 242
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "80bca589"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/dda30fff | 2023-09-28T22:55:08.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-28T22:55:07 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 164
num_examples: 10
download_size: 1316
dataset_size: 164
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "dda30fff"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/bfc3e463 | 2023-09-28T22:55:10.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-28T22:55:09 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 164
num_examples: 10
download_size: 1316
dataset_size: 164
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "bfc3e463"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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] |
result-kand2-sdxl-wuerst-karlo/c09c453b | 2023-09-29T10:08:13.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-29T10:08:13 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 195
num_examples: 10
download_size: 1353
dataset_size: 195
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "c09c453b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/ec996d80 | 2023-09-30T13:14:29.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-30T13:14:28 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 217
num_examples: 10
download_size: 1370
dataset_size: 217
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "ec996d80"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/289673e1 | 2023-09-30T13:18:28.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-30T13:18:27 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 168
num_examples: 10
download_size: 1327
dataset_size: 168
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "289673e1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/edc62945 | 2023-09-30T18:26:19.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-09-30T18:26:18 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 209
num_examples: 10
download_size: 1399
dataset_size: 209
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "edc62945"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/b776d96a | 2023-10-01T00:53:01.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-10-01T00:52:59 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 173
num_examples: 10
download_size: 1318
dataset_size: 173
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "b776d96a"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/39ceeb6b | 2023-10-01T00:53:02.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-10-01T00:53:02 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 173
num_examples: 10
download_size: 1318
dataset_size: 173
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "39ceeb6b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/dc1d52d8 | 2023-10-01T15:17:52.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-10-01T15:17:51 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 168
num_examples: 10
download_size: 1321
dataset_size: 168
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "dc1d52d8"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/5e4a199e | 2023-10-01T22:19:01.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-10-01T22:19:00 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 247
num_examples: 10
download_size: 1418
dataset_size: 247
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "5e4a199e"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/7a9ac406 | 2023-10-02T04:31:29.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-10-02T04:31:28 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 162
num_examples: 10
download_size: 1319
dataset_size: 162
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "7a9ac406"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/a9adf6d9 | 2023-10-02T09:28:03.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-10-02T09:28:02 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 182
num_examples: 10
download_size: 1395
dataset_size: 182
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "a9adf6d9"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/7162bca1 | 2023-10-02T12:26:33.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-10-02T12:26:32 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 227
num_examples: 10
download_size: 1422
dataset_size: 227
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "7162bca1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/8c351c30 | 2023-10-02T12:29:34.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-10-02T12:29:33 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 180
num_examples: 10
download_size: 1362
dataset_size: 180
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "8c351c30"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/38127251 | 2023-10-02T12:34:44.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-10-02T12:34:43 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 193
num_examples: 10
download_size: 1396
dataset_size: 193
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "38127251"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/cb0120f1 | 2023-10-02T12:45:34.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-10-02T12:45:33 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 232
num_examples: 10
download_size: 1452
dataset_size: 232
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "cb0120f1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/7fa2043a | 2023-10-02T13:22:28.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-10-02T13:22:27 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 194
num_examples: 10
download_size: 1397
dataset_size: 194
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "7fa2043a"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/6c022ac8 | 2023-10-02T13:25:18.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 354 | 2023-10-02T13:25:18 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 219
num_examples: 10
download_size: 1364
dataset_size: 219
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "6c022ac8"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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webis/tldr-17 | 2023-06-05T12:48:30.000Z | [
"task_categories:summarization",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"reddit-posts-summarization",
"region:us"
] | webis | This corpus contains preprocessed posts from the Reddit dataset.
The dataset consists of 3,848,330 posts with an average length of 270 words for content,
and 28 words for the summary.
Features includes strings: author, body, normalizedBody, content, summary, subreddit, subreddit_id.
Content is used as document and summary is used as summary. | @inproceedings{volske-etal-2017-tl,
title = {TL;DR: Mining {R}eddit to Learn Automatic Summarization},
author = {V{\"o}lske, Michael and Potthast, Martin and Syed, Shahbaz and Stein, Benno},
booktitle = {Proceedings of the Workshop on New Frontiers in Summarization},
month = {sep},
year = {2017},
address = {Copenhagen, Denmark},
publisher = {Association for Computational Linguistics},
url = {https://www.aclweb.org/anthology/W17-4508},
doi = {10.18653/v1/W17-4508},
pages = {59--63},
abstract = {Recent advances in automatic text summarization have used deep neural networks to generate high-quality abstractive summaries, but the performance of these models strongly depends on large amounts of suitable training data. We propose a new method for mining social media for author-provided summaries, taking advantage of the common practice of appending a {``}TL;DR{''} to long posts. A case study using a large Reddit crawl yields the Webis-TLDR-17 dataset, complementing existing corpora primarily from the news genre. Our technique is likely applicable to other social media sites and general web crawls.},
} | 26 | 353 | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
paperswithcode_id: webis-tldr-17-corpus
pretty_name: Reddit Webis-TLDR-17
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- summarization
task_ids: []
train-eval-index:
- config: default
task: summarization
task_id: summarization
splits:
train_split: train
col_mapping:
content: text
summary: target
metrics:
- type: rouge
name: Rouge
tags:
- reddit-posts-summarization
dataset_info:
features:
- name: author
dtype: string
- name: body
dtype: string
- name: normalizedBody
dtype: string
- name: subreddit
dtype: string
- name: subreddit_id
dtype: string
- name: id
dtype: string
- name: content
dtype: string
- name: summary
dtype: string
splits:
- name: train
num_bytes: 18940542951
num_examples: 3848330
download_size: 3141854161
dataset_size: 18940542951
---
# Dataset Card for Reddit Webis-TLDR-17
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://webis.de/data/webis-tldr-17.html](https://webis.de/data/webis-tldr-17.html)
- **Repository:** [https://github.com/webis-de/webis-tldr-17-corpus](https://github.com/webis-de/webis-tldr-17-corpus)
- **Paper:** [https://aclanthology.org/W17-4508]
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 3.14 GB
- **Size of the generated dataset:** 18.94 GB
- **Total amount of disk used:** 22.08 GB
### Dataset Summary
This corpus contains preprocessed posts from the Reddit dataset (Webis-TLDR-17).
The dataset consists of 3,848,330 posts with an average length of 270 words for content,
and 28 words for the summary.
Features includes strings: author, body, normalizedBody, content, summary, subreddit, subreddit_id.
Content is used as document and summary is used as summary.
### Supported Tasks and Leaderboards
Summarization (abstractive)
Known ROUGE scores achieved for the Webis-TLDR-17:
| Model | ROUGE-1 | ROUGE-2 | ROUGE-L | Paper/Source |
|-------|-------|-------|-------|------:|
| Transformer + Copy (Gehrmann et al., 2019) | 22 | 6 | 17 | Generating Summaries with Finetuned Language Models |
| Unified VAE + PGN (Choi et al., 2019) | 19 | 4 | 15 | VAE-PGN based Abstractive Model in Multi-stage Architecture for Text Summarization |
(Source: https://github.com/sebastianruder/NLP-progress/blob/master/english/summarization.md)
### Languages
English
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 3.14 GB
- **Size of the generated dataset:** 18.94 GB
- **Total amount of disk used:** 22.08 GB
An example of 'train' looks as follows.
```
{
"author": "me",
"body": "<>",
"content": "input document.",
"id": "1",
"normalizedBody": "",
"subreddit": "machinelearning",
"subreddit_id": "2",
"summary": "output summary."
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `author`: a `string` feature.
- `body`: a `string` feature.
- `normalizedBody`: a `string` feature.
- `subreddit`: a `string` feature.
- `subreddit_id`: a `string` feature.
- `id`: a `string` feature.
- `content`: a `string` feature.
- `summary`: a `string` feature.
### Data Splits
| name | train |
|-------|------:|
|default|3848330|
This corpus does not contain a separate test set. Thus it is up to the users to divide the corpus into appropriate training, validation and test sets.
## Dataset Creation
### Curation Rationale
In the scope of the task of absractive summarization the creators of the Webis-TLDR-17 propose mining social media for author-provided summaries and taking advantage of the common practice of appending a "TL;DR" to long posts. A large Reddit crawl was used to yield the Webis-TLDR-17 corpus. This dataset intends to complement the existing summarization corpora primarily from the news genre.
### Source Data
Reddit subreddits posts (submissions & comments) containing "TL;DR" from 2006 to 2016. Multiple subreddits are included.
#### Initial Data Collection and Normalization
Initial data: a set of 286 million submissions and 1.6 billion comments posted to Reddit between 2006 and 2016.
Then a five-step pipeline of consecutive filtering steps was applied.
#### Who are the source language producers?
The contents of the dataset are produced by human authors, bot-generated content was eliminated by filtering out all bot accounts with the help of an extensive list provided by the Reddit community, as well as manual inspection of cases where the user name contained the substring "bot."
### 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
This dataset has been created to serve as a source of large-scale summarization training data. It is primarily geared towards the automatic abstractive summarization task, that can be considered one of the most challenging variants of automatic summarization. It also aims to tackle the lack of genre diversity in the summarization datasets (most are news-related).
### 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
Reddit users write TL;DRs with various intentions, such as providing a “true” summary, asking questions or for help, or forming judgments and conclusions. As noted in the paper introducing the dataset, although the first kind of TL;DR posts are most important for training summarization models, yet, the latter allow for various alternative summarization-related tasks.
Although filtering was performed abusive language maybe still be present.
## Additional Information
### Dataset Curators
Michael Völske, Martin Potthast, Shahbaz Syed, Benno Stein
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@inproceedings{volske-etal-2017-tl,
title = "{TL};{DR}: Mining {R}eddit to Learn Automatic Summarization",
author = {V{"o}lske, Michael and
Potthast, Martin and
Syed, Shahbaz and
Stein, Benno},
booktitle = "Proceedings of the Workshop on New Frontiers in Summarization",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/W17-4508",
doi = "10.18653/v1/W17-4508",
pages = "59--63",
abstract = "Recent advances in automatic text summarization have used deep neural networks to generate high-quality abstractive summaries, but the performance of these models strongly depends on large amounts of suitable training data. We propose a new method for mining social media for author-provided summaries, taking advantage of the common practice of appending a {``}TL;DR{''} to long posts. A case study using a large Reddit crawl yields the Webis-TLDR-17 dataset, complementing existing corpora primarily from the news genre. Our technique is likely applicable to other social media sites and general web crawls.",
}
```
### Contributions
Thanks to [@mariamabarham](https://github.com/mariamabarham), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset. | 9,133 | [
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LysandreJik/glue-mnli-train | 2021-10-12T01:51:04.000Z | [
"region:us"
] | LysandreJik | null | null | 0 | 353 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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] |
thu-coai/Safety-Prompts | 2023-08-25T15:02:51.000Z | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:zh",
"license:apache-2.0",
"arxiv:2304.10436",
"region:us"
] | thu-coai | null | null | 30 | 353 | 2023-04-19T06:41:55 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- zh
pretty_name: Safety-Prompts
size_categories:
- 100K<n<1M
---
# Dataset Card for Dataset Name
GitHub Repository: https://github.com/thu-coai/Safety-Prompts
Paper: https://arxiv.org/abs/2304.10436
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bugdaryan/sql-create-context-instruction | 2023-09-07T15:22:00.000Z | [
"task_categories:text-generation",
"task_categories:question-answering",
"task_categories:table-question-answering",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-4.0",
"SQL",
"code",
"NLP",
"text-to-sql",
"context-sql",
"spider",
"wikisql",
"sqlglot",
"region:us"
] | bugdaryan | null | null | 3 | 353 | 2023-09-06T14:06:13 | ---
license: cc-by-4.0
task_categories:
- text-generation
- question-answering
- table-question-answering
language:
- en
tags:
- SQL
- code
- NLP
- text-to-sql
- context-sql
- spider
- wikisql
- sqlglot
pretty_name: sql-create-context
size_categories:
- 10K<n<100K
---
## Overview
This dataset is built upon [SQL Create Context](https://huggingface.co/datasets/b-mc2/sql-create-context), which in turn was constructed using data from [WikiSQL](https://huggingface.co/datasets/wikisql) and [Spider](https://huggingface.co/datasets/spider).
There are 78,577 examples of natural language queries, SQL CREATE TABLE statements, and SQL Query answering the question using the CREATE statement as context. This dataset was built with text-to-SQL LLMs in mind, intending to prevent hallucination of column and table names often seen when trained on text-to-SQL datasets. The CREATE TABLE statement can often be copy and pasted from different DBMS and provides table names, column names, and their data types. By providing just the CREATE TABLE statement as context, we can hopefully provide better grounding for models without having to provide actual rows of data, limiting token usage and exposure to private, sensitive, or proprietary data.
## Cleansing and Augmentation
Cleansing and data augmentation have been done on the combined WikiSQL and Spider data. [SQLGlot](https://github.com/tobymao/sqlglot) was used on queries from Spider and WikiSQL to parse them into different tables and columns. Column data types were inferred based on the usage of `>` `<` operators as well as the use of `MIN()`, `MAX()`, `AVG()`, and `SUM()` on columns. While this isn't perfect, it increases the likelihood of inferring the correct datatype for a column; otherwise, columns default to VARCHAR type. These tables and columns are then used to generate CREATE TABLE statements using the inferred types. SQLGlot is used again to ensure both the SQL queries and CREATE TABLE statements parse without errors.
Some queries that do not have column names, e.g., SELECT * FROM table, have a default Id column added to the CREATE TABLE statement. Some other queries which use the generic `table` as the FROM table have instead been changed to a variation of `table_name_1` or some other number which is also reflected in the CREATE TABLE statement.
## TODO
- Further augment the data by converting queries and CREATE TABLE statements into different SQL dialects. This can be done with SQLGlot. Reference to the dialect might also be added to the question.
**Random sample:**
```json
{
"text" : "[INST] Write SQLite query to answer the following question given the database schema. Please wrap your code answer using ```: Schema: CREATE TABLE head (age INTEGER) Question: How many heads of the departments are older than 56 ? [/INST] Here is the SQLite query to answer to the question: How many heads of the departments are older than 56 ?: ``` SELECT COUNT(*) FROM head WHERE age > 56 ```",
"text" : "[INST] Write SQLite query to answer the following question given the database schema. Please wrap your code answer using ```: Schema: CREATE TABLE head (name VARCHAR, born_state VARCHAR, age VARCHAR) Question: List the name, born state and age of the heads of departments ordered by age. [/INST] Here is the SQLite query to answer to the question: List the name, born state and age of the heads of departments ordered by age.: ``` SELECT name, born_state, age FROM head ORDER BY age ```"
}
```
The dataset was used to create code-llama-2 style prompts. The basic prompt template is:
```
[INST] Instruction/context [/INST]
Model output
``` | 3,625 | [
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result-kand2-sdxl-wuerst-karlo/4b9958b5 | 2023-09-19T02:29:31.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 353 | 2023-09-19T02:29:31 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 167
num_examples: 10
download_size: 1331
dataset_size: 167
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "4b9958b5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/ce65a06b | 2023-09-21T02:55:46.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 353 | 2023-09-21T02:55:45 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 187
num_examples: 10
download_size: 1357
dataset_size: 187
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "ce65a06b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/a95a2c5b | 2023-09-22T20:38:59.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 353 | 2023-09-22T20:38:59 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 168
num_examples: 10
download_size: 1307
dataset_size: 168
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "a95a2c5b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/86947388 | 2023-09-24T22:54:36.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 353 | 2023-09-24T22:54:35 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 168
num_examples: 10
download_size: 1325
dataset_size: 168
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "86947388"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/2b270b48 | 2023-09-25T03:43:57.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 353 | 2023-09-25T03:43:57 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 162
num_examples: 10
download_size: 1306
dataset_size: 162
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "2b270b48"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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] |
result-kand2-sdxl-wuerst-karlo/52f7d589 | 2023-09-26T06:35:54.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 353 | 2023-09-26T06:35:53 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 167
num_examples: 10
download_size: 1327
dataset_size: 167
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "52f7d589"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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] |
result-kand2-sdxl-wuerst-karlo/b6112e1b | 2023-09-26T16:29:48.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 353 | 2023-09-26T16:29:47 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 166
num_examples: 10
download_size: 1318
dataset_size: 166
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "b6112e1b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/16193144 | 2023-09-26T23:48:45.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 353 | 2023-09-26T23:48:45 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 169
num_examples: 10
download_size: 1324
dataset_size: 169
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "16193144"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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] |
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