id stringlengths 2 115 | author stringlengths 2 42 ⌀ | last_modified timestamp[us, tz=UTC] | downloads int64 0 8.87M | likes int64 0 3.84k | paperswithcode_id stringlengths 2 45 ⌀ | tags list | lastModified timestamp[us, tz=UTC] | createdAt stringlengths 24 24 | key stringclasses 1 value | created timestamp[us] | card stringlengths 1 1.01M | embedding list | library_name stringclasses 21 values | pipeline_tag stringclasses 27 values | mask_token null | card_data null | widget_data null | model_index null | config null | transformers_info null | spaces null | safetensors null | transformersInfo null | modelId stringlengths 5 111 ⌀ | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
strombergnlp/ipm_nel | strombergnlp | 2022-10-25T21:41:26Z | 14 | 1 | ipm-nel | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-4.0",
"named-entity-linking",
"region:us"
] | 2022-10-25T21:41:26Z | 2022-04-28T10:06:10.000Z | 2022-04-28T10:06:10 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets: []
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: ipm-nel
pretty_name: IPM NEL (Derczynski)
tags:
- named-entity-linking
---
# Dataset Card for "ipm-nel"
## 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:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [http://www.derczynski.com/papers/ner_single.pdf](http://www.derczynski.com/papers/ner_single.pdf)
- **Point of Contact:** [Leon Derczynski](https://github.com/leondz)
- **Size of downloaded dataset files:** 120 KB
- **Size of the generated dataset:**
- **Total amount of disk used:**
### Dataset Summary
This data is for the task of named entity recognition and linking/disambiguation over tweets. It comprises
the addition of an entity URI layer on top of an NER-annotated tweet dataset. The task is to detect entities
and then provide a correct link to them in DBpedia, thus disambiguating otherwise ambiguous entity surface
forms; for example, this means linking "Paris" to the correct instance of a city named that (e.g. Paris,
France vs. Paris, Texas).
The data concentrates on ten types of named entities: company, facility, geographic location, movie, musical
artist, person, product, sports team, TV show, and other.
The file is tab separated, in CoNLL format, with line breaks between tweets.
* Data preserves the tokenisation used in the Ritter datasets.
* PoS labels are not present for all tweets, but where they could be found in the Ritter data, they're given.
* In cases where a URI could not be agreed, or was not present in DBpedia, the linking URI is `NIL`.
See the paper, [Analysis of Named Entity Recognition and Linking for Tweets](http://www.derczynski.com/papers/ner_single.pdf) for a full description of the methodology.
### Supported Tasks and Leaderboards
* Dataset leaderboard on PWC: [Entity Linking on Derczynski](https://paperswithcode.com/sota/entity-linking-on-derczynski-1)
### Languages
English of unknown region (`bcp47:en`)
## Dataset Structure
### Data Instances
#### ipm_nel
- **Size of downloaded dataset files:** 120 KB
- **Size of the generated dataset:**
- **Total amount of disk used:**
An example of 'train' looks as follows.
```
{
'id': '0',
'tokens': ['#Astros', 'lineup', 'for', 'tonight', '.', 'Keppinger', 'sits', ',', 'Downs', 'plays', '2B', ',', 'CJ', 'bats', '5th', '.', '@alysonfooter', 'http://bit.ly/bHvgCS'],
'ner_tags': [9, 0, 0, 0, 0, 7, 0, 0, 7, 0, 0, 0, 7, 0, 0, 0, 0, 0],
'uris': "['http://dbpedia.org/resource/Houston_Astros', '', '', '', '', 'http://dbpedia.org/resource/Jeff_Keppinger', '', '', 'http://dbpedia.org/resource/Brodie_Downs', '', '', '', 'NIL', '', '', '', '', '']"
}
```
### Data Fields
- `id`: a `string` feature.
- `tokens`: a `list` of `string` features.
- `ner_tags`: a `list` of classification labels (`int`). Full tagset with indices:
- `uris`: a `list` of URIs (`string`) that disambiguate entities. Set to `NIL` when an entity has no DBpedia entry, or blank for outside-of-entity tokens.
### Data Splits
| name |train|
|---------|----:|
|ipm_nel|183 sentences|
## Dataset Creation
### Curation Rationale
To gather a social media benchmark for named entity linking that is sufficiently different from newswire data.
### Source Data
#### Initial Data Collection and Normalization
The data is partly harvested from that distributed by [Ritter / Named Entity Recognition in Tweets: An Experimental Study](https://aclanthology.org/D11-1141/),
and partly taken from Twitter by the authors.
#### Who are the source language producers?
English-speaking Twitter users, between October 2011 and September 2013
### Annotations
#### Annotation process
The authors were allocated documents and marked them for named entities (where these were not already present) and then attempted to find
the best-fitting DBpedia entry for each entity found. Each entity mention was labelled by a random set of three volunteers.
The annotation task was mediated using Crowdflower (Biewald, 2012). Our interface design was to show each volunteer the text of the tweet, any URL links contained
therein, and a set of candidate targets from DBpedia. The volunteers were encouraged to click on the URL links from the
tweet, to gain addition context and thus ensure that the correct DBpedia URI is chosen by them. Candidate entities were
shown in random order, using the text from the corresponding DBpedia abstracts (where available) or the actual DBpedia
URI otherwise. In addition, the options ‘‘none of the above’’, ‘‘not an entity’’ and ‘‘cannot decide’’ were added, to allow the
volunteers to indicate that this entity mention has no corresponding DBpedia URI (none of the above), the highlighted text
is not an entity, or that the tweet text (and any links, if available) did not provide sufficient information to reliably disambiguate the entity mention.
#### Who are the annotators?
The annotators are 10 volunteer NLP researchers, from the authors and the authors' institutions.
### Personal and Sensitive Information
The data was public at the time of collection. User names are preserved.
## Considerations for Using the Data
### Social Impact of Dataset
There's a risk of user-deleted content being in this data. The data has NOT been vetted for any content, so there's a risk of harmful text.
### Discussion of Biases
The data is annotated by NLP researchers; we know that this group has high agreement but low recall on English twitter text [C16-1111](https://aclanthology.org/C16-1111/).
### Other Known Limitations
The above limitations apply.
## Additional Information
### Dataset Curators
The dataset is curated by the paper's authors.
### Licensing Information
The authors distribute this data under Creative Commons attribution license, CC-BY 4.0. You must
acknowledge the author if you use this data, but apart from that, you're quite
free to do most things. See https://creativecommons.org/licenses/by/4.0/legalcode .
### Citation Information
```
@article{derczynski2015analysis,
title={Analysis of named entity recognition and linking for tweets},
author={Derczynski, Leon and Maynard, Diana and Rizzo, Giuseppe and Van Erp, Marieke and Gorrell, Genevieve and Troncy, Rapha{\"e}l and Petrak, Johann and Bontcheva, Kalina},
journal={Information Processing \& Management},
volume={51},
number={2},
pages={32--49},
year={2015},
publisher={Elsevier}
}
```
### Contributions
Author-added dataset [@leondz](https://github.com/leondz)
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DanielHesslow/SwissProt-EC | DanielHesslow | 2022-04-30T15:12:33Z | 14 | 0 | null | [
"Protein",
"Enzyme Commission",
"EC",
"region:us"
] | 2022-04-30T15:12:33Z | 2022-04-29T19:46:46.000Z | 2022-04-29T19:46:46 | ---
language:
- protein sequences
datasets:
- Swissprot
tags:
- Protein
- Enzyme Commission
- EC
---
Swissprot is a high quality manually annotated protein database. The dataset contains annotations with the functional properties of the proteins. Here we extract proteins with Enzyme Commission labels.
The dataset is ported from Protinfer: https://github.com/google-research/proteinfer.
The EC-labels are extracted and indexed, the mapping is provided in `idx_mapping.json`. Proteins without EC tags are removed.
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"Protein",
"PFam",
"region:us"
] | 2022-04-30T15:15:55Z | 2022-04-29T19:52:56.000Z | 2022-04-29T19:52:56 | ---
language:
- protein sequences
datasets:
- Swissprot
tags:
- Protein
- PFam
---
Swissprot is a high quality manually annotated protein database. The dataset contains annotations with the functional properties of the proteins. Here we extract proteins with PFam labels.
The dataset is ported from Protinfer: https://github.com/google-research/proteinfer.
The Pfam-labels are extracted and indexed, the mapping is provided in `idx_mapping.json`. Proteins without Pfam tags are removed.
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"Protein",
"Gene Ontology",
"GO",
"region:us"
] | 2022-04-30T15:16:48Z | 2022-04-29T19:53:34.000Z | 2022-04-29T19:53:34 | ---
language:
- protein sequences
datasets:
- Swissprot
tags:
- Protein
- Gene Ontology
- GO
---
Swissprot is a high quality manually annotated protein database. The dataset contains annotations with the functional properties of the proteins. Here we extract proteins with Gene Ontology labels.
The dataset is ported from Protinfer: https://github.com/google-research/proteinfer.
The GO-labels are extracted and indexed, the mapping is provided in `idx_mapping.json`. Proteins without GO tags are removed.
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charly/test | charly | 2022-04-30T17:17:22Z | 14 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2022-04-30T17:17:22Z | 2022-04-30T10:50:07.000Z | 2022-04-30T10:50:07 | ---
license: apache-2.0
---
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pauli31/czech-subjectivity-dataset | pauli31 | 2022-07-01T15:31:40Z | 14 | 1 | null | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"license:cc-by-nc-sa-4.0",
"arxiv:2204.13915",
"region:us"
] | 2022-07-01T15:31:40Z | 2022-05-02T18:27:17.000Z | 2022-05-02T18:27:17 | ---
annotations_creators: []
language_creators: []
language:
- cs-CZ
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
pretty_name: Czech Subjectivity Dataset
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
---
# Dataset Card for Czech Subjectivity Dataset
### Dataset Summary
Czech subjectivity dataset (Subj-CS) of 10k manually annotated subjective and objective sentences from movie reviews and descriptions. See the paper description https://arxiv.org/abs/2204.13915
### Github
https://github.com/pauli31/czech-subjectivity-dataset
### Supported Tasks and Leaderboards
Subjectivity Analysis
### Languages
Czech
### Data Instances
train/dev/test
### Licensing Information
[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.](https://creativecommons.org/licenses/by-nc-sa/4.0/)
### Citation Information
If you use our dataset or software for academic research, please cite the our [paper](https://arxiv.org/abs/2204.13915)
```
@article{pib2022czech,
title={Czech Dataset for Cross-lingual Subjectivity Classification},
author={Pavel Přibáň and Josef Steinberger},
year={2022},
eprint={2204.13915},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contact
pribanp@kiv.zcu.cz
### Contributions
Thanks to [@pauli31](https://github.com/pauli31) for adding this dataset. | [
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filwsyl/ascend | filwsyl | 2022-10-25T05:24:45Z | 14 | 0 | null | [
"task_categories:automatic-speech-recognition",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:multilingual",
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"language:en",
"language:zh",
"license:cc-by-sa-4.0",
"arxiv:2112.06223",
"region:u... | 2022-10-25T05:24:45Z | 2022-05-06T11:42:28.000Z | 2022-05-06T11:42:28 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
- zh
language_bcp47:
- en
- zh-CN
license:
- cc-by-sa-4.0
multilinguality:
- multilingual
pretty_name: 'ASCEND: A Spontaneous Chinese-English Dataset for Code-switching in
Multi-turn Conversation'
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- automatic-speech-recognition
task_ids:
- code-switching
- speech-recognition
---
# Dataset Card for ASCEND
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [Needs More Information]
- **Repository:** [Needs More Information]
- **Paper:** https://arxiv.org/abs/2112.06223
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
ASCEND (A Spontaneous Chinese-English Dataset) introduces a high-quality resource of spontaneous multi-turn conversational dialogue Chinese-English code-switching corpus collected in Hong Kong. ASCEND consists of 10.62 hours of spontaneous speech with a total of ~12.3K utterances. The corpus is split into 3 sets: training, validation, and test with a ratio of 8:1:1 while maintaining a balanced gender proportion on each set.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
Chinese and English
## 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] | [
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0.031443901360034... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
cradle-bio/FLIP_clusters | cradle-bio | 2022-05-06T13:29:51Z | 14 | 2 | null | [
"region:us"
] | 2022-05-06T13:29:51Z | 2022-05-06T13:21:38.000Z | 2022-05-06T13:21:38 | # Full FLIP stability dataset
The stability dataset from flip, which is based on the meltome atlas, data has those columns:
```
[ 'index', 'seq_id', 'sequence', 'target', 'cluster_center',
'cluster_distance']
```
- **Index** from the original dataset
- **Seq_id** a unique sequence ID string that is concatenated from several other IDs (also Unirep)
- **Sequence** The actual protein sequence as a string
- **Target** the melting point temperature of the protein TM
- **Cluster center** The seq_id of the cluster center protein this sequence is assigned to. Can also be its won seq_id if this sequence is a center.
- **Cluster distance** The levenstein distance of the protein to its cluster center. | [
-0.13913074135780334,
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-0.4964914619922638,
-0.14857591688632... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
cradle-bio/FLIP_biggest_cluster_Q0PD48 | cradle-bio | 2022-05-06T14:21:53Z | 14 | 1 | null | [
"region:us"
] | 2022-05-06T14:21:53Z | 2022-05-06T14:18:23.000Z | 2022-05-06T14:18:23 | Entry not found | [
-0.32276472449302673,
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-0.9104482531547546,
0.5715669393539429,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
kimcando/KOR-RE-natures-and-environments | kimcando | 2022-05-06T22:11:26Z | 14 | 1 | null | [
"license:apache-2.0",
"region:us"
] | 2022-05-06T22:11:26Z | 2022-05-06T21:59:28.000Z | 2022-05-06T21:59:28 | ---
license: apache-2.0
---
# Dataset Card for [KOR-RE-natures-and-environments]
You can find relation map, guidelines(written in Korean), short technical papers in this [github repo](https://github.com/boostcampaitech3/level2-data-annotation_nlp-level2-nlp-03). This work is done by as part of project for Boostcamp AI Tech supported by Naver Connect Foundation.
### Dataset Description
* Language: Korean
* Task: Relation Extraction
* Topics: Natures and Environments
* Sources: Korean wiki
### Main Data Fields
* Sentences: sentences
* Subject_entity: infos for subject entity in the sentence including words, start index, end index, type of entity
* object_entity: infos for object entity in the sentence including words, start index, end index, type of entity
* label : class ground truth label
* file : name of the file
| [
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0.105650335550308... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
nateraw/background-remover-files | nateraw | 2022-05-07T02:53:12Z | 14 | 1 | null | [
"license:apache-2.0",
"region:us"
] | 2022-05-07T02:53:12Z | 2022-05-07T02:49:48.000Z | 2022-05-07T02:49:48 | ---
license: apache-2.0
---
| [
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-0.0478260256350... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
hsiehpinghan/github-issues | hsiehpinghan | 2022-05-08T00:40:39Z | 14 | 0 | null | [
"region:us"
] | 2022-05-08T00:40:39Z | 2022-05-08T00:33:01.000Z | 2022-05-08T00:33:01 | Entry not found | [
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
parvezmrobin/MCMD | parvezmrobin | 2022-05-09T07:25:40Z | 14 | 0 | null | [
"region:us"
] | 2022-05-09T07:25:40Z | 2022-05-08T03:34:28.000Z | 2022-05-08T03:34:28 | This dataset is the CSV version of the original MCMD (Multi-programming-language Commit Message Dataset) provided by Tao et al. in their paper "On the Evaluation of Commit Message Generation Models: An Experimental Study".
The original version of the dataset can be found in [Zenodo](https://doi.org/10.5281/zenodo.5025758).
| [
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0.0686840191... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
shzhang/tutorial_datasets_github_issues | shzhang | 2022-05-08T06:52:12Z | 14 | 0 | null | [
"region:us"
] | 2022-05-08T06:52:12Z | 2022-05-08T06:48:13.000Z | 2022-05-08T06:48:13 | Entry not found | [
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-0.9104482531547546,
0.5715669393539429,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
johnowhitaker/imagewoof2-320 | johnowhitaker | 2022-05-08T09:26:37Z | 14 | 0 | null | [
"region:us"
] | 2022-05-08T09:26:37Z | 2022-05-08T09:23:24.000Z | 2022-05-08T09:23:24 | Entry not found | [
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-0.9104482531547546,
0.5715669393539429,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
johnowhitaker/imagenette2-320 | johnowhitaker | 2022-05-08T09:31:38Z | 14 | 0 | null | [
"region:us"
] | 2022-05-08T09:31:38Z | 2022-05-08T09:28:31.000Z | 2022-05-08T09:28:31 | Entry not found | [
-0.32276472449302673,
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0.5715669393539429,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
peandrew/dialy_dialogue_with_recoginized_concept_raw | peandrew | 2022-05-08T15:10:03Z | 14 | 1 | null | [
"region:us"
] | 2022-05-08T15:10:03Z | 2022-05-08T10:02:15.000Z | 2022-05-08T10:02:15 | Entry not found | [
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0.5715669393539429,
... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
bananabot/engMollywoodSummaries | bananabot | 2022-05-08T15:54:28Z | 14 | 1 | null | [
"license:wtfpl",
"region:us"
] | 2022-05-08T15:54:28Z | 2022-05-08T15:43:03.000Z | 2022-05-08T15:43:03 | ---
license: wtfpl
---
data I hand picked from https://blcklst.com/lists/ and http://cs.cmu.edu/~ark/personas/ | [
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0.6642072796821594,
-0.9299520254135132,
-0.6990591883659363,
-0.2676573693752289,
-0.1907429248094... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
nguyenvulebinh/fsd50k | nguyenvulebinh | 2022-05-08T22:18:48Z | 14 | 0 | null | [
"license:cc-by-4.0",
"region:us"
] | 2022-05-08T22:18:48Z | 2022-05-08T22:16:36.000Z | 2022-05-08T22:16:36 | ---
license: cc-by-4.0
---
| [
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-... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
leo19941227/g2p | leo19941227 | 2022-05-10T14:50:25Z | 14 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2022-05-10T14:50:25Z | 2022-05-10T14:49:19.000Z | 2022-05-10T14:49:19 | ---
license: apache-2.0
---
| [
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-... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
EMBO/sd-nlp-non-tokenized | EMBO | 2023-01-19T10:12:45Z | 14 | 0 | null | [
"task_categories:token-classification",
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:named-entity-recognition",
"task_ids:parsing",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categorie... | 2023-01-19T10:12:45Z | 2022-05-17T12:34:22.000Z | 2022-05-17T12:34:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets: []
task_categories:
- token-classification
- text-classification
task_ids:
- multi-class-classification
- named-entity-recognition
- parsing
---
# Dataset Card for sd-nlp
## Table of Contents
- [Dataset Card for [EMBO/sd-nlp-non-tokenized]](#dataset-card-for-dataset-name)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://sourcedata.embo.org
- **Repository:** https://github.com/source-data/soda-roberta
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** thomas.lemberger@embo.org, jorge.abreu@embo.org
### Dataset Summary
This dataset is based on the content of the SourceData (https://sourcedata.embo.org) database, which contains manually annotated figure legends written in English and extracted from scientific papers in the domain of cell and molecular biology (Liechti et al, Nature Methods, 2017, https://doi.org/10.1038/nmeth.4471).
Unlike the dataset [`sd-nlp`](https://huggingface.co/datasets/EMBO/sd-nlp), pre-tokenized with the `roberta-base` tokenizer, this dataset is not previously tokenized, but just splitted into words. Users can therefore use it to fine-tune other models.
Additional details at https://github.com/source-data/soda-roberta
### Supported Tasks and Leaderboards
Tags are provided as [IOB2-style tags](https://en.wikipedia.org/wiki/Inside%E2%80%93outside%E2%80%93beginning_(tagging)).
`PANELIZATION`: figure captions (or figure legends) are usually composed of segments that each refer to one of several 'panels' of the full figure. Panels tend to represent results obtained with a coherent method and depicts data points that can be meaningfully compared to each other. `PANELIZATION` provide the start (B-PANEL_START) of these segments and allow to train for recogntion of the boundary between consecutive panel lengends.
`NER`: biological and chemical entities are labeled. Specifically the following entities are tagged:
- `SMALL_MOLECULE`: small molecules
- `GENEPROD`: gene products (genes and proteins)
- `SUBCELLULAR`: subcellular components
- `CELL`: cell types and cell lines.
- `TISSUE`: tissues and organs
- `ORGANISM`: species
- `DISEASE`: diseases (see limitations)
- `EXP_ASSAY`: experimental assays
`ROLES`: the role of entities with regard to the causal hypotheses tested in the reported results. The tags are:
- `CONTROLLED_VAR`: entities that are associated with experimental variables and that subjected to controlled and targeted perturbations.
- `MEASURED_VAR`: entities that are associated with the variables measured and the object of the measurements.
`BORING`: entities are marked with the tag `BORING` when they are more of descriptive value and not directly associated with causal hypotheses ('boring' is not an ideal choice of word, but it is short...). Typically, these entities are so-called 'reporter' geneproducts, entities used as common baseline across samples, or specify the context of the experiment (cellular system, species, etc...).
### Languages
The text in the dataset is English.
## Dataset Structure
### Data Instances
```json
{
"words": [
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],
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],
"panel_start": [
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"small_mol_roles": ["O", "O", "O", "O", "O", "O", "B-CONTROLLED_VAR", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-CONTROLLED_VAR", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-CONTROLLED_VAR", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-CONTROLLED_VAR", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "B-CONTROLLED_VAR", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]
}
}
```
### Data Fields
- `words`: `list` of `strings` text tokenized into words.
- `panel_id`: ID of the panel to which the example belongs to in the SourceData database.
- `label_ids`:
- `entity_types`: `list` of `strings` for the IOB2 tags for entity type; possible value in `["O", "I-SMALL_MOLECULE", "B-SMALL_MOLECULE", "I-GENEPROD", "B-GENEPROD", "I-SUBCELLULAR", "B-SUBCELLULAR", "I-CELL", "B-CELL", "I-TISSUE", "B-TISSUE", "I-ORGANISM", "B-ORGANISM", "I-EXP_ASSAY", "B-EXP_ASSAY"]`
- `geneprod_roles`: `list` of `strings` for the IOB2 tags for experimental roles; values in `["O", "I-CONTROLLED_VAR", "B-CONTROLLED_VAR", "I-MEASURED_VAR", "B-MEASURED_VAR"]`
- `boring`: `list` of `strings` for IOB2 tags for entities unrelated to causal design; values in `["O", "I-BORING", "B-BORING"]`
- `panel_start`: `list` of `strings` for IOB2 tags `["O", "B-PANEL_START"]`
- `small_mol_roles`: `list` of `strings` for IOB2 tags showing whether the entity is the variable being measured or the control variable `["O", "B-CONTROLLED_VAR", "I-CONTROLLED_VAR", "B-MEASURED_VAR", "I-MEASURED_VAR",]`
### Data Splits
- train:
- features: ['words', 'labels', 'tag_mask', 'panel_id'],
- num_rows: 50_198
- validation:
- features: ['words', 'labels', 'tag_mask', 'panel_id'],
- num_rows: 5_946
- test:
- features: ['words', 'labels', 'tag_mask', 'panel_id'],
- num_rows: 6_222
## Dataset Creation
### Curation Rationale
The dataset was built to train models for the automatic extraction of a knowledge graph based from the scientific literature. The dataset can be used to train models for text segmentation, named entity recognition and semantic role labeling.
### Source Data
#### Initial Data Collection and Normalization
Figure legends were annotated according to the SourceData framework described in Liechti et al 2017 (Nature Methods, 2017, https://doi.org/10.1038/nmeth.4471). The curation tool at https://curation.sourcedata.io was used to segment figure legends into panel legends, tag enities, assign experiemental roles and normalize with standard identifiers (not available in this dataset). The source data was downloaded from the SourceData API (https://api.sourcedata.io) on 21 Jan 2021.
#### Who are the source language producers?
The examples are extracted from the figure legends from scientific papers in cell and molecular biology.
### Annotations
#### Annotation process
The annotations were produced manually with expert curators from the SourceData project (https://sourcedata.embo.org)
#### Who are the annotators?
Curators of the SourceData project.
### Personal and Sensitive Information
None known.
## Considerations for Using the Data
### Social Impact of Dataset
Not applicable.
### Discussion of Biases
The examples are heavily biased towards cell and molecular biology and are enriched in examples from papers published in EMBO Press journals (https://embopress.org)
The annotation of diseases has been added recently to the dataset. Although they appear, the number is very low and they are not consistently tagged through the entire dataset.
We recommend to use the diseases by filtering the examples that contain them.
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
Thomas Lemberger, EMBO.
Jorge Abreu Vicente, EMBO
### Licensing Information
CC BY 4.0
### Citation Information
We are currently working on a paper to present the dataset. It is expected to be ready by 2023 spring. In the meantime, the following paper should be cited.
```latex
@article {Liechti2017,
author = {Liechti, Robin and George, Nancy and Götz, Lou and El-Gebali, Sara and Chasapi, Anastasia and Crespo, Isaac and Xenarios, Ioannis and Lemberger, Thomas},
title = {SourceData - a semantic platform for curating and searching figures},
year = {2017},
volume = {14},
number = {11},
doi = {10.1038/nmeth.4471},
URL = {https://doi.org/10.1038/nmeth.4471},
eprint = {https://www.biorxiv.org/content/early/2016/06/20/058529.full.pdf},
journal = {Nature Methods}
}
```
### Contributions
Thanks to [@tlemberger](https://github.com/tlemberger>) and [@drAbreu](https://github.com/drAbreu>) for adding this dataset.
| [
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0.5874567031860... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
bigscience-data/roots_en_wikipedia | bigscience-data | 2022-12-12T11:03:18Z | 14 | 2 | null | [
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] | 2022-12-12T11:03:18Z | 2022-05-18T09:08:47.000Z | 2022-05-18T09:08:47 | ---
language: en
license: cc-by-sa-3.0
extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience
Ethical Charter. The charter can be found at:
https://hf.co/spaces/bigscience/ethical-charter'
extra_gated_fields:
I have read and agree to abide by the BigScience Ethical Charter: checkbox
---
ROOTS Subset: roots_en_wikipedia
# wikipedia
- Dataset uid: `wikipedia`
### Description
### Homepage
### Licensing
### Speaker Locations
### Sizes
- 3.2299 % of total
- 4.2071 % of en
- 5.6773 % of ar
- 3.3416 % of fr
- 5.2815 % of es
- 12.4852 % of ca
- 0.4288 % of zh
- 0.4286 % of zh
- 5.4743 % of indic-bn
- 8.9062 % of indic-ta
- 21.3313 % of indic-te
- 4.4845 % of pt
- 4.0493 % of indic-hi
- 11.3163 % of indic-ml
- 22.5300 % of indic-ur
- 4.4902 % of vi
- 16.9916 % of indic-kn
- 24.7820 % of eu
- 11.6241 % of indic-mr
- 9.8749 % of id
- 9.3489 % of indic-pa
- 9.4767 % of indic-gu
- 24.1132 % of indic-as
- 5.3309 % of indic-or
### BigScience processing steps
#### Filters applied to: en
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: ar
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: fr
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: es
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: ca
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
#### Filters applied to: zh
#### Filters applied to: zh
#### Filters applied to: indic-bn
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-ta
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-te
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: pt
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-hi
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-ml
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-ur
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: vi
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-kn
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: eu
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
#### Filters applied to: indic-mr
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: id
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-pa
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-gu
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
- filter_small_docs_bytes_300
#### Filters applied to: indic-as
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
#### Filters applied to: indic-or
- filter_wiki_user_titles
- dedup_document
- filter_remove_empty_docs
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0.502192139625549... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
jacklin/msmarco_passage_ranking_official_train | jacklin | 2022-06-13T21:46:30Z | 14 | 0 | null | [
"arxiv:1611.09268",
"region:us"
] | 2022-06-13T21:46:30Z | 2022-05-19T18:11:01.000Z | 2022-05-19T18:11:01 | This is the preprocessed training data from msmarco passage(v1) ranking corpus.
*[MS MARCO: A human generated MAchine Reading COmprehension dataset](https://arxiv.org/pdf/1611.09268.pdf)* SPayal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen,. | [
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jjjonathan14/mango1 | jjjonathan14 | 2022-05-19T19:10:24Z | 14 | 0 | null | [
"region:us"
] | 2022-05-19T19:10:24Z | 2022-05-19T19:06:56.000Z | 2022-05-19T19:06:56 | Entry not found | [
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tomekkorbak/pile-toxicity-balanced3 | tomekkorbak | 2022-05-20T18:36:32Z | 14 | 0 | null | [
"region:us"
] | 2022-05-20T18:36:32Z | 2022-05-20T14:22:55.000Z | 2022-05-20T14:22:55 | ## Generation procedure
The dataset was constructed using documents from [the Pile](https://pile.eleuther.ai/) scored using using [Perspective API](http://perspectiveapi.com) toxicity scores.
The procedure was the following:
1. A chunk of the Pile (2.2m documents) was scored using the Perspective API (on May 18-20 2022) giving [`tomekkorbak/pile-chunk-toxicity-scored-3`](https://huggingface.co/datasets/tomekkorbak/pile-chunk-toxicity-scored-3).
1. The first half of this dataset is 100k *most* toxic documents from `pile-chunk-toxicity-scored-3`
2. The first half of this dataset is 100k documents sampled randomly from of `pile-chunk-toxicity-scored-3`
3. Then, the dataset was shuffled and a 9:1 train-test split was done
## Basic stats
The average document-level scores of the bad and random halves are 0.34 and 0.05, respectively. The average token-level score of the whole dataset is 0.2025. The average document-level score is 0.1983.
## Score histogram
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spoiled/ecqa_model_generate_roberta | spoiled | 2022-05-22T10:54:22Z | 14 | 0 | null | [
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DigitalUmuganda/kinyarwanda-tts-dataset | DigitalUmuganda | 2022-05-20T15:24:55Z | 14 | 0 | null | [
"region:us"
] | 2022-05-20T15:24:55Z | 2022-05-20T15:20:06.000Z | 2022-05-20T15:20:06 | # Kinyarwanda dataset for text to speech model
Kinyarwanda dataset for text to speech model holds data for ai modelling of Kinyarwanda chatbots or other use cases. | [
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Rexhaif/ru-med-ner | Rexhaif | 2022-05-25T20:58:27Z | 14 | 1 | null | [
"arxiv:2201.06499",
"region:us"
] | 2022-05-25T20:58:27Z | 2022-05-20T15:55:37.000Z | 2022-05-20T15:55:37 | # Dataset Card for ru-med-ner
## 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)
- [Additional Information](#additional-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://github.com/pavel-blinov/RuMedBench
- **Repository:** https://github.com/pavel-blinov/RuMedBench
- **Paper:** https://arxiv.org/abs/2201.06499
- **Leaderboard:** https://github.com/pavel-blinov/RuMedBench
- **Point of Contact:** Blinov.P.D@sberbank.ru
### Dataset Summary
NER dataset for Russian language, extracted from medical records\\
See https://github.com/pavel-blinov/RuMedBench for details
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
- ru-RU
## Dataset Structure
### Data Instances
```javascript
{"idx": "2472239.tsv_0", "tokens": ["", "?5@2K9", "65", "45=L", "?@8<5=5=8O", "2K?8;0", "5", "B01;5B>:", ",", "?@>A=C;0AL", "=>GLN", "8", "A>=", ":0:", ">B18;>", "."], "ner_tags": ["O", "O", "O", "O", "O", "O", "O", "B-Drugform", "O", "B-ADR", "O", "O", "B-ADR", "I-ADR", "I-ADR", "O"]}
```
### Data Fields
- idx: example id
- tokens: list of words from example
- ner_tags: ner tags
### Citation Information
```
@misc{blinov2022rumedbench,
title={RuMedBench: A Russian Medical Language Understanding Benchmark},
author={Pavel Blinov and Arina Reshetnikova and Aleksandr Nesterov and Galina Zubkova and Vladimir Kokh},
year={2022},
eprint={2201.06499},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | [
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0.00395636307075619... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
scoup123/tr_movie_reviews_training | scoup123 | 2022-05-21T18:03:05Z | 14 | 0 | null | [
"license:other",
"region:us"
] | 2022-05-21T18:03:05Z | 2022-05-20T17:34:16.000Z | 2022-05-20T17:34:16 | ---
license: other
---
annotations_creators:
- found
language_creators:
- found
languages:
- tr
licenses:
- unknown
multilinguality:
- monolingual
paperswithcode_id: null
pretty_name: turkish_movie_reviews
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
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cpllab/syntaxgym_sentences | cpllab | 2022-05-20T20:13:22Z | 14 | 1 | null | [
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s3prl/flashlight | s3prl | 2022-05-21T02:32:59Z | 14 | 0 | null | [
"region:us"
] | 2022-05-21T02:32:59Z | 2022-05-21T02:15:22.000Z | 2022-05-21T02:15:22 | Entry not found | [
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
EddieChen372/react_repos | EddieChen372 | 2022-06-09T07:48:35Z | 14 | 4 | null | [
"region:us"
] | 2022-06-09T07:48:35Z | 2022-05-21T03:10:27.000Z | 2022-05-21T03:10:27 | Entry not found | [
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Aniemore/REPV | Aniemore | 2022-07-01T16:41:13Z | 14 | 2 | null | [
"task_categories:audio-classification",
"task_ids:audio-emotion-recognition",
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ru",
"license:... | 2022-07-01T16:41:13Z | 2022-05-26T22:15:17.000Z | 2022-05-26T22:15:17 | ---
annotations_creators:
- crowdsourced
language_creators:
- expert-generated
- crowdsourced
language:
- ru
license:
- mit
multilinguality:
- monolingual
pretty_name: Russian Emotional Phonetic Voices
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- audio-classification
task_ids:
- audio-emotion-recognition
---
# Citations
```
@misc{Aniemore,
author = {Артем Аментес, Илья Лубенец, Никита Давидчук},
title = {Открытая библиотека искусственного интеллекта для анализа и выявления эмоциональных оттенков речи человека},
year = {2022},
publisher = {Hugging Face},
journal = {Hugging Face Hub},
howpublished = {\url{https://huggingface.com/aniemore/Aniemore}},
email = {hello@socialcode.ru}
}
```
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Abdelrahman-Rezk/Arabic_Poem_Comprehensive_Dataset_APCD | Abdelrahman-Rezk | 2022-05-27T19:01:21Z | 14 | 0 | null | [
"region:us"
] | 2022-05-27T19:01:21Z | 2022-05-27T18:58:56.000Z | 2022-05-27T18:58:56 | Entry not found | [
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0.5715675354003906,... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Adapting/empathetic_dialogues_v2 | Adapting | 2022-06-21T17:56:26Z | 14 | 5 | null | [
"license:afl-3.0",
"region:us"
] | 2022-06-21T17:56:26Z | 2022-06-06T08:22:16.000Z | 2022-06-06T08:22:16 | ---
license: afl-3.0
---
Fine-tuned empathetic dialogue datasets from https://huggingface.co/datasets/empathetic_dialogues
With labeled chat history, system response, question or not and behavior.
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davidcechak/Arabidopsis_thaliana_DNA_v0_DNABert6tokenized | davidcechak | 2022-06-09T17:35:20Z | 14 | 1 | null | [
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EddieChen372/javascript-small | EddieChen372 | 2022-06-18T09:37:52Z | 14 | 2 | null | [
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autoevaluate/autoeval-staging-eval-project-38643302-7294782 | autoevaluate | 2022-06-24T10:11:47Z | 14 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-06-24T10:11:47Z | 2022-06-24T09:44:47.000Z | 2022-06-24T09:44:47 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: human-centered-summarization/financial-summarization-pegasus
dataset_name: xsum
dataset_config: default
dataset_split: test
col_mapping:
text: document
target: summary
---
# 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: human-centered-summarization/financial-summarization-pegasus
* Dataset: xsum
To run new evaluation jobs, visit Hugging Face's [automatic evaluation service](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. | [
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autoevaluate/autoeval-staging-eval-project-84760c85-7314784 | autoevaluate | 2022-06-24T09:51:31Z | 14 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-06-24T09:51:31Z | 2022-06-24T09:50:37.000Z | 2022-06-24T09:50:37 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- samsum
eval_info:
task: summarization
model: philschmid/bart-base-samsum
dataset_name: samsum
dataset_config: samsum
dataset_split: test
col_mapping:
text: dialogue
target: summary
---
# 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: philschmid/bart-base-samsum
* Dataset: samsum
To run new evaluation jobs, visit Hugging Face's [automatic evaluation service](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. | [
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autoevaluate/autoeval-staging-eval-project-84760c85-7314785 | autoevaluate | 2022-06-24T09:53:26Z | 14 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-06-24T09:53:26Z | 2022-06-24T09:50:41.000Z | 2022-06-24T09:50:41 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- samsum
eval_info:
task: summarization
model: philschmid/bart-large-cnn-samsum
dataset_name: samsum
dataset_config: samsum
dataset_split: test
col_mapping:
text: dialogue
target: summary
---
# 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: philschmid/bart-large-cnn-samsum
* Dataset: samsum
To run new evaluation jobs, visit Hugging Face's [automatic evaluation service](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. | [
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autoevaluate/autoeval-staging-eval-project-84760c85-7314786 | autoevaluate | 2022-06-24T09:52:53Z | 14 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-06-24T09:52:53Z | 2022-06-24T09:50:47.000Z | 2022-06-24T09:50:47 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- samsum
eval_info:
task: summarization
model: philschmid/distilbart-cnn-12-6-samsum
dataset_name: samsum
dataset_config: samsum
dataset_split: test
col_mapping:
text: dialogue
target: summary
---
# 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: philschmid/distilbart-cnn-12-6-samsum
* Dataset: samsum
To run new evaluation jobs, visit Hugging Face's [automatic evaluation service](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. | [
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LTress/British_LibriAdapt | LTress | 2022-07-29T16:29:17Z | 14 | 0 | null | [
"region:us"
] | 2022-07-29T16:29:17Z | 2022-06-24T10:19:36.000Z | 2022-06-24T10:19:36 | Entry not found | [
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IsaMaks/try_connll | IsaMaks | 2022-06-24T13:34:49Z | 14 | 0 | null | [
"license:cc0-1.0",
"region:us"
] | 2022-06-24T13:34:49Z | 2022-06-24T12:22:19.000Z | 2022-06-24T12:22:19 | ---
license: cc0-1.0
---
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BeardedJohn/ubb-endava-conll-assistant-ner | BeardedJohn | 2022-06-24T13:04:41Z | 14 | 0 | null | [
"region:us"
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autoevaluate/autoeval-staging-eval-project-d60b4e7e-7574885 | autoevaluate | 2022-06-26T20:11:28Z | 14 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-06-26T20:11:28Z | 2022-06-26T20:08:41.000Z | 2022-06-26T20:08:41 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xtreme
eval_info:
task: entity_extraction
model: MhF/xlm-roberta-base-finetuned-panx-de
metrics: []
dataset_name: xtreme
dataset_config: PAN-X.de
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: MhF/xlm-roberta-base-finetuned-panx-de
* Dataset: xtreme
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. | [
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Nexdata/Multi-class_Fashion_Item_Detection_Data | Nexdata | 2023-08-31T02:45:27Z | 14 | 3 | null | [
"region:us"
] | 2023-08-31T02:45:27Z | 2022-06-27T08:54:36.000Z | 2022-06-27T08:54:36 | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for Nexdata/Multi-class_Fashion_Item_Detection_Data
## 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://www.nexdata.ai/datasets/1057?source=Huggingface
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
144,810 Images Multi-class Fashion Item Detection Data. In this dataset, 19,968 images of male and 124,842 images of female were included. The Fashion Items were divided into 4 parts based on the season (spring, autumn, summer and winter). In terms of annotation, rectangular bounding boxes were adopted to annotate fashion items. The data can be used for tasks such as fashion items detection, fashion recommendation and other tasks.
For more details, please refer to the link: https://www.nexdata.ai/datasets/1057?source=Huggingface
### Supported Tasks and Leaderboards
object-detection, computer-vision: The dataset can be used to train a model for object detection.
### Languages
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
#### 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
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
### Citation Information
[More Information Needed]
### Contributions | [
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0.2434467375278473... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
yuningm/citesum | yuningm | 2022-10-25T10:39:26Z | 14 | 6 | citesum | [
"task_categories:summarization",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-nc-4.0",
"arxiv:2205.06207",
"region:us"
] | 2022-10-25T10:39:26Z | 2022-06-29T18:55:38.000Z | 2022-06-29T18:55:38 | ---
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
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MicPie/unpredictable_phonearena-com | MicPie | 2022-08-04T20:11:00Z | 14 | 0 | null | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:text2text-generation",
"task_categories:table-question-answering",
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:tabular-cl... | 2022-08-04T20:11:00Z | 2022-07-03T08:59:46.000Z | 2022-07-03T08:59:46 | ---
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}
}
```
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sasha/real_toxicity_continuations | sasha | 2022-07-06T16:08:28Z | 14 | 0 | null | [
"region:us"
] | 2022-07-06T16:08:28Z | 2022-07-06T16:08:16.000Z | 2022-07-06T16:08:16 | Entry not found | [
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MicPie/unpredictable_unique | MicPie | 2022-08-04T20:16:10Z | 14 | 0 | null | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:text2text-generation",
"task_categories:table-question-answering",
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:tabular-cl... | 2022-08-04T20:16:10Z | 2022-07-08T16:21:01.000Z | 2022-07-08T16:21:01 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-unique
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-unique" - 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}
}
```
| [
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0.15636181831359863,
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0.3131798803806305,
-1.0287790298461914,
-0.6711651682853699,
-0.6481857299804688,
0.21715325117111206,... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
MicPie/unpredictable_cluster12 | MicPie | 2022-08-04T19:52:07Z | 14 | 0 | null | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:text2text-generation",
"task_categories:table-question-answering",
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:tabular-cl... | 2022-08-04T19:52:07Z | 2022-07-08T17:20:21.000Z | 2022-07-08T17:20:21 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-cluster12
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-cluster12" - 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}
}
```
| [
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0.47133728861808777,
0.32834818959236145,
0.08932732790708542,
0.16209959983825684,
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0.5316260457038879,
0.265364408493042,
-1.0359324216842651,
-0.6827191114425659,
-0.6638859510421753,
0.18537966907024384... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
MicPie/unpredictable_cluster15 | MicPie | 2022-08-04T19:54:04Z | 14 | 0 | null | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:text2text-generation",
"task_categories:table-question-answering",
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:tabular-cl... | 2022-08-04T19:54:04Z | 2022-07-08T17:31:11.000Z | 2022-07-08T17:31:11 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-cluster15
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-cluster15" - 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}
}
```
| [
-0.5926979184150696,
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0.3294440805912018,
0.09061463177204132,
0.16332148015499115,
-0.14485280215740204,
-0.6067538857460022,
0.5176491141319275,
0.27946096658706665,
-1.0274699926376343,
-0.6868507862091064,
-0.6406322717666626,
0.2006579339504242,... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
MicPie/unpredictable_cluster16 | MicPie | 2022-08-04T19:54:44Z | 14 | 0 | null | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:text2text-generation",
"task_categories:table-question-answering",
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:tabular-cl... | 2022-08-04T19:54:44Z | 2022-07-08T17:32:41.000Z | 2022-07-08T17:32:41 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-cluster16
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-cluster16" - 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}
}
```
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-0.6468671560287476,
0.19426755607128143,... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
MicPie/unpredictable_cluster23 | MicPie | 2022-08-04T19:58:59Z | 14 | 0 | null | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:text2text-generation",
"task_categories:table-question-answering",
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:tabular-cl... | 2022-08-04T19:58:59Z | 2022-07-08T18:29:41.000Z | 2022-07-08T18:29:41 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-cluster23
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-cluster23" - 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}
}
```
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0.1935078352689743,... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
MicPie/unpredictable_cluster05 | MicPie | 2022-08-04T19:45:58Z | 14 | 0 | null | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:zero-shot-classification",
"task_categories:text2text-generation",
"task_categories:table-question-answering",
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:tabular-cl... | 2022-08-04T19:45:58Z | 2022-07-08T19:10:16.000Z | 2022-07-08T19:10:16 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-cluster05
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-cluster05" - 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}
}
```
| [
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0.28462857007980347,
-1.0246721506118774,
-0.6989456415176392,
-0.6536024212837219,
0.20811410248279572,... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
autoevaluate/autoeval-staging-eval-project-xsum-02414083-10505407 | autoevaluate | 2022-07-10T13:05:20Z | 14 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-07-10T13:05:20Z | 2022-07-10T12:33:59.000Z | 2022-07-10T12:33:59 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: facebook/bart-large-xsum
metrics: []
dataset_name: xsum
dataset_config: default
dataset_split: test
col_mapping:
text: document
target: summary
---
# 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: facebook/bart-large-xsum
* Dataset: xsum
* Config: default
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@AlekseyKorshuk](https://huggingface.co/AlekseyKorshuk) for evaluating this model. | [
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-0.1064066365361... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
TheNoob3131/mosquito-data | TheNoob3131 | 2022-07-22T05:34:57Z | 14 | 0 | null | [
"region:us"
] | 2022-07-22T05:34:57Z | 2022-07-12T00:05:26.000Z | 2022-07-12T00:05:26 | Entry not found | [
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0.5715674161911011,
-... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
LanceaKing/asvspoof2019 | LanceaKing | 2022-11-11T08:41:54Z | 14 | 0 | null | [
"task_categories:audio-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|vctk",
"language:en",
"license:odc-by",
"voice-anti-spoofing",
"arxiv:1911.01601",
"region:us"
] | 2022-11-11T08:41:54Z | 2022-07-20T08:29:40.000Z | 2022-07-20T08:29:40 | ---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- odc-by
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|vctk
task_categories:
- audio-classification
task_ids: []
pretty_name: asvspoof2019
tags:
- voice-anti-spoofing
---
# Dataset Card for asvspoof2019
## 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://datashare.ed.ac.uk/handle/10283/3336
- **Repository:** [Needs More Information]
- **Paper:** https://arxiv.org/abs/1911.01601
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
This is a database used for the Third Automatic Speaker Verification Spoofing
and Countermeasuers Challenge, for short, ASVspoof 2019 (http://www.asvspoof.org)
organized by Junichi Yamagishi, Massimiliano Todisco, Md Sahidullah, Héctor
Delgado, Xin Wang, Nicholas Evans, Tomi Kinnunen, Kong Aik Lee, Ville Vestman,
and Andreas Nautsch in 2019.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
English
## Dataset Structure
### Data Instances
```
{'speaker_id': 'LA_0091',
'audio_file_name': 'LA_T_8529430',
'audio': {'path': 'D:/Users/80304531/.cache/huggingface/datasets/downloads/extracted/8cabb6d5c283b0ed94b2219a8d459fea8e972ce098ef14d8e5a97b181f850502/LA/ASVspoof2019_LA_train/flac/LA_T_8529430.flac',
'array': array([-0.00201416, -0.00234985, -0.0022583 , ..., 0.01309204,
0.01339722, 0.01461792], dtype=float32),
'sampling_rate': 16000},
'system_id': 'A01',
'key': 1}
```
### Data Fields
Logical access (LA):
- `speaker_id`: `LA_****`, a 4-digit speaker ID
- `audio_file_name`: name of the audio file
- `audio`: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
- `system_id`: ID of the speech spoofing system (A01 - A19), or, for bonafide speech SYSTEM-ID is left blank ('-')
- `key`: 'bonafide' for genuine speech, or, 'spoof' for spoofing speech
Physical access (PA):
- `speaker_id`: `PA_****`, a 4-digit speaker ID
- `audio_file_name`: name of the audio file
- `audio`: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
- `environment_id`: a triplet (S,R,D_s), which take one letter in the set {a,b,c} as categorical value, defined as
| | a | b | c |
| -------------------------------- | ------ | ------- | -------- |
| S: Room size (square meters) | 2-5 | 5-10 | 10-20 |
| R: T60 (ms) | 50-200 | 200-600 | 600-1000 |
| D_s: Talker-to-ASV distance (cm) | 10-50 | 50-100 | 100-150 |
- `attack_id`: a duple (D_a,Q), which take one letter in the set {A,B,C} as categorical value, defined as
| | A | B | C |
| ----------------------------------- | ------- | ------ | ----- |
| Z: Attacker-to-talker distance (cm) | 10-50 | 50-100 | > 100 |
| Q: Replay device quality | perfect | high | low |
for bonafide speech, `attack_id` is left blank ('-')
- `key`: 'bonafide' for genuine speech, or, 'spoof' for spoofing speech
### Data Splits
| | Training set | Development set | Evaluation set |
| -------- | ------------ | --------------- | -------------- |
| Bonafide | 2580 | 2548 | 7355 |
| Spoof | 22800 | 22296 | 63882 |
| Total | 25380 | 24844 | 71237 |
## 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
This ASVspoof 2019 dataset is made available under the Open Data Commons Attribution License: http://opendatacommons.org/licenses/by/1.0/
### Citation Information
```
@InProceedings{Todisco2019,
Title = {{ASV}spoof 2019: {F}uture {H}orizons in {S}poofed and {F}ake {A}udio {D}etection},
Author = {Todisco, Massimiliano and
Wang, Xin and
Sahidullah, Md and
Delgado, H ́ector and
Nautsch, Andreas and
Yamagishi, Junichi and
Evans, Nicholas and
Kinnunen, Tomi and
Lee, Kong Aik},
booktitle = {Proc. of Interspeech 2019},
Year = {2019}
}
```
| [
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-0.8601663112640381,
-0.6576418876647949,
0.145384833216... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Vipitis/Shadertoys-fine | Vipitis | 2023-05-04T22:37:17Z | 14 | 3 | null | [
"task_categories:text-generation",
"annotations_creators:no-annotation",
"language_creators:machine-generated",
"size_categories:100K<n<1M",
"language:en",
"language:code",
"license:cc-by-nc-sa-3.0",
"code",
"region:us"
] | 2023-05-04T22:37:17Z | 2022-07-22T10:45:36.000Z | 2022-07-22T10:45:36 | ---
annotations_creators:
- no-annotation
language:
- en
- code
language_creators:
- machine-generated
license:
- cc-by-nc-sa-3.0
multilinguality: []
pretty_name: Shadertoys-fine
size_categories:
- 100K<n<1M
source_datasets: []
tags:
- code
task_categories:
- text-generation
task_ids: []
dataset_info:
- config_name: default
features:
- name: name
dtype: string
- name: code
dtype: string
- name: source
dtype: string
- name: author
dtype: string
splits:
- name: train
- name: test
download_size: 154529204
dataset_size: 0
- config_name: fine
features:
- name: name
dtype: string
- name: code
dtype: string
- name: source
dtype: string
- name: author
dtype: string
splits:
- name: train
num_bytes: 119963236
num_examples: 226910
- name: test
num_bytes: 20003783
num_examples: 38356
download_size: 154529204
dataset_size: 139967019
- config_name: return_completion
features:
- name: body
dtype: string
- name: return_statement
dtype: string
splits:
- name: train
num_bytes: 37597125
num_examples: 84843
- name: test
num_bytes: 6360131
num_examples: 14248
download_size: 154529204
dataset_size: 43957256
---
# Dataset Card for Shadertoys-fine
## 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)
- [Source Data](#source-data)
- [Licensing Information](#licensing-information)
## Dataset Description
- **Repository:** https://github.com/Vipitis/project (private placeholder)
### Dataset Summary
fine variant of the Shadertoys dataset (still WIP), where individual functions are avaialable as Datapoints.
### Supported Tasks and Leaderboards
`language-modeling`: The dataset can be used to train a model for modelling programming languages, which consists in building language models for programming languages.
### Languages
- English (names, comments)
- Shadercode **programming** language
## Dataset Structure
### Data Instances
A data point consists of the function string, it's name as well as a bit of metadata like the author and source URL. (in the future there might be a function string without comments)
```
{
'name': '<type> <name>',
'code': '<type> <name>(<inputs>) { <body> return <outputs>; }\n',
'source': 'https://shadertoy.com/view/<shaderID>',
'author': '<username>'
}
```
A data point in the `return_completion` subset for the return-completion task in [ShaderEval](https://huggingface.co/spaces/Vipitis/ShaderEval) includes just two features:
```
{
'body': '<type> <name> <type> <name>(<inputs>) { <body> return',
'return_statment': ' <outputs>: }\n',
}
```
### Data Fields
- 'name' funciton identifier composed of the type and the name of the function
- 'code' the raw code (including comments) of function.
- 'source' URL to the shader. It might be on a different renderpass
- 'author' username of the shader author
- 'body' the body of the function without the return statement (no comments)
- 'return_statment' the return statement of the function. everything infront of the semicolon is kept and white sapces are stripped in the custome Evaluator.
### Data Splits
Currently available (shuffled):
- train (85.0%)
- test (15.0%)
These splits should be indexed the same across both subsets. So if you are fine-tuning on the `fine` subset you won't get exposed to the `return_completion` test split. However there are many duplicates among both subsets and splits.
## Dataset Creation
Data retrieved starting 2022-07-20
### Source Data
#### Initial Data Collection and Normalization
All data was collected via the [Shadertoy.com API](https://www.shadertoy.com/howto#q2) and then by looking for keywords and counting curly brackets to figure out what is part of a function and what isn't.
#### Who are the source language producers?
Shadertoy.com contributers which publish shaders as 'public+API'
## Licensing Information
The Default [licnese for each Shader](https://www.shadertoy.com/terms) is CC BY-NC-SA 3.0. However, some Shaders might have a different license attached. The Dataset is currently not filtering for any licensis. | [
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-0.33760467171669006,
0.12010906636714935,
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0.1672630757093... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
autoevaluate/autoeval-staging-eval-project-squad-95d5e1fd-11835577 | autoevaluate | 2022-07-25T22:38:58Z | 14 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-07-25T22:38:58Z | 2022-07-25T22:34:47.000Z | 2022-07-25T22:34:47 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad
eval_info:
task: extractive_question_answering
model: mbartolo/roberta-large-synqa
metrics: []
dataset_name: squad
dataset_config: plain_text
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: mbartolo/roberta-large-synqa
* Dataset: squad
* Config: plain_text
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@mbartolo ](https://huggingface.co/mbartolo ) for evaluating this model. | [
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okg/turkish-poems | okg | 2022-07-31T10:22:53Z | 14 | 1 | null | [
"task_categories:text-generation",
"task_categories:text-classification",
"task_ids:language-modeling",
"task_ids:text-scoring",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:tr",
"license:unknown",
"region:us"
] | 2022-07-31T10:22:53Z | 2022-07-31T10:09:54.000Z | 2022-07-31T10:09:54 | ---
annotations_creators:
- found
language:
- tr
language_creators:
- found
license:
- unknown
multilinguality:
- monolingual
pretty_name: turkish-poems
size_categories:
- 1K<n<10K
source_datasets: []
tags: []
task_categories:
- text-generation
- text-classification
task_ids:
- language-modeling
- text-scoring
---
Turkish poems scraped from antoloji.com. Features consists of id, poet name, poem rating and the poem.
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nielsr/rvl_cdip_10_examples_per_class | nielsr | 2022-08-01T16:32:41Z | 14 | 0 | null | [
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LawalAfeez/science-dataset | LawalAfeez | 2022-08-17T11:38:40Z | 14 | 3 | null | [
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UCL-DARK/openai-tldr-filtered | UCL-DARK | 2023-10-26T09:51:30Z | 14 | 0 | null | [
"task_categories:text-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
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"source_datasets:extended",
"language:en",
"license:cc-by-4.0",
"alignment",
"text-classification",
"summarisation",
"human-feed... | 2023-10-26T09:51:30Z | 2022-08-17T13:40:08.000Z | 2022-08-17T13:40:08 | ---
license: cc-by-4.0
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- crowdsourced
multilinguality:
- monolingual
pretty_name: Filtered TL;DR
size_categories:
- 100K<n<1M
source_datasets:
- extended
tags:
- alignment
- text-classification
- summarisation
- human-feedback
task_categories:
- text-generation
task_ids: []
---
# Filtered TL;DR Dataset
This is the version of the dataset used in https://arxiv.org/abs/2310.06452.
If starting a new project we would recommend using https://huggingface.co/datasets/openai/summarize_from_feedback.
For more information see https://github.com/openai/summarize-from-feedback and for the original TL;DR dataset see https://zenodo.org/record/1168855#.YvzwJexudqs
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UKPLab/TexPrax | UKPLab | 2023-01-11T14:40:21Z | 14 | 1 | null | [
"license:cc-by-nc-4.0",
"arxiv:2208.07846",
"region:us"
] | 2023-01-11T14:40:21Z | 2022-08-23T12:03:20.000Z | 2022-08-23T12:03:20 | ---
license: cc-by-nc-4.0
---
# Dataset Card for TexPrax
## 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://texprax.de/**
- **Repository: https://github.com/UKPLab/TexPrax**
- **Paper: https://arxiv.org/abs/2208.07846**
- **Leaderboard: n/a**
- **Point of Contact: Ji-Ung Lee (http://www.ukp.tu-darmstadt.de/)**
### Dataset Summary
This dataset contains dialogues collected from German factory workers at the _Center for industrial productivity_ ([CiP](https://www.prozesslernfabrik.de/)). The dialogues mostly concern issues workers encounter during their daily work, such as machines breaking down, material missing, etc. The dialogues are further expert-annotated on a sentence level (problem, cause, solution, other) for sentence classification and on a token level for named entity recognition using a BIO tagging scheme. Note, that the dataset was collected in three rounds, each around one year apart. Here, we provide the data only split into train and test data where the test data was collected at the last round (July 2022). Additionally, the data from the first round is split into two subdomains, industry 4.0 (industrie) and machining (zerspanung). The splits were made according to the respective groups of people working at different assembly lines in the factory.
### Supported Tasks and Leaderboards
This dataset supports the following tasks:
* Sentence classification
* Named entity recognition (will be updated soon with the new indexing)
* Dialog generation (so far not evaluated)
### Languages
German
## Dataset Structure
### Data Instances
On sentence level, each instance consists of the dialog-id, turn-id, sentence-id, the sentence (raw), the label, the domain, and the subsplit.
```
{"185";"562";993";"wie kriege ich die Dichtung raus?";"P";"n/a";"3"}
```
On token level, each instance consists of a unique identifier, a list of tokens containing the whole dialog, the list of labels (bio-tagged entities), and the subsplit.
```
{"178_0";"['Hi', 'wie', 'kriege', 'ich', 'die', 'Dichtung', 'raus', '?', 'in', 'der', 'Schublade', 'gibt', 'es', 'einen', 'Dichtungszieher']";"['O', 'O', 'O', 'O', 'O', 'B-PRE', 'O', 'O', 'O', 'O', 'B-LOC', 'O', 'O', 'O', 'B-PE']";"Batch 3"}
```
### Data Fields
Sentence level:
* dialog-id: unique identifier for the dialog
* turn-id: unique identifier for the turn
* sentence-id: unique identifier for the dialog
* sentence: the respective sentence
* label: the label (_P_ for Problem, _C_ for Cause, _S_ for solution, and _O_ for Other)
* domain: the subdomains where the data was collected from. Domains are industry, machining, or n/a (for batch 2 and batch 3).
* subsplit: the respective subsplit of the data (see below)
Token level:
* id: the identifier
* tokens: a list of tokens (i.e., the tokenized dialogue)
* entities: the named entity in a BIO scheme (_B-X_, _I-X_, or O).
* subsplit: the respective subsplit of the data (see below)
### Data Splits
The dataset is split into train and test splits, but contains further subsplits (subsplit column). Note, that the splits are collected at different times with some turnaround in the workforce. Hence, later data (especially the data from batch 2) contains more turns (due to increased search for a cause) as more inexperienced workers who newly joined were employed in the factory.
Train:
* Batch 1 industrie: data collected in October 2020 from workers in the industry 4.0 assembly line
* Batch 1 zerspanung: data collected in October 2020 from workers in the machining assembly line
* Batch 2: data collected in-between October 2021-June 2022 from all workers
Test:
* Batch 3: data collected in July 2022 together with the system usability study run
Sentence level statistics:
| Batch | Dialogues | Turns | Sentences |
|---|---|---|---|
| 1 | 81 | 246 | 553 |
| 2 | 97 | 309 | 432 |
| 3 | 24 | 36 | 42 |
| Overall | 202 | 591 | 1,027 |
Token level statistics:
[Needs to be added]
## Dataset Creation
### Curation Rationale
This dataset provides task-oriented dialogues that solve a very domain specific problem.
### Source Data
#### Initial Data Collection and Normalization
The data was generated by workers at the [CiP](https://www.prozesslernfabrik.de/). The data was collected in three rounds (October 2020, October 2021-June 2022, July 2022). As the dialogues occurred during their daily work, one distinct property of the dataset is that all dialogues are very informal 'ne', contain abbreviations 'vll', and filler words such as 'ah'. For a detailed description please see the [paper](https://arxiv.org/abs/2208.07846).
#### Who are the source language producers?
German factory workers working at the [CiP](https://www.prozesslernfabrik.de/)
### Annotations
#### Annotation process
**Token level.** Token level annotation was done by researchers who are responsible for supervising and teaching workers at the CiP. The data was first split into three parts, each annotated by one researcher. Next, each researcher cross-examined the other researchers' annotations. If there were disagreements, all three researchers discussed the final label.
**Sentence level.** Sentence level annotations were collected from the factory workers who also generated the dialogues. For details about the data collection, please see the [TexPrax demo paper](https://arxiv.org/abs/2208.07846).
#### Who are the annotators?
**Token level.** Researchers working at the CiP.
**Sentence level.** The factory workers themselves.
### Personal and Sensitive Information
This dataset is fully anonymized. All occurrences of names have been manually checked during annotation and replaced with a random token.
## Considerations for Using the Data
### Social Impact of Dataset
Informal language especially used in short messages, however, seldom considered in existing NLP datasets. This dataset could serve as an interesting evaluation task for transferring language models to low-resource, but highly specific domains. Moreover, we note that despite all abbreviations, typos, and local dialects used in the messages, all workers were able to understand the questions as well as replies. This should be a standard future NLP models should be able to uphold.
### Discussion of Biases
The dialogues are very much on a professional level. The workers were informed (and gave their consent) in advance that their messages are being recorded and processed, which may have influenced them to hold only professional conversations, hence, all dialogues concern inanimate objects (i.e., machines).
### Other Known Limitations
[More Information Needed]
## Additional Information
You can download the data via:
```
from datasets import load_dataset
dataset = load_dataset("UKPLab/TexPrax") # default config is sentence classification
dataset = load_dataset("UKPLab/TexPrax", "ner") # use the ner tag for named entity recognition
```
Please find more information about the code and how the data was collected on [GitHub](https://github.com/UKPLab/TexPrax).
### Dataset Curators
Curation is managed by our [data manager](https://www.informatik.tu-darmstadt.de/ukp/research_ukp/ukp_research_data_and_software/ukp_data_and_software.en.jsp) at UKP.
### Licensing Information
[CC-by-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/)
### Citation Information
Please cite this data using:
```
@article{stangier2022texprax,
title={TexPrax: A Messaging Application for Ethical, Real-time Data Collection and Annotation},
author={Stangier, Lorenz and Lee, Ji-Ung and Wang, Yuxi and M{\"u}ller, Marvin and Frick, Nicholas and Metternich, Joachim and Gurevych, Iryna},
journal={arXiv preprint arXiv:2208.07846},
year={2022}
}
```
### Contributions
Thanks to [@Wuhn](https://github.com/Wuhn) for adding this dataset.
## Tags
annotations_creators:
- expert-generated
language:
- de
language_creators:
- expert-generated
license:
- cc-by-nc-4.0
multilinguality:
- monolingual
pretty_name: TexPrax-Conversations
size_categories:
- n<1K
- 1K<n<10K
source_datasets:
- original
tags:
- dialog
- expert to expert conversations
- task-oriented
task_categories:
- token-classification
- text-classification
task_ids:
- named-entity-recognition
- multi-class-classification | [
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SharedBailii/NER-BAILII-UK-CCA | SharedBailii | 2022-08-31T20:01:12Z | 14 | 0 | null | [
"license:apache-2.0",
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license: apache-2.0
---
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autoevaluate/autoeval-staging-eval-squad_v2-squad_v2-76c05b-14906065 | autoevaluate | 2022-08-31T21:51:46Z | 14 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
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type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: deepset/tinyroberta-squad2
metrics: ['bertscore']
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: deepset/tinyroberta-squad2
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@nonchalant-nagavalli](https://huggingface.co/nonchalant-nagavalli) for evaluating this model. | [
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source_datasets:
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task_categories:
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task_ids:
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pretty_name: GoEmotions
tags:
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---
# GoEmotions Spanish
## A Spanish translation (using [EasyNMT](https://github.com/UKPLab/EasyNMT)) of the [GoEmotions](https://huggingface.co/datasets/sst2) dataset.
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---
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mrmoor/cti-corpus-raw | mrmoor | 2022-09-14T18:54:05Z | 14 | 0 | null | [
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... | 2022-09-14T18:54:05Z | 2022-09-13T14:03:55.000Z | 2022-09-13T14:03:55 | ---
annotations_creators:
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language_creators: []
license:
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multilinguality:
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pretty_name: cti-corpus
size_categories:
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tags:
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- cybert threat intelligence
- it-security
- apt
task_categories:
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- text-generation
task_ids:
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---
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annotations_creators: []
language: []
language_creators: []
license:
- cc0-1.0
multilinguality: []
pretty_name: Full Body Anime HQ
size_categories:
- 100K<n<1M
source_datasets:
- original
tags: []
task_categories:
- unconditional-image-generation
task_ids: []
---
## Dataset Description
FBAnimeHQ is a dataset with high-quality full-body anime girl images in a resolution of 1024 × 512.
### Dataset Summary
The dataset contains 112,806 images.
All images are on white background
### Collection Method
#### v1.0
Collect from danbooru website.
Use yolov5 to detect and clip image.
Use anime-segmentation to remove background.
Use deepdanbooru to filter image.
Finally clean the dataset manually.
#### v2.0
Base on v1.0, use Novelai image-to-image to enhance and expand the dataset.
### Contributions
Thanks to [@SkyTNT](https://github.com/SkyTNT) for adding this dataset. | [
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EMBO/sd-character-level-ner | EMBO | 2022-10-23T06:41:24Z | 14 | 1 | null | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:named-entity-recognition",
"task_ids:parsing",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"language:en",
"license... | 2022-10-23T06:41:24Z | 2022-09-22T13:57:31.000Z | 2022-09-22T13:57:31 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets: []
task_categories:
- text-classification
- structure-prediction
task_ids:
- multi-class-classification
- named-entity-recognition
- parsing
---
# Dataset Card for sd-nlp
## Table of Contents
- [Dataset Card for [EMBO/sd-nlp-non-tokenized]](#dataset-card-for-dataset-name)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://sourcedata.embo.org
- **Repository:** https://github.com/source-data/soda-roberta
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** thomas.lemberger@embo.org, jorge.abreu@embo.org
### Dataset Summary
This dataset is based on the content of the SourceData (https://sourcedata.embo.org) database, which contains manually annotated figure legends written in English and extracted from scientific papers in the domain of cell and molecular biology (Liechti et al, Nature Methods, 2017, https://doi.org/10.1038/nmeth.4471).
Unlike the dataset [`sd-nlp`](https://huggingface.co/datasets/EMBO/sd-nlp), pre-tokenized with the `roberta-base` tokenizer, this dataset is not previously tokenized, but just splitted into words. Users can therefore use it to fine-tune other models.
Additional details at https://github.com/source-data/soda-roberta
### Supported Tasks and Leaderboards
Tags are provided as [IOB2-style tags](https://en.wikipedia.org/wiki/Inside%E2%80%93outside%E2%80%93beginning_(tagging)).
`PANELIZATION`: figure captions (or figure legends) are usually composed of segments that each refer to one of several 'panels' of the full figure. Panels tend to represent results obtained with a coherent method and depicts data points that can be meaningfully compared to each other. `PANELIZATION` provide the start (B-PANEL_START) of these segments and allow to train for recogntion of the boundary between consecutive panel lengends.
`NER`: biological and chemical entities are labeled. Specifically the following entities are tagged:
- `SMALL_MOLECULE`: small molecules
- `GENEPROD`: gene products (genes and proteins)
- `SUBCELLULAR`: subcellular components
- `CELL`: cell types and cell lines.
- `TISSUE`: tissues and organs
- `ORGANISM`: species
- `EXP_ASSAY`: experimental assays
`ROLES`: the role of entities with regard to the causal hypotheses tested in the reported results. The tags are:
- `CONTROLLED_VAR`: entities that are associated with experimental variables and that subjected to controlled and targeted perturbations.
- `MEASURED_VAR`: entities that are associated with the variables measured and the object of the measurements.
`BORING`: entities are marked with the tag `BORING` when they are more of descriptive value and not directly associated with causal hypotheses ('boring' is not an ideal choice of word, but it is short...). Typically, these entities are so-called 'reporter' geneproducts, entities used as common baseline across samples, or specify the context of the experiment (cellular system, species, etc...).
### Languages
The text in the dataset is English.
## Dataset Structure
### Data Instances
```json
{'text': '(E) Quantification of the number of cells without γ-Tubulin at centrosomes (γ-Tub -) in pachytene and diplotene spermatocytes in control, Plk1(∆/∆) and BI2536-treated spermatocytes. Data represent average of two biological replicates per condition. ',
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```
### Data Fields
- `text`: `str` of the text
- `label_ids` dictionary composed of list of strings on a character-level:
- `entity_types`: `list` of `strings` for the IOB2 tags for entity type; possible value in `["O", "I-SMALL_MOLECULE", "B-SMALL_MOLECULE", "I-GENEPROD", "B-GENEPROD", "I-SUBCELLULAR", "B-SUBCELLULAR", "I-CELL", "B-CELL", "I-TISSUE", "B-TISSUE", "I-ORGANISM", "B-ORGANISM", "I-EXP_ASSAY", "B-EXP_ASSAY"]`
- `panel_start`: `list` of `strings` for IOB2 tags `["O", "B-PANEL_START"]`
### Data Splits
```python
DatasetDict({
train: Dataset({
features: ['text', 'labels'],
num_rows: 66085
})
test: Dataset({
features: ['text', 'labels'],
num_rows: 8225
})
validation: Dataset({
features: ['text', 'labels'],
num_rows: 7948
})
})
```
## Dataset Creation
### Curation Rationale
The dataset was built to train models for the automatic extraction of a knowledge graph based from the scientific literature. The dataset can be used to train character-based models for text segmentation and named entity recognition.
### Source Data
#### Initial Data Collection and Normalization
Figure legends were annotated according to the SourceData framework described in Liechti et al 2017 (Nature Methods, 2017, https://doi.org/10.1038/nmeth.4471). The curation tool at https://curation.sourcedata.io was used to segment figure legends into panel legends, tag enities, assign experiemental roles and normalize with standard identifiers (not available in this dataset). The source data was downloaded from the SourceData API (https://api.sourcedata.io) on 21 Jan 2021.
#### Who are the source language producers?
The examples are extracted from the figure legends from scientific papers in cell and molecular biology.
### Annotations
#### Annotation process
The annotations were produced manually with expert curators from the SourceData project (https://sourcedata.embo.org)
#### Who are the annotators?
Curators of the SourceData project.
### Personal and Sensitive Information
None known.
## Considerations for Using the Data
### Social Impact of Dataset
Not applicable.
### Discussion of Biases
The examples are heavily biased towards cell and molecular biology and are enriched in examples from papers published in EMBO Press journals (https://embopress.org)
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
Thomas Lemberger, EMBO.
### Licensing Information
CC BY 4.0
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@tlemberger](https://github.com/tlemberger>) and [@drAbreu](https://github.com/drAbreu>) for adding this dataset. | [
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Drewd/lex_fridman_podcast_transcripts | Drewd | 2022-10-05T01:41:30Z | 14 | 0 | null | [
"annotations_creators:found",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:n<1K",
"language:en",
"podcast",
"ai",
"interviews",
"region:us"
] | 2022-10-05T01:41:30Z | 2022-10-04T03:42:55.000Z | 2022-10-04T03:42:55 | ---
annotations_creators:
- found
language:
- en
language_creators:
- machine-generated
license: []
multilinguality:
- monolingual
pretty_name: The transcripts from Lex Fridman podcast episodes on Youtube.
size_categories:
- n<1K
source_datasets: []
tags:
- podcast
- ai
- interviews
task_categories: []
task_ids: []
---
# Dataset Card for Lex Fridman Podcast Transcripts
## Table of Contents
- [Dataset Card for Lex Fridman Podcast Transcripts](#dataset-card-for-lex-fridman-podcast-transcripts)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://karpathy.ai/lexicap/
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [@drewdresser](https://twitter.com/drewdresser)
### Dataset Summary
These are transcripts from the Lex Fridman podcast. The podcast is hosted by Lex Fridman, a computer scientist at MIT. The podcast is a mix of interviews with researchers in AI and other fields, and discussions of current events in AI. The transcripts are generated using [OpenAI Whisper](https://github.com/openai/whisper), then made available on [Karpathy AI](https://karpathy.ai/lexicap/).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English
## Dataset Structure
### Data Instances
~325
### 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/<github-username>) for adding this dataset. | [
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SweetyTheCog/Test | SweetyTheCog | 2022-10-07T13:49:30Z | 14 | 0 | null | [
"region:us"
] | 2022-10-07T13:49:30Z | 2022-10-07T13:33:19.000Z | 2022-10-07T13:33:19 | Entry not found | [
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0.5715669393539429,
-0... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
arbml/Syria_Tweet_Sentiment | arbml | 2022-11-03T15:48:08Z | 14 | 0 | null | [
"region:us"
] | 2022-11-03T15:48:08Z | 2022-10-07T16:26:22.000Z | 2022-10-07T16:26:22 | Entry not found | [
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
autoevaluate/autoeval-eval-inverse-scaling__quote-repetition-inverse-scaling__quot-3aff83-1695059596 | autoevaluate | 2022-10-08T13:51:20Z | 14 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-10-08T13:51:20Z | 2022-10-08T12:54:22.000Z | 2022-10-08T12:54:22 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- inverse-scaling/quote-repetition
eval_info:
task: text_zero_shot_classification
model: inverse-scaling/opt-30b_eval
metrics: []
dataset_name: inverse-scaling/quote-repetition
dataset_config: inverse-scaling--quote-repetition
dataset_split: train
col_mapping:
text: prompt
classes: classes
target: answer_index
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: inverse-scaling/opt-30b_eval
* Dataset: inverse-scaling/quote-repetition
* Config: inverse-scaling--quote-repetition
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@MicPie](https://huggingface.co/MicPie) for evaluating this model. | [
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0.0073882383... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
autoevaluate/autoeval-eval-inverse-scaling__redefine-math-inverse-scaling__redefin-f7efd9-1695359605 | autoevaluate | 2022-10-08T16:13:43Z | 14 | 0 | null | [
"autotrain",
"evaluation",
"region:us"
] | 2022-10-08T16:13:43Z | 2022-10-08T13:07:20.000Z | 2022-10-08T13:07:20 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- inverse-scaling/redefine-math
eval_info:
task: text_zero_shot_classification
model: inverse-scaling/opt-66b_eval
metrics: []
dataset_name: inverse-scaling/redefine-math
dataset_config: inverse-scaling--redefine-math
dataset_split: train
col_mapping:
text: prompt
classes: classes
target: answer_index
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: inverse-scaling/opt-66b_eval
* Dataset: inverse-scaling/redefine-math
* Config: inverse-scaling--redefine-math
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@MicPie](https://huggingface.co/MicPie) for evaluating this model. | [
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-0.004595964... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
sweetdeal/kristen | sweetdeal | 2022-10-10T13:18:37Z | 14 | 0 | null | [
"region:us"
] | 2022-10-10T13:18:37Z | 2022-10-10T13:17:11.000Z | 2022-10-10T13:17:11 | Entry not found | [
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0.5715669393539429,
... | null | null | null | null | null | null | null | null | null | null | null | null | null |
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