author stringlengths 2 29 ⌀ | cardData null | citation stringlengths 0 9.58k ⌀ | description stringlengths 0 5.93k ⌀ | disabled bool 1 class | downloads float64 1 1M ⌀ | gated bool 2 classes | id stringlengths 2 108 | lastModified stringlengths 24 24 | paperswithcode_id stringlengths 2 45 ⌀ | private bool 2 classes | sha stringlengths 40 40 | siblings list | tags list | readme_url stringlengths 57 163 | readme stringlengths 0 977k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
yuningm | null | @misc{https://doi.org/10.48550/arxiv.2205.06207,
doi = {10.48550/ARXIV.2205.06207},
url = {https://arxiv.org/abs/2205.06207},
author = {Mao, Yuning and Zhong, Ming and Han, Jiawei},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {CiteSum: Citation Text-guided Scientific Extreme Summarization and Low-resource Domain Adaptation},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
} | CiteSum: Citation Text-guided Scientific Extreme Summarization and Low-resource Domain Adaptation.
CiteSum contains TLDR summaries for scientific papers from their citation texts without human annotation,
making it around 30 times larger than the previous human-curated dataset SciTLDR. | false | 10 | false | yuningm/citesum | 2022-10-25T10:39:26.000Z | citesum | false | 38139de09992c33d51f53531bbf3d575ca3e2e27 | [] | [
"arxiv:2205.06207",
"language:en",
"license:cc-by-nc-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:summarization"
] | https://huggingface.co/datasets/yuningm/citesum/resolve/main/README.md | ---
language:
- en
license: cc-by-nc-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- summarization
paperswithcode_id: citesum
---
# CiteSum
## Description
CiteSum: Citation Text-guided Scientific Extreme Summarization and Low-resource Domain Adaptation.
CiteSum contains TLDR summaries for scientific papers from their citation texts without human annotation, making it around 30 times larger than the previous human-curated dataset SciTLDR.
## Homepage
https://github.com/morningmoni/CiteSum
## Paper
https://arxiv.org/abs/2205.06207
## Authors
### Yuning Mao, Ming Zhong, Jiawei Han
#### University of Illinois Urbana-Champaign
{yuningm2, mingz5, hanj}@illinois.edu
## Dataset size
Train: 83304
Validation: 4721
Test: 4921
## Data details
- src (string): source text. long description of paper
- tgt (string): target text. tldr of paper
- paper_id (string): unique id for the paper
- title (string): title of the paper
- discipline (dict):
- venue (string): Where the paper was published (conference)
- journal (string): Journal in which the paper was published
- mag_field_of_study (list[str]): scientific fields that the paper falls under.
Example:
```
{
'src': 'We describe a convolutional neural network that learns feature representations for short textual posts using hashtags as a supervised signal. The proposed approach is trained on up to 5.5 billion words predicting 100,000 possible hashtags. As well as strong performance on the hashtag prediction task itself, we show that its learned representation of text (ignoring the hashtag labels) is useful for other tasks as well. To that end, we present results on a document recommendation task, where it also outperforms a number of baselines.',
'tgt': 'A convolutional neural network model for predicting hashtags was proposed in REF .',
'paper_id': '14697143',
'title': '#TagSpace: Semantic Embeddings from Hashtags',
'discipline': {
'venue': 'EMNLP',
'journal': None,
'mag_field_of_study': ['Computer Science']
}
}
```
## Using the dataset
```python
from datasets import load_dataset
ds = load_dataset("yuningm/citesum")
```
## Data location
https://drive.google.com/file/d/1ndHCREXGSPnDUNllladh9qCtayqbXAfJ/view
|
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-183be059-9075194 | 2022-06-29T20:26:38.000Z | null | false | bf7670076120164edc138d6394f6ea6820907de4 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:conll2003"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-183be059-9075194/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- conll2003
eval_info:
task: entity_extraction
model: dslim/bert-base-NER
metrics: []
dataset_name: conll2003
dataset_config: conll2003
dataset_split: test
col_mapping:
tokens: tokens
tags: ner_tags
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Token Classification
* Model: dslim/bert-base-NER
* Dataset: conll2003
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@aseifert](https://huggingface.co/aseifert) for evaluating this model. |
SerdarHelli | null | null | null | false | 5 | false | SerdarHelli/SegmentationOfTeethPanoramicXRayImages | 2022-10-29T20:05:26.000Z | null | false | da4e9f4db86e259f783e89a50fd8f811dfe3f257 | [] | [
"size_categories:n<1K",
"task_categories:image-segmentation",
"task_ids:semantic-segmentation",
"tags:teeth-segmentation",
"tags:dental-imaging",
"tags:medical-imaging"
] | https://huggingface.co/datasets/SerdarHelli/SegmentationOfTeethPanoramicXRayImages/resolve/main/README.md | ---
size_categories:
- n<1K
task_categories:
- image-segmentation
task_ids:
- semantic-segmentation
tags:
- teeth-segmentation
- dental-imaging
- medical-imaging
train-eval-index:
- config: plain_text
task: semantic_segmentation
task_id: semantic_segmentation
splits:
train_split: train
eval_split: test
col_mapping:
image: image
label: image
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/SerdarHelli/Segmentation-of-Teeth-in-Panoramic-X-ray-Image-Using-U-Net](https://github.com/SerdarHelli/Segmentation-of-Teeth-in-Panoramic-X-ray-Image-Using-U-Net)
- **Repository:** [https://github.com/SerdarHelli/Segmentation-of-Teeth-in-Panoramic-X-ray-Image-Using-U-Net](https://github.com/SerdarHelli/Segmentation-of-Teeth-in-Panoramic-X-ray-Image-Using-U-Net)
- **Paper:** [Tooth Instance Segmentation on Panoramic Dental Radiographs Using U-Nets and Morphological Processing](https://dergipark.org.tr/tr/pub/dubited/issue/68307/950568)
- **Leaderboard:**
- **Point of Contact:** S.Serdar Helli
### Dataset Summary
# Semantic-Segmentation-of-Teeth-in-Panoramic-X-ray-Image
The aim of this study is automatic semantic segmentation and measurement total length of teeth in one-shot panoramic x-ray image by using deep learning method with U-Net Model and binary image analysis in order to provide diagnostic information for the management of dental disorders, diseases, and conditions.
[***Github Link***](https://github.com/SerdarHelli/Segmentation-of-Teeth-in-Panoramic-X-ray-Image-Using-U-Net)
***Original Dataset For Only Images***
DATASET ref - H. Abdi, S. Kasaei, and M. Mehdizadeh, “Automatic segmentation of mandible in panoramic x-ray,” J. Med. Imaging, vol. 2, no. 4, p. 44003, 2015
[Link DATASET for only original images.](https://data.mendeley.com/datasets/hxt48yk462/1)
## Dataset Structure
### Data Instances
An example of 'train' looks as follows.
```
{
"image": X-ray Image (Image),
"label": Binary Image Segmentation Map (Image)
}
```
## Dataset Creation
### Source Data
***Original Dataset For Only Images***
DATASET ref - H. Abdi, S. Kasaei, and M. Mehdizadeh, “Automatic segmentation of mandible in panoramic x-ray,” J. Med. Imaging, vol. 2, no. 4, p. 44003, 2015
[Link DATASET for only original images.](https://data.mendeley.com/datasets/hxt48yk462/1)
### Annotations
#### Annotation process
The annotation was made manually.
#### Who are the annotators?
S.Serdar Helli
### Other Known Limitations
The X-Ray Images files associated with this dataset are licensed under a Creative Commons Attribution 4.0 International license.
To Check Out For More Information:
***Original Dataset For Only Images***
DATASET ref - H. Abdi, S. Kasaei, and M. Mehdizadeh, “Automatic segmentation of mandible in panoramic x-ray,” J. Med. Imaging, vol. 2, no. 4, p. 44003, 2015
[Link DATASET for only original images.](https://data.mendeley.com/datasets/hxt48yk462/1)
## Additional Information
### Citation Information
For Labelling
```
@article{helli10tooth,
title={Tooth Instance Segmentation on Panoramic Dental Radiographs Using U-Nets and Morphological Processing},
author={HELL{\.I}, Serdar and HAMAMCI, Anda{\c{c}}},
journal={D{\"u}zce {\"U}niversitesi Bilim ve Teknoloji Dergisi},
volume={10},
number={1},
pages={39--50}
}
```
For Original Images
```
@article{abdi2015automatic,
title={Automatic segmentation of mandible in panoramic x-ray},
author={Abdi, Amir Hossein and Kasaei, Shohreh and Mehdizadeh, Mojdeh},
journal={Journal of Medical Imaging},
volume={2},
number={4},
pages={044003},
year={2015},
publisher={SPIE}
}
```
### Contributions
Thanks to [@SerdarHelli](https://github.com/SerdarHelli) for adding this dataset. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-87e7c3be-9085195 | 2022-06-29T21:11:33.000Z | null | false | 51da51ef377f004e18152d6e02ed1e31eb2466d9 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:clinc_oos"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-87e7c3be-9085195/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- clinc_oos
eval_info:
task: multi_class_classification
model: dbounds/roberta-large-finetuned-clinc
metrics: []
dataset_name: clinc_oos
dataset_config: small
dataset_split: test
col_mapping:
text: text
target: intent
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: dbounds/roberta-large-finetuned-clinc
* Dataset: clinc_oos
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mxnno](https://huggingface.co/mxnno) for evaluating this model. |
davidberg | null | null | null | false | 1 | false | davidberg/inflation | 2022-06-29T21:57:10.000Z | null | false | f19a3041fde864693ebfa1a34337b0b62e055880 | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/davidberg/inflation/resolve/main/README.md | ---
license: apache-2.0
---
|
davidberg | null | null | null | false | 1 | false | davidberg/sentiment-reviews | 2022-06-29T22:38:11.000Z | null | false | c43462c3ad3938da22954e98f95a5cb506e1dd23 | [] | [
"license:postgresql"
] | https://huggingface.co/datasets/davidberg/sentiment-reviews/resolve/main/README.md | ---
license: postgresql
---
|
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-00ac2adb-9115197 | 2022-06-29T22:41:58.000Z | null | false | 74178ac21d8791035a616fd4f97bbd652b541c78 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:cifar10"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-00ac2adb-9115197/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- cifar10
eval_info:
task: image_multi_class_classification
model: abhishek/autotrain_cifar10_vit_base
metrics: []
dataset_name: cifar10
dataset_config: plain_text
dataset_split: test
col_mapping:
image: img
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Image Classification
* Model: abhishek/autotrain_cifar10_vit_base
* Dataset: cifar10
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@davidberg](https://huggingface.co/davidberg) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-00ac2adb-9115199 | 2022-06-29T22:42:09.000Z | null | false | c535f479a30ffc48bc48663ca86c6f20272e9219 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:cifar10"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-00ac2adb-9115199/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- cifar10
eval_info:
task: image_multi_class_classification
model: karthiksv/vit-base-patch16-224-cifar10
metrics: []
dataset_name: cifar10
dataset_config: plain_text
dataset_split: test
col_mapping:
image: img
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Image Classification
* Model: karthiksv/vit-base-patch16-224-cifar10
* Dataset: cifar10
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@davidberg](https://huggingface.co/davidberg) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-00ac2adb-9115200 | 2022-06-29T22:42:47.000Z | null | false | e0d72b4c4e3aa00dc38e60a88deac5c4b3c10312 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:cifar10"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-00ac2adb-9115200/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- cifar10
eval_info:
task: image_multi_class_classification
model: jimypbr/cifar10_outputs
metrics: []
dataset_name: cifar10
dataset_config: plain_text
dataset_split: test
col_mapping:
image: img
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Image Classification
* Model: jimypbr/cifar10_outputs
* Dataset: cifar10
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@davidberg](https://huggingface.co/davidberg) for evaluating this model. |
loubnabnl | null | null | null | false | 1 | false | loubnabnl/github-code-more-filtering | 2022-06-30T22:28:37.000Z | null | false | 7d4eca2f188da2be524a990684e5871f4b836b50 | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/loubnabnl/github-code-more-filtering/resolve/main/README.md | ---
license: apache-2.0
---
|
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-00ac2adb-9115202 | 2022-06-29T22:43:31.000Z | null | false | cc79d4ca52014f13aee22ec5c7872cebac96c9ed | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:cifar10"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-00ac2adb-9115202/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- cifar10
eval_info:
task: image_multi_class_classification
model: tanlq/vit-base-patch16-224-in21k-finetuned-cifar10
metrics: []
dataset_name: cifar10
dataset_config: plain_text
dataset_split: test
col_mapping:
image: img
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Image Classification
* Model: tanlq/vit-base-patch16-224-in21k-finetuned-cifar10
* Dataset: cifar10
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@davidberg](https://huggingface.co/davidberg) for evaluating this model. |
codeparrot | null | null | The GitHub Code clean dataset in a more filtered version of codeparrot/github-code dataset, it consists of 115M code files from GitHub in 32 programming languages with 60 extensions totaling in almost 1TB of text data. | false | 13,769 | false | codeparrot/github-code-clean | 2022-07-05T09:35:14.000Z | null | false | c48d40f9e70f0196f8236901ee35807f7d6c44c0 | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/codeparrot/github-code-clean/resolve/main/README.md | ---
license: apache-2.0
---
This is a cleaner version of [Github-code dataset](https://huggingface.co/datasets/codeparrot/github-code), we add the following filters:
* Average line length < 100
* Alpha numeric characters fraction > 0.25
* Remove auto-generated files (keyword search)
3.39M files are removed making up 2.94% of the dataset. |
launch | null | @inproceedings{cao-wang-2021-controllable,
title = "Controllable Open-ended Question Generation with A New Question Type Ontology",
author = "Cao, Shuyang and
Wang, Lu",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.502",
doi = "10.18653/v1/2021.acl-long.502",
pages = "6424--6439",
abstract = "We investigate the less-explored task of generating open-ended questions that are typically answered by multiple sentences. We first define a new question type ontology which differentiates the nuanced nature of questions better than widely used question words. A new dataset with 4,959 questions is labeled based on the new ontology. We then propose a novel question type-aware question generation framework, augmented by a semantic graph representation, to jointly predict question focuses and produce the question. Based on this framework, we further use both exemplars and automatically generated templates to improve controllability and diversity. Experiments on two newly collected large-scale datasets show that our model improves question quality over competitive comparisons based on automatic metrics. Human judges also rate our model outputs highly in answerability, coverage of scope, and overall quality. Finally, our model variants with templates can produce questions with enhanced controllability and diversity.",
} | Reddit question generation dataset. | false | 1 | false | launch/reddit_qg | 2022-11-09T01:58:05.000Z | null | false | 77859ef0ac63997f4e1a16f27cc4acbf8a06cc2f | [] | [
"annotations_creators:expert-generated",
"language:en",
"license:cc-by-4.0",
"multilinguality:monolingual",
"task_categories:text-classification"
] | https://huggingface.co/datasets/launch/reddit_qg/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
task_categories:
- text-classification
task_ids: []
pretty_name: RedditQG
---
# Dataset Card for RedditQG
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://shuyangcao.github.io/projects/ontology_open_ended_question/](https://shuyangcao.github.io/projects/ontology_open_ended_question/)
- **Repository:** [https://github.com/ShuyangCao/open-ended_question_ontology](https://github.com/ShuyangCao/open-ended_question_ontology)
- **Paper:** [https://aclanthology.org/2021.acl-long.502/](https://aclanthology.org/2021.acl-long.502/)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
This dataset contains answer-question pairs from QA communities of Reddit.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English
## Dataset Structure
### Data Instances
An example looks as follows.
```
{
"id": "askscience/123",
"qid": "2323",
"answer": "A test answer.",
"question": "A test question?",
"score": 20
}
```
### Data Fields
- `id`: a `string` feature.
- `qid`: a `string` feature. There could be multiple answers to the same question.
- `answer`: a `string` feature.
- `question`: a `string` feature.
- `score`: an `int` feature which is the value of `upvotes - downvotes`.
### Data Splits
- train: 647763
- valid: 36023
- test: 36202
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
Reddit users.
### Personal and Sensitive Information
Samples with abusive words are discarded, but there could be samples containing personal information.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
CC BY 4.0
### Citation Information
```
@inproceedings{cao-wang-2021-controllable,
title = "Controllable Open-ended Question Generation with A New Question Type Ontology",
author = "Cao, Shuyang and
Wang, Lu",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.502",
doi = "10.18653/v1/2021.acl-long.502",
pages = "6424--6439",
abstract = "We investigate the less-explored task of generating open-ended questions that are typically answered by multiple sentences. We first define a new question type ontology which differentiates the nuanced nature of questions better than widely used question words. A new dataset with 4,959 questions is labeled based on the new ontology. We then propose a novel question type-aware question generation framework, augmented by a semantic graph representation, to jointly predict question focuses and produce the question. Based on this framework, we further use both exemplars and automatically generated templates to improve controllability and diversity. Experiments on two newly collected large-scale datasets show that our model improves question quality over competitive comparisons based on automatic metrics. Human judges also rate our model outputs highly in answerability, coverage of scope, and overall quality. Finally, our model variants with templates can produce questions with enhanced controllability and diversity.",
}
```
|
Manuel | null | null | null | false | 1 | false | Manuel/sentencias-corte-cons-colombia-1992-2021 | 2022-06-30T02:49:09.000Z | null | false | 3b2e18d24afd6b82d6db4bb5552c64298e1ad8b2 | [] | [
"license:cc-by-4.0"
] | https://huggingface.co/datasets/Manuel/sentencias-corte-cons-colombia-1992-2021/resolve/main/README.md | ---
license: cc-by-4.0
---
sentencias-corte-cons-colombia-1992-2021.
23750 Case law of the Colombia's Corte Constitucional.
Each row is a complete text of each case law.
23750 case law from 1992-2021.
Columns:
ID
Texto: Complete text of the sentence |
superjordan | null | null | null | false | 4 | false | superjordan/insurance_qa | 2022-06-30T08:30:45.000Z | null | false | e24bf67e5967acd7359533aab1a406cbee4fc60a | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/superjordan/insurance_qa/resolve/main/README.md | ---
license: apache-2.0
---
|
Langame | null | null | null | false | 1 | false | Langame/langame-seeker | 2022-06-30T06:42:22.000Z | null | false | 094794ba406881792473d6d32a26ab95e41c1dfc | [] | [
"license:wtfpl"
] | https://huggingface.co/datasets/Langame/langame-seeker/resolve/main/README.md | ---
license: wtfpl
---
# langame-seeker
Self chat between two [Seeker Search-Augmented Language Model](https://parl.ai/projects/seeker/) using [Langame](https://langa.me/) conversation starters generated by Langame's proprietary language model. The 3000 conversation starters have been generated beforehand into an "offline" dataset and manually corrected and adjusted by psychologically and philosophically trained humans.
The search engine source code is unfortunately private yet, some work need to be done to make it open source. |
dddb | null | null | null | false | 1 | false | dddb/autotrain-data-mt5_chinese_small_finetune | 2022-06-30T12:59:06.000Z | null | false | b34049cde0f0d716b965b826b6e3ddbaae7fee48 | [] | [] | https://huggingface.co/datasets/dddb/autotrain-data-mt5_chinese_small_finetune/resolve/main/README.md | ---
task_categories:
- conditional-text-generation
---
# AutoTrain Dataset for project: mt5_chinese_small_finetune
## Dataset Descritpion
This dataset has been automatically processed by AutoTrain for project mt5_chinese_small_finetune.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "\u8fd1\u671f\uff0c\u7f8e\u56fd\u56fd\u4f1a\u4f17\u9662\u901a\u8fc7\u6cd5\u6848\uff0c\u91cd\u7533\u7f8e\u56fd\u5bf9\u53f0\u6e7e\u7684\u627f\u8bfa\u3002\u5bf9\u6b64\uff0c\u4e2d\u56fd\u5916\u4ea4\u90e8\u53d1\u8a00\u4eba\u8868\u793a\uff0c\u6709\u5173\u6cd5\u6848\u4e25\u91cd\u8fdd\u53cd\u4e00\u4e2a\u4e2d\u56fd\u539f\u5219\u548c\u4e2d\u7f8e\u4e09\u4e2a\u8054\u5408\u516c\u62a5\u89c4\u5b9a\uff0c\u7c97\u66b4\u5e72\u6d89\u4e2d\u56fd\u5185\u653f\uff0c\u4e2d\u65b9\u5bf9\u6b64\u575a\u51b3\u53cd\u5bf9\u5e76\u5df2\u5411\u7f8e\u65b9\u63d0\u51fa\u4e25\u6b63\u4ea4\u6d89\u3002\n\u4e8b\u5b9e\u4e0a\uff0c\u4e2d[...]",
"target": "\u671b\u6d77\u697c\u7f8e\u56fd\u6253\u201c\u53f0\u6e7e\u724c\u201d\u662f\u5371\u9669\u7684\u8d4c\u535a"
},
{
"text": "\u5728\u63a8\u8fdb\u201c\u53cc\u4e00\u6d41\u201d\u9ad8\u6821\u5efa\u8bbe\u8fdb\u7a0b\u4e2d\uff0c\u6211\u4eec\u8981\u7d27\u7d27\u56f4\u7ed5\u4e3a\u515a\u80b2\u4eba\u3001\u4e3a\u56fd\u80b2\u624d\uff0c\u627e\u51c6\u95ee\u9898\u3001\u7834\u89e3\u96be\u9898\uff0c\u4ee5\u4e00\u6d41\u610f\u8bc6\u548c\u62c5\u5f53\u7cbe\u795e\uff0c\u5927\u529b\u63a8\u8fdb\u9ad8\u6821\u7684\u6cbb\u7406\u80fd\u529b\u5efa\u8bbe\u3002\n\u589e\u5f3a\u653f\u6cbb\u5f15\u9886\u529b\u3002\u575a\u6301\u515a\u5bf9\u9ad8\u6821\u5de5\u4f5c\u7684\u5168\u9762\u9886\u5bfc\uff0c\u59cb\u7ec8\u628a\u653f\u6cbb\u5efa\u8bbe\u6446\u5728[...]",
"target": "\u5927\u529b\u63a8\u8fdb\u9ad8\u6821\u6cbb\u7406\u80fd\u529b\u5efa\u8bbe"
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "Value(dtype='string', id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 5850 |
| valid | 1679 |
|
PolyAI | null | @inproceedings{Spithourakis2022evi,
author = {Georgios P. Spithourakis and Ivan Vuli\'{c} and Micha\l{} Lis and I\~{n}igo Casanueva and Pawe\l{} Budzianowski},
title = {{EVI}: Multilingual Spoken Dialogue Tasks and Dataset for Knowledge-Based Enrolment, Verification, and Identification},
year = {2022},
note = {Data available at https://github.com/PolyAI-LDN/evi-paper},
url = {https://arxiv.org/abs/2204.13496},
booktitle = {Findings of NAACL (publication pending)}
} | EVI is a challenging spoken multilingual dataset with 5,506 dialogues in English, Polish, and French
that can be used for benchmarking and developing knowledge-based enrolment, identification, and identification
for spoken dialogue systems. | false | 1 | false | PolyAI/evi | 2022-10-25T10:39:33.000Z | evi-multilingual-spoken-dialogue-tasks-and-1 | false | 2e5f8d3dc550028d9ae1dbbb94476a6ae282134b | [] | [
"arxiv:2204.13496",
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"language:en",
"language:fr",
"language:pl",
"license:cc-by-4.0",
"multilinguality:multilingual",
"language_bcp47:en",
"lan... | https://huggingface.co/datasets/PolyAI/evi/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
- machine-generated
language_creators:
- crowdsourced
- expert-generated
language:
- en
- fr
- pl
license:
- cc-by-4.0
multilinguality:
- multilingual
paperswithcode_id: evi-multilingual-spoken-dialogue-tasks-and-1
language_bcp47:
- en
- en-GB
- fr
- fr-FR
- pl
---
# EVI
## Dataset Description
- **Paper:** [EVI: Multilingual Spoken Dialogue Tasks and Dataset for Knowledge-Based Enrolment, Verification, and Identification](https://arxiv.org/abs/2204.13496)
- **Repository:** [Github](https://github.com/PolyAI-LDN/evi-paper)
EVI is a challenging spoken multilingual dataset
with 5,506 dialogues in English, Polish, and French
that can be used for benchmarking and developing
knowledge-based enrolment, identification, and identification for spoken dialogue systems.
## Example
EVI can be downloaded and used as follows:
```py
from datasets import load_dataset
evi = load_dataset("PolyAI/evi", "en-GB") # for British English
# to download data from all locales use:
# evi = load_dataset("PolyAI/evi", "all")
# see structure
print(evi)
```
## Dataset Structure
We show detailed information of the example for the `en-GB` configuration of the dataset.
All other configurations have the same structure.
### Data Instances
An example of a data instance of the config `en-GB` looks as follows:
```
{
"language": 0,
"dialogue_id": "CA0007220161df7be23f4554704c8720f5",
"speaker_id": "e80e9bdd33eda593f16a1b6f2fb228ff",
"turn_id": 0,
"target_profile_id": "en.GB.608",
"asr_transcription": "w20 a b",
"asr_nbest'": ["w20 a b", "w20 a bee", "w20 a baby"],
"path": "audios/en/CA0007220161df7be23f4554704c8720f5/0.wav",
"audio": {
"path": "/home/georgios/.cache/huggingface/datasets/downloads/extracted/0335ebc25feace53243133b49ba17ba18e26f0f97cb083ffdf4e73dd7427b443/audios/en/CA0007220161df7be23f4554704c8720f5/0.wav",
"array": array([ 0.00024414, 0.00024414, 0.00024414, ..., 0.00024414,
-0.00024414, 0.00024414], dtype=float32),
"sampling_rate": 8000,
}
}
```
### Data Fields
The data fields are the same among all splits.
- **language** (int): ID of language
- **dialogue_id** (str): the ID of the dialogue
- **speaker_id** (str): the ID of the speaker
- **turn_id** (int)": the ID of the turn
- **target_profile_id** (str): the ID of the target profile
- **asr_transcription** (str): ASR transcription of the audio file
- **asr_nbest** (list): n-best ASR transcriptions of the audio file
- **path** (str): Path to the audio file
- **audio** (dict): Audio object including loaded audio array, sampling rate and path of audio
### Data Splits
Every config only has the `"test"` split containing *ca.* 1,800 dialogues.
## Dataset Creation
[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
All datasets are licensed under the [Creative Commons license (CC-BY)](https://creativecommons.org/licenses/).
### Citation Information
```
@inproceedings{Spithourakis2022evi,
author = {Georgios P. Spithourakis and
Ivan Vuli\'{c} and
Micha\l{} Lis and
I\~{n}igo Casanueva
and Pawe\l{} Budzianowski},
title = {{EVI}: Multilingual Spoken Dialogue Tasks and Dataset for Knowledge-Based Enrolment, Verification, and Identification},
year = {2022},
note = {Data available at https://github.com/PolyAI-LDN/evi-paper},
url = {https://arxiv.org/abs/2204.13496},
booktitle = {Findings of NAACL (publication pending)}
}
```
### Contributions
Thanks to [@polinaeterna](https://github.com/polinaeterna) for helping with adding this dataset |
AntoineLB | null | null | null | false | 1 | false | AntoineLB/watt-saving-agent-0 | 2022-07-19T13:21:51.000Z | null | false | bfe0aad3b98276b0223c58fba78bec540ac1e3f2 | [] | [] | https://huggingface.co/datasets/AntoineLB/watt-saving-agent-0/resolve/main/README.md | |
Tipfa | null | null | null | false | 7 | false | Tipfa/LibriAdapt | 2022-06-30T14:59:50.000Z | null | false | b42580de72377e12a2da4208246b5615d95296ae | [] | [] | https://huggingface.co/datasets/Tipfa/LibriAdapt/resolve/main/README.md | |
mesolitica | null | null | null | false | 1 | false | mesolitica/noisy-ms-en-augmentation | 2022-07-17T09:41:07.000Z | null | false | 413a9d216a483578b30e32df07ac2ecbdff14e43 | [] | [
"tags:generated_from_keras_callback"
] | https://huggingface.co/datasets/mesolitica/noisy-ms-en-augmentation/resolve/main/README.md | ---
tags:
- generated_from_keras_callback
model-index:
- name: t5-tiny-finetuned-noisy-ms-en
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# ms-en
Notebooks to gather the dataset at https://github.com/huseinzol05/malay-dataset/tree/master/translation/noisy-ms-en-augmentation |
allenai | null | null | null | false | 7,233 | false | allenai/metaicl-data | 2022-06-30T21:18:49.000Z | null | false | d24215071fc64685ee5a089688a4622b11d86786 | [] | [
"arxiv:2005.00700",
"license:cc-by-nc-4.0"
] | https://huggingface.co/datasets/allenai/metaicl-data/resolve/main/README.md | ---
license: cc-by-nc-4.0
---
This is the downloaded and processed data from Meta's [MetaICL](https://github.com/facebookresearch/MetaICL).
We follow their ["How to Download and Preprocess"](https://github.com/facebookresearch/MetaICL#how-to-download-and-preprocess) instructions to obtain their modified versions of [CrossFit](https://github.com/INK-USC/CrossFit) and [UnifiedQA](https://arxiv.org/abs/2005.00700).
## Citation information
```
@inproceedings{ min2022metaicl,
title={ Meta{ICL}: Learning to Learn In Context },
author={ Min, Sewon and Lewis, Mike and Zettlemoyer, Luke and Hajishirzi, Hannaneh },
booktitle={ NAACL-HLT },
year={ 2022 }
}
@inproceedings{ ye2021crossfit,
title={ {C}ross{F}it: A Few-shot Learning Challenge for Cross-task Generalization in NLP },
author={ Ye, Qinyuan and Lin, Bill Yuchen and Ren, Xiang },
booktitle={ EMNLP },
year={ 2021 }
}
@inproceedings{ khashabi2020unifiedqa,
title={ {U}nified{QA}: Crossing Format Boundaries With a Single QA System },
author={ Khashabi, Daniel and Min, Sewon and Khot, Tushar and Sabharwal, Ashish and Tafjord, Oyvind and Clark, Peter and Hajishirzi, Hannaneh },
booktitle={ Findings of EMNLP },
year={ 2020 }
}
```
|
richt | null | null | null | false | 1 | false | richt/Euroc | 2022-06-30T18:59:22.000Z | null | false | aafd21a31f3c31332a2140a7df63bf661e291f26 | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/richt/Euroc/resolve/main/README.md | ---
license: apache-2.0
---
|
arize-ai | null | # @InProceedings{huggingface:dataset,
# title = {A great new dataset},
# author={huggingface, Inc.
# },
# year={2020}
# }
# | This dataset was crafted to be used in our tutorial [Link to the tutorial when
ready]. It consists on product reviews from an e-commerce store. The reviews
are labeled on a scale from 1 to 5 (stars). The training & validation sets are
fully composed by reviews written in english. However, the production set has
some reviews written in spanish. At Arize, we work to surface this issue and
help you solve it. | false | 1 | false | arize-ai/xtreme_en | 2022-07-01T17:23:29.000Z | null | false | 6d6b24c4204a6731263bcd5ec76564bbdbfbca58 | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"license:mit",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|xtreme",
"task_categories:token-classification",
"task_ids:named-entity-recognition"
] | https://huggingface.co/datasets/arize-ai/xtreme_en/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
pretty_name: named-entity-recognition-en-no-drift
size_categories:
- 10K<n<100K
source_datasets:
- extended|xtreme
task_categories:
- token-classification
task_ids:
- named-entity-recognition
---
# Dataset Card for `reviews_with_drift`
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
### Dataset Summary
This dataset was crafted to be used in our tutorial [Link to the tutorial when ready]. It consists on a large Movie Review Dataset mixed with some reviews from a Hotel Review Dataset. The training/validation set are purely obtained from the Movie Review Dataset while the production set is mixed. Some other features have been added (`age`, `gender`, `context`) as well as a made up timestamp `prediction_ts` of when the inference took place.
### Supported Tasks and Leaderboards
`text-classification`, `sentiment-classification`: The dataset is mainly used for text classification: given the text, predict the sentiment (positive or negative).
### Languages
Text is mainly written in english.
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@fjcasti1](https://github.com/fjcasti1) for adding this dataset. |
arize-ai | null | # @InProceedings{huggingface:dataset,
# title = {A great new dataset},
# author={huggingface, Inc.
# },
# year={2020}
# }
# | This dataset was crafted to be used in our tutorial [Link to the tutorial when
ready]. It consists on product reviews from an e-commerce store. The reviews
are labeled on a scale from 1 to 5 (stars). The training & validation sets are
fully composed by reviews written in english. However, the production set has
some reviews written in spanish. At Arize, we work to surface this issue and
help you solve it. | false | 1 | false | arize-ai/xtreme_en_language_drift_es | 2022-07-01T17:25:51.000Z | null | false | dd67b07acb615f16950e239d3e5035ffd40b696a | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"license:mit",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|xtreme",
"task_categories:token-classification",
"task_ids:named-entity-recognition"
] | https://huggingface.co/datasets/arize-ai/xtreme_en_language_drift_es/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
pretty_name: named-entity-recognition-en-no-drift
size_categories:
- 10K<n<100K
source_datasets:
- extended|xtreme
task_categories:
- token-classification
task_ids:
- named-entity-recognition
---
# Dataset Card for `reviews_with_drift`
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
### Dataset Summary
This dataset was crafted to be used in our tutorial [Link to the tutorial when ready]. It consists on a large Movie Review Dataset mixed with some reviews from a Hotel Review Dataset. The training/validation set are purely obtained from the Movie Review Dataset while the production set is mixed. Some other features have been added (`age`, `gender`, `context`) as well as a made up timestamp `prediction_ts` of when the inference took place.
### Supported Tasks and Leaderboards
`text-classification`, `sentiment-classification`: The dataset is mainly used for text classification: given the text, predict the sentiment (positive or negative).
### Languages
Text is mainly written in english.
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@fjcasti1](https://github.com/fjcasti1) for adding this dataset. |
arize-ai | null | # @InProceedings{huggingface:dataset,
# title = {A great new dataset},
# author={huggingface, Inc.
# },
# year={2020}
# }
# | This dataset was crafted to be used in our tutorial [Link to the tutorial when
ready]. It consists on product reviews from an e-commerce store. The reviews
are labeled on a scale from 1 to 5 (stars). The training & validation sets are
fully composed by reviews written in english. However, the production set has
some reviews written in spanish. At Arize, we work to surface this issue and
help you solve it. | false | 1 | false | arize-ai/xtreme_en_token_drift | 2022-07-01T17:25:34.000Z | null | false | feb643d1f0a55643f91347b4c418d243343a94cd | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"license:mit",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|xtreme",
"task_categories:token-classification",
"task_ids:named-entity-recognition"
] | https://huggingface.co/datasets/arize-ai/xtreme_en_token_drift/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
pretty_name: named-entity-recognition-en-no-drift
size_categories:
- 10K<n<100K
source_datasets:
- extended|xtreme
task_categories:
- token-classification
task_ids:
- named-entity-recognition
---
# Dataset Card for `reviews_with_drift`
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
### Dataset Summary
This dataset was crafted to be used in our tutorial [Link to the tutorial when ready]. It consists on a large Movie Review Dataset mixed with some reviews from a Hotel Review Dataset. The training/validation set are purely obtained from the Movie Review Dataset while the production set is mixed. Some other features have been added (`age`, `gender`, `context`) as well as a made up timestamp `prediction_ts` of when the inference took place.
### Supported Tasks and Leaderboards
`text-classification`, `sentiment-classification`: The dataset is mainly used for text classification: given the text, predict the sentiment (positive or negative).
### Languages
Text is mainly written in english.
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@fjcasti1](https://github.com/fjcasti1) for adding this dataset. |
bengaliAI | null | @inproceedings{commonvoice:2020,
author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
title = {Common Voice: A Massively-Multilingual Speech Corpus},
booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)},
pages = {4211--4215},
year = 2020
} | null | false | 12 | false | bengaliAI/cvbn | 2022-07-01T02:17:25.000Z | null | false | ba204823a7b512e73ef1fe218dac19ad29c8afa1 | [] | [
"license:cc"
] | https://huggingface.co/datasets/bengaliAI/cvbn/resolve/main/README.md | ---
license: cc
---
|
launch | null | null | null | false | 8 | false | launch/ampere | 2022-11-09T01:57:52.000Z | null | false | e81e6a9ac798674b1a72239936bc4f71c4fa2c4e | [] | [
"annotations_creators:expert-generated",
"language:en",
"license:cc-by-4.0",
"multilinguality:monolingual",
"task_categories:text-classification"
] | https://huggingface.co/datasets/launch/ampere/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
task_categories:
- text-classification
task_ids: []
pretty_name: AMPERE
---
# Dataset Card for AMPERE
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Structure](#dataset-structure)
- [Dataset Creation](#dataset-creation)
## Dataset Description
This dataset is released together with our NAACL 2019 Paper "[`Argument Mining for Understanding Peer Reviews`](https://aclanthology.org/N19-1219/)". If you find our work useful, please cite:
```
@inproceedings{hua-etal-2019-argument,
title = "Argument Mining for Understanding Peer Reviews",
author = "Hua, Xinyu and
Nikolov, Mitko and
Badugu, Nikhil and
Wang, Lu",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1219",
doi = "10.18653/v1/N19-1219",
pages = "2131--2137",
}
```
This dataset includes 400 scientific peer reviews collected from ICLR 2018 hosted at the Openreview platform. Each review is segmented into multiple propositions. We include the original untokenized text for each proposition. Each proposition is labeled as one of the following types:
- **evaluation**: a proposition that is not objectively verifiable and does not require any action to be performed, such as qualitative judgement and interpretation of the paper, e.g. "The paper shows nice results on a number of small tasks."
- **request**: a proposition that is not objectively verifiable and suggests a course of action to be taken, such as recommendation and suggestion for new experiments, e.g. "I would really like to see how the method performs without this hack."
- **fact**: a proposition that is verifiable with objective evidence, such as mathematical conclusion and common knowledge of the field, e.g. "This work proposes a dynamic weight update scheme."
- **quote**: a quote from the paper or another source, e.g. "The author wrote 'where r is lower bound of feature norm'."
- **reference**: a proposition that refers to an objective evidence, such as URL link and citation, e.g. "see MuseGAN (Dong et al), MidiNet (Yang et al), etc."
- **non-arg**: a non-argumentative discourse unit that does not contribute to the overall agenda of the review, such as greetings, metadata, and clarification questions, e.g. "Aha, now I understand."
## Dataset Structure
The dataset is partitioned into train/val/test sets. Each set is uploaded as a jsonl format. Each line contains the following elements:
- `doc_id` (str): a unique id for review document
- `text` (list[str]): a list of segmented propositions
- `labels` (list[str]): a list of labels corresponding to the propositions
An example looks as follows.
```
{
"doc_id": "H1WORsdlG",
"text": [
"This paper addresses the important problem of understanding mathematically how GANs work.",
"The approach taken here is to look at GAN through the lense of the scattering transform.",
"Unfortunately the manuscrit submitted is very poorly written.",
"Introduction and flow of thoughts is really hard to follow.",
"In method sections, the text jumps from one concept to the next without proper definitions.",
"Sorry I stopped reading on page 3.",
"I suggest to rewrite this work before sending it to review.",
"Among many things: - For citations use citep and not citet to have () at the right places.",
"- Why does it seems -> Why does it seem etc.",
],
"labels": [
'fact',
'fact',
'evaluation',
'evaluation',
'evaluation',
'evaluation',
'request',
'request',
'request',
]
}
```
## Dataset Creation
For human annotators, they will be asked to first read the above definitions and controversial cases carefully. The dataset to be annotated consists of 400 reviews partitioned in 20 batches. Each annotator will follow the following steps for annotation:
- Step 1: Open a review file with a text editor. The unannotated review file contains only one line, please separate it into multiple lines with each line corresponding to one single proposition. Repeat the above actions on all 400 reviews.
- Step 2: Based on the segmented units, label the type for each proposition. Start labeling at the end of each file with the marker "## Labels:". Indicate the line number of the proposition first, then annotate the type, e.g. "1. evaluation" for the first proposition. Repeat the above actions on all 400 reviews.
A third annotator then resolves the disagreements between the two annotators on both segmentation and proposition type.
|
Maysee | null | null | null | false | 3,951 | false | Maysee/tiny-imagenet | 2022-07-12T09:04:30.000Z | imagenet | false | 5a77092c28e51558c5586e9c5eb71a7e17a5e43f | [] | [
"annotations_creators:crowdsourced",
"extra_gated_prompt:By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:\n[RESEARCHER_FULLNAME] (the \"Researcher\") has requested permission to use the ImageNet database (the \"Database\") at Princeton University and Stanford University. In ex... | https://huggingface.co/datasets/Maysee/tiny-imagenet/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
extra_gated_prompt: "By clicking on \u201CAccess repository\u201D below, you also\
\ agree to ImageNet Terms of Access:\n[RESEARCHER_FULLNAME] (the \"Researcher\"\
) has requested permission to use the ImageNet database (the \"Database\") at Princeton\
\ University and Stanford University. In exchange for such permission, Researcher\
\ hereby agrees to the following terms and conditions:\n1. Researcher shall use\
\ the Database only for non-commercial research and educational purposes.\n2. Princeton\
\ University, Stanford University and Hugging Face make no representations or warranties\
\ regarding the Database, including but not limited to warranties of non-infringement\
\ or fitness for a particular purpose.\n3. Researcher accepts full responsibility\
\ for his or her use of the Database and shall defend and indemnify the ImageNet\
\ team, Princeton University, Stanford University and Hugging Face, including their\
\ employees, Trustees, officers and agents, against any and all claims arising from\
\ Researcher's use of the Database, including but not limited to Researcher's use\
\ of any copies of copyrighted images that he or she may create from the Database.\n\
4. Researcher may provide research associates and colleagues with access to the\
\ Database provided that they first agree to be bound by these terms and conditions.\n\
5. Princeton University, Stanford University and Hugging Face reserve the right\
\ to terminate Researcher's access to the Database at any time.\n6. If Researcher\
\ is employed by a for-profit, commercial entity, Researcher's employer shall also\
\ be bound by these terms and conditions, and Researcher hereby represents that\
\ he or she is fully authorized to enter into this agreement on behalf of such employer.\n\
7. The law of the State of New Jersey shall apply to all disputes under this agreement."
language:
- en
language_creators:
- crowdsourced
license: []
multilinguality:
- monolingual
paperswithcode_id: imagenet
pretty_name: Tiny-ImageNet
size_categories:
- 100K<n<1M
source_datasets:
- extended|imagenet-1k
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
---
# Dataset Card for tiny-imagenet
## Dataset Description
- **Homepage:** https://www.kaggle.com/c/tiny-imagenet
- **Repository:** [Needs More Information]
- **Paper:** http://cs231n.stanford.edu/reports/2017/pdfs/930.pdf
- **Leaderboard:** https://paperswithcode.com/sota/image-classification-on-tiny-imagenet-1
### Dataset Summary
Tiny ImageNet contains 100000 images of 200 classes (500 for each class) downsized to 64×64 colored images. Each class has 500 training images, 50 validation images, and 50 test images.
### Languages
The class labels in the dataset are in English.
## Dataset Structure
### Data Instances
```json
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=64x64 at 0x1A800E8E190,
'label': 15
}
```
### Data Fields
- image: A PIL.Image.Image object containing the image. Note that when accessing the image column: dataset[0]["image"] the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the "image" column, i.e. dataset[0]["image"] should always be preferred over dataset["image"][0].
- label: an int classification label. -1 for test set as the labels are missing. Check `classes.py` for the map of numbers & labels.
### Data Splits
| | Train | Valid |
| ------------ | ------ | ----- |
| # of samples | 100000 | 10000 |
## Usage
### Example
#### Load Dataset
```python
def example_usage():
tiny_imagenet = load_dataset('Maysee/tiny-imagenet', split='train')
print(tiny_imagenet[0])
if __name__ == '__main__':
example_usage()
``` |
Tritkoman | null | null | null | false | 1 | false | Tritkoman/autotrain-data-Rusynpannonianpure | 2022-10-25T10:39:40.000Z | null | false | f7abfbaa550a0d2cd478151aa437b303badc4dc9 | [] | [
"language:en",
"language:es",
"task_categories:translation"
] | https://huggingface.co/datasets/Tritkoman/autotrain-data-Rusynpannonianpure/resolve/main/README.md | ---
language:
- en
- es
task_categories:
- translation
---
# AutoTrain Dataset for project: Rusynpannonianpure
## Dataset Descritpion
This dataset has been automatically processed by AutoTrain for project Rusynpannonianpure.
### Languages
The BCP-47 code for the dataset's language is en2es.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"source": "\"I came to the region to meet with the leaders of the parties and discuss the progress in normalizin[...]",
"target": "\"\u042f \u043f\u0440\u0438\u0448\u043e\u043b \u0434\u043e \u0440\u0435\u0491\u0438\u043e\u043d\u0443 \u043f\u0440\u0438\u0440\u0438\u0445\u0442\u0430\u0446 \u0448\u043b\u0457\u0434\u0443\u044e\u0446\u0438 \u0441\u0445\u043e\u0434 \u043b\u0438\u0434\u0435\u0440\u043e\u0445 \u0438 \u0431\u0435\u0448\u0435\u0434\u043e\u0432\u0430\u0446 \u043e \u043d\u0430\u043f\u0440\u0435\u0434\u043e\u0432\u0430\u043d\u044e \u0443 \u043d\u043e\u0440\u043c\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u0457 \u043e\u0434\u043d\u043e\u0448\u0435[...]"
},
{
"source": "\"We had a very good discussion yesterday evening about the situation and it is normal to look for a [...]",
"target": "\"\u041c\u0430\u043b\u0438 \u0437\u043c\u0435 \u0454\u0434\u043d\u0443 \u043e\u0437\u0431\u0438\u043b\u044c\u043d\u0443 \u0440\u043e\u0437\u0433\u0432\u0430\u0440\u043a\u0443 \u0432\u0447\u0435\u0440\u0430 \u0432\u0435\u0447\u0430\u0440 \u043e \u0441\u0438\u0442\u0443\u0430\u0446\u0438\u0457 \u0438 \u043d\u043e\u0440\u043c\u0430\u043b\u043d\u043e \u0436\u0435 \u043f\u043e\u0442\u0440\u0435\u0431\u043d\u0435 \u0433\u043b\u0454\u0434\u0430\u0446 \u0440\u0438\u0448\u0435\u043d\u0454 \u043f\u0440\u0435\u0437 \u0434[...]"
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"source": "Value(dtype='string', id=None)",
"target": "Value(dtype='string', id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 3 |
| valid | 1 |
|
AswiN037 | null | null | null | false | 1 | false | AswiN037/tamil-question-answering-dataset | 2022-07-01T07:53:56.000Z | null | false | 09feea6476dba673a37248873f4e6e9998f1913d | [] | [
"license:afl-3.0"
] | https://huggingface.co/datasets/AswiN037/tamil-question-answering-dataset/resolve/main/README.md | ---
license: afl-3.0
---
this dataset contains 5 columns
context, question, answer_start, answer_text, source
| Column | Description |
| :------------ |:---------------:|
| context | A general small paragraph in tamil language |
| question | question framed form the context |
| answer_text | text span that extracted from context |
| answer_start | index of answer_text |
| source | who framed this context, question, answer pair |
source
team KBA => (Karthi, Balaji, Azeez) these people manually created
CHAII =>a kaggle competition
XQA => multilingual QA dataset |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-a25a94fd-9305221 | 2022-07-02T12:09:46.000Z | null | false | 2ba908ef5001980a29cd652c16cebfe1a69035f8 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:big_patent"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-a25a94fd-9305221/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- big_patent
eval_info:
task: summarization
model: google/bigbird-pegasus-large-bigpatent
metrics: ['rouge']
dataset_name: big_patent
dataset_config: all
dataset_split: validation
col_mapping:
text: description
target: abstract
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: google/bigbird-pegasus-large-bigpatent
* Dataset: big_patent
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@kayvane](https://huggingface.co/kayvane) for evaluating this model. |
GEM-submissions | null | null | null | false | 2 | false | GEM-submissions/lewtun__this-is-a-test-submission-2__1656667730 | 2022-07-01T09:28:55.000Z | null | false | 13ad152ac29b49542dfbb3500c6b72a499731db8 | [] | [
"benchmark:gem",
"type:prediction",
"submission_name:This is a test submission 2",
"tags:evaluation",
"tags:benchmark"
] | https://huggingface.co/datasets/GEM-submissions/lewtun__this-is-a-test-submission-2__1656667730/resolve/main/README.md | ---
benchmark: gem
type: prediction
submission_name: This is a test submission 2
tags:
- evaluation
- benchmark
---
# GEM Submission
Submission name: This is a test submission 2
|
benschill | null | @misc{kaggle-brain-tumor-classification,
title={Kaggle: Brain Tumor Classification (MRI)},
howpublished={\\url{https://www.kaggle.com/datasets/sartajbhuvaji/brain-tumor-classification-mri?resource=download}},
note = {Accessed: 2022-06-30},
} | This dataset is intended as a test case for classification tasks (4 different kinds of brain xrays). The dataset consists of almost 1400 JPEG images grouped into two splits - training and validation. Each split contains 4 categories labeled as n0~n3, each corresponding to a cancer result of the mrt.
| Label | Xray Category | Train Images | Validation Images |
| ----- | --------------------- | ------------ | ----------------- |
| n0 | glioma_tumor | 826 | 100 |
| n1 | meningioma_tumor | 822 | 115 |
| n2 | pituitary_tumor | 827 | 74 |
| n3 | no_tumor | 395 | 105 | | false | 1 | false | benschill/brain-tumor-collection | 2022-07-04T08:26:59.000Z | null | false | fe18ffc594c9e8bade2ea0049f90d2e7d25ed7f7 | [] | [
"license:pddl"
] | https://huggingface.co/datasets/benschill/brain-tumor-collection/resolve/main/README.md | ---
license: pddl
---
|
dgrnd4 | null | null | null | false | 52 | false | dgrnd4/stanford_dog_dataset | 2022-07-01T11:27:56.000Z | null | false | 3fd4d57d14b98665aa12b9e6a359a15f203ef787 | [] | [
"license:afl-3.0"
] | https://huggingface.co/datasets/dgrnd4/stanford_dog_dataset/resolve/main/README.md | ---
license: afl-3.0
---
|
joelito | null | null | null | false | 2,705 | false | joelito/covid19_emergency_event | 2022-09-22T13:44:15.000Z | null | false | bba0491bc1ba950369eafcceb1d522537b54ab2e | [] | [
"annotations_creators:found",
"annotations_creators:other",
"language_creators:found",
"language:en",
"language:fr",
"language:hu",
"language:it",
"language:nb",
"language:nl",
"language:pl",
"license:cc0-1.0",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"source_datasets:or... | https://huggingface.co/datasets/joelito/covid19_emergency_event/resolve/main/README.md | ---
annotations_creators:
- found
- other
language_creators:
- found
language:
- en
- fr
- hu
- it
- nb
- nl
- pl
license:
- cc0-1.0
multilinguality:
- multilingual
pretty_name: EXCEPTIUS Corpus
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-label-classification
---
# Dataset Card for EXCEPTIUS Corpus
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://exceptius.com/
- **Repository:** https://github.com/tommasoc80/COVID19_emergency_event
- **Paper:** Tziafas, G., de Saint-Phalle, E., de Vries, W., Egger, C., & Caselli, T. (2021). A Multilingual Approach to Identify and Classify Exceptional Measures against {COVID}-19. Proceedings of the Natural Legal Language Processing Workshop 2021, 46–62. https://doi.org/10.18653/v1/2021.nllp-1.5
- **Leaderboard:**
- **Point of Contact:** [Joel Niklaus](mailto:joel.niklaus.2@bfh.ch)
### Dataset Summary
This dataset presents a new corpus of legislative documents from 8 European countries (Beglium, France, Hunary, Italy, Netherlands, Norway, Poland, UK) in 7 languages (Dutch, English, French, Hungarian, Italian, Norwegian Bokmål, Polish) manually annotated for exceptional measures against COVID-19. The annotation was done on the sentence level.
### Supported Tasks and Leaderboards
The dataset can be used for multi-label text classification tasks.
### Languages
Dutch, English, French, Hungarian, Italian, Norwegian Bokmål, Polish
## Dataset Structure
### Data Instances
The file format is jsonl and three data splits are present (train, validation and test).
### Data Fields
The jsonl files have the following basic columns:
- `language`: The language of the sentence (set based on the country)
- `country`: The country of the sentence
- `text`: Sentence that has been annotated
The documents have been annotated with 8 labels, each label representing a specific measurement against COVID-19. Each label is represented by one boolean field in the jsonl file. The labels, i.e. the specific measure classes, are:
- `event1`: State of Emergency
- `event2`: Restrictions of fundamental rights and civil liberties
- `event3`: Restrictions of daily liberties
- `event4`: Closures / lockdown
- `event5`: Suspension of international cooperation and commitments
- `event6`: Police mobilization
- `event7`: Army mobilization
- `event8`: Government oversight
- `all_events`: an aggregate column containing all applicable events combined
### Data Splits
All annotated sentences combined have the following split:
- train: 3312 (80%)
- dev: 418 (10%)
- test: 418 (10%)
The splits have been performed on each country and have later been merged. Therefore, each split contains sentences from each country.
The following label distribution shows the number of occurrences per label per split. `total occurrences` sums up the previous rows (total number of events per split). `split size` is the number of sentences per split.
| Event | train | validation | test |
|:----------------------|----------:|-----------:|----------:|
| event1 | 383 | 54 | 47 |
| event2 | 253 | 39 | 42 |
| event3 | 412 | 70 | 62 |
| event4 | 617 | 75 | 93 |
| event5 | 52 | 4 | 6 |
| event6 | 15 | 2 | 1 |
| event7 | 45 | 4 | 5 |
| event8 | 146 | 21 | 19 |
| **total occurrences** | **1923** | **269** | **275** |
| **split size** | **3312** | **418** | **418** |
## Dataset Creation
### Curation Rationale
*"Investigate the potential of multilingual pretrained language models in order to
facilitate the analysis, exploration, and comparison of legal texts on COVID-19 exceptional measures"* (Tziafas et al., 2021)
### Source Data
#### Initial Data Collection and Normalization
*“The corpus collection process has been overseen by four political science experts working in partnership with national legal experts. All documents were retrieved from official governmental websites that publish legal acts. The identification of the relevant documents has been done by means of 4 keywords (i.e., “COVID”, “COVID-19”, “Coronavirus” and “Health emergency”). For each language, the corresponding language specific keywords were used. In this initial phase, we focus on a sample of 19 EEA countries on measures adopted at the national level. To do so, we identify publicly available links to relevant documents 2 plus UK and Switzerland. We could not find corresponding documents for two countries of the EEA (i.e., Bulgaria and Greece). All documents have been collected either by manually downloading them or by automatic scraping. For countries with more than one official language (e.g., Switzerland), legal acts were collected in all available languages.”*(Tziafas et al., 2021)
#### Who are the source language producers?
Politicians and legal experts have been involved in producing the language material.
### Annotations
#### Annotation process
*"A subset of 281 documents in eight languages has been selected for manual annotation. The annotation of the exceptional measures applies at sentence-level. The sample is based on the French, Polish, Dutch, English, Hungarian, Belgian, Italian, and Norwegian sub-corpora. Annotators were allowed to assign as many subclasses as they consider relevant to each sentence, but with a total of eight main classes of exceptional measures. Sentences can potentially entail multiple exceptional classes, making this a multi-label annotation task. The annotation process results in eight binary annotations per sentence, with 0 if the specific class is not identified within the sentence and 1 if it is. The annotation has been conducted by three experts in political science working under the supervision of the project’s Scientific Board. Since the annotators are not fluent in all languages and due to the impossibility of recruiting expert native speakers, some documents need to be translated into English to be manually annotated. No inter-annotator agreement study has been conducted in this initial phase. We intend to remedy this limitation in the project’s next development cycle. However, during the annotation phase, annotators met on a weekly basis to discuss ambiguous cases and the guidelines. Annotators are encouraged to propose new classes or subclasses. For a new (sub)class to be accepted, the measure should have been independently identified by the majority of the annotators. In this phase, no new classes were proposed."* (Tziafas et al., 2021)
#### Who are the annotators?
*"The annotation has been conducted by three experts in political science working under the supervision of the project’s Scientific Board."* (Tziafas et al., 2021)
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
Note that the information given in this dataset card refer to the dataset version as provided by Joel Niklaus and Veton Matoshi. The dataset at hand is intended to be part of a bigger benchmark dataset. Creating a benchmark dataset consisting of several other datasets from different sources requires postprocessing. Therefore, the structure of the dataset at hand, including the folder structure, may differ considerably from the original dataset. In addition to that, differences with regard to dataset statistics as give in the respective papers can be expected. The reader is advised to have a look at the conversion script ```convert_to_hf_dataset.py``` in order to retrace the steps for converting the original dataset into the present jsonl-format. For further information on the original dataset structure, we refer to the bibliographical references and the original Github repositories and/or web pages provided in this dataset card.
## Additional Information
### Dataset Curators
The names of the original dataset curators and creators can be found in references given below, in the section *Citation Information*.
Additional changes were made by Joel Niklaus ([Email](mailto:joel.niklaus.2@bfh.ch); [Github](https://github.com/joelniklaus)) and Veton Matoshi ([Email](mailto:veton.matoshi@bfh.ch); [Github](https://github.com/kapllan)).
### Licensing Information
Creative Commons Zero v1.0 Universal
### Citation Information
```
@inproceedings{tziafas-etal-2021-multilingual,
title = "A Multilingual Approach to Identify and Classify Exceptional Measures against {COVID}-19",
author = "Tziafas, Georgios and
de Saint-Phalle, Eugenie and
de Vries, Wietse and
Egger, Clara and
Caselli, Tommaso",
booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.nllp-1.5",
pages = "46--62",
}
```
### Contributions
Thanks to [@JoelNiklaus](https://github.com/joelniklaus) and [@kapllan](https://github.com/kapllan) for adding this dataset.
|
joelito | null | null | null | false | 1,738 | false | joelito/german_argument_mining | 2022-09-22T13:44:35.000Z | null | false | 576b52004ed78dd747c0f9858fa6dacc7e4196e2 | [] | [
"annotations_creators:expert-generated",
"annotations_creators:found",
"language_creators:found",
"language:de",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:multi-class-classification... | https://huggingface.co/datasets/joelito/german_argument_mining/resolve/main/README.md | ---
annotations_creators:
- expert-generated
- found
language_creators:
- found
language:
- de
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Annotated German Legal Decision Corpus
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
---
# Dataset Card for Annotated German Legal Decision Corpus
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:** https://zenodo.org/record/3936490#.X1ed7ovgomK
- **Paper:** Urchs., S., Mitrović., J., & Granitzer., M. (2021). Design and Implementation of German Legal Decision
Corpora. Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
515–521. https://doi.org/10.5220/0010187305150521
- **Leaderboard:**
- **Point of Contact:** [Joel Niklaus](mailto:joel.niklaus.2@bfh.ch)
### Dataset Summary
This dataset consists of 200 randomly chosen judgments. In these judgments a legal expert annotated the components
conclusion, definition and subsumption of the German legal writing style Urteilsstil.
*"Overall 25,075 sentences are annotated. 5% (1,202) of these sentences are marked as conclusion, 21% (5,328) as
definition, 53% (13,322) are marked as subsumption and the remaining 21% (6,481) as other. The length of judgments in
sentences ranges from 38 to 862 sentences. The median of judgments have 97 sentences, the length of most judgments is on
the shorter side."* (Urchs. et al., 2021)
*"Judgments from 22 of the 131 courts are selected for the corpus. Most judgments originate from the VG Augsburg (59 /
30%) followed by the VG Ansbach (39 / 20%) and LSG Munich (33 / 17%)."* (Urchs. et al., 2021)
*"29% (58) of all selected judgments are issued in the year 2016, followed by 22% (44) from the year 2017 and 21% (41)
issued in the year 2015. [...] The percentages of selected judgments and decisions issued in 2018 and 2019 are roughly
the same. No judgments from 2020 are selected."* (Urchs. et al., 2021)
### Supported Tasks and Leaderboards
The dataset can be used for multi-class text classification tasks, more specifically, for argument mining.
### Languages
The language in the dataset is German as it is used in Bavarian courts in Germany.
## Dataset Structure
### Data Instances
Each sentence is saved as a json object on a line in one of the three files `train.jsonl`, `validation.jsonl`
or `test.jsonl`. The file `meta.jsonl` contains meta information for each court. The `file_number` is present in all
files for identification. Each sentence of the court decision was categorized according to its function.
### Data Fields
The file `meta.jsonl` contains for each row the following fields:
- `meta_title`: Title provided by the website, it is used for saving the decision
- `court`: Issuing court
- `decision_style`: Style of the decision; the corpus contains either *Urteil* (='judgment') or *Endurteil* (
='end-judgment')
- `date`: Date when the decision was issued by the court
- `file_number`: Identification number used for this decision by the court
- `title`: Title provided by the court
- `norm_chains`: Norms related to the decision
- `decision_guidelines`: Short summary of the decision
- `keywords`: Keywords associated with the decision
- `lower_court`: Court that decided on the decision before
- `additional_information`: Additional Information
- `decision_reference`: References to the location of the decision in beck-online
- `tenor`: Designation of the legal consequence ordered by the court (list of paragraphs)
- `legal_facts`: Facts that form the base for the decision (list of paragraphs)
The files `train.jsonl`, `validation.jsonl` and `test.jsonl` contain the following fields:
- `file_number`: Identification number for linkage with the file `meta.jsonl`
- `input_sentence`: The sentence to be classified
- `label`: In depth explanation of the court decision. Each sentence is assigned to one of the major components of
German *Urteilsstil* (Urchs. et al., 2021) (list of paragraphs, each paragraph containing list of sentences, each
sentence annotated with one of the following four labels):
- `conclusion`: Overall result
- `definition`: Abstract legal facts and consequences
- `subsumption`: Determination sentence / Concrete facts
- `other`: Anything else
- `context_before`: Context in the same paragraph before the input_sentence
- `context_after`: Context in the same paragraph after the input_sentence
### Data Splits
No split provided in the original release.
Splits created by Joel Niklaus. We randomly split the dataset into 80% (160 decisions, 19271 sentences) train, 10%
validation (20 decisions, 2726 sentences) and 10% test (20 decisions, 3078 sentences). We made sure, that a decision
only occurs in one split and is not dispersed over multiple splits.
Label Distribution
| label | train | validation | test |
|:---------------|-----------:|-------------:|----------:|
| conclusion | 975 | 115 | 112 |
| definition | 4105 | 614 | 609 |
| subsumption | 10034 | 1486 | 1802 |
| other | 4157 | 511 | 555 |
| total | **19271** | **2726** | **3078** |
## Dataset Creation
### Curation Rationale
Creating a publicly available German legal text corpus consisting of judgments that have been annotated by a legal
expert. The annotated components consist of *conclusion*, *definition* and *subsumption* of the German legal writing
style *Urteilsstil*.
### Source Data
#### Initial Data Collection and Normalization
*“The decision corpus is a collection of the decisions published on the website www.gesetze-bayern.de. At the time of
the crawling the website offered 32,748 decisions of 131 Bavarian courts, dating back to 2015. The decisions are
provided from the Bavarian state after the courts agreed to a publication. All decisions are processed by the publisher
C.H.BECK, commissioned by the Bavarian state. This processing includes anonymisation, key-wording, and adding of
editorial guidelines to the decisions.”* (Urchs. et al., 2021)
#### Who are the source language producers?
German courts from Bavaria
### Annotations
#### Annotation process
*“As stated above, the judgment corpus consist of 200 randomly chosen judgments that are annotated by a legal expert,
who holds a first legal state exam. Due to financial, staff and time reasons the presented iteration of the corpus was
only annotated by a single expert. In a future version several other experts will annotate the corpus and the
inter-annotator agreement will be calculated.”* (Urchs. et al., 2021)
#### Who are the annotators?
A legal expert, who holds a first legal state exam.
### Personal and Sensitive Information
*"All decisions are processed by the publisher C.H.BECK, commissioned by the Bavarian state. This processing includes **
anonymisation**, key-wording, and adding of editorial guidelines to the decisions.”* (Urchs. et al., 2021)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
The SoMaJo Sentence Splitter has been used. Upon manual inspection of the dataset, we could see that the sentence
splitter had poor accuracy in some cases (see ```analyze_dataset()``` in ```convert_to_hf_dataset.py```). When creating
the splits, we thought about merging small sentences with their neighbors or removing them all together. However, since
we could not find an straightforward way to do this, we decided to leave the dataset content untouched.
Note that the information given in this dataset card refer to the dataset version as provided by Joel Niklaus and Veton
Matoshi. The dataset at hand is intended to be part of a bigger benchmark dataset. Creating a benchmark dataset
consisting of several other datasets from different sources requires postprocessing. Therefore, the structure of the
dataset at hand, including the folder structure, may differ considerably from the original dataset. In addition to that,
differences with regard to dataset statistics as give in the respective papers can be expected. The reader is advised to
have a look at the conversion script ```convert_to_hf_dataset.py``` in order to retrace the steps for converting the
original dataset into the present jsonl-format. For further information on the original dataset structure, we refer to
the bibliographical references and the original Github repositories and/or web pages provided in this dataset card.
## Additional Information
### Dataset Curators
The names of the original dataset curators and creators can be found in references given below, in the section *Citation
Information*. Additional changes were made by Joel Niklaus ([Email](mailto:joel.niklaus.2@bfh.ch)
; [Github](https://github.com/joelniklaus)) and Veton Matoshi ([Email](mailto:veton.matoshi@bfh.ch)
; [Github](https://github.com/kapllan)).
### Licensing Information
[Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/legalcode)
### Citation Information
```
@dataset{urchs_stefanie_2020_3936490,
author = {Urchs, Stefanie and
Mitrović, Jelena},
title = {{German legal jugements annotated with judement
style components}},
month = jul,
year = 2020,
publisher = {Zenodo},
doi = {10.5281/zenodo.3936490},
url = {https://doi.org/10.5281/zenodo.3936490}
}
```
```
@conference{icaart21,
author = {Urchs., Stefanie and Mitrovi{\'{c}}., Jelena and Granitzer., Michael},
booktitle = {Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
doi = {10.5220/0010187305150521},
isbn = {978-989-758-484-8},
issn = {2184-433X},
organization = {INSTICC},
pages = {515--521},
publisher = {SciTePress},
title = {{Design and Implementation of German Legal Decision Corpora}},
year = {2021}
}
```
### Contributions
Thanks to [@kapllan](https://github.com/kapllan) and [@joelniklaus](https://github.com/joelniklaus) for adding this
dataset.
|
joelito | null | null | null | false | 1,652 | false | joelito/greek_legal_ner | 2022-09-22T13:44:49.000Z | null | false | 2b39f22db86ba0e9a0e22144611b9a2766edcce1 | [] | [
"annotations_creators:other",
"language_creators:found",
"language:el",
"license:cc-by-nc-sa-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:token-classification",
"task_ids:named-entity-recognition"
] | https://huggingface.co/datasets/joelito/greek_legal_ner/resolve/main/README.md | ---
annotations_creators:
- other
language_creators:
- found
language:
- el
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: null
pretty_name: Greek Legal Named Entity Recognition
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
---
# Dataset Card for Greek Legal Named Entity Recognition
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://legislation.di.uoa.gr/publications?language=en
- **Repository:**
- **Paper:** Angelidis, I., Chalkidis, I., & Koubarakis, M. (2018). Named Entity Recognition, Linking and Generation for Greek Legislation. JURIX.
- **Leaderboard:**
- **Point of Contact:** [Ilias Chalkidis](mailto:ilias.chalkidis@di.ku.dk); [Joel Niklaus](mailto:joel.niklaus.2@bfh.ch)
### Dataset Summary
This dataset contains an annotated corpus for named entity recognition in Greek legislations. It is the first of its kind for the Greek language in such an extended form and one of the few that examines legal text in a full spectrum entity recognition.
### Supported Tasks and Leaderboards
The dataset supports the task of named entity recognition.
### Languages
The language in the dataset is Greek as it used in the Greek Government Gazette.
## Dataset Structure
### Data Instances
The file format is jsonl and three data splits are present (train, validation and test).
### Data Fields
The files contain the following data fields
- `date`: The date when the document was published.
- `gazette`: The government gazette of the document. Either `A` or `D`
- `A` is the general one, publishing standard legislation
- `D` is meant for legislation on urban planning and such things
- `words`: The list of tokens obtained by applying the spacy (v 3.3.1) Greek tokenizer on the sentences. For more information see `convert_to_hf_dataset.py`.
- `ner`: The list of ner tags. The list of labels for the named entities that are covered by the dataset are the following:
- `FACILITY`: Facilities, such as police stations, departments etc.
- `GPE`: Geopolitical Entity; any reference to a geopolitical entity (e.g., country, city, Greek administrative unit, etc.)
- `LEG-REFS`: Legislation Reference; any reference to Greek or European legislation (e.g., Presidential Decrees, Laws, Decisions, EU Regulations and Directives, etc.)
- `LOCATION-NAT`: Well defined natural location, such as rivers, mountains, lakes etc.
- `LOCATION-UNK`: Poorly defined locations such "End of road X" or other locations that are not "official".
- `ORG`: Organization; any reference to a public or private organization, such as: international organizations (e.g., European Union, United Nations, etc.), Greek public organizations (e.g., Social Insurance Institution) or private ones (e.g., companies, NGOs, etc.).
- `PERSON`: Any formal name of a person mentioned in the text (e.g., Greek government members, public administration officials, etc.).
- `PUBLIC-DOCS`: Public Document Reference; any reference to documents or decisions that have been published by a public institution (organization) that are not considered a primary source of legislation (e.g., local decisions, announcements, memorandums, directives).
- `O`: No entity annotation present
The final tagset (in IOB notation) is the following: `['O', 'B-ORG', 'I-ORG', 'B-GPE', 'I-GPE', 'B-LEG-REFS', 'I-LEG-REFS', 'B-PUBLIC-DOCS', 'I-PUBLIC-DOCS', 'B-PERSON', 'I-PERSON', 'B-FACILITY', 'I-FACILITY', 'B-LOCATION-UNK', 'I-LOCATION-UNK', 'B-LOCATION-NAT', 'I-LOCATION-NAT']`
### Data Splits
The dataset has three splits: *train*, *validation* and *test*.
Split across the documents:
| split | number of documents |
|:---------------|--------------------:|
| train | 23723 |
| validation | 5478 |
| test | 5084 |
Split across NER labels
| NER label + split | number of instances |
|:-----------------------------------------------|----------------------:|
| ('FACILITY', 'test') | 142 |
| ('FACILITY', 'train') | 1224 |
| ('FACILITY', 'validation') | 60 |
| ('GPE', 'test') | 1083 |
| ('GPE', 'train') | 5400 |
| ('GPE', 'validation') | 1214 |
| ('LEG-REFS', 'test') | 1331 |
| ('LEG-REFS', 'train') | 5159 |
| ('LEG-REFS', 'validation') | 1382 |
| ('LOCATION-NAT', 'test') | 26 |
| ('LOCATION-NAT', 'train') | 145 |
| ('LOCATION-NAT', 'validation') | 2 |
| ('LOCATION-UNK', 'test') | 205 |
| ('LOCATION-UNK', 'train') | 1316 |
| ('LOCATION-UNK', 'validation') | 283 |
| ('ORG', 'test') | 1354 |
| ('ORG', 'train') | 5906 |
| ('ORG', 'validation') | 1506 |
| ('PERSON', 'test') | 491 |
| ('PERSON', 'train') | 1921 |
| ('PERSON', 'validation') | 475 |
| ('PUBLIC-DOCS', 'test') | 452 |
| ('PUBLIC-DOCS', 'train') | 2652 |
| ('PUBLIC-DOCS', 'validation') | 556 |
## Dataset Creation
### Curation Rationale
Creating a big dataset for Greek named entity recognition and entity linking.
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
Greek Government Gazette
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
According to (Angelidis et al., 2018) the authors of the paper annotated the data: *"Our group annotated all of the above documents for the 6 entity types that we examine."*
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
Note that the information given in this dataset card refer to the dataset version as provided by Joel Niklaus and Veton Matoshi. The dataset at hand is intended to be part of a bigger benchmark dataset. Creating a benchmark dataset consisting of several other datasets from different sources requires postprocessing. Therefore, the structure of the dataset at hand, including the folder structure, may differ considerably from the original dataset. In addition to that, differences with regard to dataset statistics as give in the respective papers can be expected. The reader is advised to have a look at the conversion script ```convert_to_hf_dataset.py``` in order to retrace the steps for converting the original dataset into the present jsonl-format. For further information on the original dataset structure, we refer to the bibliographical references and the original Github repositories and/or web pages provided in this dataset card.
## Additional Information
### Dataset Curators
The names of the original dataset curators and creators can be found in references given below, in the section *Citation Information*.
Additional changes were made by Joel Niklaus ([Email](mailto:joel.niklaus.2@bfh.ch); [Github](https://github.com/joelniklaus)) and Veton Matoshi ([Email](mailto:veton.matoshi@bfh.ch); [Github](https://github.com/kapllan)).
### Licensing Information
[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/)
### Citation Information
```
@inproceedings{Angelidis2018NamedER,
author = {Angelidis, Iosif and Chalkidis, Ilias and Koubarakis, Manolis},
booktitle = {JURIX},
keywords = {greek,legal nlp,named entity recognition},
title = {{Named Entity Recognition, Linking and Generation for Greek Legislation}},
year = {2018}
}
```
### Contributions
Thanks to [@JoelNiklaus](https://github.com/joelniklaus) and [@kapllan](https://github.com/kapllan) for adding this dataset.
|
joelito | null | null | null | false | 1,159 | false | joelito/legalnero | 2022-09-22T13:45:09.000Z | null | false | d7c1daa1778709f527069edc0b71d9ac144de495 | [] | [
"annotations_creators:other",
"language_creators:found",
"language:ro",
"license:cc-by-nc-nd-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:token-classification",
"task_ids:named-entity-recognition"
] | https://huggingface.co/datasets/joelito/legalnero/resolve/main/README.md | ---
annotations_creators:
- other
language_creators:
- found
language:
- ro
license:
- cc-by-nc-nd-4.0
multilinguality:
- monolingual
paperswithcode_id: null
pretty_name: Romanian Named Entity Recognition in the Legal domain (LegalNERo)
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
---
# Dataset Card for Romanian Named Entity Recognition in the Legal domain (LegalNERo)
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:** https://zenodo.org/record/4922385
- **Paper:** Pais, V., Mitrofan, M., Gasan, C. L., Coneschi, V., & Ianov, A. (2021). Named Entity Recognition in the {R}omanian Legal Domain. Proceedings of the Natural Legal Language Processing Workshop 2021, 9–18. https://doi.org/10.18653/v1/2021.nllp-1.2
- **Leaderboard:**
- **Point of Contact:** [Joel Niklaus](mailto:joel.niklaus.2@bfh.ch)
### Dataset Summary
LegalNERo is a manually annotated corpus for named entity recognition in the Romanian legal domain. It provides gold annotations for organizations, locations, persons, time and legal resources mentioned in legal documents. Additionally it offers GEONAMES codes for the named entities annotated as location (where a link could be established).
### Supported Tasks and Leaderboards
The dataset supports the task of named entity recognition.
### Languages
Since legal documents for LegalNERo are extracted from the larger [MARCELL-RO corpus](https://elrc-share.eu/repository/browse/marcell-romanian-legislative-subcorpus-v2/2da548428b9d11eb9c1a00155d026706ce94a6b59ffc4b0e9fb5cd9cebe6889e/), the language in the dataset is Romanian as it used in national legislation ranging from 1881 to 2021.
## Dataset Structure
### Data Instances
The file format is jsonl and three data splits are present (train, validation and test). Named Entity annotations are non-overlapping.
Rows only containing one word (mostly words such as `\t\t\t`, `\n` or `-----`) have been filtered out.
### Data Fields
The files contain the following data fields
- `file_name`: The file_name of the applicable annotation document
- `words`: The list of tokens obtained by applying the spacy (v 3.3.1) Greek tokenizer on the sentences. For more information see `convert_to_hf_dataset.py`.
- `ner`: The list of ner tags. The list of labels for the named entities that are covered by the dataset are the following:
- `LEGAL`: Legal reference/resources
- `LOC`: Location
- `ORG`: Organization
- `PER`: Person
- `TIME`: Time reference
- `O`: No entity annotation present
The final tagset (in IOB notation) is the following: `['O', 'B-TIME', 'I-TIME', 'B-LEGAL', 'I-LEGAL', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC', 'B-PER', 'I-PER']`
### Data Splits
Splits created by Joel Niklaus.
| split | number of documents | number of sentences |
|:---------------|--------------------:|--------------------:|
| train | 296 (80%) | 7552 |
| validation | 37 (10%) | 966 |
| test | 37 (10%) | 907 |
## Dataset Creation
### Curation Rationale
The dataset provides gold annotations for organizations, locations, persons, time and legal resources mentioned in Romanian legal documents.
### Source Data
#### Initial Data Collection and Normalization
The LegalNERo corpus consists of 370 documents from the larger [MARCELL-RO corpus](https://elrc-share.eu/repository/browse/marcell-romanian-legislative-subcorpus-v2/2da548428b9d11eb9c1a00155d026706ce94a6b59ffc4b0e9fb5cd9cebe6889e/). In the following we give a short description of the crawling process for the MARCELL-RO corpus.
*The MARCELL-RO corpus "contains 163,274 files, which represent the body of national legislation ranging from 1881 to 2021. This corpus includes mainly: governmental decisions, ministerial orders, decisions, decrees and laws. All the texts were obtained via crawling from the public Romanian legislative portal . We have not distinguished between in force and "out of force" laws because it is difficult to do this automatically and there is no external resource to use to distinguish between them. The texts were extracted from the original HTML format and converted into TXT files. Each file has multiple levels of annotation: firstly the texts were tokenized, lemmatized and morphologically annotated using the Tokenizing, Tagging and Lemmatizing (TTL) text processing platform developed at RACAI, then dependency parsed with NLP-Cube, named entities were identified using a NER tool developed at RACAI, nominal phrases were identified also with TTL, while IATE terms and EuroVoc descriptors were identified using an internal tool. All processing tools were integrated into an end-to-end pipeline available within the RELATE platform and as a dockerized version. The files were annotated with the latest version of the pipeline completed within Activity 4 of the MARCELL project."* [Link](https://elrc-share.eu/repository/browse/marcell-romanian-legislative-subcorpus-v2/2da548428b9d11eb9c1a00155d026706ce94a6b59ffc4b0e9fb5cd9cebe6889e/)
#### Who are the source language producers?
The source language producers are presumably politicians and lawyers.
### Annotations
#### Annotation process
*“Annotation of the LegalNERo corpus was performed by 5 human annotators, supervised by two senior researchers at the Institute for Artificial Intelligence "Mihai Drăgănescu" of the Romanian Academy (RACAI). For annotation purposes we used the BRAT tool4 […].
Inside the legal reference class, we considered sub-entities of type *organization* and *time*. This allows for using the LegalNERo corpus in two scenarios: using all the 5 entity classes or using only the remaining general-purpose classes. The LegalNERo corpus contains a total of 370 documents from the larger MARCELL-RO corpus. These documents were split amongst the 5 annotators, with certain documents being annotated by multiple annotators. Each annotator manually annotated 100 documents. The annotators were unaware of the overlap, which allowed us to compute an inter-annotator agreement. We used the Cohen’s Kappa measure and obtained a value of 0.89, which we consider to be a good result.”* (Pais et al., 2021)
#### Who are the annotators?
*"[...] 5 human annotators, supervised by two senior researchers at the Institute for Artificial Intelligence "Mihai Drăgănescu" of the Romanian Academy (RACAI)."*
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
Note that the information given in this dataset card refer to the dataset version as provided by Joel Niklaus and Veton Matoshi. The dataset at hand is intended to be part of a bigger benchmark dataset. Creating a benchmark dataset consisting of several other datasets from different sources requires postprocessing. Therefore, the structure of the dataset at hand, including the folder structure, may differ considerably from the original dataset. In addition to that, differences with regard to dataset statistics as give in the respective papers can be expected. The reader is advised to have a look at the conversion script ```convert_to_hf_dataset.py``` in order to retrace the steps for converting the original dataset into the present jsonl-format. For further information on the original dataset structure, we refer to the bibliographical references and the original Github repositories and/or web pages provided in this dataset card.
## Additional Information
### Dataset Curators
The names of the original dataset curators and creators can be found in references given below, in the section *Citation Information*.
Additional changes were made by Joel Niklaus ([Email](mailto:joel.niklaus.2@bfh.ch); [Github](https://github.com/joelniklaus)) and Veton Matoshi ([Email](mailto:veton.matoshi@bfh.ch); [Github](https://github.com/kapllan)).
### Licensing Information
[Creative Commons Attribution Non Commercial No Derivatives 4.0 International](https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode)
### Citation Information
```
@dataset{pais_vasile_2021_4922385,
author = {Păiș, Vasile and
Mitrofan, Maria and
Gasan, Carol Luca and
Ianov, Alexandru and
Ghiță, Corvin and
Coneschi, Vlad Silviu and
Onuț, Andrei},
title = {{Romanian Named Entity Recognition in the Legal
domain (LegalNERo)}},
month = may,
year = 2021,
publisher = {Zenodo},
doi = {10.5281/zenodo.4922385},
url = {https://doi.org/10.5281/zenodo.4922385}
}
```
```
@inproceedings{pais-etal-2021-named,
author = {Pais, Vasile and Mitrofan, Maria and Gasan, Carol Luca and Coneschi, Vlad and Ianov, Alexandru},
booktitle = {Proceedings of the Natural Legal Language Processing Workshop 2021},
doi = {10.18653/v1/2021.nllp-1.2},
month = {nov},
pages = {9--18},
publisher = {Association for Computational Linguistics},
title = {{Named Entity Recognition in the {R}omanian Legal Domain}},
url = {https://aclanthology.org/2021.nllp-1.2},
year = {2021}
}
```
### Contributions
Thanks to [@JoelNiklaus](https://github.com/joelniklaus) and [@kapllan](https://github.com/kapllan) for adding this dataset.
|
joelito | null | null | null | false | 2,734 | false | joelito/online_terms_of_service | 2022-09-22T13:45:42.000Z | null | false | 448c5caa985b8dafb275294f226120f41a7f8251 | [] | [
"annotations_creators:found",
"annotations_creators:other",
"language_creators:found",
"language:de",
"language:en",
"language:it",
"language:pl",
"license:other",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:text-classification",
"... | https://huggingface.co/datasets/joelito/online_terms_of_service/resolve/main/README.md | ---
annotations_creators:
- found
- other
language_creators:
- found
language:
- de
- en
- it
- pl
license:
- other
multilinguality:
- multilingual
pretty_name: A Corpus for Multilingual Analysis of Online Terms of Service
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
- multi-label-classification
---
# Dataset Card for A Corpus for Multilingual Analysis of Online Terms of Service
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:** http://claudette.eui.eu/corpus_multilingual_NLLP2021.zip
- **Paper:** Drawzeski, K., Galassi, A., Jablonowska, A., Lagioia, F., Lippi, M., Micklitz, H. W., Sartor, G., Tagiuri,
G., & Torroni, P. (2021). A Corpus for Multilingual Analysis of Online Terms of Service. Proceedings of the Natural
Legal Language Processing Workshop 2021, 1–8. https://doi.org/10.18653/v1/2021.nllp-1.1
- **Leaderboard:**
- **Point of Contact:** [Joel Niklaus](mailto:joel.niklaus.2@bfh.ch)
### Dataset Summary
*"We present the first annotated corpus for multilingual analysis of potentially unfair clauses in online Terms of
Service [=ToS]. The data set comprises a total of 100 contracts, obtained from 25 documents annotated in four different
languages: English, German, Italian, and Polish. For each contract, potentially unfair clauses for the consumer are
annotated, for nine different unfairness categories."* (Drawzeski et al., 2021)
### Supported Tasks and Leaderboards
The dataset can be used for multi-class multi-label text classification tasks, more specifically, for classifying unfair clauses in
ToS.
### Languages
English, German, Italian, and Polish.
## Dataset Structure
### Data Instances
The file format is jsonl and three data splits are present (train, validation and test).
### Data Fields
The dataset contains the following fields:
- `language`: The language of the sentence/document.
- `company`: The company of the document.
- `line_number`: The line number of the sentence in the document.
- `sentence`: The sentence to be classified.
- `unfairness_level`: The unfairness level assigned to the sentence (if two clauses apply, the higher unfairness level is assigned here).
The documents have been annotated using nine tags that represent different categories of clause unfairness. These boolean tags are:
- `a` = Arbitration: *”This clause requires or allows the parties to resolve their disputes through an arbitration process, before the case could go to court. It is therefore considered a kind of forum selection clause. However, such a clause may or may not specify that arbitration should occur within a specific jurisdiction. Clauses stipulating that the arbitration should (1) take place in a state other than the state of consumer’s residence and/or (2) be based not on law but on arbiter’s discretion were marked as clearly unfair.”* (Lippi et al., 2019)
- `ch` = Unilateral change: *"This clause specifies the conditions under which the service provider could amend and modify the terms of service and/or the service itself. Such clauses were always considered as potentially unfair. This is because the ECJ has not yet issued a judgment in this regard, though the Annex to the Direc- tive contains several examples supporting such a qualification."* (Lippi et al., 2019)
- `cr` = Content removal : *"This gives the provider a right to modify/delete user’s content, including in-app purchases, and sometimes specifies the conditions under which the service provider may do so. As in the case of unilateral termination, clauses that indicate conditions for content removal were marked as potentially unfair, whereas clauses stipulating that the service provider may remove content in his full discretion, and/or at any time for any or no reasons and/or without notice nor possibility to retrieve the content were marked as clearly unfair."* (Lippi et al., 2019)
- `j` = Jurisdiction : *"This type of clause stipulates what courts will have the competence to adjudicate disputes under the contract. Jurisdiction clauses giving consumers a right to bring disputes in their place of residence were marked as clearly fair, whereas clauses stating that any judicial proceeding takes a residence away (i.e. in a different city, different country) were marked as clearly unfair. This assessment is grounded in ECJ’s case law, see for example Oceano case number C-240/98."* (Lippi et al., 2019)
- `law` = Choice of law: *"This clause specifies what law will govern the contract, meaning also what law will be applied in potential adjudication of a dispute arising under the contract. Clauses defining the applicable law as the law of the consumer’s country of residence were marked as clearly fair [...]"* (Lippi et al., 2019)
- `ltd` = Limitation of liability: *"This clause stipulates that the duty to pay damages is limited or excluded, for certain kinds of losses and under certain conditions. Clauses that explicitly affirm non-excludable providers’ liabilities were marked as clearly fair."* (Lippi et al., 2019)
- `ter` = Unilateral termination: *"This clause gives provider the right to suspend and/or terminate the service and/or the contract, and sometimes details the circumstances under which the provider claims to have a right to do so. Unilateral termination clauses that specify reasons for termination were marked as potentially unfair. Whereas clauses stipulating that the service provider may suspend or terminate the service at any time for any or no reasons and/or without notice were marked as clearly unfair."* (Lippi et al., 2019)
- `use` = Contract by using: *"This clause stipulates that the consumer is bound by the terms of use of a specific service, simply by using the service, without even being required to mark that he or she has read and accepted them. We always marked such clauses as potentially unfair. The reason for this choice is that a good argument can be offered for these clauses to be unfair, because they originate an imbalance in rights and duties of the parties, but this argument has no decisive authoritative backing yet, since the ECJ has never assessed a clause of this type."* (Lippi et al., 2019)
- `pinc` = Privacy included: This tag identifies *"clauses stating that consumers consent to the privacy policy simply by using the service. Such clauses have been always considered potentially unfair"* (Drawzeski et al., 2021)
- `all_topics` = an aggregate column containing all applicable topics combined
*”We assumed that each type of clause could be classified as either clearly fair, or potentially unfair, or clearly unfair. In order to mark the different degrees of (un)fairness we appended a numeric value to each XML tag, with 1 meaning clearly fair, 2 potentially unfair, and 3 clearly unfair. Nested tags were used to annotate text segments relevant to more than one type of clause. With clauses covering multiple paragraphs, we chose to tag each paragraph separately, possibly with different degrees of (un)fairness.”* (Lippi et al., 2019)
### Data Splits
No splits provided in the original paper.
Joel Niklaus created the splits manually. The train split contains the 20 (80%) first companies in alphabetic order (*Booking, Dropbox, Electronic_Arts, Evernote, Facebook, Garmin, Google, Grindr, Linkedin, Mozilla,
Pinterest, Quora, Ryanair, Skype, Skyscanner, Snap, Spotify, Terravision, Tinder, Tripadvisor*). The
validation split contains the 2 (8%) companies *Tumblr* and *Uber*. The test split contains the 3 (12%) companies *Weebly*,
*Yelp*, *Zynga*.
There are two tasks possible for this dataset.
#### Clause Topics
By only considering the clause topic, we separated the clause topic from the fairness level classification. Thus, the label set could be reduced to just 9 classes.
This dataset poses a multi-label multi-class sentence classification problem.
The following label distribution shows the number of occurrences per label per split. `total occurrences` sums up the previous rows (number of clause topics per split). `split size` is the number of sentences per split.
| clause topic | train | validation | test |
|:----------------------|------------:|-----------------:|-----------:|
| a | 117 | 6 | 21 |
| ch | 308 | 45 | 53 |
| cr | 155 | 4 | 44 |
| j | 206 | 8 | 36 |
| law | 178 | 8 | 26 |
| ltd | 714 | 84 | 161 |
| ter | 361 | 39 | 83 |
| use | 185 | 14 | 32 |
| pinc | 71 | 0 | 8 |
| **total occurrences** | **2295** | **208** | **464** |
| **split size** | **19942** | **1690** | **4297** |
#### Unfairness Levels
When predicting unfairness levels, all untagged sentences can be removed. This reduces the dataset size considerably.
This dataset poses a single-label multi-class sentence classification problem.
| unfairness_level | train | validation | test |
|:---------------------------|------------:|-----------:|----------:|
| untagged | 17868 | 1499 | 3880 |
| potentially_unfair | 1560 | 142 | 291 |
| clearly_unfair | 259 | 31 | 65 |
| clearly_fair | 156 | 5 | 32 |
| **total without untagged** | **1975** | **178** | **388** |
| **total** | **19942** | **1690** | **4297** |
## Dataset Creation
### Curation Rationale
The EU legislation is published in all official languages. This multilingualism comes with costs and challenges, such as limited cross-linguistical interpretability. The EU has refrained from regulating languages in which standard terms in consumer contracts should be drafted, allowing for differing approaches to emerge in various jurisdictions. Consumer protection authorities and non-governmental organizations in Europe tend to operate only in their respective languages. Therefore, consumer protection technologies are needed that are capable of dealing with multiple languages. The dataset at hand can be used for the automated detection of unfair clauses in ToS which, in most cases, are available in multiple languages. (Drawzeski et al., 2021)
### Source Data
#### Initial Data Collection and Normalization
*"The analysed ToS were retrieved from the [Claudette pre-existing corpus](http://claudette.eui.eu/ToS.zip), covering 100 English ToS (Lippi et al., 2019; Ruggeri et al., 2021). Such terms mainly concern popular digital services provided to consumers, including leading online platforms (such as search engines and social media). The predominant language of drafting of these ToS is English, with differing availability of corresponding ToS in other languages. To carry out the present study, the ultimate 25 ToS were selected on the basis of three main criteria: a) their availability in the four selected languages; b) the possibility of identifying a correspondence between the different versions, given their publication date; and c) the similarity of their structure (e.g. number of clauses, sections, etc.). To illustrate, while ToS in both German and Italian were identified for 63 out of the 100 ToS contained in the pre-existing Claudette training corpus, Polish versions were found for only 42 of these 63 ToS. Out of the 42 ToS available in the four languages, we selected those with the more closely corresponding versions based on criteria b) and c) above. Perfect correspondence across the 4 languages, however, could not be achieved for all 25 ToS."* (Drawzeski et al., 2021)
#### Who are the source language producers?
The source language producers are likely to be lawyers.
### Annotations
#### Annotation process
The dataset at hand is described by Drawzeski et al. (2021). The ToS of the dataset were retrieved from the pre-existing
and mono-lingual (English) Claudette corpus which is described in (Lippi et al., 2019). Drawzeski et al. (2021) *“investigate methods for automatically transferring the annotations made on ToS in the context of the Claudette project
onto the corresponding versions of the same documents in a target language, where such resources and expertise may be
lacking.”*
Therefore, in the following, we will present the annotation process for the Claudette corpus as described in (Lippi et
al., 2019).
*”The corpus consists of 50 relevant on-line consumer contracts, i.e., ToS of on-line platforms. Such contracts were
selected among those offered by some of the major players in terms of number of users, global relevance, and time the
service was established. Such contracts are usually quite detailed in content, are frequently updated to reflect changes
both in the service and in the applicable law, and are often available in different versions for different
jurisdictions. Given multiple versions of the same contract, we selected the most recent version available on-line to
European customers. The mark-up was done in XML by three annotators, which jointly worked for the formulation of the
annotation guidelines. The whole annotation process included several revisions, where some corrections were also
suggested by an analysis of the false positives and false negatives retrieved by the initial machine learning
prototypes. Due to the large interaction among the annotators during this process, in order to assess inter-annotation
agreement, a further test set consisting of 10 additional contracts was tagged, following the final version of the
guidelines. […] We produced an additional test set consisting of 10 more annotated contracts. Such documents were
independently tagged by two distinct annotators who had carefully studied the guidelines. In order to quantitatively
measure the inter-annotation agreement, for this test set we computed the standard Cohen’s 𝜅 metric […] which resulted
to be 0.871 […].”*
#### Who are the annotators?
Not specified.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
It is very likely that some ToS in German, Italian and Polish are direct translations from English. Drawzeski et al. (2021) write: *“Although we could not assess this comprehensively in the present study, we infer from the wording of the ToS that at least in 9 out of 25 cases, German, Italian and Polish documents were indeed translations of the English originals.”*
### Other Known Limitations
Note that the information given in this dataset card refer to the dataset version as provided by Joel Niklaus and Veton Matoshi. The dataset at hand is intended to be part of a bigger benchmark dataset. Creating a benchmark dataset consisting of several other datasets from different sources requires postprocessing. Therefore, the structure of the dataset at hand, including the folder structure, may differ considerably from the original dataset. In addition to that, differences with regard to dataset statistics as give in the respective papers can be expected. The reader is advised to have a look at the conversion script ```convert_to_hf_dataset.py``` in order to retrace the steps for converting the original dataset into the present jsonl-format. For further information on the original dataset structure, we refer to the bibliographical references and the original Github repositories and/or web pages provided in this dataset card.
## Additional Information
### Dataset Curators
The names of the original dataset curators and creators can be found in references given below, in the section *Citation Information*.
Additional changes were made by Joel Niklaus ([Email](mailto:joel.niklaus.2@bfh.ch); [Github](https://github.com/joelniklaus)) and Veton Matoshi ([Email](mailto:veton.matoshi@bfh.ch); [Github](https://github.com/kapllan)).
### Licensing Information
cc-by-nc-2.5
### Citation Information
```
@inproceedings{drawzeski-etal-2021-corpus,
address = {Punta Cana, Dominican Republic},
author = {Drawzeski, Kasper and Galassi, Andrea and Jablonowska, Agnieszka and Lagioia, Francesca and Lippi, Marco and Micklitz, Hans Wolfgang and Sartor, Giovanni and Tagiuri, Giacomo and Torroni, Paolo},
booktitle = {Proceedings of the Natural Legal Language Processing Workshop 2021},
doi = {10.18653/v1/2021.nllp-1.1},
month = {nov},
pages = {1--8},
publisher = {Association for Computational Linguistics},
title = {{A Corpus for Multilingual Analysis of Online Terms of Service}},
url = {https://aclanthology.org/2021.nllp-1.1},
year = {2021}
}
```
### Contributions
Thanks to [@JoelNiklaus](https://github.com/joelniklaus) and [@kapllan](https://github.com/kapllan) for adding this
dataset.
|
lapix | null | @misc{CCAgTDataset,
doi = {10.17632/WG4BPM33HJ.2},
url = {https://data.mendeley.com/datasets/wg4bpm33hj/2},
author = {Jo{\\~{a}}o Gustavo Atkinson Amorim and Andr{\'{e}} Vict{\'{o}}ria Matias and Tainee Bottamedi and Vinícius Sanches and Ane Francyne Costa and Fabiana Botelho De Miranda Onofre and Alexandre Sherlley Casimiro Onofre and Aldo von Wangenheim},
title = {CCAgT: Images of Cervical Cells with AgNOR Stain Technique},
publisher = {Mendeley},
year = {2022},
copyright = {Attribution-NonCommercial 3.0 Unported}
} | The CCAgT (Images of Cervical Cells with AgNOR Stain Technique) dataset contains 9339 images (1600x1200 resolution where each pixel is 0.111µmX0.111µm) from 15 different slides stained using the AgNOR technique.
Each image has at least one label. In total, this dataset has more than 63K instances of annotated object.
The images are from the patients of the Gynecology and Colonoscopy Outpatient Clinic of the Polydoro Ernani de São Thiago University Hospital of the Universidade Federal de Santa Catarina (HU-UFSC). | false | 1 | false | lapix/CCAgT | 2022-07-27T21:11:52.000Z | null | false | 2f9aa77e76373edaf9fd26f2b4b42a14d230c956 | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"license:cc-by-nc-3.0",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:image-segmentation",
"task_categories:object-detection",
"task_ids:semantic-... | https://huggingface.co/datasets/lapix/CCAgT/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- cc-by-nc-3.0
multilinguality:
- monolingual
paperswithcode_id: null
pretty_name: Images of Cervical Cells with AgNOR Stain Technique
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- image-segmentation
- object-detection
task_ids:
- semantic-segmentation
- instance-segmentation
---
# Dataset Card for Images of Cervical Cells with AgNOR Stain Technique
## 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:** [CCAgT homepage](https://data.mendeley.com/datasets/wg4bpm33hj/)
- **Repository:** [CCAgT-utils](https://github.com/johnnv1/CCAgT-utils)
- **Paper:** [Semantic Segmentation for the Detection of Very Small Objects on Cervical Cell Samples Stained with the AgNOR Technique](https://dx.doi.org/10.2139/ssrn.4126881)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [João G. A. Amorim](mailto:joao.atkinson@posgrad.ufsc.br)
### Dataset Summary
The CCAgT (Images of Cervical Cells with AgNOR Stain Technique) dataset contains 9339 images (1600x1200 resolution where each pixel is 0.111µmX0.111µm) from 15 different slides stained using the AgNOR technique. Each image has at least one label. In total, this dataset has more than 63K instances of annotated object. The images are from the patients of the Gynecology and Colonoscopy Outpatient Clinic of the [Polydoro Ernani de São Thiago University Hospital of the Universidade Federal de Santa Catarina (HU-UFSC)](https://unihospital.ufsc.br/).
### Supported Tasks and Leaderboards
- `image-segmentation`: The dataset can be used to train a model for semantic segmentation or instance segmentation. Semantic segmentation consists in classifying each pixel of the image. Success on this task is typically measured by achieving high values of [mean iou](https://huggingface.co/spaces/evaluate-metric/mean_iou) or [f-score](https://huggingface.co/spaces/evaluate-metric/f1) for pixels results. Instance segmentation consists of doing object detection first and then using a semantic segmentation model inside detected objects. For instances results, this task is typically measured by achieving high values of [recall](https://huggingface.co/spaces/evaluate-metric/recall), [precision](https://huggingface.co/spaces/evaluate-metric/precision) and [f-score](https://huggingface.co/spaces/evaluate-metric/f1).
- `object-detection`: The dataset can be used to train a model for object detection to detect the nuclei categories or the nucleolus organizer regions (NORs), which consists of locating instances of objects and then classifying each one. This task is typically measured by achieving a high values of [recall](https://huggingface.co/spaces/evaluate-metric/recall), [precision](https://huggingface.co/spaces/evaluate-metric/precision) and [f-score](https://huggingface.co/spaces/evaluate-metric/f1).
### Languages
The class labels in the dataset are in English.
## Dataset Structure
### Data Instances
An example looks like the one below:
#### `semantic segmentation` (default configuration)
```
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1200x1600 at 0x276021C5EB8>,
'annotation': <PIL.PngImagePlugin.PngImageFile image mode=L size=1200x1600 at 0x385021C5ED7>
}
```
#### `object detection`
```
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1200x1600 at 0x276021C5EB8>,
'objects': {
'bbox': [
[36, 7, 13, 32],
[50, 7, 12, 32]
],
'label': [1, 5]
}
```
#### `instance segmentation`
```
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1200x1600 at 0x276021C5EB8>,
'objects': {
'bbox': [
[13.3, 7.5, 47.6, 38.3],
[10.2, 7.5, 50.7, 38.3]
],
'segment': [
[[36.2, 7.5, 13.3, 32.1, 52.1, 40.6, 60.9, 45.8, 50.1, 40, 40, 33.2, 35.2]],
[[10.2, 7.5, 10.3, 32.1, 52.1, 40.6, 60.9, 45.8, 50.1, 40, 40, 33.2, 35.2]],
],
'label': [1, 5]
}
```
### Data Fields
The data annotations have the following fields:
#### `semantic segmentation` (default configuration)
- `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`.
- `annotation`: A `PIL.Image.Image` object containing the annotation mask. The mask has a single channel and the following pixel values are possible: `BACKGROUND` (0), `NUCLEUS` (1), `CLUSTER` (2), `SATELLITE` (3), `NUCLEUS_OUT_OF_FOCUS` (4), `OVERLAPPED_NUCLEI` (5), `NON_VIABLE_NUCLEUS` (6) and `LEUKOCYTE_NUCLEUS` (7).
#### `object detection`
- `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`.
- `objects`: a dictionary containing bounding boxes and labels of the cell objects
- `bbox`: a list of bounding boxes (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format) corresponding to the objects present on the image
- `label`: a list of integers representing the category (7 categories to describe the objects in total; two to differentiate nucleolus organizer regions), with the possible values including `NUCLEUS` (0), `CLUSTER` (1), `SATELLITE` (2), `NUCLEUS_OUT_OF_FOCUS` (3), `OVERLAPPED_NUCLEI` (4), `NON_VIABLE_NUCLEUS` (5) and `LEUKOCYTE_NUCLEUS` (6).
#### `instance segmentation`
- `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`.
- `objects`: a dictionary containing bounding boxes and labels of the cell objects
- `bbox`: a list of bounding boxes (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format) corresponding to the objects present on the image
- `segment`: a list of segments in format of `[polygon_0, ..., polygon_n]`, where each polygon is `[x0, y0, ..., xn, yn]`.
- `label`: a list of integers representing the category (7 categories to describe the objects in total; two to differentiate nucleolus organizer regions), with the possible values including `NUCLEUS` (0), `CLUSTER` (1), `SATELLITE` (2), `NUCLEUS_OUT_OF_FOCUS` (3), `OVERLAPPED_NUCLEI` (4), `NON_VIABLE_NUCLEUS` (5) and `LEUKOCYTE_NUCLEUS` (6).
### Data Splits
The data is split randomly using the fixed seed into training, test and validation set. The training data contains 70% of the images and the testing and the validation data contain 15% of the images each. In total, the training set contains 6533 images and the testing and the validation set 1403 images each.
<details>
<summary>
Click here to see additional statistics:
</summary>
| Slide id | Diagnostics | images | annotations | NUCLEUS | CLUSTER | SATELLITE | NUCLEUS_OUT_OF_FOCUS | OVERLAPPED_NUCLEI | NON_VIABLE_NUCLEUS | LEUKOCYTE_NUCLEUS |
| :-------: | :---------: | :----: | :---------: | :-----: | :------: | :-------: | :------------------: | :---------------: | :---------------: | :-------: |
| A | CIN 3 | 1311 | 3164 | 763 | 1038 | 922 | 381 | 46 | 14 | 0 |
| B | SCC | 561 | 911 | 224 | 307 | 112 | 132 | 5 | 1 | 130 |
| C | AC | 385 | 11420 | 2420 | 3584 | 1112 | 1692 | 228 | 477 | 1907 |
| D | CIN 3 | 2125 | 1258 | 233 | 337 | 107 | 149 | 12 | 8 | 412 |
| E | CIN 3 | 506 | 11131 | 2611 | 6249 | 1648 | 476 | 113 | 34 | 0 |
| F | CIN 1 | 318 | 3365 | 954 | 1406 | 204 | 354 | 51 | 326 | 70 |
| G | CIN 2 | 249 | 2759 | 691 | 1279 | 336 | 268 | 49 | 51 | 85 |
| H | CIN 2 | 650 | 5216 | 993 | 983 | 425 | 2562 | 38 | 214 | 1 |
| I | No lesion | 309 | 474 | 56 | 55 | 19 | 170 | 2 | 23 | 149 |
| J | CIN 1 | 261 | 1786 | 355 | 304 | 174 | 743 | 18 | 33 | 159 |
| K | No lesion | 1503 | 13102 | 2464 | 6669 | 638 | 620 | 670 | 138 | 1903 |
| L | CIN 2 | 396 | 3289 | 842 | 796 | 387 | 1209 | 27 | 23 | 5 |
| M | CIN 2 | 254 | 1500 | 357 | 752 | 99 | 245 | 16 | 12 | 19 |
| N | CIN 3 | 248 | 911 | 258 | 402 | 67 | 136 | 10 | 6 | 32 |
| O | AC | 262 | 2904 | 792 | 1549 | 228 | 133 | 88 | 52 | 62 |
| **Total** | - | 9339 | 63190 | 14013 | 25710 | 6478 | 9270 | 1373 | 1412 | 4934 |
Lesion types:
- Cervical intraepithelial neoplasia 1 - CIN 1
- Cervical intraepithelial neoplasia 2 - CIN 2
- Cervical intraepithelial neoplasia 3 - CIN 3
- Squamous cell carcinoma - SCC
- Adenocarcinoma - AC
- No lesion
</details>
## Dataset Creation
### Curation Rationale
CCAgT was built to provide a dataset for machines to learn how to identify nucleus and nucleolus organizer regions (NORs).
### Source Data
#### Initial Data Collection and Normalization
The images are collected as patches/tiles of whole slide images (WSIs) from cervical samples stained with AgNOR technique to allow the detection of nucleolus organizer regions (NORs). NORs are DNA loops containing genes responsible for the transcription of ribosomal RNA located in the cell nucleolus. They contain a set of argyrophilic proteins, selectively stained by silver nitrate, which can be identified as black dots located throughout the nucleoli area and called AgNORs.
#### Who are the source language producers?
The dataset was built using images from examinations (a gynecological exam, colposcopy and biopsy) of 15 women patients who were treated at the Gynecology and Colposcopy Outpatient Clinic of the [University Hospital Professor Polydoro Ernani de São Thiago of Federal University of Santa Catarina (HU-UFSC)](https://unihospital.ufsc.br/) and had 6 different diagnoses in their oncological exams. The samples were collected by the members of the Clinical Analyses Department: Ane Francyne Costa, Fabiana Botelho De Miranda Onofre, and Alexandre Sherlley Casimiro Onofre.
### Annotations
#### Annotation process
The instances were annotated using the [labelbox](https://labelbox.com/) tool. The satellite category was labeled as a single dot, and the other categories were labeled as polygons. After the annotation process, all annotations were reviewed.
#### Who are the annotators?
Members of the Clinical Analyses Department and the Image Processing and Computer Graphics Lab. — LAPiX from [Universidade Federal de Santa Catarina (UFSC)](https://en.ufsc.br/).
- Tainee Bottamedi
- Vinícius Sanches
- João H. Telles de Carvalho
- Ricardo Thisted
### Personal and Sensitive Information
This research was approved by the UFSC Research Ethics Committee (CEPSH), protocol number 57423616.3.0000.0121. All involved patients were informed about the study's objectives, and those who agreed to participate signed an informed consent form.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset's purpose is to help spread the AgNOR as a support method for cancer diagnosis since this method is not standardized among pathologists.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
Satellite annotation is not as accurate for pixel-level representation due to single-point annotations.
## Additional Information
### Dataset Curators
Members of the Clinical Analyses Department from [Universidade Federal de Santa Catarina (UFSC)](https://en.ufsc.br/) collected the dataset samples: Ane Francyne Costa, Fabiana Botelho De Miranda Onofre, and Alexandre Sherlley Casimiro Onofre.
### Licensing Information
The files associated with this dataset are licensed under an [Attribution-NonCommercial 3.0 Unported](https://creativecommons.org/licenses/by-nc/3.0/) license.
Users are free to adapt, copy or redistribute the material as long as they attribute it appropriately and do not use it for commercial purposes.
### Citation Information
```bibtex
% Dataset oficial page
@misc{CCAgTDataset,
doi = {10.17632/WG4BPM33HJ.2},
url = {https://data.mendeley.com/datasets/wg4bpm33hj/2},
author = {Jo{\~{a}}o Gustavo Atkinson Amorim and Andr{\'{e}} Vict{\'{o}}ria Matias and Tainee Bottamedi and Vin{\'{i}}us Sanches and Ane Francyne Costa and Fabiana Botelho De Miranda Onofre and Alexandre Sherlley Casimiro Onofre and Aldo von Wangenheim},
title = {CCAgT: Images of Cervical Cells with AgNOR Stain Technique},
publisher = {Mendeley},
year = {2022},
copyright = {Attribution-NonCommercial 3.0 Unported}
}
% Dataset second version
% pre-print:
@article{AtkinsonAmorim2022,
doi = {10.2139/ssrn.4126881},
url = {https://doi.org/10.2139/ssrn.4126881},
year = {2022},
publisher = {Elsevier {BV}},
author = {Jo{\~{a}}o Gustavo Atkinson Amorim and Andr{\'{e}} Vict{\'{o}}ria Matias and Allan Cerentini and Fabiana Botelho de Miranda Onofre and Alexandre Sherlley Casimiro Onofre and Aldo von Wangenheim},
title = {Semantic Segmentation for the Detection of Very Small Objects on Cervical Cell Samples Stained with the {AgNOR} Technique},
journal = {{SSRN} Electronic Journal}
}
% Dataset first version
% Link: https://arquivos.ufsc.br/d/373be2177a33426a9e6c/
% Paper:
@inproceedings{AtkinsonSegmentationAgNORCBMS2020,
author={Jo{\~{a}}o Gustavo Atkinson Amorim and Luiz Antonio Buschetto Macarini and Andr{\'{e}} Vict{\'{o}}ria Matias and Allan Cerentini and Fabiana Botelho De Miranda Onofre and Alexandre Sherlley Casimiro Onofre and Aldo von Wangenheim},
booktitle={2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)},
title={A Novel Approach on Segmentation of AgNOR-Stained Cytology Images Using Deep Learning},
year={2020},
pages={552-557},
doi={10.1109/CBMS49503.2020.00110},
url={https://doi.org/10.1109/CBMS49503.2020.00110}
}
```
### Contributions
Thanks to [@johnnv1](https://github.com/johnnv1) for adding this dataset. |
davanstrien | null | null | null | false | 1 | false | davanstrien/newspaper_navigator | 2022-10-14T08:15:42.000Z | null | false | 3b109dff6af7c935dda7835e33d6ecf9f2fa0eea | [] | [
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"task_categories:image-to-text",
"task_categories:text-to-image",
"task_categories:feature-extraction",
"task_categories:zero-shot-image-classification",
"task_ids:image-captioning"
] | https://huggingface.co/datasets/davanstrien/newspaper_navigator/resolve/main/README.md | ---
annotations_creators:
- machine-generated
language: []
language_creators:
- machine-generated
license: []
multilinguality: []
pretty_name: Newspaper Navigator
size_categories: []
source_datasets: []
tags: []
task_categories:
- image-to-text
- text-to-image
- feature-extraction
- zero-shot-image-classification
task_ids:
- image-captioning
--- |
gegham | null | null | null | false | 1 | false | gegham/tensor | 2022-07-13T13:07:12.000Z | null | false | 64f93ccd51d919efda61d1cdde92dc31e52deadd | [] | [] | https://huggingface.co/datasets/gegham/tensor/resolve/main/README.md | efdsv |
jonaskoenig | null | null | null | false | 2 | false | jonaskoenig/trump_administration_statement | 2022-07-15T10:53:15.000Z | null | false | 6ac51b5ccffa328054172eac65dc6084c2f6a1c4 | [] | [
"license:mit"
] | https://huggingface.co/datasets/jonaskoenig/trump_administration_statement/resolve/main/README.md | ---
license: mit
---
|
paren8esis | null | @ARTICLE{
9749916,
author={Sykas, Dimitrios and Sdraka, Maria and Zografakis, Dimitrios and Papoutsis, Ioannis},
journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
title={A Sentinel-2 multi-year, multi-country benchmark dataset for crop classification and segmentation with deep learning},
year={2022},
doi={10.1109/JSTARS.2022.3164771}
} | Sen4AgriNet is a Sentinel-2 based time series multi country benchmark dataset, tailored for
agricultural monitoring applications with Machine and Deep Learning. It is annotated from
farmer declarations collected via the Land Parcel Identification System (LPIS) for harmonizing
country wide labels. These declarations have only recently been made available as open data,
allowing for the first time the labelling of satellite imagery from ground truth data.
We proceed to propose and standardise a new crop type taxonomy across Europe that address
Common Agriculture Policy (CAP) needs, based on the Food and Agriculture Organization (FAO)
Indicative Crop Classification scheme. Sen4AgriNet is the only multi-country, multi-year dataset
that includes all spectral information. It is constructed to cover the period 2016-2020 for
Catalonia and France, while it can be extended to include additional countries. | false | 5 | false | paren8esis/S4A | 2022-07-04T14:05:06.000Z | null | false | 6ee48afecc22f3a08f6a01267d55a6d0c6d3ebb2 | [] | [
"arxiv:2204.00951"
] | https://huggingface.co/datasets/paren8esis/S4A/resolve/main/README.md | ---
YAML tags:
---
## Dataset Description
- **Homepage:** [www.sen4agrinet.space.noa.gr](https://www.sen4agrinet.space.noa.gr/)
- **Repository:** [github.com/Orion-AI-Lab/S4A](https://github.com/Orion-AI-Lab/S4A)
- **Paper:** ["A Sentinel-2 multi-year, multi-country benchmark dataset for crop classification and segmentation with deep learning" (D. Sykas, M. Sdraka, D. Zografakis, I. Papoutsis](https://arxiv.org/abs/2204.00951)
### Dataset Summary
Sen4AgriNet is a Sentinel-2 based time series multi-country benchmark dataset, tailored for agricultural monitoring applications with Machine and Deep Learning. It is annotated from farmer declarations collected via the Land Parcel Identification System (LPIS) for harmonizing country wide labels. These declarations have only recently been made available as open data, allowing for the first time the labelling of satellite imagery from ground truth data. We proceed to propose and standardise a new crop type taxonomy across Europe that address Common Agriculture Policy (CAP) needs, based on the Food and Agriculture Organization (FAO) Indicative Crop Classification scheme. Sen4AgriNet is the only multi-country, multi-year dataset that includes all spectral information. The current version covers the period 2019-2020 for Catalonia and France, while it can be extended to include additional countries.
### Languages
All information in the dataset is in English (`en_GB`).
## Dataset Structure
### Data Instances
A typical sample in Sen4AgriNet consists of the following fields:
```
{
'patch_full_name': '2019_31TCF_patch_10_14',
'patch_year': '2019',
'patch_name': 'patch_10_14',
'patch_country_code': 'ES',
'patch_tile': '31TCF',
'B01': array([...]),
'B02': array([...]),
'B03': array([...]),
'B04': array([...]),
'B05': array([...]),
'B06': array([...]),
'B07': array([...]),
'B08': array([...]),
'B09': array([...]),
'B10': array([...]),
'B11': array([...]),
'B12': array([...]),
'B8A': array([...]),
'parcels': array([...]),
'labels': array([...]),
'timestamp': [...]
}
```
### Data Fields
Below we provide a brief explanation of each field:
- `patch_full_name`: The full name of the patch.
- `patch_year`: The year of the observations included in the patch.
- `patch_name`: The name of the patch. It is of the form: `patch_xx_yy` where `xx` and `yy` are the indices of the patch inside the tile.
- `patch_country_code`: The country code of the observations included in the patch. Currently it is either `ES` for Catalonia or `FR` for France.
- `B01`, ..., `B8A`: Each one is an array containing the observations of the corresponding Sentinel-2 band. The shape of each array is (T, H, W) where T is the number of observations, H the height of the image and W the width of the image.
- `parcels`: A mask containing the parcels code number.
- `labels`: A mask containing the class codes for each crop in the taxonomy.
- `timestamp`: The timestamps of the observations.
### Data Splits
In this version of the dataset there are no predefined train/val/test splits so that the users can define their own.
### Data configurations
There are the following configurations in the current version of Sen4AgriNet:
- `complete`: The complete Sen4AgriNet dataset.
- `cat_2019`: Only Catalonia data for 2019.
- `cat_2020`: Only Catalonia data for 2020.
- `fr_2019`: Only France data for 2019.
## Dataset Creation
### Curation Rationale
One of the major problems faced by researchers in the fields of Remote Sensing and AI is the absence of country-wide labelled data that are harmonized along space and time. Specifically in the EU, the Common Agriculture Policy (CAP) has placed a stepping stone to overcome this issue by legally establishing Paying Agencies in each EU country which are responsible for distributing subsidies to farmers. In order to fulfill their objectives, Paying Agencies systematically collect the cultivated crop type and parcel geometries for every farmer and record it via the Land Parcel Identification System (LPIS) in a standardized way for each country. Unfortunately, public access to these farmer declarations has been restricted for several years, thus making it almost impossible to get country-wide ground truth data. However, since 2019 and for the
first time these datasets are gradually becoming open (e.g. France, Catalonia, Estonia, Croatia, Slovenia, Slovakia and Luxemburg). This change offers a significant opportunity for the Earth Observation (EO) community to explore novel and innovative data-driven agricultural applications, by exploiting this abundance of new LPIS information.
In principle, this fusion of the LPIS data sources has tremendous potential but there are still some barriers to overcome. First of all, the LPIS system of each country is customly configured to utilize the local language of the crop types and the specific taxonomy structure of the crops that matches the local subsidies policy implementation. This non-standardization of the labels prohibits the spatial generalization of Deep Learning (DL) models and thus needs to be carefully handled to achieve a common representation consistent among countries. On top of these contextual/semantic barriers, parcels are mapped in the corresponding national cartographic projection which in all cases is different from the cartographic projection of the satellite images and pose an additional challenge on the preparation of a consistent, proper and at scale DL-ready dataset.
Aiming to overcome the above limitations in this repository we offer Sen4AgriNet, a unique benchmark EO dataset for agricultural monitoring with the following key characteristics:
- it is **pixel based** to capture spatial parcel variability
- it is **multi-temporal** to capture the crop phenology phases
- it is **multi-annual** to model the seasonal variability
- it is **multi-country** to model the geographic spatial variability
- it is **object-aggregated** to further incorporate ground truth data (parcel geometries) in the process
- it is **modular** since it can be enlarged with parcels from more EU countries or expanded in a straightforward way to include additional sensor and non-EO data (e.g. meteorological data)
### Source Data
1) The LPIS data for the region of Catalonia for 2019–2020 provided by the "Agricultura, Ramaderia, Pesca i Alimentacio" with an Open Data Commons Attribution License.
2) France LPIS data for 2019 provided by the French Paying Agency with an Open Data Commons Attribution License.
3) All Sentinel-2 L1C images with less than 10% cloud coverage for the above tiles.
#### Initial Data Collection and Normalization
The Sentinel-2 L1C images were downloaded from Copernicus and each image was split into 900 non-overlapping patches. A single patch contains 366x366 images for the 10-meter bands, 183x183 for the 20-meter bands and 61x61 for the 60-meter bands. The size of the patches was chosen in order to have integer division of the size of the tile with all 3 different spatial resolutions of Sentinel-2.
#### Annotation process
The Indicative Crop Classification (ICC) scheme was developed by the United Nations FAO organization. It is an approach to produce a harmonized vocabulary and taxonomy for crops and plants that are used in food production. Sen4AgriNet adopts and customises an extended version of FAO ICC in order to create a universally applicable crop label nomenclature for the collected LPIS data with the following benefits:
- Single language (English) is used and naming for all classes across all participating countries.
- Classes are normalized among different datasets.
- Hierarchical class structure is adopted. Depending on the application different levels of classes can be used.
- Additional non-agricultural classes are used (e.g. "fallow land", "barren land", etc.) to model Remote Sensing spectral signatures since agricultural parcels co-exist with other unrelated classes in satellite images.
The presented custom FAO/CLC classification scheme has a total of 9 groups, 168 classes and sub-classes. The 161 classes/sub-classes are crop related, 4 are some major CLC classes (as sub-classes in this hierarchy), 2 are the fallow and barren lands, and 1 is the no data sub-class.
This crop taxonomy was used to create the `labels` mask. In addition, a second annotation mask is provided (`parcels`) where each parcel obtains a unique identifier, regardless of the crops cultivated in it.
### Personal and Sensitive Information
None.
## Considerations for Using the Data
### Social Impact of Dataset
We believe that Sen4AgriNet can be regarded as a labelled benchmark dataset, tailored for CAP and the use of Sentinel-2 imagery that come at no cost, and can spur numerous DL-based applications for crop type classification, parcel extraction, parcel counting and semantic segmentation. More importantly, the dataset can be extended to include other input data sources, including Sentinel-1 Synthetic Aperture Radar data, and meteorological data, allowing a new family of applications on early warning risk assessment and agricultural insurance.
## Additional Information
### Licensing Information
MIT License.
### Citation Information
```
@ARTICLE{
9749916,
author={Sykas, Dimitrios and Sdraka, Maria and Zografakis, Dimitrios and Papoutsis, Ioannis},
journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
title={A Sentinel-2 multi-year, multi-country benchmark dataset for crop classification and segmentation with deep learning},
year={2022},
doi={10.1109/JSTARS.2022.3164771}
}
```
|
Yehor | null | null | null | false | 1 | false | Yehor/ukrainian-news-headlines | 2022-07-30T17:39:30.000Z | null | false | cecdd5845d29aa5a62fbcecf294d1b72d8fd860b | [] | [
"language:uk",
"license:cc-by-nc-sa-4.0",
"tags:uk"
] | https://huggingface.co/datasets/Yehor/ukrainian-news-headlines/resolve/main/README.md | ---
language:
- uk
license: cc-by-nc-sa-4.0
tags:
- uk
---
This dataset contains **5,242,391** samples of Ukrainian news headlines.
Usage:
```python
from datasets import load_dataset
ds = load_dataset('Yehor/ukrainian-news-headlines', split='train')
for row in ds:
print(row['headline'])
```
Attribution to the dataset:
- Chaplynskyi, D. et al. (2021) lang-uk Ukrainian Ubercorpus [Data set]. https://lang.org.ua/uk/corpora/#anchor4
|
Shlepa | null | null | null | false | 1 | false | Shlepa/Krop | 2022-07-02T03:10:52.000Z | null | false | aafb58901d736dab1d50526db670f2f7df58fac9 | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/Shlepa/Krop/resolve/main/README.md | ---
license: apache-2.0
---
|
bryanbocao | null | null | null | false | 1 | false | bryanbocao/coco_minitrain | 2022-07-03T00:10:15.000Z | null | false | aced9745c97a9790fe99638dba7fd4d8f03e44b2 | [] | [
"license:cc-by-4.0"
] | https://huggingface.co/datasets/bryanbocao/coco_minitrain/resolve/main/README.md | ---
license: cc-by-4.0
---
|
nev | null | null | null | false | 1 | false | nev/nsd-general-clip | 2022-08-10T20:32:52.000Z | null | false | 7b9d89fae2feed46732ce28327fafab410bb9863 | [] | [
"license:isc"
] | https://huggingface.co/datasets/nev/nsd-general-clip/resolve/main/README.md | ---
license: isc
---
|
MicPie | null | @misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
} | The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card. | false | 1 | false | MicPie/unpredictable_full | 2022-08-04T20:07:28.000Z | null | false | 4e75b1b7fabb453a60a571bc9ccc2b95b9789fe0 | [] | [
"arxiv:2208.01009",
"annotations_creators:no-annotation",
"language_creators:found",
"language:en",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",... | https://huggingface.co/datasets/MicPie/unpredictable_full/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-full
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-full" - Dataset of Few-shot Tasks from Tables
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### Dataset Summary
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
There are several dataset versions available:
* [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### Supported Tasks and Leaderboards
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
### Languages
English
## Dataset Structure
### Data Instances
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
### Data Fields
'task': task identifier
'input': column elements of a specific row in the table.
'options': for multiple choice classification, it provides the options to choose from.
'output': target column element of the same row as input.
'pageTitle': the title of the page containing the table.
'outputColName': output column name
'url': url to the website containing the table
'wdcFile': WDC Web Table Corpus file
### Data Splits
The UnpredicTable datasets do not come with additional data splits.
## Dataset Creation
### Curation Rationale
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
### Source Data
#### Initial Data Collection and Normalization
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
#### Who are the source language producers?
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### Personal and Sensitive Information
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
### Discussion of Biases
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
### Other Known Limitations
No additional known limitations.
## Additional Information
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
|
bigscience-biomedical | null | @inproceedings{scitail,
author = {Tushar Khot and Ashish Sabharwal and Peter Clark},
booktitle = {AAAI}
title = {SciTail: A Textual Entailment Dataset from Science Question Answering},
year = {2018}
} | The SciTail dataset is an entailment dataset created from multiple-choice science exams and
web sentences. Each question and the correct answer choice are converted into an assertive
statement to form the hypothesis. We use information retrieval to obtain relevant text from
a large text corpus of web sentences, and use these sentences as a premise P. We crowdsource
the annotation of such premise-hypothesis pair as supports (entails) or not (neutral), in order
to create the SciTail dataset. The dataset contains 27,026 examples with 10,101 examples with
entails label and 16,925 examples with neutral label. | false | 32 | false | bigscience-biomedical/scitail | 2022-10-16T19:22:02.000Z | scitail | false | 0fdbed1e3345a91d2f22be471dd24396c2877052 | [] | [
"language:en",
"license:apache-2.0",
"multilinguality:monolingual"
] | https://huggingface.co/datasets/bigscience-biomedical/scitail/resolve/main/README.md | ---
language: en
license: apache-2.0
multilinguality: monolingual
pretty_name: SciTail
paperswithcode_id: scitail
---
# Dataset Card for SciTail
## Dataset Description
- **Homepage:** https://allenai.org/data/scitail
- **Pubmed:** False
- **Public:** True
- **Tasks:** Textual Entailment
The SciTail dataset is an entailment dataset created from multiple-choice science exams and web sentences. Each question and the correct answer choice are converted into an assertive statement to form the hypothesis. We use information retrieval to obtain relevant text from a large text corpus of web sentences, and use these sentences as a premise P. We crowd source the annotation of such premise-hypothesis pair as supports (entails) or not (neutral), in order to create the SciTail dataset. The dataset contains 27,026 examples with 10,101 examples with entails label and 16,925 examples with neutral label.
## Citation Information
```
@inproceedings{scitail,
author = {Tushar Khot and Ashish Sabharwal and Peter Clark},
booktitle = {AAAI}
title = {SciTail: A Textual Entailment Dataset from Science Question Answering},
year = {2018}
```
|
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-854c0218-9415245 | 2022-07-02T22:28:44.000Z | null | false | dad2cadd8d501bf91facd78bbd7a598d98f32e7e | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:emotion"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-854c0218-9415245/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- emotion
eval_info:
task: multi_class_classification
model: lewtun/sagemaker-distilbert-emotion
metrics: []
dataset_name: emotion
dataset_config: default
dataset_split: test
col_mapping:
text: text
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: lewtun/sagemaker-distilbert-emotion
* Dataset: emotion
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@gabrielaltay](https://huggingface.co/gabrielaltay) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-562e1223-9425246 | 2022-07-02T23:01:39.000Z | null | false | f68d414189a214d5a52b5842006e55eb8b95a337 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:bigscience-biomedical/tmp-scitail"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-562e1223-9425246/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- bigscience-biomedical/tmp-scitail
eval_info:
task: binary_classification
model: gabrielaltay/autotrain-at-test-bb-tmp-scitail-1078438446
metrics: []
dataset_name: bigscience-biomedical/tmp-scitail
dataset_config: scitail_bigbio_te
dataset_split: test
col_mapping:
text: premise
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Binary Text Classification
* Model: gabrielaltay/autotrain-at-test-bb-tmp-scitail-1078438446
* Dataset: bigscience-biomedical/tmp-scitail
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@gabrielaltay](https://huggingface.co/gabrielaltay) for evaluating this model. |
dddb | null | null | null | false | 1 | false | dddb/autotrain-data-new_model | 2022-07-03T04:34:26.000Z | null | false | d40231fb47c493a4a6cbdc01e69ef4193b27bd2c | [] | [] | https://huggingface.co/datasets/dddb/autotrain-data-new_model/resolve/main/README.md | ---
task_categories:
- conditional-text-generation
---
# AutoTrain Dataset for project: new_model
## Dataset Descritpion
This dataset has been automatically processed by AutoTrain for project new_model.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "\u8fd1\u671f\uff0c\u7f8e\u56fd\u56fd\u4f1a\u4f17\u9662\u901a\u8fc7\u6cd5\u6848\uff0c\u91cd\u7533\u7f8e\u56fd\u5bf9\u53f0\u6e7e\u7684\u627f\u8bfa\u3002\u5bf9\u6b64\uff0c\u4e2d\u56fd\u5916\u4ea4\u90e8\u53d1\u8a00\u4eba\u8868\u793a\uff0c\u6709\u5173\u6cd5\u6848\u4e25\u91cd\u8fdd\u53cd\u4e00\u4e2a\u4e2d\u56fd\u539f\u5219\u548c\u4e2d\u7f8e\u4e09\u4e2a\u8054\u5408\u516c\u62a5\u89c4\u5b9a\uff0c\u7c97\u66b4\u5e72\u6d89\u4e2d\u56fd\u5185\u653f\uff0c\u4e2d\u65b9\u5bf9\u6b64\u575a\u51b3\u53cd\u5bf9\u5e76\u5df2\u5411\u7f8e\u65b9\u63d0\u51fa\u4e25\u6b63\u4ea4\u6d89\u3002\n\u4e8b\u5b9e\u4e0a\uff0c\u4e2d[...]",
"target": "\u671b\u6d77\u697c\u7f8e\u56fd\u6253\u201c\u53f0\u6e7e\u724c\u201d\u662f\u5371\u9669\u7684\u8d4c\u535a"
},
{
"text": "\u5728\u63a8\u8fdb\u201c\u53cc\u4e00\u6d41\u201d\u9ad8\u6821\u5efa\u8bbe\u8fdb\u7a0b\u4e2d\uff0c\u6211\u4eec\u8981\u7d27\u7d27\u56f4\u7ed5\u4e3a\u515a\u80b2\u4eba\u3001\u4e3a\u56fd\u80b2\u624d\uff0c\u627e\u51c6\u95ee\u9898\u3001\u7834\u89e3\u96be\u9898\uff0c\u4ee5\u4e00\u6d41\u610f\u8bc6\u548c\u62c5\u5f53\u7cbe\u795e\uff0c\u5927\u529b\u63a8\u8fdb\u9ad8\u6821\u7684\u6cbb\u7406\u80fd\u529b\u5efa\u8bbe\u3002\n\u589e\u5f3a\u653f\u6cbb\u5f15\u9886\u529b\u3002\u575a\u6301\u515a\u5bf9\u9ad8\u6821\u5de5\u4f5c\u7684\u5168\u9762\u9886\u5bfc\uff0c\u59cb\u7ec8\u628a\u653f\u6cbb\u5efa\u8bbe\u6446\u5728[...]",
"target": "\u5927\u529b\u63a8\u8fdb\u9ad8\u6821\u6cbb\u7406\u80fd\u529b\u5efa\u8bbe"
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "Value(dtype='string', id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 5850 |
| valid | 1679 |
|
mahdiAsefi | null | null | null | false | 1 | false | mahdiAsefi/autotrain-data-persina-paraphrase | 2022-07-03T06:53:16.000Z | null | false | fc9df95d425ad80e3a96ff6a7738b8fb93ee3c80 | [] | [] | https://huggingface.co/datasets/mahdiAsefi/autotrain-data-persina-paraphrase/resolve/main/README.md | ---
task_categories:
- conditional-text-generation
---
# AutoTrain Dataset for project: persina-paraphrase
## Dataset Descritpion
This dataset has been automatically processed by AutoTrain for project persina-paraphrase.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": " \u0686\u0631\u0627 \u0645\u06cc \u06af\u0648\u06cc\u06cc\u0645 \"\u06cc\u06a9 \u0634\u0644\u0648\u0627\u0631\" \u0627\u06af\u0631 \u0641\u0642\u0637 \u06cc\u06a9 \u0686\u06cc\u0632 \u0627\u0633\u062a\u061f",
"target": " \u0686\u0631\u0627 \u0645\u06cc \u06af\u0648\u06cc\u06cc\u0645 \u0634\u0644\u0648\u0627\u0631\u061f"
},
{
"text": " \u0647\u0646\u062f \u0631\u0627 \u062f\u0631 \u06cc\u06a9 \u062e\u0637 \u0686\u06af\u0648\u0646\u0647 \u062a\u0639\u0631\u06cc\u0641 \u0645\u06cc \u06a9\u0646\u06cc\u062f\u061f",
"target": " \u0686\u06af\u0648\u0646\u0647 \u0647\u0646\u062f \u0631\u0627 \u062f\u0631 \u06cc\u06a9 \u062c\u0645\u0644\u0647 \u062a\u0639\u0631\u06cc\u0641 \u06a9\u0646\u06cc\u0645\u061f"
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "Value(dtype='string', id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 119410 |
| valid | 29853 |
|
MicPie | null | @misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
} | The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card. | false | 2 | false | MicPie/unpredictable_mmo-champion-com | 2022-08-04T20:09:49.000Z | null | false | 85aaa66a7843304692990eea17bc3b89ef99aac5 | [] | [
"arxiv:2208.01009",
"annotations_creators:no-annotation",
"language_creators:found",
"language:en",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",... | https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-mmo-champion-com
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-mmo-champion-com" - Dataset of Few-shot Tasks from Tables
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### Dataset Summary
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
There are several dataset versions available:
* [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### Supported Tasks and Leaderboards
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
### Languages
English
## Dataset Structure
### Data Instances
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
### Data Fields
'task': task identifier
'input': column elements of a specific row in the table.
'options': for multiple choice classification, it provides the options to choose from.
'output': target column element of the same row as input.
'pageTitle': the title of the page containing the table.
'outputColName': output column name
'url': url to the website containing the table
'wdcFile': WDC Web Table Corpus file
### Data Splits
The UnpredicTable datasets do not come with additional data splits.
## Dataset Creation
### Curation Rationale
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
### Source Data
#### Initial Data Collection and Normalization
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
#### Who are the source language producers?
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### Personal and Sensitive Information
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
### Discussion of Biases
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
### Other Known Limitations
No additional known limitations.
## Additional Information
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
|
MicPie | null | @misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
} | The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card. | false | 2 | false | MicPie/unpredictable_baseball-fantasysports-yahoo-com | 2022-08-04T19:37:41.000Z | null | false | ccd340079cf7705fabed9a460fdff394abac01bd | [] | [
"arxiv:2208.01009",
"annotations_creators:no-annotation",
"language_creators:found",
"language:en",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",... | https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-baseball-fantasysports-yahoo-com
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-baseball-fantasysports-yahoo-com" - Dataset of Few-shot Tasks from Tables
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### Dataset Summary
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
There are several dataset versions available:
* [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### Supported Tasks and Leaderboards
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
### Languages
English
## Dataset Structure
### Data Instances
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
### Data Fields
'task': task identifier
'input': column elements of a specific row in the table.
'options': for multiple choice classification, it provides the options to choose from.
'output': target column element of the same row as input.
'pageTitle': the title of the page containing the table.
'outputColName': output column name
'url': url to the website containing the table
'wdcFile': WDC Web Table Corpus file
### Data Splits
The UnpredicTable datasets do not come with additional data splits.
## Dataset Creation
### Curation Rationale
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
### Source Data
#### Initial Data Collection and Normalization
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
#### Who are the source language producers?
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### Personal and Sensitive Information
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
### Discussion of Biases
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
### Other Known Limitations
No additional known limitations.
## Additional Information
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
|
MicPie | null | @misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
} | The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card. | false | 2 | false | MicPie/unpredictable_phonearena-com | 2022-08-04T20:11:00.000Z | null | false | f4f1dcb833270d8e0319a2a86cfa3805fb3e4081 | [] | [
"arxiv:2208.01009",
"annotations_creators:no-annotation",
"language_creators:found",
"language:en",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",... | https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-phonearena-com
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-phonearena-com" - Dataset of Few-shot Tasks from Tables
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### Dataset Summary
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
There are several dataset versions available:
* [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### Supported Tasks and Leaderboards
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
### Languages
English
## Dataset Structure
### Data Instances
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
### Data Fields
'task': task identifier
'input': column elements of a specific row in the table.
'options': for multiple choice classification, it provides the options to choose from.
'output': target column element of the same row as input.
'pageTitle': the title of the page containing the table.
'outputColName': output column name
'url': url to the website containing the table
'wdcFile': WDC Web Table Corpus file
### Data Splits
The UnpredicTable datasets do not come with additional data splits.
## Dataset Creation
### Curation Rationale
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
### Source Data
#### Initial Data Collection and Normalization
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
#### Who are the source language producers?
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### Personal and Sensitive Information
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
### Discussion of Biases
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
### Other Known Limitations
No additional known limitations.
## Additional Information
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
|
djagatiya | null | null | null | false | 3 | false | djagatiya/ner-ontonotes-v5-eng-v4 | 2022-07-03T11:36:33.000Z | null | false | 944fe3f43c298ca526eeb51927210795ab4721a0 | [] | [
"language:eng",
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"source_datasets:subset"
] | https://huggingface.co/datasets/djagatiya/ner-ontonotes-v5-eng-v4/resolve/main/README.md | ---
language:
- eng
task_categories:
- token-classification
task_ids:
- named-entity-recognition
source_datasets:
- subset
---
# (NER) ontonotes-v5-eng-v4
This dataset is subset of [conll2012_ontonotesv5](https://huggingface.co/datasets/conll2012_ontonotesv5) original dataset.
- Language: english
- Version: v4
| Dataset | Examples |
| --- | --- |
| Training | 75187 |
| Testing | 9479 |
|
MicPie | null | @misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
} | The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card. | false | 2 | false | MicPie/unpredictable_support-google-com | 2022-08-04T20:15:33.000Z | null | false | 0b76fc0ecb5ea9fe99a5d5be9812716664061013 | [] | [
"arxiv:2208.01009",
"annotations_creators:no-annotation",
"language_creators:found",
"language:en",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",... | https://huggingface.co/datasets/MicPie/unpredictable_support-google-com/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-support-google-com
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-support-google-com" - Dataset of Few-shot Tasks from Tables
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### Dataset Summary
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
There are several dataset versions available:
* [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### Supported Tasks and Leaderboards
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
### Languages
English
## Dataset Structure
### Data Instances
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
### Data Fields
'task': task identifier
'input': column elements of a specific row in the table.
'options': for multiple choice classification, it provides the options to choose from.
'output': target column element of the same row as input.
'pageTitle': the title of the page containing the table.
'outputColName': output column name
'url': url to the website containing the table
'wdcFile': WDC Web Table Corpus file
### Data Splits
The UnpredicTable datasets do not come with additional data splits.
## Dataset Creation
### Curation Rationale
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
### Source Data
#### Initial Data Collection and Normalization
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
#### Who are the source language producers?
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### Personal and Sensitive Information
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
### Discussion of Biases
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
### Other Known Limitations
No additional known limitations.
## Additional Information
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
|
MicPie | null | @misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
} | The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card. | false | 2 | false | MicPie/unpredictable_dividend-com | 2022-08-04T20:04:10.000Z | null | false | a74dbc1675b4a257fa3312c56efdc297cdc2361f | [] | [
"arxiv:2208.01009",
"annotations_creators:no-annotation",
"language_creators:found",
"language:en",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",... | https://huggingface.co/datasets/MicPie/unpredictable_dividend-com/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-dividend-com
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-dividend-com" - Dataset of Few-shot Tasks from Tables
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### Dataset Summary
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
There are several dataset versions available:
* [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### Supported Tasks and Leaderboards
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
### Languages
English
## Dataset Structure
### Data Instances
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
### Data Fields
'task': task identifier
'input': column elements of a specific row in the table.
'options': for multiple choice classification, it provides the options to choose from.
'output': target column element of the same row as input.
'pageTitle': the title of the page containing the table.
'outputColName': output column name
'url': url to the website containing the table
'wdcFile': WDC Web Table Corpus file
### Data Splits
The UnpredicTable datasets do not come with additional data splits.
## Dataset Creation
### Curation Rationale
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
### Source Data
#### Initial Data Collection and Normalization
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
#### Who are the source language producers?
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### Personal and Sensitive Information
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
### Discussion of Biases
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
### Other Known Limitations
No additional known limitations.
## Additional Information
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
|
MicPie | null | @misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
} | The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card. | false | 2 | false | MicPie/unpredictable_bulbapedia-bulbagarden-net | 2022-08-04T19:40:16.000Z | null | false | 121fb00f1583e20e3457e130c80e05a68c3c7f39 | [] | [
"arxiv:2208.01009",
"annotations_creators:no-annotation",
"language_creators:found",
"language:en",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",... | https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-bulbapedia-bulbagarden-net
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-bulbapedia-bulbagarden-net" - Dataset of Few-shot Tasks from Tables
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### Dataset Summary
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
There are several dataset versions available:
* [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### Supported Tasks and Leaderboards
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
### Languages
English
## Dataset Structure
### Data Instances
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
### Data Fields
'task': task identifier
'input': column elements of a specific row in the table.
'options': for multiple choice classification, it provides the options to choose from.
'output': target column element of the same row as input.
'pageTitle': the title of the page containing the table.
'outputColName': output column name
'url': url to the website containing the table
'wdcFile': WDC Web Table Corpus file
### Data Splits
The UnpredicTable datasets do not come with additional data splits.
## Dataset Creation
### Curation Rationale
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
### Source Data
#### Initial Data Collection and Normalization
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
#### Who are the source language producers?
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### Personal and Sensitive Information
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
### Discussion of Biases
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
### Other Known Limitations
No additional known limitations.
## Additional Information
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
|
MicPie | null | @misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
} | The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card. | false | 2 | false | MicPie/unpredictable_wkdu-org | 2022-08-04T20:18:48.000Z | null | false | f05478ec2e00f9b85c0076a44b771504dffaa14f | [] | [
"arxiv:2208.01009",
"annotations_creators:no-annotation",
"language_creators:found",
"language:en",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",... | https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-wkdu-org
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-wkdu-org" - Dataset of Few-shot Tasks from Tables
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### Dataset Summary
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
There are several dataset versions available:
* [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### Supported Tasks and Leaderboards
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
### Languages
English
## Dataset Structure
### Data Instances
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
### Data Fields
'task': task identifier
'input': column elements of a specific row in the table.
'options': for multiple choice classification, it provides the options to choose from.
'output': target column element of the same row as input.
'pageTitle': the title of the page containing the table.
'outputColName': output column name
'url': url to the website containing the table
'wdcFile': WDC Web Table Corpus file
### Data Splits
The UnpredicTable datasets do not come with additional data splits.
## Dataset Creation
### Curation Rationale
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
### Source Data
#### Initial Data Collection and Normalization
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
#### Who are the source language producers?
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### Personal and Sensitive Information
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
### Discussion of Biases
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
### Other Known Limitations
No additional known limitations.
## Additional Information
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
|
MicPie | null | @misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
} | The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card. | false | 2 | false | MicPie/unpredictable_dummies-com | 2022-08-04T20:04:46.000Z | null | false | 36db98c5f36305fb63229fd88b9c1f50bca7b140 | [] | [
"arxiv:2208.01009",
"annotations_creators:no-annotation",
"language_creators:found",
"language:en",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",... | https://huggingface.co/datasets/MicPie/unpredictable_dummies-com/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-dummies-com
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-dummies-com" - Dataset of Few-shot Tasks from Tables
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### Dataset Summary
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
There are several dataset versions available:
* [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### Supported Tasks and Leaderboards
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
### Languages
English
## Dataset Structure
### Data Instances
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
### Data Fields
'task': task identifier
'input': column elements of a specific row in the table.
'options': for multiple choice classification, it provides the options to choose from.
'output': target column element of the same row as input.
'pageTitle': the title of the page containing the table.
'outputColName': output column name
'url': url to the website containing the table
'wdcFile': WDC Web Table Corpus file
### Data Splits
The UnpredicTable datasets do not come with additional data splits.
## Dataset Creation
### Curation Rationale
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
### Source Data
#### Initial Data Collection and Normalization
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
#### Who are the source language producers?
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### Personal and Sensitive Information
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
### Discussion of Biases
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
### Other Known Limitations
No additional known limitations.
## Additional Information
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
|
MicPie | null | @misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
} | The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card. | false | 2 | false | MicPie/unpredictable_mgoblog-com | 2022-08-04T20:09:03.000Z | null | false | 13f3febb67413609d9cb25545f3587fa2ca5604d | [] | [
"arxiv:2208.01009",
"annotations_creators:no-annotation",
"language_creators:found",
"language:en",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",... | https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: AdapTable-mgoblog-com
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "AdapTable-mgoblog-com" - Dataset of Few-shot Tasks from Tables
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### Dataset Summary
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
There are several dataset versions available:
* [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### Supported Tasks and Leaderboards
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
### Languages
English
## Dataset Structure
### Data Instances
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
### Data Fields
'task': task identifier
'input': column elements of a specific row in the table.
'options': for multiple choice classification, it provides the options to choose from.
'output': target column element of the same row as input.
'pageTitle': the title of the page containing the table.
'outputColName': output column name
'url': url to the website containing the table
'wdcFile': WDC Web Table Corpus file
### Data Splits
The UnpredicTable datasets do not come with additional data splits.
## Dataset Creation
### Curation Rationale
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
### Source Data
#### Initial Data Collection and Normalization
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
#### Who are the source language producers?
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### Personal and Sensitive Information
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
### Discussion of Biases
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
### Other Known Limitations
No additional known limitations.
## Additional Information
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
|
MicPie | null | @misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
} | The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card. | false | 2 | false | MicPie/unpredictable_gamefaqs-com | 2022-08-04T20:08:30.000Z | null | false | 91c25524931b0f421ab607c20c1a7bc6199be922 | [] | [
"arxiv:2208.01009",
"annotations_creators:no-annotation",
"language_creators:found",
"language:en",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",... | https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-gamefaqs-com
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-gamefaqs-com" - Dataset of Few-shot Tasks from Tables
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### Dataset Summary
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
There are several dataset versions available:
* [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### Supported Tasks and Leaderboards
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
### Languages
English
## Dataset Structure
### Data Instances
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
### Data Fields
'task': task identifier
'input': column elements of a specific row in the table.
'options': for multiple choice classification, it provides the options to choose from.
'output': target column element of the same row as input.
'pageTitle': the title of the page containing the table.
'outputColName': output column name
'url': url to the website containing the table
'wdcFile': WDC Web Table Corpus file
### Data Splits
The UnpredicTable datasets do not come with additional data splits.
## Dataset Creation
### Curation Rationale
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
### Source Data
#### Initial Data Collection and Normalization
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
#### Who are the source language producers?
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### Personal and Sensitive Information
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
### Discussion of Biases
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
### Other Known Limitations
No additional known limitations.
## Additional Information
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
|
MicPie | null | @misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
} | The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card. | false | 2 | false | MicPie/unpredictable_studystack-com | 2022-08-04T20:15:01.000Z | null | false | 832c7304a9b4dbd1c3f7a436d5e644c78084962d | [] | [
"arxiv:2208.01009",
"annotations_creators:no-annotation",
"language_creators:found",
"language:en",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",... | https://huggingface.co/datasets/MicPie/unpredictable_studystack-com/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-studystack-com
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-studystack-com" - Dataset of Few-shot Tasks from Tables
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### Dataset Summary
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
There are several dataset versions available:
* [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### Supported Tasks and Leaderboards
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
### Languages
English
## Dataset Structure
### Data Instances
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
### Data Fields
'task': task identifier
'input': column elements of a specific row in the table.
'options': for multiple choice classification, it provides the options to choose from.
'output': target column element of the same row as input.
'pageTitle': the title of the page containing the table.
'outputColName': output column name
'url': url to the website containing the table
'wdcFile': WDC Web Table Corpus file
### Data Splits
The UnpredicTable datasets do not come with additional data splits.
## Dataset Creation
### Curation Rationale
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
### Source Data
#### Initial Data Collection and Normalization
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
#### Who are the source language producers?
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### Personal and Sensitive Information
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
### Discussion of Biases
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
### Other Known Limitations
No additional known limitations.
## Additional Information
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
|
MicPie | null | @misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
} | The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card. | false | 2 | false | MicPie/unpredictable_sittercity-com | 2022-08-04T20:13:09.000Z | null | false | 2af65195e39bd9839053773b0afb1a330165449c | [] | [
"arxiv:2208.01009",
"annotations_creators:no-annotation",
"language_creators:found",
"language:en",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",... | https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-sittercity-com
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-sittercity-com" - Dataset of Few-shot Tasks from Tables
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### Dataset Summary
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
There are several dataset versions available:
* [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### Supported Tasks and Leaderboards
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
### Languages
English
## Dataset Structure
### Data Instances
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
### Data Fields
'task': task identifier
'input': column elements of a specific row in the table.
'options': for multiple choice classification, it provides the options to choose from.
'output': target column element of the same row as input.
'pageTitle': the title of the page containing the table.
'outputColName': output column name
'url': url to the website containing the table
'wdcFile': WDC Web Table Corpus file
### Data Splits
The UnpredicTable datasets do not come with additional data splits.
## Dataset Creation
### Curation Rationale
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
### Source Data
#### Initial Data Collection and Normalization
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
#### Who are the source language producers?
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### Personal and Sensitive Information
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
### Discussion of Biases
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
### Other Known Limitations
No additional known limitations.
## Additional Information
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
|
MicPie | null | @misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
} | The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card. | false | 2 | false | MicPie/unpredictable_msdn-microsoft-com | 2022-08-04T20:10:19.000Z | null | false | 107a673e9688ef4bc63e27884e16e2c741ee494d | [] | [
"arxiv:2208.01009",
"annotations_creators:no-annotation",
"language_creators:found",
"language:en",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",... | https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-msdn-microsoft-com
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-msdn-microsoft-com" - Dataset of Few-shot Tasks from Tables
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### Dataset Summary
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
There are several dataset versions available:
* [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### Supported Tasks and Leaderboards
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
### Languages
English
## Dataset Structure
### Data Instances
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
### Data Fields
'task': task identifier
'input': column elements of a specific row in the table.
'options': for multiple choice classification, it provides the options to choose from.
'output': target column element of the same row as input.
'pageTitle': the title of the page containing the table.
'outputColName': output column name
'url': url to the website containing the table
'wdcFile': WDC Web Table Corpus file
### Data Splits
The UnpredicTable datasets do not come with additional data splits.
## Dataset Creation
### Curation Rationale
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
### Source Data
#### Initial Data Collection and Normalization
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
#### Who are the source language producers?
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### Personal and Sensitive Information
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
### Discussion of Biases
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
### Other Known Limitations
No additional known limitations.
## Additional Information
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
|
MicPie | null | @misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
} | The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card. | false | 2 | false | MicPie/unpredictable_cappex-com | 2022-08-04T19:41:09.000Z | null | false | eaf057b4650acaee32eefcc413131ab5e64ff2c4 | [] | [
"arxiv:2208.01009",
"annotations_creators:no-annotation",
"language_creators:found",
"language:en",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",... | https://huggingface.co/datasets/MicPie/unpredictable_cappex-com/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-cappex.com
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-cappex.com" - Dataset of Few-shot Tasks from Tables
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### Dataset Summary
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
There are several dataset versions available:
* [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### Supported Tasks and Leaderboards
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
### Languages
English
## Dataset Structure
### Data Instances
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
### Data Fields
'task': task identifier
'input': column elements of a specific row in the table.
'options': for multiple choice classification, it provides the options to choose from.
'output': target column element of the same row as input.
'pageTitle': the title of the page containing the table.
'outputColName': output column name
'url': url to the website containing the table
'wdcFile': WDC Web Table Corpus file
### Data Splits
The UnpredicTable datasets do not come with additional data splits.
## Dataset Creation
### Curation Rationale
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
### Source Data
#### Initial Data Collection and Normalization
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
#### Who are the source language producers?
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### Personal and Sensitive Information
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
### Discussion of Biases
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
### Other Known Limitations
No additional known limitations.
## Additional Information
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
|
MicPie | null | @misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
} | The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card. | false | 1 | false | MicPie/unpredictable_en-wikipedia-org | 2022-08-04T20:05:44.000Z | null | false | 848fe8a39fc1bb84dce9e3a26818376eb810e77d | [] | [
"arxiv:2208.01009",
"annotations_creators:no-annotation",
"language_creators:found",
"language:en",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",... | https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-en-wikipedia-org
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-en-wikipedia-org" - Dataset of Few-shot Tasks from Tables
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### Dataset Summary
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
There are several dataset versions available:
* [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### Supported Tasks and Leaderboards
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
### Languages
English
## Dataset Structure
### Data Instances
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
### Data Fields
'task': task identifier
'input': column elements of a specific row in the table.
'options': for multiple choice classification, it provides the options to choose from.
'output': target column element of the same row as input.
'pageTitle': the title of the page containing the table.
'outputColName': output column name
'url': url to the website containing the table
'wdcFile': WDC Web Table Corpus file
### Data Splits
The UnpredicTable datasets do not come with additional data splits.
## Dataset Creation
### Curation Rationale
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
### Source Data
#### Initial Data Collection and Normalization
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
#### Who are the source language producers?
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### Personal and Sensitive Information
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
### Discussion of Biases
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
### Other Known Limitations
No additional known limitations.
## Additional Information
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
|
MicPie | null | @misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
} | The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card. | false | 2 | false | MicPie/unpredictable_cram-com | 2022-08-04T20:03:25.000Z | null | false | c295ef60a01bb8a33a8702ddda18308eacba4a31 | [] | [
"arxiv:2208.01009",
"annotations_creators:no-annotation",
"language_creators:found",
"language:en",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",... | https://huggingface.co/datasets/MicPie/unpredictable_cram-com/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-cram-com
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-cram-com" - Dataset of Few-shot Tasks from Tables
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### Dataset Summary
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
There are several dataset versions available:
* [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### Supported Tasks and Leaderboards
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
### Languages
English
## Dataset Structure
### Data Instances
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
### Data Fields
'task': task identifier
'input': column elements of a specific row in the table.
'options': for multiple choice classification, it provides the options to choose from.
'output': target column element of the same row as input.
'pageTitle': the title of the page containing the table.
'outputColName': output column name
'url': url to the website containing the table
'wdcFile': WDC Web Table Corpus file
### Data Splits
The UnpredicTable datasets do not come with additional data splits.
## Dataset Creation
### Curation Rationale
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
### Source Data
#### Initial Data Collection and Normalization
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
#### Who are the source language producers?
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### Personal and Sensitive Information
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
### Discussion of Biases
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
### Other Known Limitations
No additional known limitations.
## Additional Information
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
|
MicPie | null | @misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
} | The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card. | false | 1 | false | MicPie/unpredictable_w3-org | 2022-08-04T20:16:53.000Z | null | false | 13acf265ff28b4809f0e95a5b41a5b96e831cdb4 | [] | [
"arxiv:2208.01009",
"annotations_creators:no-annotation",
"language_creators:found",
"language:en",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",... | https://huggingface.co/datasets/MicPie/unpredictable_w3-org/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-w3-org
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-w3-org" - Dataset of Few-shot Tasks from Tables
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### Dataset Summary
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
There are several dataset versions available:
* [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### Supported Tasks and Leaderboards
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
### Languages
English
## Dataset Structure
### Data Instances
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
### Data Fields
'task': task identifier
'input': column elements of a specific row in the table.
'options': for multiple choice classification, it provides the options to choose from.
'output': target column element of the same row as input.
'pageTitle': the title of the page containing the table.
'outputColName': output column name
'url': url to the website containing the table
'wdcFile': WDC Web Table Corpus file
### Data Splits
The UnpredicTable datasets do not come with additional data splits.
## Dataset Creation
### Curation Rationale
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
### Source Data
#### Initial Data Collection and Normalization
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
#### Who are the source language producers?
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### Personal and Sensitive Information
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
### Discussion of Biases
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
### Other Known Limitations
No additional known limitations.
## Additional Information
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
|
MicPie | null | @misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
} | The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card. | false | 2 | false | MicPie/unpredictable_sporcle-com | 2022-08-04T20:13:59.000Z | null | false | 7d11a3ddcd818ce988ae8d89e5e997e8eea2c0a1 | [] | [
"arxiv:2208.01009",
"annotations_creators:no-annotation",
"language_creators:found",
"language:en",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",... | https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-sporcle-com
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-sporcle-com" - Dataset of Few-shot Tasks from Tables
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### Dataset Summary
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
There are several dataset versions available:
* [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### Supported Tasks and Leaderboards
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
### Languages
English
## Dataset Structure
### Data Instances
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
### Data Fields
'task': task identifier
'input': column elements of a specific row in the table.
'options': for multiple choice classification, it provides the options to choose from.
'output': target column element of the same row as input.
'pageTitle': the title of the page containing the table.
'outputColName': output column name
'url': url to the website containing the table
'wdcFile': WDC Web Table Corpus file
### Data Splits
The UnpredicTable datasets do not come with additional data splits.
## Dataset Creation
### Curation Rationale
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
### Source Data
#### Initial Data Collection and Normalization
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
#### Who are the source language producers?
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### Personal and Sensitive Information
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
### Discussion of Biases
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
### Other Known Limitations
No additional known limitations.
## Additional Information
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
|
MicPie | null | @misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
} | The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card. | false | 2 | false | MicPie/unpredictable_wiki-openmoko-org | 2022-08-04T20:17:59.000Z | null | false | 00686de27a07cdda04f8fe2bdeafb534e2c8a839 | [] | [
"arxiv:2208.01009",
"annotations_creators:no-annotation",
"language_creators:found",
"language:en",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",... | https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-wiki-openmoko-org
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-wiki-openmoko-org" - Dataset of Few-shot Tasks from Tables
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### Dataset Summary
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
There are several dataset versions available:
* [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### Supported Tasks and Leaderboards
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
### Languages
English
## Dataset Structure
### Data Instances
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
### Data Fields
'task': task identifier
'input': column elements of a specific row in the table.
'options': for multiple choice classification, it provides the options to choose from.
'output': target column element of the same row as input.
'pageTitle': the title of the page containing the table.
'outputColName': output column name
'url': url to the website containing the table
'wdcFile': WDC Web Table Corpus file
### Data Splits
The UnpredicTable datasets do not come with additional data splits.
## Dataset Creation
### Curation Rationale
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
### Source Data
#### Initial Data Collection and Normalization
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
#### Who are the source language producers?
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### Personal and Sensitive Information
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
### Discussion of Biases
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
### Other Known Limitations
No additional known limitations.
## Additional Information
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
|
MicPie | null | @misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
} | The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card. | false | 1 | false | MicPie/unpredictable_ensembl-org | 2022-08-04T20:06:23.000Z | null | false | a9b29ebafb43b2a8e2f6f3c3253aa0df2e920688 | [] | [
"arxiv:2208.01009",
"annotations_creators:no-annotation",
"language_creators:found",
"language:en",
"license:apache-2.0",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",... | https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-ensembl-org
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-ensembl-org" - Dataset of Few-shot Tasks from Tables
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### Dataset Summary
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
There are several dataset versions available:
* [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### Supported Tasks and Leaderboards
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
### Languages
English
## Dataset Structure
### Data Instances
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
### Data Fields
'task': task identifier
'input': column elements of a specific row in the table.
'options': for multiple choice classification, it provides the options to choose from.
'output': target column element of the same row as input.
'pageTitle': the title of the page containing the table.
'outputColName': output column name
'url': url to the website containing the table
'wdcFile': WDC Web Table Corpus file
### Data Splits
The UnpredicTable datasets do not come with additional data splits.
## Dataset Creation
### Curation Rationale
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
### Source Data
#### Initial Data Collection and Normalization
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
#### Who are the source language producers?
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### Personal and Sensitive Information
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
### Discussion of Biases
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
### Other Known Limitations
No additional known limitations.
## Additional Information
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
|
FIdo-AI | null | null | null | false | 2 | false | FIdo-AI/ua-squad | 2022-07-09T20:55:51.000Z | null | false | 9d9df9f4f8531f0033aa1a9ec78925759ef84c0a | [] | [] | https://huggingface.co/datasets/FIdo-AI/ua-squad/resolve/main/README.md | annotations_creators:
- crowdsourced
language:
- uk
language_creators:
- crowdsourced
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: squad
pretty_name: ''
size_categories:
- 100K<n<1M
source_datasets:
- extended|squad_v2
task_categories:
- question-answering
task_ids:
- open-domain-qa
- extractive-qa
train-eval-index:
- col_mapping:
answers:
answer_start: answer_start
text: text
context: context
question: question
config: squad_v2
metrics:
- name: SQuAD v2
type: squad_v2
splits:
eval_split: validation
train_split: train
task: question-answering
task_id: extractive_question_answering
# Dataset Card for ua-squad
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://github.com/fido-ai/ua-datasets
- **Repository:** https://huggingface.co/datasets/FIdo-AI/ua-squad
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
Ukrainian translation of the Stanford Question Answering Dataset (SQuAD) 2.0
### Supported Tasks and Leaderboards
question-answering
### Languages
Ukrainian
## Dataset Structure
### Data Instances
[Needs More Information]
### Data Fields
[Needs More Information]
### Data Splits
[Needs More Information]
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
[Needs More Information] |
1989shack | null | null | null | false | 1 | false | 1989shack/1989shack.com | 2022-07-03T15:36:48.000Z | null | false | d6c1735480b71f983a3c1c28cc89e64a5ac34fc2 | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/1989shack/1989shack.com/resolve/main/README.md | ---
license: apache-2.0
---
|
tonne | null | null | null | false | 1 | false | tonne/trader | 2022-07-03T17:56:43.000Z | null | false | 9024105e51db311744cc449d11e6b14f583175d3 | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/tonne/trader/resolve/main/README.md | ---
license: apache-2.0
---
|
FJC | null | null | null | false | 1 | false | FJC/corpusELE.csv | 2022-07-06T22:06:44.000Z | null | false | c2dd5ab3983839f23d24c455c115d39634fe2f2c | [] | [] | https://huggingface.co/datasets/FJC/corpusELE.csv/resolve/main/README.md | # Dataset Card for [corpusELE.csv]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
corpusELE is a dataset made of texts from students of Spanish as Foreign Language (**ELE**), all from a set of files of **CAES** (Corpus de Aprendices de Español como Lengua Extranjera) downloaded from the website of the **Instituto Cervantes**. The main objective of this dataset is the creation and subsequent training, by means of Deep Learning, of a classification model that, based on these data, allows to establish, given an expression in Spanish, the level of knowledge of Spanish and even the mother tongue of the speaker.
In linguistics, a corpus is a more or less extensive set of texts in electronic format that have been assembled in a computer application, according to a certain design, to facilitate the study of the language or linguistic variety from which these texts have been extracted. Among the many types and subtypes of corpora that currently exist, the so-called 'learner corpora' contain texts produced by people who are learning a given language and who speak different initial, familiar or mother tongues and different degrees of knowledge of the target language (levels) and CAES is one of those.
* **File Name**: corpusELE.csv
* **Content Description**: Set of text from ELE students of different levels of proficiency and with different mother tongues.
* **File Type**: CSV separated by COMMA
* **Header Descriptions**: Included in the dataset (first row)
* **Encoding type**: UTF-8
### Dataset Summary
* Number of Columns: 6
* Number of Rows: 46.787
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Spanish
## Dataset Structure
1. **numero** (float): CAES phrase or text number.
2. **nivel** (string): Level of knowledge of Spanish of the ELE student who has provided the text. It will be one of those established in the Common European Framework of Reference in the learning of foreign languages.
3. **lenguaM** (string): Mother tongue of the ELE student to which the registered text belongs.
4. **pClave** (string): Key Word in the phrase or text. As indicated, it may be a punctuation mark, a mark or any other element or character in the sentence considered prominent or characteristic.
5. **frase** (string): Complete phrase or text provided by the ELE student. It is made up of the concatenation of the two parts into which it has been segmented in the source files and it also includes the word or key element.
6. **archivo** (string): It is and additional information included in the dataset in order to be able to use it in the preprocessing of the data. It refers to the name of the file from which the corresponding text has been taken. Although this information is not necessary for the object of the work, it is of interest when debugging the data capture. Later it will be information that we can do without.
### Data Instances
Each instance of the dataset consists of a single sentence or text
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
The Cervantes Institute, on its website (www.institutocervantes.es), makes freely available to users the so-called CAES or Corpus de Aprendices de Español, currently in its version 2.1. published in March 2022.
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset. |
mf99 | null | null | null | false | 1 | false | mf99/autotrain-data-sum-200-random | 2022-10-23T06:22:05.000Z | null | false | b76b66326552bd73cd041a6090c8b3eb5f7e3f55 | [] | [
"language:en"
] | https://huggingface.co/datasets/mf99/autotrain-data-sum-200-random/resolve/main/README.md | ---
language:
- en
task_categories:
- conditional-text-generation
---
# AutoTrain Dataset for project: sum-200-random
## Dataset Descritpion
This dataset has been automatically processed by AutoTrain for project sum-200-random.
### Languages
The BCP-47 code for the dataset's language is en.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "aen: {Forest hermit to Professor, it's never too late to change. | Dr. Gregory P. Smith | TEDxByronB[...]",
"target": "Fire, plenty of ferns to sleep on and an endless supply of alcohol. 65"
},
{
"text": "aen: {William Noel: Revealing the lost codex of Archimedes}{from 62% to 72%}{And combinatorics is a [...]",
"target": "The really astonishing thing though about this manuscript is that we looked at the other manuscripts[...]"
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "Value(dtype='string', id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 451280 |
| valid | 112821 |
|
santiagxf | null | null | null | false | 1 | false | santiagxf/spanish-marketing-tweets | 2022-07-03T21:49:27.000Z | null | false | 6ade0ffc1c18044e853676892c4933509f1c7f4e | [] | [
"license:unlicense"
] | https://huggingface.co/datasets/santiagxf/spanish-marketing-tweets/resolve/main/README.md | ---
license: unlicense
---
|
fce-m72109 | null | null | null | false | 1 | false | fce-m72109/mascorpus | 2022-07-03T21:51:58.000Z | null | false | 3a15ceec53f10ce3576701cfcce7541f0e667fea | [] | [
"license:unlicense"
] | https://huggingface.co/datasets/fce-m72109/mascorpus/resolve/main/README.md | ---
license: unlicense
---
|
EnZon3 | null | null | null | false | 1 | false | EnZon3/The-Worlds-Sentiment | 2022-07-04T23:08:24.000Z | null | false | 7c23e66a959c1f7198c25fd82111d6c633e8d514 | [] | [] | https://huggingface.co/datasets/EnZon3/The-Worlds-Sentiment/resolve/main/README.md | annotations_creators:
- other
language:
- en
language_creators:
- found
license:
- gpl-3.0
multilinguality:
- monolingual
pretty_name: The World's Sentiment
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- other
task_ids: []
# Dataset Card for The World's Sentiment
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [TWS Homepage](the-worlds-sentiment.enzon3.repl.co)
- **Repository:** [GitHub](https://github.com/EnZon3/TWS-dataset_gen)
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
The World's Sentiment is a dataset of news headlines' titles and their sentiment scores. This dataset was for a project of mine to see how positive or negative events in the world are. There are some use cases for this dataset, if you use only the headlines, you could train an AI to generate fake, but realistic headlines. But if you opt for what I did, which was to analyze the dataset, you'll find quite a bit of interesting stuff.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
This dataset is in English, text is from news articles' titles, provided by [News API](https://newsapi.org)
The associated BCP-47 code is `en`.
## Dataset Structure
### Data Instances
A typical data point comprises of a title, and the title's sentiment score. The dataset is a CSV file, so I cannot provide JSON data, but I can provide an example of what it would look like in Excel.
| Headline | Sentiment score |
| ----- | ---- |
| Russia Ukraine: Russian missile strike hits crowded shopping mall in Kremenchuk - 9News | -0.181818182 |
Here's what the example would look like in plain-text:
Russia Ukraine: Russian missile strike hits crowded shopping mall in Kremenchuk - 9News,-0.181818182
### Data Fields
Headline:
The title to a headline
Sentiment:
The sentiment score of the headline's title.
### Data Splits
[N/A]
## Dataset Creation
### Curation Rationale
I created the TWS dataset after a question popped up in my head on June 27th. It kind of went like this: 'How negative, or positive are news headlines?'
### Source Data
#### Initial Data Collection and Normalization
The data was collected by getting the top headlines in every English-speaking country that [News API](https://newsapi.org) supported and running through the responses and logging only the titles, while also simultaneously using Sentiment analysis to get the sentiment score (The sentiment dataset I used was afinn). The data was slightly modified in it's final form to correct any syntax errors in the CSV file using [CSVlint](https://csvlint.io/) to find them. The dataset is not tokenized.
#### Who are the source language producers?
The data was made by humans, and the news sources I used are located [here](https://newsapi.org/docs/endpoints/sources), because there are so many that I can't put them here.
### Annotations
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
Names might show up in the titles.
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
[Needs More Information] |
Yincen | null | null | null | false | 218 | false | Yincen/SalienceEvaluation | 2022-07-04T02:36:58.000Z | null | false | dd93e7ba97dd8c0776cb50249b0e1d53e4076b2c | [] | [
"annotations_creators:crowdsourced",
"language:zh",
"language_creators:found",
"license:gpl-3.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:multi-input-text-classification"
] | https://huggingface.co/datasets/Yincen/SalienceEvaluation/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language:
- zh
language_creators:
- found
license:
- gpl-3.0
multilinguality:
- monolingual
pretty_name: Yincen/SalienceEvaluation
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-input-text-classification
---
# Dataset Card for Yincen/SalienceEvaluation
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@github-username](https://github.com/qyccc) for adding this dataset. |
holylovenia | null | null | null | false | 1 | false | holylovenia/TITML-IDN | 2022-10-25T06:23:17.000Z | null | false | eb915043fa53039237e47183108b7aaf19b7da9e | [] | [
"annotations_creators:expert-generated",
"language_creators:found",
"language:id",
"license:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:automatic-speech-recognition",
"tags:speech-recognition"
] | https://huggingface.co/datasets/holylovenia/TITML-IDN/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- id
license:
- other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- automatic-speech-recognition
task_ids: []
pretty_name: 'TITML-IDN: A large vocabulary continuous speech recognition system for
Indonesian language'
tags:
- speech-recognition
---
# IndoLVCSR
TITML-IDN (Tokyo Institute of Technology Multilingual - Indonesian) is collected and proposed by the authors of "A Large Vocabulary Continuous Speech Recognition System for Indonesian Language". The text transcriptions are obtained from newspaper and magazine articles. The speech is recorded from 20 speakers (11 males and 9 females).
# How to cite
If you use this dataset, you have to cite this paper:
```
@inproceedings{lestari2006titmlidn,
title={A large vocabulary continuous speech recognition system for Indonesian language},
author={Lestari, Dessi Puji and Iwano, Koji and Furui, Sadaoki},
booktitle={15th Indonesian Scientific Conference in Japan Proceedings},
pages={17--22},
year={2006}
}
``` |
Li-Tang | null | null | null | false | 1 | false | Li-Tang/test_dataset | 2022-07-05T03:52:56.000Z | null | false | b6ecea87dc9076cbdc6138d23499264cb6c7649e | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/Li-Tang/test_dataset/resolve/main/README.md | ---
license: apache-2.0
---
|
joheras | null | null | null | false | 1 | false | joheras/prueba | 2022-07-04T09:43:30.000Z | null | false | 4f31650cfbb35f343399d3d11591f139738ce8f3 | [] | [
"license:cc"
] | https://huggingface.co/datasets/joheras/prueba/resolve/main/README.md | ---
license: cc
---
|
joheras | null | null | null | false | 1 | false | joheras/caes | 2022-07-04T10:21:43.000Z | null | false | 95771eb06730cd63c66ed6ecf892a55c06956fef | [] | [
"license:cc"
] | https://huggingface.co/datasets/joheras/caes/resolve/main/README.md | ---
license: cc
---
|
msalnikov | null | @InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
} | HuggingFace wrapper for https://github.com/askplatypus/wikidata-simplequestions dataset
Simplequestions dataset based on Wikidata. | false | 1 | false | msalnikov/wikidata_simplequestions | 2022-07-04T12:21:52.000Z | null | false | e1c3445fcff645e27b0253b205b1017b8f2126aa | [] | [] | https://huggingface.co/datasets/msalnikov/wikidata_simplequestions/resolve/main/README.md | # Wikidata Simplequestions
Huggingface Dataset wrapper for Wikidata-simplequestion dataset
```python3
from datasets import load_dataset;
load_dataset('../wikidata_simplequestions', 'answerable_en', cache_dir='/YOUR_PATH_TO_CACHE/')
```
|
CShorten | null | null | null | false | 1 | false | CShorten/ArXiv-ML-Abstract-Embeddings | 2022-07-04T13:13:37.000Z | null | false | ee6ef3917f0210c08e7337f318b99b48c4c4c4c0 | [] | [] | https://huggingface.co/datasets/CShorten/ArXiv-ML-Abstract-Embeddings/resolve/main/README.md | This dataset contains embeddings of the abstracts of ArXiv Machine Learning papers.
The embeddings are produced from sentence-transformers/paraphrase-MiniLM-L6-v2. The model can be accessed here: <a href = "https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L6-v2/discussions/2">HuggingFace Sentence Transformers </a>
The original dataset before embedding can be accessed here: <a href = "https://huggingface.co/datasets/CShorten/ML-ArXiv-Papers">ML ArXiv Papers</a> |
CShorten | null | null | null | false | 1 | false | CShorten/ArXiv-ML-Title-Embeddings | 2022-07-04T13:44:15.000Z | null | false | 7c2cd16a06fdbd304e68d85877485fde46e97312 | [] | [] | https://huggingface.co/datasets/CShorten/ArXiv-ML-Title-Embeddings/resolve/main/README.md | This dataset contains embeddings of the titles of ArXiv Machine Learning papers.
The embeddings are produced from sentence-transformers/paraphrase-MiniLM-L6-v2. The model can be accessed here: HuggingFace Sentence Transformers
The original dataset before embedding can be accessed here: ML ArXiv Papers |
mounikaiiith | null | null | null | false | 1 | false | mounikaiiith/Telugu_Clickbait | 2022-07-04T14:59:27.000Z | null | false | df94aa548fa4f93e16c6c269a99f0bd746a2ed1f | [] | [
"license:cc-by-4.0"
] | https://huggingface.co/datasets/mounikaiiith/Telugu_Clickbait/resolve/main/README.md | ---
license: cc-by-4.0
---
Do cite the below reference for using the dataset:
@inproceedings{marreddy2021clickbait,
title={Clickbait Detection in Telugu: Overcoming NLP Challenges in Resource-Poor Languages using Benchmarked Techniques},
author={Marreddy, Mounika and Oota, Subba Reddy and Vakada, Lakshmi Sireesha and Chinni, Venkata Charan and Mamidi, Radhika},
booktitle={2021 International Joint Conference on Neural Networks (IJCNN)},
pages={1--8},
year={2021},
organization={IEEE}
} |
mideind | null | null | null | false | 1 | false | mideind/icelandic-winogrande | 2022-07-04T15:44:19.000Z | null | false | 0a9a45fb300768e0443a1907eea6fa846da5837d | [] | [
"license:cc-by-4.0"
] | https://huggingface.co/datasets/mideind/icelandic-winogrande/resolve/main/README.md | ---
license: cc-by-4.0
---
|
BDas | null | ----Turkish Data---- | The dataset, prepared in Turkish, includes 53.000 tests, 53.000 validations and 160600 train data.
The data is composed of customer comments and created from e-commerce sites. | false | 10 | false | BDas/Turkish-Dataset | 2022-09-16T07:34:57.000Z | null | false | 4d23111fad1b11390bf0ac0124e54c8f125e0dc9 | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:tr",
"license:other",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:multi-label... | https://huggingface.co/datasets/BDas/Turkish-Dataset/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- tr
license:
- other
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
- multi-label-classification
pretty_name: 'Turkish NLP Dataset'
---
# Dataset Card for "Turkish-NLP-Dataset"
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Preprocessing](#dataset-preprocessing)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/BihterDass/turkish-nlp-dataset]
- **Repository:**[https://github.com/BihterDass/turkish-nlp-dataset]
- **Size of downloaded dataset files:** 125.5 MB
- **Size of the generated dataset:** 125.5 MB
### Dataset Summary
The dataset was compiled from user comments from e-commerce sites. It consists of 53,000 validations, 53,000 tests and 160600 train data. Data were classified into 3 classes (positive(pos), negative(neg) and natural(nor). The data is available to you on github.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
#### turkish-dataset-v1
- **Size of downloaded dataset files:** 125.5 MB
- **Size of the generated dataset:** 125.5 MB
### Data Fields
The data fields are the same among all splits.
#### turkish-dataset-v-v1
- `text`: a `string` feature.
- `label`: a classification label, with possible values including `positive` (2), `natural` (1), `negative` (0).
### Data Splits
| |train |validation|test |
|----|--------:|---------:|---------:|
|Data| 160600 | 53000| 53000|
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@PnrSvc](https://github.com/PnrSvc) for adding this dataset. |
lyakaap | null | null | null | false | 1 | false | lyakaap/laion-mini-ja | 2022-07-05T02:30:45.000Z | null | false | 8f65fde41b0e3362383eaf9e7f0dbfa53bf5e487 | [] | [] | https://huggingface.co/datasets/lyakaap/laion-mini-ja/resolve/main/README.md | #samples=5007831
```
dataset = load_dataset('lyakaap/laion2B-japanese-subset', split='train')
dataset = dataset.remove_columns(['LANGUAGE', 'NSFW', 'LICENSE', 'SAMPLE_ID'])
dataset = dataset.filter(lambda x: x['HEIGHT'] <= 384 and x['WIDTH'] <= 384)
dataset = dataset.filter(lambda x: x['HEIGHT'] >= 128 and x['WIDTH'] >= 128)
dataset = dataset.filter(lambda x: x['similarity'] >= 0.31)
dataset.push_to_hub('lyakaap/laion-mini-ja', token='XXX')
``` |
HTTP404ERROR | null | null | null | false | 1 | false | HTTP404ERROR/huggingface | 2022-07-05T02:42:14.000Z | null | false | 6359aba5ca767bae5c184ebea62c9a0127c9dd7a | [] | [
"license:afl-3.0"
] | https://huggingface.co/datasets/HTTP404ERROR/huggingface/resolve/main/README.md | ---
license: afl-3.0
---
|
Paul | null | null | null | false | 2 | false | Paul/hatecheck-spanish | 2022-07-05T10:27:07.000Z | null | false | a7ea759535bb9fad6361cca151cf94a46e88edf3 | [] | [
"arxiv:2206.09917",
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
"language:es",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:hate-speech-detection"
] | https://huggingface.co/datasets/Paul/hatecheck-spanish/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- expert-generated
language:
- es
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Spanish HateCheck
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- hate-speech-detection
---
# Dataset Card for Multilingual HateCheck
## Dataset Description
Multilingual HateCheck (MHC) is a suite of functional tests for hate speech detection models in 10 different languages: Arabic, Dutch, French, German, Hindi, Italian, Mandarin, Polish, Portuguese and Spanish.
For each language, there are 25+ functional tests that correspond to distinct types of hate and challenging non-hate.
This allows for targeted diagnostic insights into model performance.
For more details, please refer to our paper about MHC, published at the 2022 Workshop on Online Abuse and Harms (WOAH) at NAACL 2022. If you are using MHC, please cite our work!
- **Paper:** Röttger et al. (2022) - Multilingual HateCheck: Functional Tests for Multilingual Hate Speech Detection Models. https://arxiv.org/abs/2206.09917
- **Repository:** https://github.com/rewire-online/multilingual-hatecheck
- **Point of Contact:** paul@rewire.online
## Dataset Structure
The csv format mostly matches the original HateCheck data, with some adjustments for specific languages.
**mhc_case_id**
The test case ID that is unique to each test case across languages (e.g., "mandarin-1305")
**functionality**
The shorthand for the functionality tested by the test case (e.g, "target_obj_nh"). The same functionalities are tested in all languages, except for Mandarin and Arabic, where non-Latin script required adapting the tests for spelling variations.
**test_case**
The test case text.
**label_gold**
The gold standard label ("hateful" or "non-hateful") of the test case. All test cases within a given functionality have the same gold standard label.
**target_ident**
Where applicable, the protected group that is targeted or referenced in the test case. All HateChecks cover seven target groups, but their composition varies across languages.
**ref_case_id**
For hateful cases, where applicable, the ID of the hateful case which was perturbed to generate this test case. For non-hateful cases, where applicable, the ID of the hateful case which is contrasted by this test case.
**ref_templ_id**
The equivalent to ref_case_id, but for template IDs.
**templ_id**
The ID of the template from which the test case was generated.
**case_templ**
The template from which the test case was generated (where applicable).
**gender_male** and **gender_female**
For gender-inflected languages (French, Spanish, Portuguese, Hindi, Arabic, Italian, Polish, German), only for cases where gender inflection is relevant, separate entries for gender_male and gender_female replace case_templ.
**label_annotated**
A list of labels given by the three annotators who reviewed the test case (e.g., "['hateful', 'hateful', 'hateful']").
**label_annotated_maj**
The majority vote of the three annotators (e.g., "hateful"). In some cases this differs from the gold label given by our language experts.
**disagreement_in_case**
True if label_annotated_maj does not match label_gold for the entry.
**disagreement_in_template**
True if the test case is generated from an IDENT template and there is at least one case with disagreement_in_case generated from the same template. This can be used to exclude entire templates from MHC. |
Paul | null | null | null | false | 21 | false | Paul/hatecheck-portuguese | 2022-07-05T10:27:47.000Z | null | false | 323bdf67e0fbd3d7f8086fad0971b5bd5a62524b | [] | [
"arxiv:2206.09917",
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
"language:pt",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:hate-speech-detection"
] | https://huggingface.co/datasets/Paul/hatecheck-portuguese/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- expert-generated
language:
- pt
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Portuguese HateCheck
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- hate-speech-detection
---
# Dataset Card for Multilingual HateCheck
## Dataset Description
Multilingual HateCheck (MHC) is a suite of functional tests for hate speech detection models in 10 different languages: Arabic, Dutch, French, German, Hindi, Italian, Mandarin, Polish, Portuguese and Spanish.
For each language, there are 25+ functional tests that correspond to distinct types of hate and challenging non-hate.
This allows for targeted diagnostic insights into model performance.
For more details, please refer to our paper about MHC, published at the 2022 Workshop on Online Abuse and Harms (WOAH) at NAACL 2022. If you are using MHC, please cite our work!
- **Paper:** Röttger et al. (2022) - Multilingual HateCheck: Functional Tests for Multilingual Hate Speech Detection Models. https://arxiv.org/abs/2206.09917
- **Repository:** https://github.com/rewire-online/multilingual-hatecheck
- **Point of Contact:** paul@rewire.online
## Dataset Structure
The csv format mostly matches the original HateCheck data, with some adjustments for specific languages.
**mhc_case_id**
The test case ID that is unique to each test case across languages (e.g., "mandarin-1305")
**functionality**
The shorthand for the functionality tested by the test case (e.g, "target_obj_nh"). The same functionalities are tested in all languages, except for Mandarin and Arabic, where non-Latin script required adapting the tests for spelling variations.
**test_case**
The test case text.
**label_gold**
The gold standard label ("hateful" or "non-hateful") of the test case. All test cases within a given functionality have the same gold standard label.
**target_ident**
Where applicable, the protected group that is targeted or referenced in the test case. All HateChecks cover seven target groups, but their composition varies across languages.
**ref_case_id**
For hateful cases, where applicable, the ID of the hateful case which was perturbed to generate this test case. For non-hateful cases, where applicable, the ID of the hateful case which is contrasted by this test case.
**ref_templ_id**
The equivalent to ref_case_id, but for template IDs.
**templ_id**
The ID of the template from which the test case was generated.
**case_templ**
The template from which the test case was generated (where applicable).
**gender_male** and **gender_female**
For gender-inflected languages (French, Spanish, Portuguese, Hindi, Arabic, Italian, Polish, German), only for cases where gender inflection is relevant, separate entries for gender_male and gender_female replace case_templ.
**label_annotated**
A list of labels given by the three annotators who reviewed the test case (e.g., "['hateful', 'hateful', 'hateful']").
**label_annotated_maj**
The majority vote of the three annotators (e.g., "hateful"). In some cases this differs from the gold label given by our language experts.
**disagreement_in_case**
True if label_annotated_maj does not match label_gold for the entry.
**disagreement_in_template**
True if the test case is generated from an IDENT template and there is at least one case with disagreement_in_case generated from the same template. This can be used to exclude entire templates from MHC. |
Paul | null | null | null | false | 8 | false | Paul/hatecheck-polish | 2022-07-05T10:26:41.000Z | null | false | 28d7098e2e5a211c4810d0a4d8deccc5889e55b6 | [] | [
"arxiv:2206.09917",
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
"language:pl",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:hate-speech-detection"
] | https://huggingface.co/datasets/Paul/hatecheck-polish/resolve/main/README.md | ---
annotations_creators:
- crowdsourced
language_creators:
- expert-generated
language:
- pl
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Polish HateCheck
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- hate-speech-detection
---
# Dataset Card for Multilingual HateCheck
## Dataset Description
Multilingual HateCheck (MHC) is a suite of functional tests for hate speech detection models in 10 different languages: Arabic, Dutch, French, German, Hindi, Italian, Mandarin, Polish, Portuguese and Spanish.
For each language, there are 25+ functional tests that correspond to distinct types of hate and challenging non-hate.
This allows for targeted diagnostic insights into model performance.
For more details, please refer to our paper about MHC, published at the 2022 Workshop on Online Abuse and Harms (WOAH) at NAACL 2022. If you are using MHC, please cite our work!
- **Paper:** Röttger et al. (2022) - Multilingual HateCheck: Functional Tests for Multilingual Hate Speech Detection Models. https://arxiv.org/abs/2206.09917
- **Repository:** https://github.com/rewire-online/multilingual-hatecheck
- **Point of Contact:** paul@rewire.online
## Dataset Structure
The csv format mostly matches the original HateCheck data, with some adjustments for specific languages.
**mhc_case_id**
The test case ID that is unique to each test case across languages (e.g., "mandarin-1305")
**functionality**
The shorthand for the functionality tested by the test case (e.g, "target_obj_nh"). The same functionalities are tested in all languages, except for Mandarin and Arabic, where non-Latin script required adapting the tests for spelling variations.
**test_case**
The test case text.
**label_gold**
The gold standard label ("hateful" or "non-hateful") of the test case. All test cases within a given functionality have the same gold standard label.
**target_ident**
Where applicable, the protected group that is targeted or referenced in the test case. All HateChecks cover seven target groups, but their composition varies across languages.
**ref_case_id**
For hateful cases, where applicable, the ID of the hateful case which was perturbed to generate this test case. For non-hateful cases, where applicable, the ID of the hateful case which is contrasted by this test case.
**ref_templ_id**
The equivalent to ref_case_id, but for template IDs.
**templ_id**
The ID of the template from which the test case was generated.
**case_templ**
The template from which the test case was generated (where applicable).
**gender_male** and **gender_female**
For gender-inflected languages (French, Spanish, Portuguese, Hindi, Arabic, Italian, Polish, German), only for cases where gender inflection is relevant, separate entries for gender_male and gender_female replace case_templ.
**label_annotated**
A list of labels given by the three annotators who reviewed the test case (e.g., "['hateful', 'hateful', 'hateful']").
**label_annotated_maj**
The majority vote of the three annotators (e.g., "hateful"). In some cases this differs from the gold label given by our language experts.
**disagreement_in_case**
True if label_annotated_maj does not match label_gold for the entry.
**disagreement_in_template**
True if the test case is generated from an IDENT template and there is at least one case with disagreement_in_case generated from the same template. This can be used to exclude entire templates from MHC. |
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